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WO2025050067A1 - Systèmes, procédés, kits et appareils pour puces spécialisées pour couches d'intelligence robotique - Google Patents

Systèmes, procédés, kits et appareils pour puces spécialisées pour couches d'intelligence robotique Download PDF

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Publication number
WO2025050067A1
WO2025050067A1 PCT/US2024/044898 US2024044898W WO2025050067A1 WO 2025050067 A1 WO2025050067 A1 WO 2025050067A1 US 2024044898 W US2024044898 W US 2024044898W WO 2025050067 A1 WO2025050067 A1 WO 2025050067A1
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WO
WIPO (PCT)
Prior art keywords
neural network
robots
environment
robotic
integrated chipset
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/US2024/044898
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English (en)
Inventor
Charles H. CELLA
Teymour S. EL-TAHRY
Bennett Ingvoldstad
Jenna PARENTI
Brent BLIVEN
Andrew Cardno
Brad Kell
Eric P. VETTER
Anthony CASCIO
Nicholas ROGOSIN
Leon FORTIN JR.
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Strong Force VCN Portfolio 2019 LLC
Original Assignee
Strong Force VCN Portfolio 2019 LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Strong Force VCN Portfolio 2019 LLC filed Critical Strong Force VCN Portfolio 2019 LLC
Priority to AU2024220202A priority Critical patent/AU2024220202A1/en
Publication of WO2025050067A1 publication Critical patent/WO2025050067A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

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    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
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    • G05D1/222—Remote-control arrangements operated by humans
    • G05D1/224—Output arrangements on the remote controller, e.g. displays, haptics or speakers
    • G05D1/2244—Optic
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    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
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    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/008—Artificial life, i.e. computing arrangements simulating life based on physical entities controlled by simulated intelligence so as to replicate intelligent life forms, e.g. based on robots replicating pets or humans in their appearance or behaviour
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0633—Managing shopping lists, e.g. compiling or processing purchase lists
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B82—NANOTECHNOLOGY
    • B82Y—SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES; MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES; MANUFACTURE OR TREATMENT OF NANOSTRUCTURES
    • B82Y10/00—Nanotechnology for information processing, storage or transmission, e.g. quantum computing or single electron logic
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2105/00—Specific applications of the controlled vehicles
    • G05D2105/20—Specific applications of the controlled vehicles for transportation
    • G05D2105/28—Specific applications of the controlled vehicles for transportation of freight
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2107/00—Specific environments of the controlled vehicles
    • G05D2107/70—Industrial sites, e.g. warehouses or factories
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/5017—Task decomposition
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G06F2209/50—Indexing scheme relating to G06F9/50
    • G06F2209/5021—Priority
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q2220/00—Business processing using cryptography

Definitions

  • the techniques described herein relate to a robotic system including: a robotic control circuit configured to control one or more robotic functions of a robot; a plurality of sensors configured to collect data; a governance analysis circuit configured to analyze the data and select one or more governance frameworks based on the analyzed data; and a governance model circuit configured to generate a model that applies the one or more governance frameworks to determine one or more governance actions, wherein the robotic control circuit is configured to control the one or more robotic functions in accordance with the one or more governance actions; wherein the robotic control circuit, the governance analysis circuit, and the governance model circuit are integrated on a single substrate.
  • the techniques described herein relate to a robotic system, wherein the one or more governance frameworks include at least one of: safety standards, security standards, quality standards, regulatory standards, or financial standards.
  • the techniques described herein relate to a robotic system, wherein the analyzed data indicates a state of an environment containing the robotic system.
  • the techniques described herein relate to a robotic system, wherein the one or more governance frameworks include a plurality of governance frameworks, wherein the governance model circuit is further configured to: prioritize the plurality of governance frameworks; and resolve a conflict between a first governance framework of the plurality of governance frameworks and a second governance framework of the plurality of governance frameworks based on respective priorities of the first governance framework and the second governance framework.
  • the techniques described herein relate to a robotic system, wherein the governance model circuit is further configured to apply the generated model to a second set of data captured after the generation of the model to determine the one or more governance actions. [0009] In some aspects, the techniques described herein relate to a robotic system, wherein the governance model circuit is configured to continually adjust the model based on real-time data captured from the plurality of sensors.
  • the techniques described herein relate to a robotic system, wherein the robotic control circuit is further configured to control one or more functions of a second robotic system in accordance with the one or more governance actions.
  • the techniques described herein relate to a robotic system, wherein the robotic system is further configured to transmit an instruction to the second robotic system to control the one or more functions of the second robotic system.
  • the techniques described herein relate to a robotic system, wherein the one or more governance actions include changing a state of the robotic system.
  • the techniques described herein relate to a robotic system, wherein the one or more governance actions include changing a task assigned to the robotic system.
  • the techniques described herein relate to a robotic system, wherein the one or more governance actions include transmitting a warning or alarm.
  • the techniques described herein relate to a robotic system, wherein the one or more governance actions include transforming data to comply with the one or more governance frameworks.
  • the techniques described herein relate to a robotic system, wherein the governance model circuit is configured to simulate the one or more governance actions within a digital twin environment. [0017] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the governance analysis circuit, and/or the governance model circuit are implemented using specialized Al chips.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the governance analysis circuit, or the governance model circuit are implemented using a combination of CPUs and GPUs.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the governance analysis circuit, or the governance model circuit are configured to use dynamic voltage and frequency scaling.
  • the techniques described herein relate to a robotic system, wherein the single substrate includes a 2.5D or 3D stack of chips.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the governance analysis circuit, or the governance model circuit are connected using a high-speed bridge.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the governance analysis circuit, or the governance model circuit are connected to high bandwidth memory.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the governance analysis circuit, and/or the governance model circuit are modular elements connected using die-to-die connectivity.
  • the techniques described herein relate to a robotic system including: a robotic control circuit configured to control one or more robotic functions of a robot; a plurality of sensors configured to collect data; a predictive modeling circuit configured to use one or more artificial intelligence models to generate a prediction based on the data; and a predictive model optimization circuit configured to re-train a predictive model of the one or more artificial intelligence models based on one or more conditions detected after generating the prediction, wherein the robotic control circuit, the predictive modeling circuit, and the predictive model optimization circuit are integrated on a single substrate.
  • the techniques described herein relate to a robotic system, wherein the predictive model optimization circuit is further configured to train the predictive model based on training data generated by the robotic system.
  • the techniques described herein relate to a robotic system, wherein the training data generated by the robotic system includes classification data generated based on the data captured by the plurality of sensors.
  • the techniques described herein relate to a robotic system, wherein the predictive model optimization circuit is further configured to train the predictive model based on training data generated by an environment digital twin.
  • the techniques described herein relate to a robotic system, wherein the predictive model optimization circuit is configured to re-train the predictive model based on an accuracy of the prediction generated by the predictive modeling circuit. [0029] In some aspects, the techniques described herein relate to a robotic system, further including a recommendation circuit configured to provide a recommended action for the robotic system based on the prediction.
  • the techniques described herein relate to a robotic system, wherein the robotic control system is configured to control the one or more robotic functions of the robot based on the recommended action.
  • the techniques described herein relate to a robotic system, wherein the recommended action includes an action and an entity on which the action will be taken.
  • the techniques described herein relate to a robotic system, wherein the recommended action further includes a modifier for the action.
  • the techniques described herein relate to a robotic system, wherein the robotic system is further configured to simulate the recommended action using an environment digital twin.
  • the techniques described herein relate to a robotic system, wherein the recommendation circuit is further configured to provide a second recommended action for controlling one or more robotic functions of a second robotic system.
  • the techniques described herein relate to a robotic system, wherein the robotic system is further configured to transmit the second recommended action to the second robotic system.
  • the techniques described herein relate to a robotic system, wherein the robotic system is further configured to generate a report indicating an outcome of the recommended action.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are implemented using specialized Al chips.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are implemented using a combination of CPUs and GPUs.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are connected to high bandwidth memory. [0043] In some aspects, the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the predictive modeling circuit, or the predictive model optimization circuit are modular elements connected using die-to-die connectivity.
  • the techniques described herein relate to a robotic system including: a robotic control circuit configured to control one or more robotic functions of a robot; a plurality of sensors configured to collect data; a network interface circuit configured to communicate with other robotic systems via a network; a network analysis circuit configured to use one or more artificial intelligence models to analyze the data and the communication with other robotic systems; and a network optimization circuit configured to optimize the communication with other robotic systems based on the analysis by the network analysis circuit, wherein the robotic control circuit, the network interface circuit, the network analysis circuit, and the network optimization circuit are integrated on a single substrate.
  • the techniques described herein relate to a robotic system, wherein the data includes at least one of: a physical signal measurement, network traffic, network device information, or network configuration data.
  • the techniques described herein relate to a robotic system, wherein the network analysis circuit is configured to predict a future network condition.
  • the techniques described herein relate to a robotic system, wherein the network optimization circuit is configured to optimize one or more of traffic flows between robotic systems on the network, data prioritization on the network, or protocols used by the robotic systems on the network.
  • the techniques described herein relate to a robotic system, wherein the network analysis circuit is further configured to generate or update a network digital twin based on the analysis performed by the network analysis circuit.
  • the techniques described herein relate to a robotic system, wherein the network optimization circuit is configured to simulate the optimization using the network digital twin.
  • the techniques described herein relate to a robotic system, wherein the optimization performed by the network optimization circuit includes optimizing a schedule of the network, a quality of data transmitted between robotic systems via the network, or a security of data transmitted between robotic systems via the network.
  • the techniques described herein relate to a robotic system, wherein the optimization performed by the network optimization circuit includes instructing a robotic system to power up or down, switch networks, adjust a transmission schedule, adjust a communication protocol, re-route traffic, or perform compression on data.
  • the techniques described herein relate to a robotic system, wherein the optimization performed by the network optimization circuit includes compressing, decompressing, up-sampling, down-sampling, reformatting, delaying, buffering, or rescheduling traffic transmitted to or from robotic systems via the network interface circuit.
  • the techniques described herein relate to a robotic system, wherein the optimization performed by the network optimization circuit includes modifying an instruction being routed to a robotic system via the network.
  • the techniques described herein relate to a robotic system, wherein the optimization performed by the network optimization circuit includes changing a topology of the network.
  • the techniques described herein relate to a robotic system, wherein the optimization performed by the network optimization circuit includes changing a header of a data packet being routed to a robotic system via the network.
  • the techniques described herein relate to a robotic system, further including a governance circuit configured to monitor and apply governance actions to traffic transmitted between robotic systems via the network.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are implemented using specialized Al chips.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are implemented using a combination of CPUs and GPUs.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are configured to use dynamic voltage and frequency scaling. [0060] In some aspects, the techniques described herein relate to a robotic system, wherein the single substrate includes a 2.5d or 3d stack of chips.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are connected using a high-speed bridge.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are connected to high bandwidth memory.
  • the techniques described herein relate to a robotic system, wherein one or more of the robotic control circuit, the network interface circuit, the network analysis circuit, or the network optimization circuit are modular elements connected using die-to-die connectivity.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share a set of I/O capabilities of the integrated chipset.
  • the techniques described herein relate to an integrated chipset, wherein the shared set of I/O capabilities include shared I/O ports.
  • the techniques described herein relate to an integrated chipset, wherein the shared set of I/O capabilities include shared data.
  • the techniques described herein relate to an integrated chipset, wherein the shared set of I/O capabilities include shared sensors.
  • the techniques described herein relate to an integrated chipset, wherein the shared set of I/O capabilities include the set of functions for the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier includes an FPGA.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment. [0081] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment. [0082] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are manufactured on a single silicon wafer, wherein the common substrate is the single silicon wafer.
  • the techniques described herein relate to an integrated chipset, wherein the common substrate is a single chip.
  • the techniques described herein relate to an integrated chipset, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are separately manufactured chips that are bonded to the common substrate.
  • the techniques described herein relate to an integrated chipset, wherein the common substrate is a package, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are enclosed in the package.
  • the techniques described herein relate to an integrated chipset, wherein the package encloses a plurality of packages, wherein the plurality of packages are connected via a common interface.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment. [0099] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to classify a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, an output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a common substrate
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier circuit to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are configured as layers in a 3D chipset architecture.
  • the techniques described herein relate to an integrated chipset, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are connected vertically using through-silicon vias.
  • the techniques described herein relate to an integrated chipset, wherein the integrated chipset is within a package enclosing a plurality of vertically stacked packages, wherein the plurality of vertically stacked packages are connected via a common interface.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment. [0117] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are configured as layers in a 3D chipset architecture.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0123] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share a set of I/O capabilities that are disposed on a perimeter of the chipset.
  • the techniques described herein relate to an integrated chipset, wherein the shared set of I/O capabilities include shared I/O ports disposed on a perimeter of the chipset. [0126] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared I/O ports are used to send and receive data to and from shared sensors and actuators. [0127] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared set of I/O capabilities include the set of functions for the set of robots. [0128] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share a set of I/O capabilities that are disposed on a perimeter of the chipset.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset having a set of processing cores concentrated in a middle portion of the chipset served by a set of I/O capabilities located on a perimeter of the chipset with an off-chip interconnection capability substantially at the
  • the techniques described herein relate to an integrated chipset, wherein the shared set of I/O capabilities include shared I/O ports disposed on the perimeter of the chipset.
  • the techniques described herein relate to an integrated chipset, wherein the shared I/O ports are used to send and receive data to and from shared sensors and actuators. [0147] In some aspects, the techniques described herein relate to an integrated chipset, wherein the shared set of I/O capabilities include the set of functions for the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding. [0152] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment. [0157] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier circuit to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset having a set of processing cores concentrated in a middle portion of the chipset served by a set of I/O capabilities located on a perimeter of the chipset with an off-chip interconnection capability substantially at the center of the chipset.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are integrated on the chipset having a set of modular processing clusters connected by a set of embedded multi -chip interconnect bridges to a set of high bandwidth memory modules.
  • a first module processing cluster includes the neural network classifier and the neural network control circuit.
  • a second module processing cluster includes the robotic control circuit and the data collection circuit.
  • each of the high bandwidth memory modules is a HBM module, a HBM2 module, a HBM2E module, or a HBM3 module.
  • the techniques described herein relate to an integrated chipset, wherein the embedded multi-chip interconnect bridge is one of a network on chip (NoC) bridge, an advanced extensible interface (AXI) bridge, a PCI express (PCIe) bridge, a high-speed inter-chip (HSIC) bridge, or a hypertransport bridge.
  • NoC network on chip
  • AXI advanced extensible interface
  • PCIe PCI express
  • HSIC high-speed inter-chip
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment. [0175] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit are integrated on the chipset having a set of modular processing clusters connected by a set of embedded multi -chip interconnect bridges to a set of high bandwidth memory modules.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset having a bi-directional torus network on chip architecture.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset having a bi-directional torus network on chip architecture.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0201] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0203] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit share are integrated on the chipset as a set of modular elements using die-to-die connectivity.
  • the techniques described herein relate to an integrated chipset, wherein at least a subset of the modular elements are arranged in a 2.5D or 3D stacked configuration.
  • the techniques described herein relate to an integrated chipset, wherein the die-to-die connectivity uses silicon interposers.
  • the techniques described herein relate to an integrated chipset, wherein the die-to-die connectivity is one or more of Embedded Multi -Die Interconnect Bridge (EMIB), Advanced Interconnect Bus (AIB), Chip-to-Chip Direct Connect (C2C).
  • EMIB Embedded Multi -Die Interconnect Bridge
  • AIB Advanced Interconnect Bus
  • C2C Chip-to-Chip Direct Connect
  • the techniques described herein relate to an integrated chipset, wherein the chipset is integrated within a package using Wafer-Level Fan-Out (WLFO) and/or Fan-Out Wafer-Level Packaging (FOWLP).
  • WLFO Wafer-Level Fan-Out
  • FOWLP Fan-Out Wafer-Level Packaging
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots. [0210] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier, and the neural network control circuit share are integrated on the chipset as a set of modular elements using die-to-die connectivity.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset using dynamic voltage and frequency scaling.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment. [0235] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset using dynamic voltage and frequency scaling.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0241] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0243] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on an optical chipset where optical communication is partitioned by wavelength to allow selective prioritization by wavelength.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on an optical chipset where optical communication is partitioned by wavelength to allow selective prioritization by wavelength.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0261] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0263] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset using integrated fan-out packaging.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment. [0270] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier circuit to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit share are integrated on the chipset using integrated fan-out packaging.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0281] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0283] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a high numerical aperture, extreme ultraviolet optical chipset.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a high numerical aperture, extreme ultraviolet optical chipset.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0301] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0303] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around field effect transistors.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots. [0306] In some aspects, the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around field effect transistors.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots. [0318] In some aspects, the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0321] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0323] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around nanowire field effect transistors.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment. [0331] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around nanowire field effect transistors.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0341] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0343] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around nanosheet field effect transistors.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around nanosheet field effect transistors.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0361] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0363] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around complementary field effect transistors.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots. [0367] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset using a set of gate-all-around complementary field effect transistors.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment. [0380] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0381] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0383] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a carbon nanotube chipset.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment. [0394] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a carbon nanotube chipset.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0401] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0403] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset having high-bandwidth SRAM memory.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier circuit to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset having high-bandwidth SRAM memory.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0421] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0423] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset having 3D-NAND flash memory.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment. [0430] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a chipset having 3D-NAND flash memory.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0441] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0443] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to an integrated chipset, including: a robotic control circuit configured to control a set of functions for a set of robots; a data collection circuit configured to handle data from a set of loT devices; a neural network classifier configured to process the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and a neural network control circuit configured to operate on the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a hybrid- bonded chipset.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize objects within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to perform scene understanding.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine an activity of a human within the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to recognize sounds in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a pose of at least one robot of the set of robots.
  • the techniques described herein relate to an integrated chipset, wherein the neural network classifier is configured to determine a map of the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control navigation within the environment. [0454] In some aspects, the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with objects in the environment.
  • the techniques described herein relate to an integrated chipset, wherein the neural network control circuit is configured to control interactions with humans in the environment.
  • the techniques described herein relate to a method performed by an integrated chipset, the method including: controlling, by a robotic control circuit of the integrated chipset, a set of functions for a set of robots; collecting, by a data collection circuit of the integrated chipset, data from a set of loT devices; processing, by a neural network classifier of the integrated chipset, the data from the loT devices to output a classification of a state of an environment wherein the robots operate; and processing, by a neural network control circuit of the integrated chipset, the output of the neural network classifier to output an operational control parameter to the robotic control circuit, wherein the robotic control circuit, the data collection circuit, the neural network classifier and the neural network control circuit are integrated on a hybrid-bonded chipset.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a robot that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein the set of robots includes a plurality of robots, wherein the integrated chipset is on board a platform that manages the plurality of robots.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes recognizing objects within the environment.
  • the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a pose of at least one robot of the set of robots. [0461] In some aspects, the techniques described herein relate to a method, wherein classifying the state of the environment includes determining a map of the environment.
  • the techniques described herein relate to a method, wherein the operational control parameter is configured to control navigation within the environment. [0463] In some aspects, the techniques described herein relate to a method, wherein the operational control parameter is configured to control interactions with humans or objects in the environment.
  • the techniques described herein relate to a robotic fleet including: a plurality of robotic systems, wherein each robotic system of the plurality of robotic systems includes an integrated chipset and a set of sensors, wherein each integrated chipset is configured to generate synthetic training data; and a robotic system in communication with at least a subset of the plurality of robotic systems, wherein the robotic system includes an integrated chipset configured to perform steps including: measuring a performance of an Al model being executed by the integrated chipset; determining that additional training data is required to optimize the performance of the Al model; transmitting, to the subset of the plurality of robotic systems, a request for training data, wherein the request specifies one or more context factors for the robotic system; receiving synthetic training data generated by the subset of the plurality of robotic systems; and fine-tuning the Al model using the synthetic training data to optimize the performance of the Al model.
  • the techniques described herein relate to a robotic fleet, wherein the subset of the plurality of robotic systems generate the synthetic
  • the techniques described herein relate to a robotic fleet, wherein the subset of the plurality of robotic systems generate the synthetic training data using a plurality of digital twins.
  • the techniques described herein relate to a robotic fleet, wherein the subset of the plurality of robotic systems generates the synthetic training data using generative Al models.
  • the techniques described herein relate to a robotic fleet, wherein the generative Al models include one or more of generative adversarial networks (GANs) or large language models (LLMs).
  • GANs generative adversarial networks
  • LLMs large language models
  • the techniques described herein relate to a robotic fleet, wherein a first robotic system of the plurality of robotic systems generates a first portion of the synthetic training data based on a first set of sensors on board the first robotic system and a second robotic system of the plurality of robotic systems generates a second portion of the synthetic training data based on a second set of sensors on board the second robotic system, wherein the first set of sensors and the second set of sensors include different sensors.
  • the techniques described herein relate to a robotic fleet, wherein a first robotic system of the plurality of robotic systems generates a first portion of the synthetic training data using a first Al model and a second robotic system of the plurality of robotic systems generates a second portion of the synthetic training data using a second Al model, wherein the first Al model and second Al model are different types of Al models.
  • the techniques described herein relate to a robotic fleet, wherein the synthetic training data includes data captured by the set of sensors.
  • the techniques described herein relate to a robotic fleet, wherein the one or more context factors include a task being performed by the robotic system.
  • the techniques described herein relate to a robotic fleet, wherein the subset of the plurality of robotic systems generate the synthetic training data by simulating performance of the task.
  • the techniques described herein relate to a robotic fleet, wherein measuring the performance of the Al model includes measuring the performance of the Al model for the task being performed by the robotic system.
  • the techniques described herein relate to a robotic fleet, wherein the one or more context factors describe a local environment of the robotic system.
  • the techniques described herein relate to a robotic fleet, wherein the one or more context factors describe one or more humans nearby the robotic system.
  • the techniques described herein relate to a robotic fleet, wherein the one or more context factors describe one or more devices nearby the robotic system. [0478] In some aspects, the techniques described herein relate to a robotic fleet, wherein the subset of the plurality of robotic systems generate the synthetic training data by simulating the local environment of the robotic system.
  • the techniques described herein relate to a robotic fleet, wherein measuring the performance of the Al model includes measuring the performance of the Al model with respect to the local environment of the robotic system.
  • the techniques described herein relate to a robotic fleet, wherein the Al model is one of a navigation model, an object manipulation model, a language model, or a network optimization model.
  • the techniques described herein relate to a robotic fleet, wherein the Al model is multimodal, wherein the synthetic training data includes one or more of audio data, image data, or video data.
  • the techniques described herein relate to a robotic fleet, wherein the robotic system includes a network enhancement chipset that optimizes the communication with the subset of the plurality of robotic systems.
  • the techniques described herein relate to a robotic fleet, wherein the integrated chipset includes a plurality of processing units integrated on a single substrate.
  • the techniques described herein relate to a robotic system including: an integrated chipset; and an intelligence layer configured to perform steps including: determining a task for performance by the integrated chipset of the robotic system, wherein the integrated chipset includes a plurality of processing units; selecting an Al model for execution by the integrated chipset for performing the task; obtaining training data for optimizing the Al model's performance on the determined task; causing the integrated chipset to retrain the Al model using the training data; and causing the integrated chipset to execute the retrained Al model to perform the task;
  • the techniques described herein relate to a robotic system, wherein obtaining the training data includes retrieving data from storage on board the robotic system. [0486] In some aspects, the techniques described herein relate to a robotic system, wherein obtaining the training data includes causing the integrated chipset to generate synthetic training data.
  • the techniques described herein relate to a robotic system, wherein the integrated chipset generates the synthetic training data using an environment digital twin and one or more context factors for the robotic system.
  • the techniques described herein relate to a robotic system, wherein the one or more context factors include one or more requirements, objectives, or methods of performing the task.
  • the techniques described herein relate to a robotic system, wherein the one or more context factors describe one or more humans nearby the robotic system.
  • the techniques described herein relate to a robotic system, wherein the one or more context factors describe one or more devices nearby the robotic system. [0491] In some aspects, the techniques described herein relate to a robotic system, wherein the integrated chipset generates the synthetic training data by simulating the performance of the task.
  • the techniques described herein relate to a robotic system, wherein the integrated chipset generates the synthetic training data by: transmitting a request for training data to a plurality of other robotic systems in communication with the robotic system; and receiving the synthetic training data from the other robotic systems.
  • the techniques described herein relate to a robotic system, wherein the synthetic training data includes data captured by sensors of the other robotic systems.
  • the techniques described herein relate to a robotic system, wherein the synthetic training data is generated based on simulations performed by the other robotic systems. [0495] In some aspects, the techniques described herein relate to a robotic system, wherein causing the integrated chipset to retrain the Al model using the training data includes: selecting a most optimal processing unit of the integrated chipset for retraining; and causing the most optimal processing unit to retrain the Al model using the training data.
  • the techniques described herein relate to a robotic system, wherein selecting the most optimal processing unit is based on one or more of processing capability or power efficiency of each processing unit.
  • the techniques described herein relate to a robotic system, wherein the most optimal processing unit is an FPGA, wherein causing the most optimal processing unit to retrain the Al model using the training data includes reprogramming the FPGA to execute the retraining.
  • the techniques described herein relate to a robotic system, wherein the intelligence layer is further configured to perform steps including: determining that a context for performing the task has ended; and deleting the retrained Al model.
  • the techniques described herein relate to a robotic system, wherein the intelligence layer is further configured to perform steps including: measuring the performance of the task; updating the training data in real-time based on the measured performance of the task; and causing the integrated chipset to further retrain the Al model using the updated training data.
  • the techniques described herein relate to a robotic system, wherein the Al model is one of a navigation model, an object manipulation model, a language model, or a network optimization model.
  • the techniques described herein relate to a robotic system, wherein the Al model is multimodal, wherein the training data includes one or more of audio data, image data, or video data.
  • the techniques described herein relate to a robotic system, wherein the plurality of processing units is integrated on a single substrate.
  • the techniques described herein relate to a robotic system, wherein the plurality of processing units is manufactured on a single silicon wafer.
  • the techniques described herein relate to a robotic fleet including: a fleet management platform configured to assign a plurality of roles to a plurality of robotic systems of the robotic fleet; and a robotic system in communication with the fleet management platform, wherein the robotic system includes an integrated chipset including a plurality of processing units, wherein the robotic system is configured to perform steps including: receiving a role assignment from the fleet management platform; configuring the plurality of processing units to perform tasks associated with the role; determining a next task from a task queue for performance by the robotic system; assigning the next task to a configured processing unit based on one or more context factors; and executing the next task using the configured processing unit.
  • the techniques described herein relate to a robotic fleet, wherein the fleet management platform is configured to dynamically re-assign the plurality of roles among the plurality of robotic systems of the robotic fleet based on a current state of the robotic fleet.
  • the techniques described herein relate to a robotic fleet, wherein the current state of the robotic fleet includes a status of each robotic system of the plurality of robotic systems.
  • the techniques described herein relate to a robotic fleet, wherein the status of each robotic system includes one or more of a power level of each robotic system, an availability to perform additional tasks of each robotic system, and a processing capability of each robotic system.
  • the techniques described herein relate to a robotic fleet, wherein the current state of the robotic fleet includes an environment of each robotic system.
  • the techniques described herein relate to a robotic fleet, wherein the current state of the robotic fleet includes a set of tasks assigned to the robotic fleet.
  • the techniques described herein relate to a robotic fleet, wherein the role assignment is one or more of an enhanced vision role, a data analysis role, or a decisionmaking role.
  • the techniques described herein relate to a robotic fleet, wherein the fleet management platform is onboard a controller robotic system.
  • the techniques described herein relate to a robotic fleet, wherein the controller robotic system is configured to assign a fleet management role to another robotic system of the robotic fleet.
  • the techniques described herein relate to a robotic fleet, wherein the context factors include an Al model used to perform the task.
  • the techniques described herein relate to a robotic fleet, wherein the context factors include a power efficiency of the configured processing unit.
  • the techniques described herein relate to a robotic fleet, wherein the context factors include a parallelizability of the next task.
  • the techniques described herein relate to a robotic fleet, wherein the context factors include one or more timing requirements for the next task. [0517] In some aspects, the techniques described herein relate to a robotic fleet, wherein the context factors include a communication speed of the configured processing unit.
  • the techniques described herein relate to a robotic fleet, wherein the robotic system is further configured to perform steps including, responsive to receiving the role assignment, requesting an Al model associated with the role from another device in communication with the robotic system.
  • the techniques described herein relate to a robotic fleet, wherein configuring the plurality of processing units to perform tasks associated with the role includes assigning one or more Al models to one or more processing units.
  • the techniques described herein relate to a robotic fleet, wherein assigning one or more Al models to one or more processing units includes reprogramming an FPGA to execute an assigned Al model.
  • the techniques described herein relate to a robotic system, wherein the one or more Al models include a navigation model, an object manipulation model, a language model, or a network optimization model.
  • the techniques described herein relate to a robotic system, wherein the plurality of processing units is integrated on a single substrate.
  • the techniques described herein relate to a robotic system, wherein the plurality of processing units is manufactured on a single silicon wafer.
  • the techniques described herein relate to a robotic system including: an integrated chipset including a plurality of processing units; and an intelligence layer configured to perform steps including: receiving a role assignment from a fleet management platform; configuring the plurality of processing units to perform tasks associated with the role; determining a next task from a task queue for performance by the robotic system; selecting an algorithm for execution by the integrated chipset for performing sub-task generation; causing the integrated chipset to execute the selected algorithm to generate one or more sub-tasks for performance of the next task; assigning the one or more sub-tasks to the plurality of processing units; and causing the plurality of processing units of the integrated chipset to execute the one or more sub-tasks to perform the next task.
  • the techniques described herein relate to a robotic system, wherein the selected algorithm for performing sub-task generation includes reinforcement learning based on simulating actions within a digital twin environment.
  • the techniques described herein relate to a robotic system, wherein the selected algorithm for performing sub-task generation includes one or more of a goal decomposition algorithm, a hierarchical task network, or a genetic algorithm.
  • the techniques described herein relate to a robotic system, wherein selecting the algorithm includes selecting an algorithm that is most suitable for sub-task generation based on the next task.
  • selecting the algorithm includes: selecting multiple algorithms for parallel generation of sub- tasks based on the next task; and selecting the one or more sub-tasks based on corresponding sub-tasks generated by the multiple algorithms.
  • the techniques described herein relate to a robotic system, wherein the role assignment is one or more of an enhanced vision role, a data analysis role, or a decisionmaking role.
  • the techniques described herein relate to a robotic system, wherein the robotic system is further configured to perform steps including, responsive to receiving the role assignment, requesting an Al model associated with the role from another device in communication with the robotic system.
  • the techniques described herein relate to a robotic system, wherein configuring the plurality of processing units to perform tasks associated with the role includes assigning one or more Al models to one or more of the plurality of processing units.
  • the techniques described herein relate to a robotic fleet, wherein assigning one or more Al models to one or more of the plurality of processing units includes reprogramming an FPGA to execute an assigned Al model.
  • the techniques described herein relate to a robotic system, wherein the one or more Al models include a navigation model, an object manipulation model, a language model, or a network optimization model.
  • the techniques described herein relate to a robotic system, wherein the one or more sub-tasks include optimization of a network.
  • the techniques described herein relate to a robotic system, wherein the one or more sub-tasks include applying a plurality of governance frameworks to control the robotic system.
  • the techniques described herein relate to a robotic system, wherein the one or more sub-tasks include a navigation task.
  • the techniques described herein relate to a robotic system, wherein the next task includes training an Al model, wherein the one or more sub-tasks include generating synthetic data for training the Al model.
  • the techniques described herein relate to a robotic system, wherein the one or more sub-tasks further include requesting synthetic data from other robotic systems in communication with the robotic system.
  • the techniques described herein relate to a robotic system, wherein the received role assignment is a fleet controller role, wherein the next task includes dynamically assigning roles to other robotic systems in communication with the robotic system.
  • the techniques described herein relate to a robotic system, wherein the intelligence layer is further configured to perform steps including assigning at least one of the sub-tasks to another robotic system.
  • the techniques described herein relate to a robotic system, wherein the plurality of processing units is integrated on a single substrate. [0542] In some aspects, the techniques described herein relate to a robotic system, wherein the plurality of processing units is manufactured on a single silicon wafer.
  • the techniques described herein relate to a robotic system, wherein the plurality of processing units are separately manufactured and bonded to a single substrate.
  • FIG. 1 is a block diagram showing prior art relationships of various entities and facilities in a supply chain.
  • FIG. 2 is a block diagram showing components and interrelationships of systems and processes of a value chain network in accordance with the present disclosure.
  • FIG. 3 is another block diagram showing components and interrelationships of systems and processes of a value chain network in accordance with the present disclosure.
  • FIG. 4 is a block diagram showing components and interrelationships of systems and processes of a digital products network of FIGS. 2 and 3 in accordance with the present disclosure.
  • FIG. 5 is a block diagram showing components and interrelationships of systems and processes of a value chain network technology stack in accordance with the present disclosure.
  • FIG. 6 is a block diagram showing a platform and relationships for orchestrating controls of various entities in a value chain network in accordance with the present disclosure.
  • FIG. 7 is a block diagram showing components and relationships in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 8 is a block diagram showing components and relationships of value chain entities managed by embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 9 is a block diagram showing network relationships of entities in a value chain network in accordance with the present disclosure.
  • FIG. 10 is a block diagram showing a set of applications supported by unified data handling layers in a value chain network management platform in accordance with the present disclosure.
  • FIG. 11 is a block diagram showing components and relationships in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 12 is a block diagram showing components and relationships of a data storage layer in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 13 is a block diagram showing components and relationships of an adaptive intelligent systems layer in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 14 is a block diagram that depicts providing adaptive intelligence systems for coordinated intelligence for sets of demand and supply applications for a category of goods in accordance with the present disclosure.
  • FIG. 15 is a block diagram that depicts providing hybrid adaptive intelligence systems for coordinated intelligence for sets of demand and supply applications or a category of goods in accordance with the present disclosure.
  • FIG. 16 is a block diagram that depicts providing adaptive intelligence systems for predictive intelligence for sets of demand and supply applications for a category of goods in accordance with the present disclosure.
  • FIG. 17 is a block diagram that depicts providing adaptive intelligence systems for classification intelligence for sets of demand and supply applications for a category of goods in accordance with the present disclosure.
  • FIG. 18 is a block diagram that depicts providing adaptive intelligence systems to produce automated control signals for sets of demand and supply applications for a category of goods in accordance with the present disclosure.
  • FIG. 19 is a block diagram that depicts training artificial intelligence/machine learning systems to produce information routing recommendations for a selected value chain network in accordance with the present disclosure.
  • FIG. 20 is a block diagram that depicts a semi-sentient problem recognition system for recognition of pain points/problem states in a value chain network in accordance with the present disclosure.
  • FIG. 21 is a block diagram that depicts a set of artificial intelligence systems operating on value chain information to enable automated coordination of value chain activities for an enterprise in accordance with the present disclosure.
  • FIG. 22 is a block diagram showing components and relationships involved in integrating a set of digital twins in an embodiment of a value chain network management platform in accordance with the present disclosure.
  • FIG. 23 is a block diagram showing a set of digital twins involved in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 24 is a block diagram showing components and relationships of entity discovery and management systems in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 25 is a block diagram showing components and relationships of a robotic process automation system in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 26 is a block diagram showing components and relationships of a set of opportunity miners in an embodiment of a value chain network management platform in accordance with the present disclosure.
  • FIG. 27 is a block diagram showing components and relationships of a set of edge intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 28 is a block diagram showing components and relationships in an embodiment of a value chain network management platform in accordance with the present disclosure.
  • FIG. 29 is a block diagram showing additional details of components and relationships in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 30 is a block diagram showing components and relationships in an embodiment of a value chain network management platform that enables centralized orchestration of value chain network entities in accordance with the present disclosure.
  • FIG. 31 is a block diagram showing components and relationships of a unified database in an embodiment of a value chain network management platform in accordance with the present disclosure.
  • FIG. 32 is a block diagram showing components and relationships of a set of unified data collection systems in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 33 is a block diagram showing components and relationships of a set of Internet of Things monitoring systems in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 34 is a block diagram showing components and relationships of a machine vision system and a digital twin in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 35 is a block diagram showing components and relationships of a set of adaptive edge intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 36 is a block diagram showing additional details of components and relationships of a set of adaptive edge intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 37 is a block diagram showing components and relationships of a set of unified adaptive intelligence systems in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 38 is a schematic of a system configured to train an artificial system that is leveraged by a value chain system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.
  • FIG. 39 is a schematic of a system configured to train an artificial system that is leveraged by a container fleet management system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.
  • FIG. 40 is a schematic of a system configured to train an artificial system that is leveraged by a logistics design system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.
  • FIG. 41 is a schematic of a system configured to train an artificial system that is leveraged by a packaging design system using real world outcome data and a digital twin system according to some embodiments of the present disclosure.
  • FIG. 43 is a schematic illustrating an example of a portion of an information technology system for value chain artificial intelligence leveraging digital twins according to some embodiments of the present disclosure.
  • FIG. 44 is a block diagram showing components and relationships of a set of intelligent project management facilities in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 45 is a block diagram showing components and relationships of an intelligent task recommendation system in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 46 is a block diagram showing components and relationships of a routing system among nodes of a value chain network in embodiments of a value chain network management platform in accordance with the present disclosure.
  • FIG. 47 is a block diagram showing components and relationships of a dashboard for managing a set of digital twins in embodiments of a value chain network management platform.
  • FIG. 48 is a block diagram showing components and relationships in embodiments of a value chain network management platform that uses a microservices architecture.
  • FIG. 49 is a block diagram showing components and relationships of an Internet of Things data collection architecture and sensor recommendation system in embodiments of a value chain network management platform.
  • FIG. 50 is a block diagram showing components and relationships of a social data collection architecture in embodiments of a value chain network management platform.
  • FIG. 51 is a block diagram showing components and relationships of a crowdsourcing data collection architecture in embodiments of a value chain network management platform.
  • FIG. 52 is a diagrammatic view that depicts embodiments of a set of value chain network digital twins representing virtual models of a set of value chain network entities in accordance with the present disclosure.
  • FIG. 53 is a diagrammatic view that depicts embodiments of a warehouse digital twin kit system in accordance with the present disclosure.
  • FIG. 54 is a diagrammatic view that depicts embodiments of a stress test performed on a value chain network in accordance with the present disclosure.
  • FIG. 55 is a diagrammatic view that depicts embodiments of methods used by a machine for detecting faults and predicting any future failures of the machine in accordance with the present disclosure.
  • FIG. 56 is a diagrammatic view that depicts embodiments of deployment of machine twins to perform predictive maintenance on a set of machines in accordance with the present disclosure.
  • FIG. 57 is a schematic illustrating an example of a portion of a system for value chain customer digital twins and customer profile digital twins according to some embodiments of the present disclosure.
  • FIG. 58 is a schematic illustrating an example of an advertising application that interfaces with the adaptive intelligent systems layer in accordance with the present disclosure.
  • FIG. 59 is a schematic illustrating an example of an e-commerce application integrated with the adaptive intelligent systems layer in accordance with the present disclosure.
  • FIG. 60 is a schematic illustrating an example of a demand management application integrated with the adaptive intelligent systems layer in accordance with the present disclosure.
  • FIG. 61 is a schematic illustrating an example of a portion of a system for value chain smart supply component digital twins according to some embodiments of the present disclosure.
  • FIG. 62 is a schematic illustrating an example of a risk management application that interfaces with the adaptive intelligent systems layer in accordance with the present disclosure.
  • FIG. 63 is a diagrammatic view of maritime assets associated with a value chain network management platform including components of a port infrastructure in accordance with the present disclosure.
  • FIGS. 64 and 65 are diagrammatic views of maritime assets associated with a value chain network management platform including components of a ship in accordance with the present disclosure.
  • FIG. 66 is a diagrammatic view of maritime assets associated with a value chain network management platform including components of a barge in accordance with the present disclosure.
  • FIG. 67 is a diagrammatic view of maritime assets associated with a value chain network management platform including those involved in maritime events, legal proceedings and making use of geofenced parameters in accordance with the present disclosure.
  • FIG. 68 is a schematic illustrating an example environment of the enterprise and executive control tower and management platform, including data sources in communication therewith, according to some embodiments of the present disclosure.
  • FIG. 69 is a schematic illustrating an example set of components of the enterprise control tower and management platform according to some embodiments of the present disclosure.
  • FIG. 70 is a schematic illustrating and example of an enterprise data model according to some embodiments of the disclosure.
  • FIG. 71 is a schematic illustrating examples of different types of enterprise digital twins, including executive digital twins, in relation to the data layer, processing layer, and application layer of the enterprise digital twin framework according to some embodiments of the present disclosure.
  • FIG. 72 is a schematic illustrating an example implementation of the enterprise and executive control tower and management platform according to some embodiments of the present disclosure.
  • FIG. 73 is a flow chart illustrating an example set of operations for configuring and serving an enterprise digital twin.
  • FIG. 74 illustrates an example set of operations of a method for configuring an organizational digital twin.
  • FIGS. 76-103 are schematic diagrams of embodiments of neural net systems that may connect to, be integrated in, and be accessible by the platform for enabling intelligent transactions including ones involving expert systems, self-organization, machine learning, artificial intelligence and including neural net systems trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes in accordance with embodiments of the present disclosure.
  • FIG. 104 is a schematic illustrating an example intelligence services system according to some embodiments of the present disclosure.
  • FIG. 105 is a schematic illustrating an example neural network with multiple layers according to some embodiments of the present disclosure.
  • FIG. 106 is a schematic illustrating an example convolutional neural network (CNN) according to some embodiments of the present disclosure.
  • FIG. 107 is a schematic illustrating an example neural network for implementing natural language processing according to some embodiments of the present disclosure.
  • FIG. 108 is a schematic illustrating an example reinforcement learning-based approach for executing one or more tasks by a mobile system according to some embodiments of the present disclosure.
  • FIG. 109 is a schematic illustrating an example physical orientation determination chip according to some embodiments of the present disclosure.
  • FIG. 110 is a schematic illustrating an example network enhancement chip according to some embodiments of the present disclosure.
  • FIG. I l l is a schematic illustrating an example diagnostic chip according to some embodiments of the present disclosure.
  • FIG. 112 is a schematic illustrating an example governance chip according to some embodiments of the present disclosure.
  • FIG. 113 is a schematic illustrating an example prediction, classification, and recommendation chip according to some embodiments of the present disclosure.
  • FIG. 114 is a diagrammatic view illustrating an example environment of an autonomous additive manufacturing platform according to some embodiments of the present disclosure.
  • FIG. 117 is a schematic view illustrating a system for learning on data from an autonomous additive manufacturing platform to train an artificial learning system to use digital twins for classification, predictions and decision making according to some embodiments of the present disclosure.
  • FIG. 118 is a schematic illustrating an example implementation of an autonomous additive manufacturing platform including various components along with other entities of a distributed manufacturing network according to some embodiments of the present disclosure.
  • FIG. 119 is a schematic illustrating an example implementation of an autonomous additive manufacturing platform for automating and managing manufacturing functions and sub- processes including process and material selection, hybrid part workflows, feedstock formulation, part design optimization, risk prediction and management, marketing and customer service according to some embodiments of the present disclosure.
  • FIG. 121 is a schematic illustrating an example implementation of a distributed manufacturing network where the digital thread data is tokenized and stored in a distributed ledger so as to ensure traceability of parts printed at one or more manufacturing nodes in the distributed manufacturing network according to some embodiments of the present disclosure.
  • FIG. 122 is a diagrammatic view illustrating an example implementation of a conventional computer vision system for creating an image of an object of interest.
  • FIG. 123 is a schematic illustrating an example implementation of a dynamic vision system for dynamically learning an object concept about an object of interest according to some embodiments of the present disclosure.
  • FIG. 125 is a flow diagram illustrating a method for object recognition by a dynamic vision system according to some embodiments of the present disclosure.
  • FIG. 126 is a schematic illustrating an example implementation of a dynamic vision system for modelling, simulating and optimizing various optical, mechanical, design and lighting parameters of the dynamic vision system according to some embodiments of the present disclosure.
  • FIG. 127 is a schematic view illustrating an example implementation of a dynamic vision system depicting detailed view of various components along with integration of the dynamic vision system with one or more third party systems according to some embodiments of the present disclosure.
  • FIG. 128 is a schematic illustrating an example environment of a fleet management platform according to some embodiments of the present disclosure.
  • FIG. 129 is a schematic illustrating example configurations of a multi-purpose robot and a special purpose robot according to some embodiments of the present disclosure.
  • FIG. 130 is a schematic illustrating an example platform -level intelligence layer of a fleet management platform according to some embodiments of the present disclosure.
  • FIG. 131 is a schematic illustrating an example configuration of an intelligence layer according to some embodiments of the present disclosure.
  • FIG. 132 is a schematic illustrating an example security framework according to some embodiments of the present disclosure.
  • FIG. 133 is a schematic illustrating an example environment of a fleet management platform according to some embodiments of the present disclosure.
  • FIG. 134 is a schematic illustrating an example data flow of a job configuration system according to some embodiments of the present disclosure.
  • FIG. 135 is a schematic illustrating an example data flow of a fleet operations system according to some embodiments of the present disclosure.
  • FIG. 136 is a schematic illustrating an example job parsing system and task definition system and an example data flow thereof according to some embodiments of the present disclosure.
  • FIG. 137 is a schematic illustrating an example fleet configuration system and an example data flow thereof according to some embodiments of the present disclosure.
  • FIG. 140 is a schematic illustrating an example architecture of the robot control system according to some embodiments of the present disclosure.
  • FIG. 141 is a schematic illustrating an example architecture of the robot control system 12150 that utilizes data from multiple sensors in the vision and sensing system according to some embodiments of the present disclosure.
  • FIG. 142 is a schematic illustrating an example vision and sensing system of a robot according to some embodiments of the present disclosure.
  • FIG. 145 is a schematic illustrating example configurations of a smart container according to some embodiments of the present disclosure.
  • FIG. 146 is a schematic illustrating an intelligence service adapted to provide intelligence services to the smart intermodal container system according to some embodiments of the present disclosure.
  • FIG. 147 is a schematic illustrating a digital twin module according to some embodiments of the present disclosure according to some embodiments of the present disclosure.
  • FIG. 148 illustrates an example embodiment of a method of receiving requests to update one or more properties of digital twins of shipping entities and/or environments.
  • FIG. 149 illustrates an example embodiment of a method for updating a set of cost of downtime values in the digital twin of a smart container according to some embodiments of the present disclosure.
  • FIG. 150 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.
  • FIG. 151 is a schematic illustrating an example environment of a connected product according to some embodiments of the present disclosure.
  • FIG. 153 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.
  • FIG. 154 is a flow diagram illustrating a method of using product level data according to some embodiments of the disclosure.
  • FIG. 155 is a schematic illustrating an example environment of a digital product network according to some embodiments of the present disclosure.
  • FIG. 156 is a schematic illustrating an example of a smart futures contract system according to some embodiments of the present disclosure.
  • FIG. 157 is a schematic illustrating an example environment of an edge networking system according to some embodiments of the present disclosure.
  • FIG. 159 is a schematic illustrating an example environment of an edge networking system according to some embodiments of the present disclosure including a configured device EDNW system.
  • FIG. 160 is a block diagram showing a schematic of a dual-process artificial neural network system according to some embodiments of the present disclosure.
  • FIG. 163B is a schematic view of another example control architecture for system facilitation and/or management.
  • FIG. 163C is a schematic view of an example control architecture for system facilitation and/or management.
  • FIG. 163D is a schematic view of another example control architecture for system facilitation and/or management.
  • FIG. 164A is a schematic view of an example management stack that includes a control architecture similar to FIGS. 163A and 163B.
  • FIG. 164B is a schematic view of an example management stack capable of implementing a control architecture similar to that of FIGS. 163 A and 163B for a value chain network.
  • FIG. 164C is a schematic view of an example management stack that includes a control architecture similar to FIGS. 163C and 163D.
  • FIG. 164D is a schematic view of an example management stack capable of implementing a control architecture similar to that of FIGS. 163C and 163D for a value chain network.
  • FIG. 165 A is a flow diagram of an example arrangement for a control architecture similar to that of FIGS. 163A and 163B.
  • FIG. 165B is a flow diagram of an example arrangement for a control architecture similar to that of FIGS. 163C and 163D.
  • FIG. 166 is an example flowchart of one or more VCN processes that may be used with one or more example implementations of the disclosure.
  • FIGS. 167-173 are example flowcharts of one or more VCN processes that may be used with one or more example implementations of the disclosure.
  • FIG. 174 is a schematic view of an example generative Al system.
  • FIG. 175 is a schematic view of an example of a determination of attention by a machine learning model.
  • FIG. 176 is a schematic view of an example of a transformer model.
  • FIG. 177 is a schematic view of a value chain network (VCN) converging technology stack.
  • VCN value chain network
  • FIG. 179 is a schematic view of an example robotic fleet operations platform.
  • FIG. 180 is a schematic view of an example specialized integrated chipset.
  • FIG. 183 is a schematic view of an example environment including a fleet management platform and/or robot.
  • systems and processes of this disclosure may include information technology processes and systems for management of value chain network entities, including supply chain and demand management entities.
  • enterprise management platforms more particularly involving an edge-distributed database and query language for storing and retrieving value chain data may also be used.
  • Orders for products were fulfilled by manufacturers through a supply chain, such as depicted in FIG. 1, where suppliers 122 in various supply environments 160, operating production facilities 134 or acting as resellers or distributors for others, made a product 130 available at a point of origin 102 in response to an order.
  • the product 130 was passed through the supply chain, being conveyed and stored via various hauling facilities 138 and distribution facilities 134, such as warehouses 132, fulfillment centers 112 and delivery systems 114, such as trucks and other vehicles, trains, and the like.
  • various hauling facilities 138 and distribution facilities 134 such as warehouses 132, fulfillment centers 112 and delivery systems 114, such as trucks and other vehicles, trains, and the like.
  • logistics facilities 138 and distribution facilities 134 such as warehouses 132, fulfillment centers 112 and delivery systems 114, such as trucks and other vehicles, trains, and the like.
  • maritime facilities and infrastructure such as ships, barges, docks and ports provided transport over waterways between the points of origin 102 and one or more destinations 104.
  • wearables may provide insight into the movement, health indicators, physiological states, activity states, movements, and other characteristics of workers.
  • organizations implement CRM systems, ERP systems, operations systems, information technology systems, advanced analytics and other systems that leverage information and information technology
  • organizations have access to an increasingly wide array of other large data sets, such as marketing data, sales data, operational data, information technology data, performance data, customer data, financial data, market data, pricing data, supply chain data, and the like, including data sets generated by or for the organization and third-party data sets.
  • the presence of more data and data of new types offers many opportunities for organizations to achieve competitive advantages; however, it also presents problems, such as of complexity and volume, such that users can be overwhelmed, missing opportunities for insight.
  • RFID Radio Frequency Identification
  • value chain network refers to elements and interconnections of historically segregated demand management systems and processes and supply chain management systems and processes, enabled by the development and convergence of numerous diverse technologies.
  • a value chain control tower 260 may be connected to, in communication with, or otherwise operatively coupled with data processing facilities including, but not limited to, big data centers (e.g., big data processing 230) and related processing functionalities that receive data flow, data pools, data streams and/or other data configurations and transmission modalities received from, for example, digital product networks 21002, directly from customers (e.g., direct connected customer 250), or some other third party 220.
  • big data centers e.g., big data processing 230
  • processing functionalities that receive data flow, data pools, data streams and/or other data configurations and transmission modalities received from, for example, digital product networks 21002, directly from customers (e.g., direct connected customer 250), or some other third party 220.
  • Communications related to market orchestration activities and communications 210, analytics 232, or some other type of input may also be utilized by the value chain control tower for demand enhancement 262, synchronized planning 234, intelligent procurement 238, dynamic fulfillment 240 or some other smart operation informed by coordinated and adaptive intelligence, as described herein.
  • the value chain control tower 360 may coordinate market orchestration activities 310 including, but not limited to, demand curve management 352, synchronization of an ecosystem 348, intelligent procurement 344, dynamic fulfillment 350, value chain analytics 340, and/or smart supply chain operations 342.
  • the value chain control tower 360 may be connected to, in communication with, or otherwise operatively coupled with adaptive data pipelines 302 and processing facilities that may be further connected to, in communication with, or otherwise operationally coupled with external data sources 320 and a data handling stack 330 (e.g., value chain network technology) that may include intelligent, user-adaptive interfaces, adaptive intelligence and control 332, and/or adaptive data monitoring and storage 334, as described herein.
  • the value chain control tower 302 may also be further connected to, in communication with, or otherwise operatively coupled with additional value chain entities including, but not limited to, digital product networks 21002, customers (e.g., directed connected customers 362), and/or other connected operations 364 and entities of a value chain network.
  • DPN DIGITAL PRODUCT NETWORKS
  • products may create and transmit data, such as product level data, to a communication layer within the value chain network technology stack and/or to an edge data processing facility.
  • This data may produce enhanced product level data and may be combined with third party data for further processing, modeling or other adaptive or coordinated intelligence activity, as described herein. This may include, but is not limited to, producing and/or simulating product and value chain use cases, the data for which may be utilized by products, product development processes, product design, and the like.
  • the platforms or the value chain network technology stack may include a development environment, APIs for connectivity, cloud and/or hosting applications, and device discovery.
  • the data aggregation facilities or layer may include, but is not limited to, modules for data normalization for common transmission and heterogeneous data collection from disparate devices.
  • the data facilities or layer may include, but is not limited to, loT and big data access, control, and collection and alternatives.
  • the value chain network technology stack may be further associated with additional data sources and/or technology enablers.
  • FIG. 6 illustrates a connected value chain network 668 in which a value chain network management platform 604 (referred to herein in some cases as a “value chain control tower,” the “VCNP,” or simply as “the system,” or “the platform”) orchestrates a variety of factors involved in planning, monitoring, controlling, and optimizing various entities and activities involved in the value chain network 668, such as supply and production factors, demand factors, logistics and distribution factors, and the like.
  • a value chain network management platform 604 referred to herein in some cases as a “value chain control tower,” the “VCNP,” or simply as “the system,” or “the platform”
  • a unified platform 604 for monitoring and managing supply factors and demand factors as well as status information can be shared about and between various entities (e.g., including custom ers/consumers, suppliers, distribution such as distributors, suppliers, and production such as producers or production facilities) as demand factors are understood and accounted for, as orders are generated and fulfilled, and as products are created and moved through a supply chain.
  • the value chain network 668 may include not only an intelligent product 1510, but all of the equipment, infrastructure, personnel and other entities involved in planning and satisfying demand for it.
  • the value chain network 668 managed by a value chain management platform 604 may include a set of value chain network entities 652, such as, without limitation: a product 1510, which may be an intelligent product 1510; a set of production facilities 674 involved in producing finished goods, components, systems, subsystems, materials used in goods, or the like; various entities, activities and other supply factors 648 involved in supply environments 670, such as suppliers 642, points of origin 610, and the like; various entities, activities and other demand factors 644 involved in demand environments 672, such as customers 662 (including consumers, businesses, and intermediate customers such as value added resellers and distributors), retailers 664 (including online retailers, mobile retailers, conventional bricks and mortar retailers, pop-up shops and the like) and the like located and/or operating at various destinations 612; various distribution environments 678 and distribution facilities 658, such as warehousing facilities 654, fulfillment facilities 628, and delivery
  • the value chain network management platform 604 monitors, controls, and otherwise enables management (and in some cases autonomous or semi -autonomous behavior) of a wide range of value chain network 668 processes, workflows, activities, events and applications 630 (collectively referred to in some cases simply as “applications 630”).
  • the value chain network management platform 604 may include a set of systems, applications, processes, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, and other elements working in coordination to enable intelligent management of a set of value chain entities 652 that may occur, operate, transact or the like within, or own, operate, support or enable, one or more value chain network processes, workflows, activities, events and/or applications 630 or that may otherwise be part of, integrated with, linked to, or operated on by the VCNP 604 in connection with a product 1510 (which may be any category of product, such as a finished good, software product, hardware product, component product, material, item of equipment, item of consumer packaged goods, consumer product, food product, beverage product, home product, business supply product, consumable product, pharmaceutical product, medical device product, technology product, entertainment product, or any other type of product and/or set of related services, and which may,
  • the management platform 604 may include a set of data handling layers 608 each of which is configured to provide a set of capabilities that facilitate development and deployment of intelligence, such as for facilitating automation, machine learning, applications of artificial intelligence, intelligent transactions, state management, event management, process management, and many others, for a wide variety of value chain network applications and end uses.
  • the data handling layers 608 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604 by a value chain monitoring systems layer 614.
  • the value chain monitoring systems layer 614 may include, integrate with, and/or cooperate with various data collection and management systems 640, referred to for convenience in some cases as data collection systems 640, for collecting and organizing data collected from or about value chain entities 652, as well as data collected from or about the various data layers 624 or services or components thereof.
  • the data handling layers 608 are configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 by a value chain network-oriented data storage systems layer 624, referred to herein for convenience in some cases simply as a data storage layer 624 or storage layer 624. As shown in FIG. 7, the data handling layers 608 may also include an adaptive intelligent systems layer 614.
  • the adaptive intelligence systems layer 614 may include a set of data processing, artificial intelligence and computational systems 634 that are described in more detail elsewhere throughout this disclosure.
  • the data processing, artificial intelligence and computational systems 634 may relate to artificial intelligence (e.g., expert systems, artificial intelligence, neural, supervised, machine learning, deep learning, model-based systems, and the like).
  • the data processing, artificial intelligence and computational systems 634 may relate to various examples, in some embodiments, such as use of a recurrent network as adaptive intelligence system operating on a blockchain of transactions in a supply chain to determine a pattern, use with biological systems, opportunity mining (e.g., where artificial intelligence system may be used to monitor for new data sources as opportunities for automatically deploying intelligence), robotic process automation (e.g., automation of intelligent agents for various workflows), edge and network intelligence (e.g., implicated on monitoring systems such as adaptively using available RF spectrum, adaptively using available fixed network spectrum, adaptively storing data based on available storage conditions, adaptively sensing based on a kind of contextual sensing), and the like.
  • opportunity mining e.g., where artificial intelligence system may be used to monitor for new data sources as opportunities for automatically deploying intelligence
  • robotic process automation e.g., automation of intelligent agents for various workflows
  • edge and network intelligence e.g., implicated on monitoring systems such as adaptively using available RF spectrum, adaptively using available fixed network spectrum,
  • the data handling layers 608 may be depicted in vertical stacks or ribbons in the figures and may represent many functionalities available to the platform 604 including storage, monitoring, and processing applications and resources and combinations thereof.
  • the set of capabilities of the data handling layers 608 may include a shared microservices architecture.
  • the set of capabilities may be deployed to provide multiple distinct services or applications, which can be configured as one or more services, workflows, or combinations thereof.
  • the set of capabilities may be deployed within or be resident to certain applications or processes.
  • the set of capabilities can include one or more activities marshaled for the benefit of the platform.
  • the set of capabilities may include one or more events organized for the benefit of the platform.
  • one of the sets of capabilities of the platform may be deployed within at least a portion of a common architecture such as common architecture that supports a common data schema. In embodiments, one of the sets of capabilities of the platform may be deployed within at least a portion of a common architecture that can support a common storage. In embodiments, one of the sets of capabilities of the platform may be deployed within at least a portion of a common architecture that can support common monitoring systems. In embodiments, one or more sets of capabilities of the platform may be deployed within at least a portion of a common architecture that can support one or more common processing frameworks.
  • the set of capabilities of the data handling layers 608 can include examples where the storage functionality supports scalable processing capabilities, scalable monitoring systems, digital twin systems, payments interface systems, and the like.
  • one or more software development kits can be provided by the platform along with deployment interfaces to facilitate connections and use of the capabilities of the data handling layers 608.
  • adaptive intelligence systems may analyze, learn, configure, and reconfigure one or more of the capabilities of the data handling layers 608.
  • the platform 604 may, for example, include a common data storage schema serving a shipyard entity related service and a warehousing entity service. There are many other applicable examples and combinations applicable to the foregoing example including the many value chain entities disclosed herein. By way of these examples, the platform 604 may be shown to create connectivity (e.g., supply of capabilities and information) across many value chain entities.
  • the value chain network management platform 604 is illustrated in connection with a set of value chain entities 652 that may be subject to management by the platform 604, may integrate with or into the platform 604, and/or may supply inputs to and/or take outputs from the platform 604, such as ones involved in or for a wide range of value chain activities (such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities, events and applications 630 (collectively “applications 630” or simply “activities”)).
  • applications 630 or simply “activities”.
  • Connections with the value chain entities 652 may be facilitated by a set of connectivity facilities 642 and interfaces 702, including a wide range of components and systems described throughout this disclosure and in greater detail below. This may include connectivity and interface capabilities for individual services of the platform, for the data handling layers, for the platform as a whole, and/or among value chain entities 652, among others.
  • the product 1510 may be encompassed as an intelligent product 1510 or the VCNP 604 may include the intelligent product 1510.
  • the intelligent product 1510 may be enabled with a set of capabilities such as, without limitation data processing, networking, sensing, autonomous operation, intelligent agent, natural language processing, speech recognition, voice recognition, touch interfaces, remote control, self-organization, self- healing, process automation, computation, artificial intelligence, analog or digital sensors, cameras, sound processing systems, data storage, data integration, and/or various Internet of Things capabilities, among others.
  • the intelligent product 1510 may include a form of information technology.
  • the intelligent product 1510 may have a processor, computer random access memory, and a communication module.
  • the monitoring systems layer 614 may monitor any or all of the value chain entities 652 in a value chain network 668, may exchange data with the value chain entities 652, may provide control instructions to or take instructions from any of the value chain entities 652, or the like, such as through the various capabilities of the data handling layers 608 described throughout this disclosure.
  • the next step may be to establish a common data schema that enables services that work on or in any one of these applications. This may involve taking any of the data that is flowing through or about any of these entities 652 and pull the data into a framework where other applications across supply and demand may interact with the entities 652.
  • This may be a shared data pipeline coming from an loT system and other external data sources, feeding into the monitoring layer, being stored in a common data schema in the storage layer, and then various intelligence may be trained to identify implications across these entities 652.
  • a supplier may be bankrupt, or a determination is made that the supplier is bankrupt, and then the VCNP 604 may automatically trigger a substitute smart contract to be sent to a secondary supplier with altered terms.
  • the VCNP 604 may automatically trigger a substitute smart contract to be sent to a secondary supplier with altered terms.
  • There may be management of different aspects of the supply chain. For example, changing pricing instantly and automatically on the demand side in response to one more supplier’s being identified as bankrupt (e.g., from bankruptcy announcement). Other similar examples may be used based on what occurs in that automation layer which may be enabled by the VCNP 604.
  • a digital twin may be used by user to view all these entities 652 that are not typically shown together and monitor what is going on with each of these entities 652 including identification of problem states. For example, after viewing three quarters of bad financial reports on a supplier, a report may be flagged to watch it closely for potential future bankruptcy, etc.
  • a port infrastructure facility 660 may inform a fleet of floating assets 620 via connections to the floating assets 620 (such as ships, barges, or the like) that the port is near capacity, thereby kicking off a negotiation process (which may include an automated negotiation based on a set of rules and governed by a smart contract) for the remaining capacity and enabling some assets 620 to be redirected to alternative ports or holding facilities.
  • a negotiation process which may include an automated negotiation based on a set of rules and governed by a smart contract
  • These and many other connections among value chain network entities 652, whether one-to-one connections, one-to-many connections, many-to-many connections, or connections among defined groups of entities 652 (such as ones controlled by the same owner or operator), are encompassed herein as applications 630 managed by the VCNP 604.
  • the value chain management platform 604 may host an enable interaction among a wide range of disparate applications 630 (such term including the above-referenced and other value chain applications, services, solutions, and the like), such that by virtue of shared microservices, shared data infrastructure, and shared intelligence, any pair or larger combination or permutation of such services may be improved relative to an isolated application of the same type.
  • disparate applications 630 such term including the above-referenced and other value chain applications, services, solutions, and the like
  • the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set of value chain network entities 652 may further include, without limitation: a payments application 860 (such as for calculating payments (including based on situational factors such as applicable taxes, duties and the like for the geography of an entity 652), transferring funds, resolving payments to parties, and the like, for any of the applications 630 noted herein); a process management application 862 (such as for managing any of the processes or workflows described throughout this disclosure, including supply processes, demand processes, logistics processes, delivery processes, fulfillment processes, distribution processes, ordering processes, navigation processes, and many others); a compatibility testing application 864, such as for assessing compatibility among value chain network entities 652 or activities involved in any of the processes, workflows, activities, or other applications 630 described herein (such as for determining compatibility of a container or package with a product 1510, the compatibility of a product 1510 with a
  • the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set of value chain network entities 652 may further include, without limitation: a predictive maintenance application 910 (such as for anticipating, predicting, and undertaking actions to manage faults, failures, shutdowns, damage, required maintenance, required repairs, required service, required support, or the like for a set of value chain network entities 652, such as products 650, equipment, infrastructure, buildings, vehicles, and others); a logistics application 912 (such as for managing logistics for pickups, deliveries, transfer of goods onto hauling facilities, loading, unloading, packing, picking, shipping, driving, and other activities involving in the scheduling and management of the movement of products 650 and other items between points of origin and points of destination through various intermediate locations; a reverse logistic application 914 (such as for handling logistics for returned products 650, waste products, damaged goods, or other items that can be transferred on a return logistics path
  • the set of applications 614 provided on the VCNP 604, integrated with the VCNP 604 and/or managed by or for the VCNP 604 and/or involving a set of value chain network entities 652 may further include, without limitation a maritime fleet management application 880 (for managing a set of maritime assets, such as container ships, barges, boats, and the like, as well as related infrastructure facilities such as docks, cranes, ports, and others, such as to determine optimal routes for fleet assets based on weather, market, traffic, and other conditions, to ensure compliance with policies and regulations, to ensure safety, to improve environmental factors, to improve financial metrics, and many others); a shipping management application 882 (such as for managing a set of shipping assets, such as trucks, trains, airplanes, and the like, such as to optimize financial yield, to improve safety, to reduce energy consumption, to reduce delays, to mitigate environmental impact, and for many other purposes); an opportunity matching application 884 (such as for matching one or more demand factors with one or more supply factors, for matching needs
  • Value chain entities 652 such as involved in or for a wide range of value chain activities (such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities, events and applications 630 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference.
  • value chain activities such as supply chain activities, logistics activities, demand management and planning activities, delivery activities, shipping activities, warehousing activities, distribution and fulfillment activities, inventory aggregation, storage and management activities, marketing activities, and many others, as involved in various value chain network processes, workflows, activities, events and applications 630 may include any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference.
  • the value chain network management platform 604 may include the data handling layers 608 such that the value chain network management platform 604 may provide management of the value chain network management platform 604 and/or management of the other layers such as the value chain network monitoring systems layer 614, the value chain network entity-oriented data storage systems layer 624 (e.g., data storage layer 624), and the adaptive intelligent systems layer 614.
  • Each of the data handling layers 608 may include a variety of services, programs, applications, workflows, systems, components and modules, as further described herein and in the documents incorporated herein by reference.
  • each of the data handling layers 608 (and optionally the platform 604 as a whole) is configured such that one or more of its elements can be accessed as a service by other layers 624 or by other systems (e.g., being configured as a platform-as-a-service deployed on a set of cloud infrastructure components in a microservices architecture).
  • the platform 604 may have (or may configure and/or provision), and a data handling layer 608 may use, a set of connectivity facilities 642, such as network connections (including various configurations, types and protocols), interfaces, ports, application programming interfaces (APIs), brokers, services, connectors, wired or wireless communication links, human-accessible interfaces, software interfaces, micro-services, SaaS interfaces, PaaS interfaces, laaS interfaces, cloud capabilities, or the like by which data or information may be exchanged between a data handling layer 608 and other layers, systems or sub-systems of the platform 604, as well as with other systems, such as value chain entities 652 or external systems, such as cloud-based or on-premises enterprise systems (e.g., accounting systems, resource management systems, CRM systems, supply chain management systems and many others).
  • connectivity facilities 642 such as network connections (including various configurations, types and protocols), interfaces, ports, application programming interfaces (APIs), brokers, services, connectors, wired or wireless communication links, human-accessible
  • Each of the data handling layers 608 may include a set of services (e.g., microservices), for data handling, including facilities for data extraction, transformation and loading; data cleansing and deduplication facilities; data normalization facilities; data synchronization facilities; data security facilities; computational facilities (e.g., for performing pre-defined calculation operations on data streams and providing an output stream); compression and de-compression facilities; analytic facilities (such as providing automated production of data visualizations) and others.
  • services e.g., microservices
  • data handling including facilities for data extraction, transformation and loading; data cleansing and deduplication facilities; data normalization facilities; data synchronization facilities; data security facilities; computational facilities (e.g., for performing pre-defined calculation operations on data streams and providing an output stream); compression and de-compression facilities; analytic facilities (such as providing automated production of data visualizations) and others.
  • each data handling layer 608 has a set of application programming connectivity facilities 642 for automating data exchange with each of the other data handling layers 608. These may include data integration capabilities, such as for extracting, transforming, loading, normalizing, compression, decompressing, encoding, decoding, and otherwise processing data packets, signals, and other information as it exchanged among the layers and/or the applications 630, such as transforming data from one format or protocol to another as needed in order for one layer to consume output from another.
  • the data handling layers 608 are configured in a topology that facilitates shared data collection and distribution across multiple applications and uses within the platform 604 by the value chain monitoring systems layer 614.
  • the value chain monitoring systems layer 614 may include, integrate with, and/or cooperate with various data collection and management systems 640, referred to for convenience in some cases as data collection systems 640, for collecting and organizing data collected from or about value chain entities 652, as well as data collected from or about the various data layers 624 or services or components thereof.
  • data collection systems 640 for collecting and organizing data collected from or about value chain entities 652, as well as data collected from or about the various data layers 624 or services or components thereof.
  • a stream of physiological data from a wearable device worn by a worker undertaking a task or a consumer engaged in an activity can be distributed via the monitoring systems layer 614 to multiple distinct applications in the value chain management platform 604, such as one that facilitates monitoring the physiological, psychological, performance level, attention, or other state of a worker and another that facilitates operational efficiency and/or effectiveness.
  • the monitoring systems layer 614 facilitates alignment, such as time-synchronization, normalization, or the like of data that is collected with respect to one or more value chain network entities 652.
  • one or more video streams or other sensor data collected of or with respect to a worker 718 or other entity in a value chain network facility or environment such as from a set of camera-enabled loT devices, may be aligned with a common clock, so that the relative timing of a set of videos or other data can be understood by systems that may process the videos, such as machine learning systems that operate on images in the videos, on changes between images in different frames of the video, or the like.
  • the monitoring systems layer 614 may further align a set of videos, camera images, sensor data, or the like, with other data, such as a stream of data from wearable devices, a stream of data produced by value chain network systems (such as ships, lifts, vehicles, containers, cargo handling systems, packing systems, delivery systems, drones/robots, and the like), a stream of data collected by mobile data collectors, and the like.
  • Configuration of the monitoring systems layer 614 as a common platform, or set of microservices, that are accessed across many applications may dramatically reduce the number of interconnections required by an owner or other operator within a value chain network in order to have a growing set of applications monitoring a growing set of loT devices and other systems and devices that are under its control.
  • the data handling layers 608 are configured in a topology that facilitates shared or common data storage across multiple applications and uses of the platform 604 by the value chain network-oriented data storage systems layer 624, referred to herein for convenience in some cases simply as the data storage layer 624 or storage layer 624.
  • various data collected about the value chain entities 652, as well as data produced by the other data handling layers 608, may be stored in the data storage layer 624, such that any of the services, applications, programs, or the like of the various data handling layers 608 can access a common data source (which may comprise a single logical data source that is distributed across disparate physical and/or virtual storage locations).
  • a supply chain or inventory management application in the value chain management platform 604 such as one for ordering replacement parts for a machine or item of equipment, may access the same data set about what parts have been replaced for a set of machines as a predictive maintenance application that is used to predict whether a component of a ship, or facility of a port is likely to require replacement parts. Similarly, prediction may be used with respect to the resupply of items.
  • value chain network data objects 1004 may be provided according to an object-oriented data model that defines classes, objects, attributes, parameters and other features of the set of data objects (such as associated with value chain network entities 652 and applications 630) that are handled by the platform 604.
  • the data storage systems layer 624 may provide an extremely rich environment for collection of data that can be used for extraction of features or inputs for intelligence systems, such as expert systems, analytic systems, artificial intelligence systems, robotic process automation systems, machine learning systems, deep learning systems, supervised learning systems, or other intelligent systems as disclosed throughout this disclosure and the documents incorporated herein by reference.
  • intelligence systems such as expert systems, analytic systems, artificial intelligence systems, robotic process automation systems, machine learning systems, deep learning systems, supervised learning systems, or other intelligent systems as disclosed throughout this disclosure and the documents incorporated herein by reference.
  • each application 630 in the platform 604 and each adaptive intelligent system in the adaptive intelligent systems layer 614 can benefit from the data collected or produced by or for each of the others.
  • the data storage systems layer 624 may facilitate collection of data that can be used for extraction of features or inputs for intelligence systems such as a development framework from artificial intelligence.
  • the collections of data may pull in and/or house event logs (naturally stored or ad-hoc, as needed), perform periodic checks on onboard diagnostic data, or the like.
  • pre calculation of features may be deployed using AWS Lambda, for example, or various other cloud-based on-demand compute capabilities, such as pre-calculations, multiplexing signals.
  • there are pairings (doubles, triples, quadruplets, etc.) of similar kinds of value chain entities that may use one or more sets of capabilities of the data handling layers 608 to deploy connectivity and services across value chain entities and across applications used by the entities even when amassing hundreds and hundreds of data types from relatively disparate entities.
  • various pairings of similar types of value chain entities using, at least in part, the connectivity and services across value chain entities and applications may direct the information from the pairings of connected data to artificial intelligence services including the various neural networks disclosed herein and hybrid combinations thereof.
  • genetic programming techniques may be deployed to prune some of the input features in the information from the pairings of connected data.
  • genetic programming techniques may also be deployed to add to and augment the input features in the information from the pairings. These genetic programming techniques may be shown to increase the efficacy of the determinations established by the artificial intelligence services.
  • the information from the pairings of connected data may be migrated to other layers on the platform including to support or deploy robotic process automation, prediction, forecasting, and other resources such that the shared data schema may facilitate as capabilities and resources for the platform 604.
  • a wide range of data types may be stored in the storage layer 624 using various storage media and data storage types, data architectures 1002, and formats, including, without limitation: asset and facility data 1030, state data 1140 (such as indicating a state, condition status, or other indicator with respect to any of the value chain network entities 652, any of the applications 630 or components or workflows thereof, or any of the components or elements of the platform 604, among others), worker data 1032 (including identity data, role data, task data, workflow data, health data, attention data, mood data, stress data, physiological data, performance data, quality data and many other types); event data 1034 ((such as with respect to any of a wide range of events, including operational data, transactional data, workflow data, maintenance data, and many other types of data that includes or relates to events that occur within a value chain network 668 or with respect to one or more applications 630, including process events, financial events, transaction events, output events, input events, state-change events, operating events, workflow events, repair events, maintenance events, service events, damage
  • the data handling layers 608 are configured in a topology that facilitates shared adaptation capabilities, which may be provided, managed, mediated and the like by one or more of a set of services, components, programs, systems, or capabilities of the adaptive intelligent systems layer 614, referred to in some cases herein for convenience as the adaptive intelligence layer 614.
  • the adaptive intelligence systems layer 614 may include a set of data processing, artificial intelligence and computational systems 634 that are described in more detail elsewhere throughout this disclosure.
  • computing resources such as available processing cores, available servers, available edge computing resources, available on-device resources (for single devices or peered networks), and available cloud infrastructure, among others
  • data storage resources including local storage on devices, storage resources in or on value chain entities or environments (including on-device storage, storage on asset tags, local area network storage and the like), network storage resources, cloudbased storage resources, database resources and others), networking resources (including cellular network spectrum, wireless network resources, fixed network resources and others), energy resources (such as available battery power, available renewable energy, fuel, grid-based power, and many others) and others
  • energy resources such as available battery power, available renewable energy, fuel, grid-based power, and many others
  • others may be optimized in a coordinated or shared way on behalf of an operator, enterprise, or the like, such as for the benefit of multiple applications, programs, workflows, or the like.
  • the adaptive intelligence layer 614 may manage and provision available network resources for both a supply chain management application and for a demand planning application (among many other possibilities), such that low latency resources are used for supply chain management application (where rapid decisions may be important) and longer latency resources are used for the demand planning application.
  • a wide variety of adaptations may be provided on behalf of the various services and capabilities across the various layers 624, including ones based on application requirements, quality of service, on- time delivery, service objectives, budgets, costs, pricing, risk factors, operational objectives, efficiency objectives, optimization parameters, returns on investment, profitability, uptime/downtime, worker utilization, and many others.
  • the value chain management platform 604 may include, integrate with, and enable the various value chain network processes, workflows, activities, events and applications 630 described throughout this disclosure that enable an operator to manage more than one aspect of a value chain network environment or entity 652 in a common application environment (e.g., shared, pooled, similarly licenses whether shared data for one person, multiple people, or anonymized), such as one that takes advantage of common data storage in the data storage layer 624, common data collection or monitoring in the monitoring systems layer 614 and/or common adaptive intelligence of the adaptive intelligence layer 614.
  • Outputs from the applications 630 in the platform 604 may be provided to the other data handing layers 624.
  • state and status information for various objects, entities, processes, flows and the like; object information, such as identity, attribute and parameter information for various classes of objects of various data types; event and change information, such as for workflows, dynamic systems, processes, procedures, protocols, algorithms, and other flows, including timing information; outcome information, such as indications of success and failure, indications of process or milestone completion, indications of correct or incorrect predictions, indications of correct or incorrect labeling or classification, and success metrics (including relating to yield, engagement, return on investment, profitability, efficiency, timeliness, quality of service, quality of product, customer satisfaction, and others) among others.
  • Outputs from each application 630 can be stored in the data storage layer 624, distributed for processing by the data collection layer 614, and used by the adaptive intelligence layer 614.
  • the cross-application nature of the platform 604 thus facilitates convenient organization of all of the necessary infrastructure elements for adding intelligence to any given application, such as by supplying machine learning on outcomes across applications, providing enrichment of automation of a given application via machine learning based on outcomes from other applications or other elements of the platform 604, and allowing application developers to focus on application-native processes while benefiting from other capabilities of the platform 604.
  • outputs and outcomes 1040 from various applications 630 may be used to facilitate automated learning and improvement of classification, prediction, or the like that is involved in a step of a process that is intended to be automated.
  • the storage layer 624 may store data in one or more knowledge graphs (such as a directed acyclic graph, a data map, a data hierarchy, a data cluster including links and nodes, a self-organizing map, or the like) in the graph database architectures 1124.
  • the knowledge graph may be a prevalent example of when a graph database and graph database architecture may be used.
  • the knowledge graph may be used to graph a workflow. For a linear workflow, a directed acyclic graph may be used. For a contingent workflow, a cyclic graph may be used.
  • the graph database (e.g., graph database architectures 1124) may include the knowledge graph or the knowledge graph may be an example of the graph database.
  • the knowledge graph may include ontology and connections (e.g., relationships) between the ontology of the knowledge graph.
  • the knowledge graph may be used to capture an articulation of knowledge domains of a human expert such that there may be an identification of opportunities to design and build robotic process automation or other intelligence that may replicate this knowledge set.
  • the platform may be used to recognize that a type of expert is using this factual knowledge base (from the knowledge graph) coupled with competencies that may be replicable by artificial intelligence that may be different depending on type of expertise involved.
  • artificial intelligence such as a convolutional neural network may be used with spatiotemporal aspects that may be used to diagnose issues or packing up a box in a warehouse.
  • the data storage layer 624 may use and enable an asset tag 1178, which may include a data structure that is associated with an asset and accessible and managed, such as by use of access controls, so that storage and retrieval of data is optionally linked to local processes, but also optionally open to remote retrieval and storage options.
  • the storage layer 624 may include one or more blockchains 1180, such as ones that store identity data, transaction data, historical interaction data, and the like, such as with access control that may be role-based or may be based on credentials associated with a value chain entity 652, a service, or one or more applications 630.
  • the management platform 604 may, in various optional embodiments, include the set of applications 614, by which an operator or owner of a value chain network entity, or other users, may manage, monitor, control, analyze, or otherwise interact with one or more elements of a value chain network entity 652, such as any of the elements noted in connection above and throughout this disclosure.
  • These adaptive intelligent systems 614 may include a robotic process automation system 1442, a set of protocol adaptors 1110, a packet acceleration system 1410, an edge intelligence system 1420 (which may be a self-adaptive system), an adaptive networking system 1430, a set of state and event managers 1450, a set of opportunity miners 1460, a set of artificial intelligence systems 1160, a set of digital twin systems 1700, a set of entity interaction systems 1920 (such as for setting up, provisioning, configuring and otherwise managing sets of interactions between and among sets of value chain network entities 652 in the value chain network 668), and other systems.
  • a robotic process automation system 1442 a set of protocol adaptors 1110, a packet acceleration system 1410, an edge intelligence system 1420 (which may be a self-adaptive system), an adaptive networking system 1430, a set of state and event managers 1450, a set of opportunity miners 1460, a set of artificial intelligence systems 1160, a set of digital twin systems 1700, a set of entity interaction systems 1920
  • the value chain monitoring systems layer 614 and its data collection systems 640 may include a wide range of systems for the collection of data.
  • This layer may include, without limitation, real time monitoring systems 1520 (such as onboard monitoring systems like event and status reporting systems on ships and other floating assets, on delivery vehicles, on trucks and other hauling assets, and in shipyards, ports, warehouses, distribution centers and other locations; on-board diagnostic (OBD) and telematics systems on floating assets, vehicles and equipment; systems providing diagnostic codes and events via an event bus, communication port, or other communication system; monitoring infrastructure (such as cameras, motion sensors, beacons, RFID systems, smart lighting systems, asset tracking systems, person tracking systems, and ambient sensing systems located in various environments where value chain activities and other events take place), as well as removable and replaceable monitoring systems, such as portable and mobile data collectors, RFID and other tag readers, smart phones, tablets and other mobile devices that are capable of data collection and the like); software interaction observation systems 1500 (such as for logging and tracking events involved in interactions of users with software user interfaces
  • a management platform of an information technology system such as a management platform for a value chain of goods and/or services is depicted as a block diagram of functional elements and representative interconnections.
  • the management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614.
  • the adaptive intelligence systems 614 provide coordinated intelligence (including artificial intelligence 1160, expert systems 3002, machine learning 3004, and the like) for a set of demand management applications 824 and for a set of supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain.
  • the adaptive intelligence systems 614 may deliver artificial intelligence 1160 through a set of data processing, artificial intelligence and computational systems 634.
  • the adaptive intelligence systems 614 are selectable and/or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the sets of value chain applications (e.g., demand management applications 824 and supply chain applications 812).
  • the adaptive intelligence systems 614 may include artificial intelligence, including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.
  • user interface may include interfaces for configuring an artificial intelligence system 1160 to take inputs from selected data sources of the value chain (such as data sources used by the set of demand management applications 824 and/or the set of supply chain applications 812) and supply them, such as to a neural network, artificial intelligence system 1160 or any of the other adaptive intelligence systems 614 described throughout this disclosure and in the documents incorporated herein by reference to enhance, control, improve, optimize, configure, adapt or have another impact on a value chain for the category of goods 3010.
  • the selected data sources of the value chain may be applied either as inputs for classification or prediction, or as outcomes relating to the value chain, the category of goods 3010 and the like.
  • providing coordinated intelligence may include providing artificial intelligence capabilities, such as artificial intelligence systems 1160 and the like.
  • Artificial intelligence systems may facilitate coordinated intelligence for the set of demand management applications 824 or the set of supply chain applications 812 or both, such as for a category of goods, such as by processing data that is available in any of the data sources of the value chain, such as value chain processes, bills of materials, manifests, delivery schedules, weather data, traffic data, goods design specifications, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System, and the like.
  • ERP Enterprise Resource Planning
  • CRM Customer Relationship Management
  • CEM Customer Experience Management
  • SLM Service Lifecycle Management
  • PLM Product Lifecycle Management
  • the user interface 3020 may provide access to, among other things artificial intelligence capabilities, applications, systems and the like for coordinating intelligence for applications of the value chain and particularly for value chain applications for the category of goods 3010.
  • the user interface 3020 may be adapted to receive information descriptive of the category of goods 3010 and configure user access to the artificial intelligence capabilities responsive thereto, so that the user, through the user interface is guided to artificial intelligence capabilities that are suitable for use with value chain applications (e.g., the set of demand management applications 824 and supply chain applications 812) that contribute to goods/ services in the category of goods 3010.
  • the user interface 3020 may facilitate providing coordinated intelligence that comprises artificial intelligence capabilities that provide coordinated intelligence for a specific operator and/or enterprise that participates in the supply chain for the category of goods.
  • the user interface 3020 may be configured to facilitate the user selecting and/or configuring multiple artificial intelligence systems 1160 for use with the value chain.
  • the user interface may present the set of demand management applications 824 and supply chain applications 812 as connected entities that receive, process, and produce outputs each of which may be shared among the applications.
  • Types of artificial intelligence systems 1160 may be indicated in the user interface 3020 responsive to sets of connected applications or their data elements being indicated in the user interface, such as by the user placing a pointer proximal to a connected set of applications and the like.
  • the user interface 3020 may facilitate access to the set of adaptive intelligence systems provides a set of capabilities that facilitate development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management.
  • the adaptive intelligence systems 614 may be configured with data processing, artificial intelligence and computational systems 634 that may operate cooperatively to provide coordinated intelligence, such as when an artificial intelligence system 1160 operates on or responds to data collected by or produced by other systems of the adaptive intelligence systems 614, such as a data processing system and the like.
  • providing coordinated intelligence may include operating a portion of a set of artificial intelligence systems 1160 that employs one or more types of neural network that is described herein and in the documents incorporated herein by reference and that processes any of the demand management application outputs and supply chain application outputs to provide the coordinated intelligence.
  • providing coordinated intelligence for the set of demand management applications 824 may include configuring at least one of the adaptive intelligence systems 614 (e.g., through the user interface 3020 and the like) for at least one or more demand management applications selected from a list of demand management applications including a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service, and the like.
  • providing coordinated intelligence for the set of supply chain applications 812 may include configuring at least one of the adaptive intelligence systems 614 for at least one or more supply chain applications selected from a list of supply chain applications including a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, an order for components management application, and the like.
  • the management platform 102 may, such as through the user interface 3020 facilitate access to the set of adaptive intelligence systems 614 that provide coordinated intelligence for a set of demand management applications 824 and supply chain applications 812 through the application of artificial intelligence.
  • the user may seek to align supply with demand while ensuring profitability and the like of a value chain for a category of goods 3010.
  • the management platform allows the user to focus on the applications of demand and supply while gaining advantages of techniques such as expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and the like.
  • the management platform 102 may, through the user interface 3020 and the like provide a set of adaptive intelligence systems 614 that provide coordinated artificial intelligence 1160 for the sets of demand management applications 824 and supply chain applications 812 for the category of goods 3020 by, for example, determining (automatically) relationships among demand management and supply chain applications based on inputs used by the applications, results produced by the applications, and value chain outcomes.
  • the artificial intelligence 1160 may be coordinated by, for example, the set of data processing, artificial intelligence and computational systems 634 available through the adaptive intelligence systems 614.
  • the management platform 102 may be configured with a set of artificial intelligence systems 1160 as part of a set of adaptive intelligence systems 614 that provide the coordinated intelligence for the sets of demand management applications 824 and supply chain applications 812 for a category of goods 3010.
  • the set of artificial intelligence systems 1160 may provide the coordinated intelligence so that at least one supply chain application of the set of supply chain applications 812 produces results that address at least one aspect of supply for at least one of the goods in the category of goods as determined by at least one demand management application of the set of demand management applications 824.
  • a behavioral tracking demand management application may generate results for behavior of uses of a good in the category of goods 3010.
  • the artificial intelligence systems 1160 may process the behavior data and conclude that there is a perceived need for greater consumer access to a second product in the category of goods 3010.
  • This coordinated intelligence may be, optionally automatically, applied to the set of supply chain applications 812 so that, for example, production resources or other resources in the value chain for the category of goods are allocated to the second product.
  • a distributor who handles stocking retailer shelves may receive a new stocking plan that allocates more retail shelf space for the second product, such as by taking away space from a lower margin product and the like.
  • the set of artificial intelligence systems 1160 and the like may provide coordinated intelligence for the sets of supply chain and demand management applications by, for example, determining an optionally temporal prioritization of demand management application outputs that impact control of supply chain applications so that an optionally temporal demand for at least one of the goods in the category of goods 3010 can be met.
  • Seasonal adjustments in prioritization of demand application results are one example of a temporal change. Adjustments in prioritization may also be localized, such as when a large college football team is playing at their home stadium and local supply of tailgating supplies may temporally be adjusted even though demand management application results suggest that small propane stoves are not currently in demand in a wider region.
  • a set of adaptive intelligence systems 614 that provide coordinated intelligence, such as by providing artificial intelligence capabilities 1160 and the like may also facilitate development and deployment of intelligence for at least one function selected from a list of functions consisting of supply chain application automation, demand management application automation, machine learning, artificial intelligence, intelligent transactions, intelligent operations, remote control, analytics, monitoring, reporting, state management, event management, and process management.
  • the set of adaptive intelligence systems 614 may be configured as a layer in the platform and an artificial intelligence system therein may operate on or be responsive to data collected by and/or produced by other systems (e.g., data processing systems, expert systems, machine learning systems and the like) of the adaptive intelligence systems layer.
  • the coordinated intelligence may be provided for a specific value chain entity 652, such as a supply chain operator, business, enterprise, and the like that participates in the supply chain for the category of goods.
  • a specific value chain entity 652 such as a supply chain operator, business, enterprise, and the like that participates in the supply chain for the category of goods.
  • Providing coordinated intelligence may include employing a neural network to process at least one of the inputs and outputs of the sets of demand management and supply chain applications.
  • Neural networks may be used with demand applications, such as a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service, and the like.
  • Neural networks may also be used with supply chain applications such as a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, an order for components management application, and the like.
  • Neural networks may provide coordinated intelligence by processing data that is available in any of a plurality of value chain data sources for the category of goods including without limitation processes, bill of materials, weather, traffic, design specification, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System, and the like.
  • ERP Enterprise Resource Planning
  • CRM Customer Relationship Management
  • CEM Customer Experience Management
  • SLM Service Lifecycle Management
  • PLM Product Lifecycle Management
  • Neural networks configured for providing coordinated intelligence may share adaptation capabilities with other adaptive intelligence systems 614, such as when these systems are configured in a topology that facilitates such shared adaptation.
  • neural networks may facilitate provisioning available value chain/supply chain network resources for both the set of demand management applications and for the set of supply chain applications.
  • neural networks may provide coordinated intelligence to improve at least one of the list of outputs consisting of a process output, an application output, a process outcome, an application outcome, and the like.
  • a management platform of an information technology system such as a management platform for a value chain of goods and/or services is depicted as a block diagram of functional elements and representative interconnections.
  • the management platform includes a user interface 3020 that provides, among other things, a hybrid set of adaptive intelligence systems 614.
  • the hybrid set of adaptive intelligence systems 614 provide coordinated intelligence through the application of artificial intelligence, such as through application of a hybrid artificial intelligence system 3060, and optionally through one or more expert systems, machine learning systems, and the like for use with a set of demand management applications 824 and for a set of supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain.
  • the hybrid adaptive intelligence systems 614 may deliver two types of artificial intelligence systems, type A 3052 and type B 3054 through a set of data processing, artificial intelligence and computational systems 634.
  • the hybrid adaptive intelligence systems 614 are selectable and/or configurable through the user interface 3020 so that one or more of the hybrid adaptive intelligence systems 614 can operate on or in cooperation with the sets of supply chain applications (e.g., demand management applications 824 and supply chain applications 812).
  • the hybrid adaptive intelligence systems 614 may include a hybrid artificial intelligence system 3060 that may include at least two types of artificial intelligence capabilities including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.
  • the hybrid adaptive intelligence systems 614 may facilitate applying a first type of artificial intelligence system 1160 to the set of demand management applications 824 and a second type of artificial intelligence system 1160 to the set of supply chain applications 812, wherein each of the first type and second type of artificial intelligence system 1160 can operate independently, cooperatively, and optionally coordinate operation to provide coordinated intelligence for operation of the value chain that produces at least one of the goods in the category of goods 3010.
  • the user interface 3020 may include interfaces for configuring a hybrid artificial intelligence system 3060 to take inputs from selected data sources of the value chain (such as data sources used by the set of demand management applications 824 and/or the set of supply chain applications 812) and supply them, such as to at least one of the two types of artificial intelligence systems in the hybrid artificial intelligence system 3060, types of which are described throughout this disclosure and in the documents incorporated herein by reference to enhance, control, improve, optimize, configure, adapt or have another impact on a value chain for the category of goods 3010.
  • the selected data sources of the value chain may be applied either as inputs for classification or prediction, or as outcomes relating to the value chain, the category of goods 3010 and the like.
  • the hybrid adaptive intelligence systems 614 provides a plurality of distinct artificial intelligence systems 1160, a hybrid artificial intelligence system 3060, and combinations thereof.
  • any of the plurality of distinct artificial intelligence systems 1160 and the hybrid artificial intelligence system 3060 may be configured as a plurality of neural network-based systems, such as a classification-adapted neural network, a prediction- adapted neural network and the like.
  • a machine learning-based artificial intelligence system may be provided for the set of demand management applications 824 and a neural network-based artificial intelligence system may be provided for the set of supply chain applications 812.
  • a hybrid artificial intelligence system 3060 may provide two types of artificial intelligence to different applications, such as different demand management applications 824 (e.g., a sales management application and a demand prediction application) or different supply chain applications 812 (e.g., a logistics control application and a production quality control application).
  • different demand management applications 824 e.g., a sales management application and a demand prediction application
  • different supply chain applications 812 e.g., a logistics control application and a production quality control application.
  • hybrid adaptive intelligence systems 614 may be applied as distinct artificial intelligence capabilities to distinct demand management applications 824.
  • coordinated intelligence through a hybrid artificial intelligence capabilities may be provided to a demand planning application by a feed-forward neural network, to a demand prediction application by a machine learning system, to a sales application by a self-organizing neural network, to a future demand aggregation application by a radial basis function neural network, to a marketing application by a convolutional neural network, to an advertising application by a recurrent neural network, to an e-commerce application by a hierarchical neural network, to a marketing analytics application by a stochastic neural network, to a customer relationship management application by an associative neural network and the like.
  • a management platform of an information technology system such as a management platform for a value chain of goods and/or services is depicted as a block diagram of functional elements and representative interconnections for providing a set of predictions 3070.
  • the management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614.
  • the adaptive intelligence systems 614 provide a set of predictions 3070 through the application of artificial intelligence, such as through application of an artificial intelligence system 1160, and optionally through one or more expert systems, machine learning systems, and the like for use with a coordinated set of demand management applications 824 and supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain.
  • the adaptive intelligence systems 614 may deliver the set of prediction 3070 through a set of data processing, artificial intelligence and computational systems 634.
  • the adaptive intelligence systems 614 are selectable and/or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the coordinated sets of value chain applications.
  • the adaptive intelligence systems 614 may include an artificial intelligence system that provides artificial intelligence capabilities known to be associated with artificial intelligence including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.
  • the adaptive intelligence systems 614 may facilitate applying adapted intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812 such as by producing a set of predictions 3070 that may facilitate coordinating the two sets of value chain applications, or at least facilitate coordinating at least one demand management application and at least one supply chain application from their respective sets.
  • the set of predictions 3070 includes a least one prediction of an impact on a supply chain application based on a current state of a coordinated demand management application, such as a prediction that a demand for a good will decrease earlier than previously anticipated.
  • the set of predictions 3070 includes at least one prediction of an impact on a demand management application based on a current state of a coordinated supply chain application, such as a prediction that a lack of supply of a good will likely impact a measure of demand of related goods.
  • the set of predictions 3070 is a set of predictions of adjustments in supply required to meet demand. Other predictions include at least one prediction of change in demand that impacts supply.
  • predictions in the set of predictions predict a change in supply that impacts at least one of the set of demand management applications, such as a promotion application for at least one good in the category of goods.
  • a prediction in the set of predictions may be as simple as setting a likelihood that a supply of a good in the category of goods will not meet demand set by a demand setting application.
  • the adaptive intelligence systems 614 may provide a set of artificial intelligence capabilities to facilitate providing the set of predictions for the coordinated set of demand management applications and supply chain applications.
  • the set of artificial intelligence capabilities may include a probabilistic neural network that may be used to predict a fault condition or a problem state of a demand management application such as a lack of sufficient validated feedback.
  • the probabilistic neural network may be used to predict a problem state with a machine performing a value chain operation (e.g., a production machine, an automated handling machine, a packaging machine, a shipping machine and the like) based on a collection of machine operating information and preventive maintenance information for the machine.
  • the set of predictions 3070 may be provided by the management platform 102 directly through a set of adaptive artificial intelligence systems.
  • the set of predictions 3070 may be provided for the coordinated set of demand management applications and supply chain applications for a category of goods by applying artificial intelligence capabilities for coordinating the set of demand management applications and supply chain applications.
  • the set of predictions 3070 may be predictions of outcomes for operating a value chain with the coordinated set demand management applications and supply chain applications for the category of goods, so that a user may conduct test cases of coordinated sets of demand management applications and supply chain applications to determine which sets may produce desirable outcomes (viable candidates for a coordinated set of applications) and which may produce undesirable outcomes.
  • a management platform of an information technology system such as a management platform for a value chain of goods and/or services is depicted as a block diagram of functional elements and representative interconnections for providing a set of classifications 3080.
  • the management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614.
  • the adaptive intelligence systems 614 provide a set of classifications 3080 through, for example, the application of artificial intelligence, such as through application of an artificial intelligence system 1160, and optionally through one or more expert systems, machine learning systems, and the like for use with a coordinated set of demand management applications 824 and supply chain applications 812 for a category of goods 3010, which may be produced, marketed, sold, resold, rented, leased, given away, serviced, recycled, renewed, enhanced, and the like through the value chain.
  • the adaptive intelligence systems 614 may deliver the set of classifications 3080 through a set of data processing, artificial intelligence and computational systems 634.
  • the adaptive intelligence systems 614 are selectable and/or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 can operate on or in cooperation with the coordinated sets of value chain applications.
  • the adaptive intelligence systems 614 may include an artificial intelligence system that provides, among other things classification capabilities through any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.
  • the adaptive intelligence systems 614 may facilitate applying adapted intelligence capabilities to the coordinated set of demand management applications 824 and supply chain applications 812 such as by producing a set of classifications 3080 that may facilitate coordinating the two sets of value chain applications, or at least facilitate coordinating at least one demand management application and at least one supply chain application from their respective sets.
  • the set of classifications 3080 includes at least one classification of a current state of a supply chain application for use by a coordinated demand management application, such as a classification of a problem state that may impact operation of a demand management application, such as a marketing application and the like. Such a classification may be useful in determining how to adjust a market expectation for a good that is going to have a lower yield than previously anticipated. The converse may also be true in that the set of classifications 3080 includes at least one classification of a current state of a demand management application and its relationship to a coordinated supply chain application.
  • the set of classifications 3080 is a set of classifications of adjustments in supply required to meet demand, such as adjustments to production worker needs would be classified differently that adjustments in third-party logistics providers.
  • Other classifications may include at least one classification of perceived changes in demand and a resulting potential impact on supply management.
  • Yet other classifications in the set of classifications may include a supply chain application impact on at least one of the sets of demand management applications, such as a promotion application for at least one good in the category of goods.
  • a classification in the set of classifications may be as simple as classifying a likelihood that a supply of a good in the category of goods will not meet demand set by a demand setting application.
  • the adaptive intelligence systems 614 may provide a set of artificial intelligence capabilities to facilitate providing the set of classifications 3080 for the coordinated set of demand management applications and supply chain applications.
  • the set of artificial intelligence capabilities may include a probabilistic neural network that may be used to classify fault conditions or problem states of a demand management application, such as a classification of a lack of sufficient validated feedback.
  • the probabilistic neural network may be used to classify a problem state of a machine performing a value chain operation (e.g., a production machine, an automated handling machine, a packaging machine, a shipping machine and the like) as pertaining to at least one of machine operating information and preventive maintenance information for the machine.
  • the set of classifications 3080 may be provided by the management platform 102 directly through a set of adaptive artificial intelligence systems. Further, the set of classifications 3080 may be provided for the coordinated set of demand management applications and supply chain applications for a category of goods by applying artificial intelligence capabilities for coordinating the set of demand management applications and supply chain applications.
  • the set of classifications 3080 may be classifications of outcomes for operating a value chain with the coordinated set demand management applications and supply chain applications for the category of goods, so that a user may conduct test cases of coordinated sets of demand management applications and supply chain applications to determine which sets may produce outcomes that are classified as desirable (e.g., viable candidates for a coordinated set of applications) and outcomes that are classified as undesirable.
  • the set of classifications may comprise a set of adaptive intelligence functions, such as a neural network that may be adapted to classify information associated with the category of goods.
  • the neural network may be a multilayered feed forward neural network.
  • the set of classifications 3080 may be achieved through use of artificial intelligence systems 1160 for coordinating the set of coordinated demand management and supply chain applications. Artificial intelligence systems may configure and generate sets of classifications 3080 as a means by which demand management applications and supply chain applications can be coordinated. In an example, classification of information flow throughout a value chain may be classified as being relevant to both a demand management application and a supply chain application; this common relevance may be a point of coordination among the applications. In embodiments, the set of classifications may be artificial intelligence generated classifications of outcomes of operating a supply chain that is dependent on the coordinated demand management applications 824 and supply chain applications 812.
  • a management platform of an information technology system such as a management platform for a value chain of goods and/or services is depicted as a block diagram of functional elements and representative interconnections for achieving automated control intelligence.
  • the management platform includes a user interface 3020 that provides, among other things, a set of adaptive intelligence systems 614.
  • the adaptive intelligence systems 614 provide automated control signaling 3092 for a coordinated set of demand management applications 824 and supply chain applications 812 for a category of goods 3010, which may be produced and sold through the value chain.
  • the adaptive intelligence systems 614 may deliver the automated control signals 3092 through a set of data processing, artificial intelligence and computational systems 634.
  • the adaptive intelligence systems 614 are selectable and/or configurable through the user interface 3020 so that one or more of the adaptive intelligence systems 614 can automatically control the sets of supply chain applications (e.g., demand management applications 824 and supply chain applications 812).
  • the adaptive intelligence systems 614 may include artificial intelligence including any of the various expert systems, artificial intelligence systems, neural networks, supervised learning systems, machine learning systems, deep learning systems, and other systems described throughout this disclosure and in the documents incorporated by reference.
  • the user interface 3020 may include interfaces for configuring an adaptive intelligence systems 614 to take inputs from selected data sources of the value chain 3094 (such as data sources used by the coordinated set of demand management applications 824 and/or the set of supply chain applications 812) and supply them, such as to a neural network, artificial intelligence system 1160 or any of the other adaptive intelligence systems 614 described throughout this disclosure and in the documents incorporated herein by reference for producing automated control signals 3092, such as to enhance, control, improve, optimize, configure, adapt or have another impact on a value chain for the category of goods 3010.
  • the selected data sources of the value chain may be used for determining aspects of the automated control signals, such as for temporal adjustments to control outcomes relating to the value chain at least for the category of goods 3010 and the like.
  • the adaptive intelligence systems 614 may apply machine learning to outcomes of supply to automatically adapt a set of demand management application control signals. Similarly, the adaptive intelligence systems 614 may apply machine learning to outcomes of demand management to automatically adapt a set of supply chain application control signals.
  • the adaptive intelligence systems 614 may provide further processing for automated control signal generation, such as by applying artificial intelligence to determine aspects of a value chain that impact automated control of the coordinated set of demand management applications and supply chain applications for a category of goods. The determined aspects could be used in the generation and operation of automated control intelligence/signals, such as by filtering out value chain information for aspects that do not impact the targeted demand management and supply chain applications.
  • Embodiments are described herein for using artificial intelligence systems or capabilities to identify, configure and regulate automated control signals.
  • Such embodiments may further include a closed loop of feedback from the coordinated set of demand management and supply chain applications (e.g., state information, output information, outcomes and the like) that is optionally processed with machine learning and used to adapt the automated control signals for at least one of the goods in the category of goods.
  • An automated control signal may be adapted based on, for example, an indication of feedback from a supply chain application that yield of a good suggests a production problem.
  • the automated control signal may impact production rate and the feedback may cause the signal to automatically self-adjust to a slower production rate until the production problem is resolved.
  • a management platform of an information technology system such as a management platform for a value chain of goods and/or services is depicted as a block diagram of functional elements and representative interconnections for providing information routing recommendations.
  • the management platform includes a set of value chain networks 3102 from which network data 3110 is collected from a set of information routing activities, the information including outcomes, parameters, routing activity information and the like. Within the set of value chain networks 3102 is selected a select value chain network 3104 for which at least one information routing recommendation 3130 is provided.
  • An artificial intelligence system 1160 may include a machine learning system and may be trained using a training set derived from the network data 3110 outcomes, parameters and routing activity information for the set of value chain networks 3102.
  • the artificial intelligence system 1160 may further provide an information routing recommendation 3130 based on a current status 3120 of the select value chain network 3104.
  • the artificial intelligence system may use machine learning to train on information transaction types within the set of value chain networks 3102, thereby learning pertinent factors regarding different transaction types (e.g., real-time inventory updates, buyer credit checks, engineering signoff, and the like) and contributing to the information routing recommendation accordingly.
  • the artificial intelligence system may also use machine learning to train on information value for different types and/or classes of information routed in and throughout the set of value chain networks 3102.
  • Information may be valued on a wide range of factors, including timing of information availability and timing of information consumption as well as information content-based value, such as information without which a value chain network element (e.g., a production provider) cannot perform a desired action (e.g., starting volume production without a work order). Therefore, information routing recommendations may be based on training on transaction type, information value, and a combination thereof. These are merely exemplary information routing recommendation training and recommendation basis factors and are presented here without limitation on other elements for training and recommendation basis.
  • the artificial intelligence system 1160 may provide an information routing recommendation 3130 based on transaction type, transaction type and information type, network type and the like.
  • An information routing recommendation may be based on combinations of factors, such as information type and network type, such as when an information type (streaming) is not compatible with a network type (small transactions).
  • the artificial intelligence system 1160 may use machine learning to develop an understanding of networks within the selected value chain network 3104, such as network topology, network loading, network reliability, network latency and the like. This understanding may be combined with, for example, detected or anticipated network conditions to form an information routing recommendation. Aspects such as existence of edge intelligence in a value chain network 3104 can influence one or more information routing recommendations.
  • a type of information may be incompatible with a network type; however, the network may be configured with edge intelligence that can be leveraged by the artificial intelligence system 1160 to adapt the form of the information being routed so that it is compatible with a targeted network type.
  • an information routing recommendation may avoid routing information that is confidential to a first supplier in the value chain through network nodes controlled by competitors of the supplier.
  • an information routing recommendation may include routing information to a first node where it is partially consumed and partially processed for further routing, such as by splitting up the portion partially processed for further routing into destination-specific information sets.
  • an artificial intelligence system 1160 may provide an information routing recommendation based on goals, such as goals of a value chain network, goals of information routing, and the like.
  • Goal-based information routing recommendations may include routing goals, such as Quality of Service routing goals, routing reliability goals (which may be measured based on a transmission failure rate and the like). Other goals may include a measure of latency associated with one or more candidate routes.
  • An information routing recommendation may be based on the availability of information in a selected value chain network, such as when information is available and when it needs to be delivered.
  • routing recommendations may include using resources that are lower cost, may involve short delays in routing and the like.
  • resources that are lower cost may involve short delays in routing and the like.
  • a result of product testing is needed within a few hundred milliseconds of when the test is finished to maintain a production operation rate, and the like.
  • An information routing recommendation may be formed by the artificial intelligence system 1160 based on information persistence factors, such as how long information is available for immediate routing within the value chain network.
  • An information routing recommendation that factors information persistence may select network resources based on availability, cost and the like during a time of information persistence.
  • Information value and an impact on information value may factor into an information routing recommendation.
  • information that is valid for a single shipment e.g., a production run of a good
  • an information routing recommendation may indicate routing the relevant information to all of the highest priority consumers of the information while it is still valid.
  • routing of information that is consumed by more than one value chain entity may need to be coordinated so that each value chain entity receives the information at a desired time/moment, such as during the same production shift, at their start of day, which may be different if the entities are in different time zones, and the like.
  • information routing recommendations may be based on a topology of a value chain, based on location and availability of network storage resources, and the like.
  • one or more information routing recommendations may be adapted while the information is routed based on, for example, changes in network resource availability, network resource discovery, network dynamic loading, priority of recommendations that are generated after information for a first recommendation is in-route, and the like.
  • a management platform of an information technology system such as a management platform for a value chain of goods and/or services is depicted as a block diagram of functional elements and representative interconnections for semi-sentient problem recognitions of pain points in a value chain network.
  • the management platform includes a set of value chain network entities 3152 from which entity -related data 3160 is collected and includes outcomes, parameters, activity information and the like associated with the entities.
  • entity -related data 3160 is collected and includes outcomes, parameters, activity information and the like associated with the entities.
  • select value chain network entities 3154 for which at least one pain point problem state 3172 is detected.
  • An artificial intelligence system 1160 may be training on a training set derived from the entity-related data 3160 including training on outcomes associated with value chain entities, parameters associated with, for example, operation of the value chain, value chain activity information and the like.
  • the artificial intelligence system may further employ machine learning to facilitate learning problem state factors 3180 that may characterize problem states input as training data. These factors 3180 may further be used by an instance of artificial intelligence 1160’ that operates on computing resources 3170 that are local to value chain network entities that are experiencing the problem/result of a pain point.
  • a goal of such a configuration of artificial intelligence systems, data sets, and value chain networks is to recognize a problem state in a portion of the selected value chain.
  • recognizing problem states may be based on variance analysis, such as variances that occur in value chain measures (e.g., loading, latency, delivery time, cost, and the like), particularly in a specific measure over time. Variances that exceed a variance threshold (e.g., an optionally dynamic range of results of a value chain operation, such as production, shipping, clearing customs, and the like) may be indicative of a pain point.
  • variances that occur in value chain measures e.g., loading, latency, delivery time, cost, and the like
  • a variance threshold e.g., an optionally dynamic range of results of a value chain operation, such as production, shipping, clearing customs, and the like
  • the platform 102 In addition to detecting problem states, the platform 102, such as through the methods of semi-sentient problem recognition, predicts a pain point based at least in part on a correlation with a detected problem state.
  • the correlation may be derived from the value chain, such as a shipper cannot deliver international goods until they are processed through customs, or a sales forecast cannot be provided with a high degree of confidence without high quality field data and the like.
  • a predicted pain point may be a point of value chain activity further along a supply chain, an activity that occurs in a related activity (e.g., tax planning is related to tax laws), and the like.
  • a predicted pain point may be assigned a risk value based on aspects of the detected problem state and correlations between the predicted pain point activity and the problem state activity. If a production operation can receive materials from two suppliers, a problem state with one of the suppliers may indicate a low risk of a pain point of use of the material. Likewise, if a demand management application indicates high demand for a good and a problem is detected with information on which the demand is based, a risk of excess inventory (pain point) may be high depending on, for example how far along in the value chain the good has progressed.
  • semi-sentient problem recognition may involve more than mere linkages of data and operational states of entities engaged in a value chain. Problem recognition may also be based on human factors, such as perceived stress of production supervisors, shippers, and the like. Human factors for use in semi-sentient problem recognition may be collected from sensors that facilitate detection of human stress level and the like (e.g., wearable physiological sensors, and the like).
  • semi-sentient problem recognition may also be based on unstructured information, such as digital communication, voice messaging, and the like that may be shared among, originate with, or be received by humans involved in the value chain operations.
  • unstructured information such as digital communication, voice messaging, and the like that may be shared among, originate with, or be received by humans involved in the value chain operations.
  • natural language processing of email communications among workers in an enterprise may indicate a degree of discomfort with, for example, a supplier to a value chain. While data associated with the supplier (e.g., on-time production, quality, and the like) may be within a variance range deemed acceptable, information within this unstructured content may indicate a potential pain point, such as a personal issue with a key participant at the supplier and the like.
  • semi-sentient problem recognition may be based on analysis of variances of measures of a value chain operation/entity/application including variance of a given measure over time, variance of two related measures, and the like.
  • variance in outcomes over time may indicate a problem state and/or suggest a pain point.
  • an artificial intelligence-based system may determine an acceptable range of outcome variance and apply that range to measures of a select set of value chain network entities, such as entities that share one or more similarities, to facilitate detection of a problem state.
  • an acceptable range of outcome variance may indicate a problem state trigger threshold that may be used by a local instance of artificial intelligence to signal a problem state.
  • a problem state may be detected when at least one measure of the value chain activity/entity and the like is greater than the artificial intelligence-determined problem state threshold.
  • Variance analysis for problem state detection may include detecting variances in start/end times of scheduled value chain network entity activities, variances in at least one of production time, production quality, production rate, production start time, production resource availability or trends thereof, variances in a measure of shipping supply chain entity, variances in a duration of time for transfer from one mode of transport to another (e.g., when the variance is greater than a transport mode problem state threshold), variances in quality testing, and the like.
  • a semi-sentient problem recognition system may include a machine learning/artificial intelligence prediction of a correlated pain point further along a supply chain due to a detected pain point, such as a risk and/or need for overtime, expedited shipping, discounting goods prices, and the like.
  • a management platform of an information technology system such as a management platform for a value chain of goods and/or services is depicted as a block diagram of functional elements and representative interconnections automated coordination of a set of value chain network activities for a set of products of an enterprise.
  • the management platform includes a set of network-connected value chain network entities 3202 that produce activity information 3208 that is used by an artificial intelligence system 1160 to provide automate coordination 3220 of value chain network activities 3212 for a set of products 3210 for an enterprise 3204.
  • a value chain may include a plurality of interconnected entities that each perform several activities for completing the value chain. While humans play a critical role in some activities within a value chain network, greater automated coordination and unified orchestration of supply and demand may be achieved using artificial intelligence-type systems (e.g., machine learning, expert systems, self-organizing systems, and the like including such systems describe herein and in the documents incorporated herein by reference) for coordinating supply chain activities.
  • artificial intelligence may further enrich the emerging nature of self-adapting systems, including Internet of Things (loT) devices and intelligent products and the like that not only provide greater capabilities to end users, but can play a critical role in automated coordination of supply chain activities.
  • LoT Internet of Things
  • an loT system deployed in a fulfillment center 628 may coordinate with an intelligent product 1510 that takes customer feedback about the product 1510, and an application 630 for the fulfillment center 628 may, upon receiving customer feedback via a connection path to the intelligent product 1510 about a problem with the product 1510, initiate a workflow to perform corrective actions on similar products 650 before the products 650 are sent out from the fulfillment center 628.
  • the workflow may be configured by an artificial intelligence system 1160 that analyzes the problem with the product 1510, develops an understanding of value chain network activities that produce the product, determines resources required for the workflow, coordinates with inventory and production systems to adapt any existing workflows and the like. Artificial intelligence systems 1160 may further coordinate with demand management applications to address any temporary impact on product availability and the like.
  • Automated coordination of value chain network activities within and across value chain network entity activities may benefit from advanced artificial intelligence systems that may enable use of differing artificial intelligence capabilities for any given value chain set of entities, applications, or conditions.
  • Use of hybrid artificial intelligence systems may provide benefits by applying more than one type of intelligence to a set of conditions to facilitate human and/or computer automated selection thereof.
  • Artificial intelligence can further enhance automated coordination of value chain network entity activities through intelligent operations such as generating sets of predictions, sets of classifications, generation of automate control signals (that may be communicated across value chain network entities and the like).
  • Artificial intelligence systems may facilitate automated coordination of value chain network entity activities for a set of products or an enterprise based on adaptive intelligence provided by the platform for a category of goods under which the set of products of an enterprise may be grouped.
  • adaptive intelligence may be provided by the platform for a drapery hanging category of goods and a set of products for an enterprise may include a line of adaptable drapery hangers.
  • artificial intelligence capabilities may be applied to value chain network activities of the enterprise for automating aspects of the value chain, such as information exchange among activities and the like.
  • Each of these may have characteristics of digital twins described throughout this disclosure and the documents incorporated by reference herein, such as mirroring or reflecting changes in states of associated physical objects or other entities, providing capabilities for modeling behavior or interactions of associated physical objects or other entities, enabling simulations, providing indications of status, and many others.
  • the artificial intelligence system 1160 may process image frames of the video feed to find markings (such as produce labels, SKUs, images, logos, or the like), shapes (such as packages of a particular size or shape), activities (such as loading or unloading activities) or the like that may indicate that a product has moved through the loading dock.
  • markings such as produce labels, SKUs, images, logos, or the like
  • shapes such as packages of a particular size or shape
  • activities such as loading or unloading activities
  • This information may substitute for, augment, or be used to validate other information, such as RFID tracking information or the like.
  • Similar discovery and interaction management activities may be undertaken with any of the types of value chain network entities 652 described throughout this disclosure.
  • the value chain network entities 652 may include, for example, products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, port infrastructure facilities, or many others.
  • one of the processes automated by robotic process automation involves configuration of a website navigation path related to a product. In embodiments, one of the processes automated by robotic process automation involves determination of an inventory level for a product. In embodiments, one of the processes automated by robotic process automation involves selection of a logistics type. In embodiments, one of the processes automated by robotic process automation involves configuration of a schedule for product delivery. In embodiments, one of the processes automated by robotic process automation involves configuration of a logistics schedule. In embodiments, one of the processes automated by robotic process automation involves configuration of a set of inputs for machine learning. In embodiments, one of the processes automated by robotic process automation involves preparation of product documentation. In embodiments, one of the processes automated by robotic process automation involves preparation of required disclosures about a product.
  • one of the processes automated by robotic process automation involves inspection of product quality data from a set of sensors. In embodiments, one of the processes automated by robotic process automation involves inspection of data from a set of onboard diagnostics on a product. In embodiments, one of the processes automated by robotic process automation involves inspection of diagnostic data from an Internet of Things system. In embodiments, one of the processes automated by robotic process automation involves review of sensor data from environmental sensors in a set of supply chain environments.
  • one of the processes automated by robotic process automation involves selection of inputs for a digital twin. In embodiments, one of the processes automated by robotic process automation involves selection of outputs from a digital twin. In embodiments, one of the processes automated by robotic process automation involves selection of visual elements for presentation in a digital twin. In embodiments, one of the processes automated by robotic process automation involves diagnosis of sources of delay in a supply chain. In embodiments, one of the processes automated by robotic process automation involves diagnosis of sources of scarcity in a supply chain. In embodiments, one of the processes automated by robotic process automation involves diagnosis of sources of congestion in a supply chain.
  • the opportunity miners 1460 may include a set of systems that collect information within the VCNP 102 and collect information within, about and for a set of value chain network entities 652 and environments, where the collected information has the potential to help identify and prioritize opportunities for increased automation and/or intelligence about the value chain network 668, about applications 630, about value chain network entities 652, or about the VCNP 102 itself.
  • the opportunity miners 1460 may include systems that observe clusters of value chain network workers by time, by type, and by location, such as using cameras, wearables, or other sensors, such as to identify labor-intensive areas and processes in a set of value chain network 668 environments.
  • analytics 838 may be used to identify which environments or activities would most benefit from automation for purposes of improved delivery times, mitigation of congestion, and other performance improvements.
  • the library may include videos that are specifically developed as instructional videos, such as to facilitate developing an automation map that can follow instructions in the video, such as providing a sequence of steps according to a procedure or protocol, breaking down the procedure or protocol into sub-steps that are candidates for automation, and the like.
  • videos may be processed by natural language processing, such as to automatically develop a sequence of labeled instructions that can be used by a developer to facilitate a map, a graph, or other models of a process that assists with development of automation for the process.
  • a specified set of training data sets may be configured to operate as inputs to learning.
  • the adaptive intelligent systems 614 may include value translators 1470.
  • the value translators 1470 may relate to demand side of transactions. Specifically, for example, the value translators 1470 may understand negative currencies of two marketplaces and may be able to translate value currencies into other currencies (e.g., not only fiat currencies that already have clear translation functions).
  • value translators 1470 may be associated with points of a point-based system (e.g., in a cost-based routing system).
  • value translators 1470 may be loyalty points offered that may be convertible into airline seats and/or may translate to refund policies for staying in a hotel room.
  • value translators 1470 may be used with network prioritization or cost-based routing that happens in networks off of priorities where the point system in these cost-based routing systems is not monetary-based.
  • FIG. 28 additional details of an embodiment of the platform 604 are provided, in particular relating to an overall architecture for the platform 604. These may include, for the cloud-based management platform 604, employing a micro-services architecture, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 of various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 614, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808.
  • the platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases and applications) for enabling an enterprise to manage a set of value chain network entities 652, such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • applications 614 including processes, workflows, activities, events, use cases and applications
  • a set of value chain network entities 652 such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • an information technology system may include: a cloud-based management platform with a micro-services architecture; a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities; and a set of applications for enabling an enterprise to manage a set of value chain network entities from a point of origin to a point of customer use.
  • Also provided herein are methods, systems, components and other elements for an information technology system may include: a cloud-based management platform with a micro-services architecture, the platform having: a set of interfaces for accessing and configuring features of the platform; a set of network connectivity facilities for enabling a set of value chain network entities to connect to the platform; a set of adaptive intelligence facilities for automating a set of capabilities of the platform; a set of data storage facilities for storing data collected and handled by the platform; and a set of monitoring facilities for monitoring the value chain network entities; wherein the platform hosts a set of applications for enabling an enterprise to manage a set of value chain network entities from a point of origin of a product of the enterprise to a point of customer use.
  • FIG. 29 additional details of an embodiment of the platform 604 are provided, in particular relating to an overall architecture for the platform 604. These may include, for the cloud-based management platform 604, employing a micro-services architecture, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 of various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 614, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808.
  • the platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases and applications) for enabling an enterprise to manage a set of value chain network entities 652, such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • applications 614 including processes, workflows, activities, events, use cases and applications
  • a set of value chain network entities 652 such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • the set of interfaces 702 may include a demand management interface 1402 and a supply chain management interface 1404.
  • the set of network connectivity facilities 642 for enabling a set of value chain network entities 652 to connect to the platform 604 may include a 5G network system 1410, such as one that is deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of network connectivity facilities 642 for enabling a set of value chain network entities 652 to connect to the platform 604 may include an Internet of Things system 1172, such as one that is deployed in a supply chain infrastructure facility operated by the enterprise, in, on or near a value chain network entity 652, in a network system, and/or in a cloud computing environment (such as where data collection systems 640 are configured to collect and organize loT data).
  • an Internet of Things system 1172 such as one that is deployed in a supply chain infrastructure facility operated by the enterprise, in, on or near a value chain network entity 652, in a network system, and/or in a cloud computing environment (such as where data collection systems 640 are configured to collect and organize loT data).
  • the set of network connectivity facilities 642 for enabling a set of value chain network entities 652 to connect to the VCNP 102 may include a cognitive networking system 1420 deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of network connectivity facilities 642 for enabling a set of value chain network entities 652 to connect to the VCNP 102 may include a peer-to-peer network system 1430, such as one that is deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of adaptive intelligence facilities or adaptive intelligent systems 614 for automating a set of capabilities of the platform 604 may include an edge intelligence system 1420, such as one that is deployed in a supply chain infrastructure facility operated by the enterprise.
  • the set of adaptive intelligence facilities or adaptive intelligent systems 614 for automating a set of capabilities of the platform 604 may include a robotic process automation system 1442.
  • the set of adaptive intelligence facilities or adaptive intelligent systems 614 for automating a set of capabilities of the platform 604 may include or may integrate with a self-configuring data collection system 1440, such as one that deployed in a supply chain infrastructure facility operated by the enterprise, one that is deployed in a network, and/or one that is deployed in a cloud computing environment. This may include elements of the data collection systems 640 of the data handling layers 608 that interact with or integrate with elements of the adaptive intelligent systems 614.
  • the set of adaptive intelligence facilities or adaptive intelligent systems 614 for automating a set of capabilities of the platform 604 may include a digital twin system 1700, such as one representing attributes of a set of value chain network entities, such as the ones controlled by an enterprise.
  • the set of adaptive intelligence facilities or adaptive intelligent systems 614 for automating a set of capabilities of the platform 604 may include a smart contract system 848, such as one for automating a set of interactions or transactions among a set of value chain network entities 652 based on status data, event data, or other data handled by the data handling layers 608.
  • a smart contract system 848 such as one for automating a set of interactions or transactions among a set of value chain network entities 652 based on status data, event data, or other data handled by the data handling layers 608.
  • the set of data storage facilities or data storage systems 624 for storing data collected and handled by the platform 604 uses a distributed data architecture 1122.
  • the set of data storage facilities for storing data collected and handled by the platform uses a blockchain 844.
  • the set of data storage facilities for storing data collected and handled by the platform uses a distributed ledger 1452.
  • the set of data storage facilities for storing data collected and handled by the platform uses graph database 1124 representing a set of hierarchical relationships of value chain network entities.
  • the set of monitoring facilities 614 for monitoring the value chain network entities 652 includes an Internet of Things monitoring system 1172, such as for collecting data from loT systems and devices deployed throughout a value chain network.
  • the set of monitoring facilities 614 for monitoring the value chain network entities 652 includes a set of sensor systems 1462, such as ones deployed in a value chain environment or in, one or near a value chain network entity 652, such as in or on a product 1510.
  • the set of applications 614 includes a set of applications, which may include a variety of types from among, for example, a set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510 and enterprise resource management applications 1520.
  • the set of applications includes an asset management application 1530.
  • the value chain network entities 652 as mentioned throughout this disclosure may include, for example, without limitation, products, suppliers, producers, manufacturers, retailers, businesses, owners, operators, operating facilities, customers, consumers, workers, mobile devices, wearable devices, distributors, resellers, supply chain infrastructure facilities, supply chain processes, logistics processes, reverse logistics processes, demand prediction processes, demand management processes, demand aggregation processes, machines, ships, barges, warehouses, maritime ports, airports, airways, waterways, roadways, railways, bridges, tunnels, online retailers, ecommerce sites, demand factors, supply factors, delivery systems, floating assets, points of origin, points of destination, points of storage, points of use, networks, information technology systems, software platforms, distribution centers, fulfillment centers, containers, container handling facilities, customs, export control, border control, drones, robots, robotic handling systems, 3D printers, vehicles, autonomous vehicles, hauling facilities, waterways, port infrastructure facilities, or others.
  • the platform 604 manages a set of demand factors 1540, a set of supply factors 1550 and a set of value chain infrastructure facilities 1560.
  • the supply factors 1550 as mentioned throughout this disclosure may include, for example and without limitation, ones involving component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, route safety, and many others.
  • the demand factors 1540 as mentioned throughout this disclosure may include, for example and without limitation, ones involving product availability, product pricing, delivery timing, need for refill, need for replacement, manufacturer recall, need for upgrade, need for maintenance, need for update, need for repair, need for consumable, taste, preference, inferred need, inferred want, group demand, individual demand, family demand, business demand, need for workflow, need for process, need for procedure, need for treatment, need for improvement, need for diagnosis, compatibility to system, compatibility to product, compatibility to style, compatibility to brand, demographic, psychographic, geolocation, indoor location, destination, route, home location, visit location, workplace location, business location, personality, mood, emotion, customer behavior, business type, business activity, personal activity, wealth, income, purchasing history, shopping history, search history, engagement history, clickstream history, website history, online navigation history, group behavior, family behavior, family membership, customer identity, group identity, business identity, customer profile, business profile, group profile, family profile, declared interest, inferred interest, and many others.
  • the supply chain infrastructure facilities 1560 as mentioned throughout this disclosure may include, for example and without limitation, ship, container ship, boat, barge, maritime port, crane, container, container handling, shipyard, maritime dock, warehouse, distribution, fulfillment, fueling, refueling, nuclear refueling, waste removal, food supply, beverage supply, drone, robot, autonomous vehicle, aircraft, automotive, truck, train, lift, forklift, hauling facilities, conveyor, loading dock, waterway, bridge, tunnel, airport, depot, vehicle station, train station, weigh station, inspection, roadway, railway, highway, customs house, border control, and other facilities.
  • the set of applications 614 as mentioned throughout this disclosure may include, for example and without limitation, supply chain, asset management, risk management, inventory management, demand management, demand prediction, demand aggregation, pricing, positioning, placement, promotion, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, self-configuration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, demand customer profiling, entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, product design, product configuration, product updating, product maintenance, product support, product testing, warehousing, distribution, fulfillment, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing
  • the platform 604 may employ a micro-services architecture with the various data handling layers 608, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 of various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 614, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808.
  • the platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases and applications) for enabling an enterprise to manage a set of value chain network entities 652, such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • the platform 604 may include a user interface 1570 that provides a set of unified views for a set of demand management information and supply chain information for a category of goods, such as one that displays status information, event information, activity information, analytics, reporting, or other elements of, relating to, or produced by a set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510 and enterprise resource management applications 1520 that monitor and/or manage a value chain network and a set of value chain network entities 652.
  • a user interface 1570 that provides a set of unified views for a set of demand management information and supply chain information for a category of goods, such as one that displays status information, event information, activity information, analytics, reporting, or other elements of, relating to, or produced by a set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510 and enterprise resource management applications 1520 that monitor and/or manage a value chain network and a set of value chain network entities 652.
  • the unified view interface 1570 may thus provide, in embodiments, a control tower for an enterprise over a range of assets, such as supply chain infrastructure facilities 1560 and other value chain network entities 652 that are involved as a product 1510 travels from a point of origin through distribution and retail channels to an environment where it is used by a customer. These may include views of demand factors 1540 and supply factors 1550, so that a user may develop insights about connections among the factors and control one or both of them with coordinated intelligence. Population of a set of unified views may be adapted over time, such as by learning on outcomes 1040 or other operations of the adaptive intelligent systems 614, such as to determine which views of the interface 1570 provide the most impactful insights, control features, or the like.
  • assets such as supply chain infrastructure facilities 1560 and other value chain network entities 652 that are involved as a product 1510 travels from a point of origin through distribution and retail channels to an environment where it is used by a customer.
  • These may include views of demand factors 1540 and supply factors 1550, so that a user may
  • the user interface includes a voice operated assistant 1580.
  • the user interface includes a set of digital twins 1700 for presenting a visual representation of a set of attributes of a set of value chain network entities 652.
  • the user interface 1570 may include capabilities for configuring the adaptive intelligent systems 614 or adaptive intelligence facilities, such as to allow user selection of attributes, parameters, data sources, inputs to learning, feedback to learning, views, formats, arrangements, or other elements.
  • an information technology system may include: a cloud-based management platform with a micro-services architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities that are coordinated for monitoring and management of a set of value chain network entities; a set of applications for enabling an enterprise to manage a set of value chain network entities from a point of origin to a point of customer use; and a user interface that provides a set of unified views for a set of demand management information and supply chain information for a category of goods.
  • a cloud-based management platform with a micro-services architecture a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities that are coordinated for monitoring and management of a set of value chain network entities
  • a set of applications for enabling an enterprise to manage a set of value chain network entities from a point of origin to a point of customer use
  • a user interface that provides a set of unified views for a set of demand management information and supply chain information for a category of goods.
  • the platform 604 may employ a micro-services architecture with the various data handling layers 608, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 of various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 614, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808.
  • the platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases and applications) for enabling an enterprise to manage a set of value chain network entities 652, such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • the platform 604 may include a unified database 1590 that supports a set of applications of multiple types, such as ones among a set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510 and enterprise resource management applications 1520 that monitor and/or manage a value chain network and a set of value chain network entities 652.
  • the unified database 1590 may thus provide, in embodiments, unification of data storage, access and handling for an enterprise over a range of assets, such as supply chain infrastructure facilities 1560 and other value chain network entities 652 that are involved as a product 1510 travels from a point of origin through distribution and retail channels to an environment where it is used by a customer.
  • This unification may provide a number of advantages, including reduced need for data entry, consistency across applications 630, reduced latency (and better real-time reporting), reduced need for data transformation and integration, and others. These may include data relating to demand factors 1540 and supply factors 1550, so that an application 630 may benefit from information collected by, processed, or produced by other applications 630 of the platform 604 and a user can develop insights about connections among the factors and control one or both of them with coordinated intelligence.
  • Population of the unified database 1590 may be adapted over time, such as by learning on outcomes 1040 or other operations of the adaptive intelligent systems 614, such as to determine which elements of the database 1590 should be made available to which applications, what data structures provide the most benefit, what data should be stored or cached for immediate retrieval, what data can be discarded versus saved, what data is most beneficial to support adaptive intelligent systems 614, and for other uses.
  • an information technology system may include: a cloud-based management platform with a micro-services architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities that are coordinated for monitoring and management of a set of value chain network entities; a set of applications for enabling an enterprise to manage a set of value chain network entities from a point of origin to a point of customer use; and a unified database that supports a set of applications of at least two types from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications and a set of enterprise resource management applications for a category of goods.
  • the unified database that supports a set of demand management applications, a set of supply chain applications, a set of intelligent product applications and a set of enterprise resource management applications for a category of goods is a distributed database.
  • the unified database that supports a set of demand management applications, a set of supply chain applications, a set of intelligent product applications and a set of enterprise resource management applications for a category of goods uses a graph database architecture.
  • the set of demand management applications includes a demand prediction application.
  • the set of demand management applications includes a demand aggregation application.
  • the set of demand management applications includes a demand activation application.
  • the set of supply chain management applications includes a vendor search application. In embodiments, the set of supply chain management applications includes a route configuration application. In embodiments, the set of supply chain management applications includes a logistics scheduling application.
  • the platform 604 may employ a micro-services architecture with the various data handling layers 608, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 of various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 1160, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808.
  • the platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases and applications) for enabling an enterprise to manage a set of value chain network entities 652, such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • applications 614 including processes, workflows, activities, events, use cases and applications
  • a set of value chain network entities 652 such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • the platform 604 may include a set of unified set of data collection and management systems 640 of the set of monitoring facilities or systems 808 that support a set of applications 614 of various types, including a set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510 and enterprise resource management applications 1520 that monitor and/or manage a value chain network and a set of value chain network entities 652.
  • the unified data collection and management systems 640 may thus provide, in embodiments, unification of data monitoring, search, discovery, collection, access and handling for an enterprise or other user over a range of assets, such as supply chain infrastructure facilities 1560 and other value chain network entities 652 that are involved as a product 1510 travels from a point of origin through distribution and retail channels to an environment where it is used by a customer.
  • This unification may provide a number of advantages, including reduced need for data entry, consistency across applications 630, reduced latency (and better real-time reporting), reduced need for data transformation and integration, and others.
  • the unified data collection and management systems 640 may be adapted over time, such as by learning on outcomes 1040 or other operations of the adaptive intelligent systems 614, such as to determine which elements of the data collection and management systems 640 should be made available to which applications 630, what data types or sources provide the most benefit, what data should be stored or cached for immediate retrieval, what data can be discarded versus saved, what data is most beneficial to support adaptive intelligent systems 614, and for other uses.
  • the unified data collection and management systems 640 may use a unified data schema which relates data collection and management for various applications. This may be a single point of truth database at the most tightly bound or a set of distributed data systems that may follow a schema that may be sufficiently common enough that a wide variety of applications may consume the same data as received. For example, sensor data may be pulled from a smart product that may be consumed by a logistics application, a financial application, a demand prediction application, or a genetic programming artificial intelligence (Al) application to change the product, and the like. All of these applications may consume data from a data framework.
  • a unified data schema which relates data collection and management for various applications. This may be a single point of truth database at the most tightly bound or a set of distributed data systems that may follow a schema that may be sufficiently common enough that a wide variety of applications may consume the same data as received. For example, sensor data may be pulled from a smart product that may be consumed by a logistics application, a financial application, a demand prediction application, or a genetic
  • this may occur from blockchains that may contain a distributed ledger or transactional data for purchase and sales or blockchains where there may be an indication of whether or not events had occurred.
  • this data flow may occur through distributed databases, relational databases, graph databases of all types, and the like that may be part of the unified data collection and management systems 640.
  • the unified data collection and management systems 640 may utilize memory that may be dedicated memory on an asset, in a tag or part of a memory structure of the device itself that may come from a robust pipeline tied to the value chain network entities.
  • the unified data collection and management systems 640 may use classic data integration capabilities that may include adapting protocols such that they can ultimately get to the unified system or schema.
  • an information technology system may include: a cloud-based management platform with a micro-services architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities that are coordinated for monitoring and management of a set of value chain network entities; a set of applications for enabling an enterprise to manage a set of value chain network entities from a point of origin to a point of customer use; and a unified set of data collection systems that support a set of applications of at least two types from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications and a set of enterprise resource management applications for a category of goods.
  • the unified set of data collection systems includes a set of crowdsourcing data collection systems. In embodiments, the unified set of data collection systems includes a set of Internet of Things data collection systems. In embodiments, the unified set of data collection systems includes a set of self-configuring sensor systems. In embodiments, the unified set of data collection systems includes a set of data collection systems that interact with a network-connected product.
  • the unified set of data collection systems includes a set of mobile data collectors deployed in a set of value chain network environments operated by an enterprise.
  • the unified set of data collection systems includes a set of edge intelligence systems deployed in set of value chain network environments operated by an enterprise.
  • the unified set of data collection systems includes a set of crowdsourcing data collection systems.
  • the unified set of data collection systems includes a set of Internet of Things data collection systems.
  • the unified set of data collection systems includes a set of self-configuring sensor systems.
  • the unified set of data collection systems includes a set of data collection systems that interact with a network- connected product.
  • the unified set of data collection systems includes a set of mobile data collectors deployed in a set of value chain network environments operated by an enterprise. In embodiments, the unified set of data collection systems includes a set of edge intelligence systems deployed in a set of value chain network environments operated by an enterprise.
  • the platform 604 may employ a micro-services architecture with the various data handling layers 608, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 of various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 1160, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808.
  • the platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases and applications) for enabling an enterprise to manage a set of value chain network entities 652, such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • applications 614 including processes, workflows, activities, events, use cases and applications
  • a set of value chain network entities 652 such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • the platform 604 may include a unified set of Internet of Things systems 1172 that provide coordinated monitoring of various value chain entities 652 in service of a set of multiple applications 630 of various types, such as a set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510 and enterprise resource management applications 1520 that monitor and/or manage a value chain network and a set of value chain network entities 652.
  • a set of supply chain management applications 21004 demand management applications 1502
  • intelligent product applications 1510 and enterprise resource management applications 1520 that monitor and/or manage a value chain network and a set of value chain network entities 652.
  • the unified set of Internet of Things systems 1172 may thus provide, in embodiments, unification of monitoring of, and communication with, a wide range of facilities, devices, systems, environments, and assets, such as supply chain infrastructure facilities 1560 and other value chain network entities 652 that are involved as a product 1510 travels from a point of origin through distribution and retail channels to an environment where it is used by a customer.
  • This unification may provide a number of advantages, including reduced need for data entry, consistency across applications 630, reduced latency, real-time reporting and awareness, reduced need for data transformation and integration, and others.
  • These may include Internet of Things systems 1172 that are used in connection with demand factors 1540 and supply factors 1550, so that an application 630 may benefit from information collected by, processed, or produced by the unified set of Internet of Things systems 1172 for other applications 630 of the platform 604, and a user can develop insights about connections among the factors and control one or both of them with coordinated intelligence.
  • the unified set of Internet of Things systems 1172 may be adapted over time, such as by learning on outcomes 1040 or other operations of the adaptive intelligent systems 614, such as to determine which elements of the unified set of Internet of Things systems 1172 should be made available to which applications 630, what loT systems 1172 provide the most benefit, what data should be stored or cached for immediate retrieval, what data can be discarded versus saved, what data is most beneficial to support adaptive intelligent systems 614, and for other uses.
  • the unified set of Internet of Things (loT) systems 1172 may be loT devices that may be installed in various environments.
  • One goal of the unified set of Internet of Things systems 1172 may be coordination across a city or town involving citywide deployments where collectively a set of IOT devices may be connected by wide area network protocols (e.g., longer range protocols).
  • the unified set of Internet of Things systems 1172 may involve connecting a mesh of devices across several different distribution facilities.
  • the loT devices may identify collection for each warehouse and the warehouses may use the loT devices to communicate with each other.
  • the loT devices may be configured to process data without using the cloud.
  • an information technology system may include: a cloud-based management platform with a micro-services architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities that are coordinated for monitoring and management of a set of value chain network entities; a set of applications integrated with the platform for enabling an enterprise user of the platform to manage a set of value chain network entities from a point of origin to a point of customer use; and a unified set of Internet of Things systems that provide coordinated monitoring of a set of applications of at least two types from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications and a set of enterprise resource management applications for a category of goods.
  • the unified set of Internet of Things systems includes a set of smart home Internet of Things devices to enable monitoring of a set of demand factors and a set of Internet of Things devices deployed in proximity to a set of supply chain infrastructure facilities to enable monitoring of a set of supply factors.
  • the unified set of Internet of Things systems includes a set of workplace Internet of Things devices to enable monitoring of a set of demand factors for a set of business customers and a set of Internet of Things devices deployed in proximity to a set of supply chain infrastructure facilities to enable monitoring of a set of supply factors.
  • the unified set of Internet of Things systems includes a set of Internet of Things devices to monitor a set of consumer goods stores to enable monitoring of a set of demand factors for a set of consumers and a set of Internet of Things devices deployed in proximity to a set of supply chain infrastructure facilities to enable monitoring of a set of supply factors.
  • the Internet of Things systems may include, for example and without limitations, camera systems, lighting systems, motion sensing systems, weighing systems, inspection systems, machine vision systems, environmental sensor systems, onboard sensor systems, onboard diagnostic systems, environmental control systems, sensor-enabled network switching and routing systems, RF sensing systems, magnetic sensing systems, pressure monitoring systems, vibration monitoring systems, temperature monitoring systems, heat flow monitoring systems, biological measurement systems, chemical measurement systems, ultrasonic monitoring systems, radiography systems, LIDAR-based monitoring systems, access control systems, penetrating wave sensing systems, SONAR-based monitoring systems, radar-based monitoring systems, computed tomography systems, magnetic resonance imaging systems, network monitoring systems, and many others.
  • the platform 604 may employ a micro-services architecture with the various data handling layers 608, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 of various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 1160, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808.
  • the platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases and applications) for enabling an enterprise to manage a set of value chain network entities 652, such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • applications 614 including processes, workflows, activities, events, use cases and applications
  • a set of value chain network entities 652 such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • the platform 604 may include a machine vision system 1600 and a digital twin system 1700, wherein the machine vision system 1600 feeds data to the digital twin system 1700 (which may be enabled by a set of adaptive intelligent systems 614, including artificial intelligence 1160, and may be used as interfaces or components of interfaces 702, such as ones by which an operator may monitor twins 1700 of various value chain network entities 652).
  • the machine vision system 1600 and digital twin system 1700 may operate in coordination for a set of multiple applications 630 of various types, such as a set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510 and enterprise resource management applications 1520 that monitor and/or manage a value chain network and a set of value chain network entities 652.
  • the machine vision system 1600 and digital twin system 1700 may thus provide, in embodiments, image-based monitoring (with automated processing of image data) a wide range of facilities, devices, systems, environments, and assets, such as supply chain infrastructure facilities 1560 and other value chain network entities 652 that are involved as a product 1510 travels from a point of origin through distribution and retail channels to an environment where it is used by a customer, as well as representation of images, as well as extracted data from images, in a digital twin 1700.
  • This unification may provide a number of advantages, including improved monitoring, improved visualization and insight, improved visibility, and others.
  • machine vision systems 1600 and digital twin systems 1700 may include machine vision systems 1600 and digital twin systems 1700 that are used in connection with demand factors 1540 and supply factors 1550, so that an application 630 may benefit from information collected by, processed, or produced by the machine vision system 1600 and digital twin system 1700 for other applications 630 of the platform 604, and a user can develop insights about connections among the factors and control one or both of them with coordinated intelligence.
  • the machine vision system 1600 and/or digital twin system 1700 may be adapted over time, such as by learning on outcomes 1040 or other operations of the adaptive intelligent systems 614, such as to determine which elements collected and/or processed by the machine vision system 1600 and/or digital twin system 1700 should be made available to which applications 630, what elements and/or content provide the most benefit, what data should be stored or cached for immediate retrieval, what data can be discarded versus saved, what data is most beneficial to support adaptive intelligent systems 614, and for other uses.
  • an information technology system may include: a cloud-based management platform with a micro-services architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities that are coordinated for monitoring and management of a set of value chain network entities; a set of applications for enabling an enterprise to manage a set of value chain network entities from a point of origin to a point of customer use; and for a set of applications of at least two types from among a set of supply chain applications, a set of demand management applications, a set of intelligent product applications and a set of enterprise resource management applications and having a machine vision system and a digital twin system, wherein the machine vision system feeds data to the digital twin system.
  • the set of supply chain applications and demand management applications is among any described throughout this disclosure or in the documents incorporated by reference herein.
  • the set of supply chain applications and demand management applications includes, for example and without limitation one or more involving inventory management, demand prediction, demand aggregation, pricing, blockchain, smart contract, positioning, placement, promotion, analytics, finance, trading, arbitrage, customer identity management, store planning, shelf-planning, customer route planning, customer route analytics, commerce, ecommerce, payments, customer relationship management, sales, marketing, advertising, bidding, customer monitoring, customer process monitoring, customer relationship monitoring, collaborative filtering, customer profiling, customer feedback, similarity analytics, customer clustering, product clustering, seasonality factor analytics, customer behavior tracking, customer behavior analytics, product design, product configuration, A/B testing, product variation analytics, augmented reality, virtual reality, mixed reality, customer demand profiling, customer mood, emotion or affect detection, customer mood, emotion of affect analytics, business entity profiling, customer enterprise profiling, demand matching, location-based targeting, location-based offering, point of sale interface, point of use interface, search, advertisement, entity discovery, entity search, enterprise resource planning, workforce management, customer digital twin, product pricing,
  • the set of supply chain applications and demand management applications may include, without limitation, one or more of supply chain, asset management, risk management, inventory management, blockchain, smart contract, infrastructure management, facility management, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, vendor management, process management, compatibility testing, compatibility management, infrastructure testing, incident management, predictive maintenance, logistics, monitoring, remote control, automation, selfconfiguration, self-healing, self-organization, logistics, reverse logistics, waste reduction, augmented reality, virtual reality, mixed reality, supply chain digital twin, vendor profiling, supplier profiling, manufacturer profiling, logistics entity profiling, enterprise profiling, worker profiling, workforce profiling, component supply policy management, warehousing, distribution, fulfillment, shipping fleet management, vehicle fleet management, workforce management, maritime fleet management, navigation, routing, shipping management, opportunity matching, search, entity discovery, entity search, distribution, delivery, enterprise resource planning or other applications.
  • the set of supply chain applications and demand management applications may include, without limitation, one or more of asset management, risk management, inventory management, blockchain, smart contract, analytics, finance, trading, tax, regulatory, identity management, commerce, ecommerce, payments, security, safety, compatibility testing, compatibility management, incident management, predictive maintenance, monitoring, remote control, automation, self-configuration, self-healing, self-organization, waste reduction, augmented reality, virtual reality, mixed reality, product design, product configuration, product updating, product maintenance, product support, product testing, kit configuration, kit deployment, kit support, kit updating, kit maintenance, kit modification, kit management, product digital twin, opportunity matching, search, advertisement, entity discovery, entity search, variation, simulation, user interface, application programming interface, connectivity management, natural language interface, voice/speech interface, robotic interface, touch interface, haptic interface, vision system interface, enterprise resource planning, or other applications.
  • the set of supply chain applications and demand management applications may include, without limitation, one or more of operations, finance, asset management, supply chain management, demand management, human resource management, product management, risk management, regulatory and compliance management, inventory management, infrastructure management, facilities management, analytics, trading, tax, identity management, vendor management, process management, project management, operations management, customer relationship management, workforce management, incident management, research and development, sales management, marketing management, fleet management, opportunity analytics, decision support, strategic planning, forecasting, resource management, property management, or other applications.
  • the machine vision system includes an artificial intelligence system that is trained to recognize a type of value chain asset based on a labeled data set of images of such type of value chain assets.
  • the digital twin presents an indicator of the type of asset based on the output of the artificial intelligence system.
  • the machine vision system includes an artificial intelligence system that is trained to recognize a type of activity involving a set of value chain entities based on a labeled data set of images of such type of activity.
  • the digital twin presents an indicator of the type of activity based on the output of the artificial intelligence system.
  • the machine vision system includes an artificial intelligence system that is trained to recognize a safety hazard involving a value chain entity based on a training data set that includes a set of images of value chain network activities and a set of value chain network safety outcomes.
  • the digital twin presents an indicator of the hazard based on the output of the artificial intelligence system.
  • the machine vision system includes an artificial intelligence system that is trained to predict a delay based on a training data set that includes a set of images of value chain network activities and a set of value chain network timing outcomes.
  • the digital twin presents an indicator of a likelihood of delay based on the output of the artificial intelligence system.
  • artificial intelligence in connection with value chain network entities 652 and related processes and applications may be used to facilitate, among other things: (a) the optimization, automation and/or control of various functions, workflows, applications, features, resource utilization and other factors, (b) recognition or diagnosis of various states, entities, patterns, events, contexts, behaviors, or other elements; and/or (c) the forecasting of various states, events, contexts or other factors.
  • artificial intelligence improves, a large array of domain-specific and/or general artificial intelligence systems have become available and are likely to continue to proliferate.
  • an artificial intelligence store 3504 that is configured to enable collection, organization, recommendation and presentation of relevant sets of artificial intelligence systems based on one or more attributes of a domain and/or a domain- related problem.
  • an artificial intelligence store 3504 may include a set of interfaces to artificial intelligence systems, such as enabling the download of relevant artificial intelligence applications, establishment of links or other connections to artificial intelligence systems (such as links to cloud-deployed artificial intelligence systems via APIs, ports, connectors, or other interfaces) and the like.
  • an interface to the application store 3504 may take input from a developer and/or from the platform (such as from an opportunity miner 1460) that indicates one or more attributes of a problem that may be addressed through artificial intelligence and may provide a set of recommendations, such as via an artificial intelligence attribute search engine, for a subset of artificial intelligence solutions that may represent favorable candidates based on the developer’s domain-specific problem.
  • Search results or recommendations may, in embodiments, be based at least in part on collaborative filtering, such as by asking developers to indicate or select elements of favorable models, as well as by clustering, such as by using similarity matrices, k-means clustering, or other clustering techniques that associate similar developers, similar domain-specific problems, and/or similar artificial intelligence solutions.
  • the artificial intelligence store 3504 may include e-commerce features, such as ratings, reviews, links to relevant content, and mechanisms for provisioning, licensing, delivery and payment (including allocation of payments to affiliates and or contributors), including ones that operate using smart contract and/or blockchain features to automate purchasing, licensing, payment tracking, settlement of transactions, or other features.
  • the artificial intelligence system 1160 may define a machine learning model 3000 for performing analytics, simulation, decision making, and prediction making related to data processing, data analysis, simulation creation, and simulation analysis of one or more of the value chain entities 652.
  • the machine learning model 3000 is an algorithm and/or statistical model that performs specific tasks without using explicit instructions, relying instead on patterns and inference.
  • the machine learning model 3000 builds one or more mathematical models based on training data to make predictions and/or decisions without being explicitly programmed to perform the specific tasks.
  • the machine learning model 3000 may receive inputs of sensor data as training data, including event data 1034 and state data 1140 related to one or more of the value chain entities 652.
  • the sensor data input to the machine learning model 3000 may be used to train the machine learning model 3000 to perform the analytics, simulation, decision making, and prediction making relating to the data processing, data analysis, simulation creation, and simulation analysis of the one or more of the value chain entities 652.
  • the machine learning model 3000 may also use input data from a user or users of the information technology system.
  • the machine learning model 3000 may include an artificial neural network, a decision tree, a support vector machine, a Bayesian network, a genetic algorithm, any other suitable form of machine learning model, or a combination thereof.
  • the machine learning model 3000 may be configured to learn through supervised learning, unsupervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, association rules, a combination thereof, or any other suitable algorithm for learning.
  • the artificial intelligence system 1160 may also define the digital twin system 1700 to create a digital replica of one or more of the value chain entities 652.
  • the digital replica of the one or more of the value chain entities 652 may use substantially real-time sensor data to provide for substantially real-time virtual representation of the value chain entity 652 and provides for simulation of one or more possible future states of the one or more value chain entities 652.
  • the digital replica exists simultaneously with the one or more value chain entities 652 being replicated.
  • the digital replica provides one or more simulations of both physical elements and properties of the one or more value chain entities 652 being replicated and the dynamics thereof, in embodiments, throughout the lifestyle of the one or more value chain entities 652 being replicated.
  • the digital replica may provide a hypothetical simulation of the one or more value chain entities 652, for example during a design phase before the one or more value chain entities are constructed or fabricated, or during or after construction or fabrication of the one or more value chain entities by allowing for hypothetical extrapolation of sensor data to simulate a state of the one or more value chain entities 652, such as during high stress, after a period of time has passed during which component wear may be an issue, during maximum throughput operation, after one or more hypothetical or planned improvements have been made to the one or more value chain entities 652, or any other suitable hypothetical situation.
  • the machine learning model 3000 may automatically predict hypothetical situations for simulation with the digital replica, such as by predicting possible improvements to the one or more value chain entities 652, predicting when one or more components of the one or more value chain entities 652 may fail, and/or suggesting possible improvements to the one or more value chain entities 652, such as changes to timing settings, arrangement, components, or any other suitable change to the value chain entities 652.
  • the digital replica allows for simulation of the one or more value chain entities 652 during both design and operation phases of the one or more value chain entities 652, as well as simulation of hypothetical operation conditions and configurations of the one or more value chain entities 652.
  • the digital replica allows for invaluable analysis and simulation of the one or more value chain entities, by facilitating observation and measurement of nearly any type of metric, including temperature, wear, light, vibration, etc. not only in, on, and around each component of the one or more value chain entities 652, but in some embodiments within the one or more value chain entities 652.
  • the machine learning model 3000 may process the sensor data including the event data 1034 and the state data 1140 to define simulation data for use by the digital twin system 1700.
  • the machine learning model 3000 may, for example, receive state data 1140 and event data 1034 related to a particular value chain entity 652 of the plurality of value chain entities 652 and perform a series of operations on the state data 1140 and the event data 1034 to format the state data 1140 and the event data 1034 into a format suitable for use by the digital twin system 1700 in creation of a digital replica of the value chain entity 652.
  • one or more value chain entities 652 may include a robot configured to augment products on an adjacent assembly line.
  • the machine learning model 3000 may collect data from one or more sensors positioned on, near, in, and/or around the robot.
  • the machine learning model 3000 may perform operations on the sensor data to process the sensor data into simulation data and output the simulation data to the digital twin system 1700.
  • the machine learning model 3000 may automatically predict hypothetical situations for simulation with the digital replica, such as by predicting possible improvements to the set of value chain entities, predicting when one or more components of the set of value chain entities may fail, and/or suggesting possible improvements to the set of value chain entities, such as changes to timing settings, arrangement, components, or any other suitable change to the value chain entities 652.
  • the digital replica allows for simulation of the set of value chain entities during both design and operation phases of the set of value chain entities, as well as simulation of hypothetical operation conditions and configurations of the set of value chain entities.
  • the digital replica allows for invaluable analysis and simulation of the one or more value chain entities, by facilitating observation and measurement of nearly any type of metric, including temperature, wear, light, vibration, etc.
  • the machine learning model 3000 may process the sensor data including the event data 1034 and the state data 1140 to define simulation data for use by the digital twin system 1700.
  • the machine learning model 3000 may, for example, receive state data 1140 and event data 1034 related to a particular value chain entity 652 of the plurality of value chain entities 652 and perform a series of operations on the state data 1140 and the event data 1034 to format the state data 1140 and the event data 1034 into a format suitable for use by the digital twin system 1700 in the creation of a digital replica of the set of value chain entities.
  • the machine learning model 3000 may automatically increase or decrease collection rates, processing, storage, sampling rates, bandwidth allocation, bitrates, and other attributes of sensor data collection to achieve or better achieve the modeling goal. In some embodiments, the machine learning model 3000 may make suggestions or predictions to a user of the information technology system related to increasing or decreasing collection rates, processing, storage, sampling rates, bandwidth allocation, bitrates, and other attributes of sensor data collection to achieve or better achieve the modeling goal. In some embodiments, the machine learning model 3000 may use sensor data, simulation data, previous, current, and/or future digital replica simulations of one or more value chain entities 652 of the plurality of value chain entities 652 to automatically create and/or propose modeling goals.
  • the machine learning model 3000 may be configured to evaluate a set of hypothetical simulations of one or more of the value chain entities 652.
  • the set of hypothetical simulations may be created by the machine learning model 3000 and the digital twin system 1700 as a result of one or more modeling commands, as a result of one or more modeling goals, one or more modeling commands, by prediction by the machine learning model 3000, or a combination thereof.
  • the machine learning model 3000 may evaluate the set of hypothetical simulations based on one or more metrics defined by the user, one or more metrics defined by the machine learning model 3000, or a combination thereof.
  • the machine learning model 3000 may evaluate each of the hypothetical simulations of the set of hypothetical simulations independently of one another.
  • the machine learning model 3000 may evaluate one or more of the hypothetical simulations of the set of hypothetical simulations in relation to one another, for example by ranking the hypothetical simulations or creating tiers of the hypothetical simulations based on one or more metrics.
  • the machine learning model 3000 may be and/or include an artificial neural network, e.g., a connectionist system configured to “learn” to perform tasks by considering examples and without being explicitly programmed with task-specific rules.
  • the machine learning model 3000 may be based on a collection of connected units and/or nodes that may act like artificial neurons that may in some ways emulate neurons in a biological brain.
  • the units and/or nodes may each have one or more connections to other units and/or nodes.
  • the units and/or nodes may be configured to transmit information, e.g., one or more signals, to other units and/or nodes, process signals received from other units and/or nodes, and forward processed signals to other units and/or nodes.
  • One or more of the units and/or nodes and connections therebetween may have one or more numerical “weights” assigned.
  • the assigned weights may be configured to facilitate learning, i.e., training, of the machine learning model 3000.
  • the weights assigned weights may increase and/or decrease one or more signals between one or more units and/or nodes, and in some embodiments may have one or more thresholds associated with one or more of the weights.
  • the one or more thresholds may be configured such that a signal is only sent between one or more units and/or nodes, if a signal and/or aggregate signal crosses the threshold.
  • the units and/or nodes may be assigned to a plurality of layers, each of the layers having one or both of inputs and outputs.
  • a first layer may be configured to receive training data, transform at least a portion of the training data, and transmit signals related to the training data and transformation thereof to a second layer.
  • a final layer may be configured to output an estimate, conclusion, product, or other consequence of processing of one or more inputs by the machine learning model 3000.
  • Each of the layers may perform one or more types of transformations, and one or more signals may pass through one or more of the layers one or more times.
  • the machine learning model 3000 may employ deep learning and being at least partially modeled and/or configured as a deep neural network, a deep belief network, a recurrent neural network, and/or a convolutional neural network, such as by being configured to include one or more hidden layers.
  • the machine learning model 3000 may be configured to perform regression analysis to determine and/or estimate a relationship between one or more inputs and one or more features of the one or more inputs.
  • Regression analysis may include linear regression, wherein the machine learning model 3000 may calculate a single line to best fit input data according to one or more mathematical criteria.
  • inputs to the machine learning model 3000 may be tested, such as by using a set of testing data that is independent from the data set used for the creation and/or training of the machine learning model, such as to test the impact of various inputs to the accuracy of the model 3000.
  • inputs to the regression model may be removed, including single inputs, pairs of inputs, triplets, and the like, to determine whether the absence of inputs creates a material degradation of the success of the model 3000. This may assist with recognition of inputs that are in fact correlated (e.g., are linear combinations of the same underlying data), that are overlapping, or the like.
  • the machine learning model 3000 may be and/or include a Bayesian network.
  • the Bayesian network may be a probabilistic graphical model configured to represent a set of random variables and conditional independence of the set of random variables.
  • the Bayesian network may be configured to represent the random variables and conditional independence via a directed acyclic graph.
  • the Bayesian network may include one or both of a dynamic Bayesian network and an influence diagram.
  • the machine learning model 3000 may be defined via supervised learning, i.e., one or more algorithms configured to build a mathematical model of a set of training data containing one or more inputs and desired outputs.
  • the training data may consist of a set of training examples, each of the training examples having one or more inputs and desired outputs, i.e., a supervisory signal.
  • Each of the training examples may be represented in the machine learning model 3000 by an array and/or a vector, i.e., a feature vector.
  • the training data may be represented in the machine learning model 3000 by a matrix.
  • the machine learning model 3000 may learn one or more functions via iterative optimization of an objective function, thereby learning to predict an output associated with new inputs.
  • the objective function may provide the machine learning model 3000 with the ability to accurately determine an output for inputs other than inputs included in the training data.
  • the machine learning model 3000 may be defined via one or more supervised learning algorithms such as active learning, statistical classification, regression analysis, and similarity learning.
  • Active learning may include interactively querying, by the machine learning model AILD102T, a user and/or an information source to label new data points with desired outputs.
  • Statistical classification may include identifying, by the machine learning model 3000, to which a set of subcategories, i.e., subpopulations, a new observation belongs based on a training set of data containing observations having known categories.
  • Regression analysis may include estimating, by the machine learning model 3000 relationships between a dependent variable, i.e., an outcome variable, and one or more independent variables, i.e., predictors, covariates, and/or features.
  • Similarity learning may include learning, by the machine learning model 3000, from examples using a similarity function, the similarity function being designed to measure how similar or related two objects are.
  • the machine learning model 3000 may be defined via unsupervised learning, i.e., one or more algorithms configured to build a mathematical model of a set of data containing only inputs by finding structure in the data such as grouping or clustering of data points.
  • the machine learning model 3000 may learn from test data, i.e., training data, that has not been labeled, classified, or categorized.
  • the unsupervised learning algorithm may include identifying, by the machine learning model 3000, commonalities in the training data and learning by reacting based on the presence or absence of the identified commonalities in new pieces of data.
  • the machine learning model 3000 may generate one or more probability density functions.
  • the machine learning model 3000 may learn by performing cluster analysis, such as by assigning a set of observations into subsets, i.e., clusters, according to one or more predesignated criteria, such as according to a similarity metric of which internal compactness, separation, estimated density, and/or graph connectivity are factors.
  • the machine learning model 3000 may be defined via semisupervised learning, i.e., one or more algorithms using training data wherein some training examples may be missing training labels.
  • the semi-supervised learning may be weakly supervised learning, wherein the training labels may be noisy, limited, and/or imprecise.
  • the noisy, limited, and/or imprecise training labels may be cheaper and/or less labor intensive to produce, thus allowing the machine learning model 3000 to train on a larger set of training data for less cost and/or labor.
  • the machine learning model 3000 may be defined via reinforcement learning, such as one or more algorithms using dynamic programming techniques such that the machine learning model 3000 may train by taking actions in an environment in order to maximize a cumulative reward.
  • the training data is represented as a Markov Decision Process.
  • the machine learning model 3000 may be defined via feature learning, i.e., one or more algorithms designed to discover increasingly accurate and/or apt representations of one or more inputs provided during training, e.g., training data.
  • Feature learning may include training via principal component analysis and/or cluster analysis.
  • Feature learning algorithms may include attempting, by the machine learning model 3000, to preserve input training data while also transforming the input training data such that the transformed input training data is useful.
  • the machine learning model 3000 may be configured to transform the input training data prior to performing one or more classifications and/or predictions of the input training data.
  • the machine learning model 3000 may be configured to reconstruct input training data from one or more unknown data-generating distributions without necessarily conforming to implausible configurations of the input training data according to the distributions.
  • the feature learning algorithm may be performed by the machine learning model 3000 in a supervised, unsupervised, or semisupervised manner.
  • the machine learning model 3000 may be defined via anomaly detection, i.e., by identifying rare and/or outlier instances of one or more items, events and/or observations.
  • the rare and/or outlier instances may be identified by the instances differing significantly from patterns and/or properties of a majority of the training data.
  • Unsupervised anomaly detection may include detecting of anomalies, by the machine learning model 3000, in an unlabeled training data set under an assumption that a majority of the training data is “normal.”
  • Supervised anomaly detection may include training on a data set wherein at least a portion of the training data has been labeled as “normal” and/or “abnormal.”
  • the machine learning model 3000 may be defined via robot learning.
  • Robot learning may include generation, by the machine learning model 3000, of one or more curricula, the curricula being sequences of learning experiences, and cumulatively acquiring new skills via exploration guided by the machine learning model 3000 and social interaction with humans by the machine learning model 3000. Acquisition of new skills may be facilitated by one or more guidance mechanisms such as active learning, maturation, motor synergies, and/or imitation.
  • the machine learning model 3000 can be defined via association rule learning.
  • Association rule learning may include discovering relationships, by the machine learning model 3000, between variables in databases, in order to identify strong rules using some measure of “interestingness.”
  • Association rule learning may include identifying, learning, and/or evolving rules to store, manipulate and/or apply knowledge.
  • the machine learning model 3000 may be configured to learn by identifying and/or utilizing a set of relational rules, the relational rules collectively representing knowledge captured by the machine learning model 3000.
  • Association rule learning may include one or more of learning classifier systems, inductive logic programming, and artificial immune systems.
  • Learning classifier systems are algorithms that may combine a discovery component, such as one or more genetic algorithms, with a learning component, such as one or more algorithms for supervised learning, reinforcement learning, or unsupervised learning.
  • Inductive logic programming may include rule-learning, by the machine learning model 3000, using logic programming to represent one or more of input examples, background knowledge, and hypothesis determined by the machine learning model 3000 during training.
  • the machine learning model 3000 may be configured to derive a hypothesized logic program entailing all positive examples given an encoding of known background knowledge and a set of examples represented as a logical database of facts.
  • another set of solutions which may be deployed alone or in connection with other elements of the platform, including the artificial intelligence store 3504, may include a set of functional imaging capabilities 3502, which may comprise monitoring systems 640 and in some cases physical process observation systems 1510 and/or software interaction observation systems 1500, such as for monitoring various value chain entities 652.
  • Functional imaging systems 3502 may, in embodiments, provide considerable insight into the types of artificial intelligence that are likely to be most effective in solving particular types of problems most effectively.
  • computational and networking systems as they grow in scale, complexity and interconnections, manifest problems of information overload, noise, network congestion, energy waste, and many others.
  • the human brain operates with a massive neural network organized into interconnected modular systems, each of which has a degree of adaptation to solve particular problems, from regulation of biological systems and maintenance of homeostasis, to detection of a wide range of static and dynamic patterns, to recognition of threats and opportunities, among many others.
  • Functional imaging 3502 such as functional magnetic resonance imaging (fMRI), electroencephalogram (EEG), computed tomography (CT) and other brain imaging systems have improved to the point that patterns of brain activity can be recognized in real time and temporally associated with other information, such behaviors, stimulus information, environmental condition data, gestures, eye movements, and other information, such that via functional imaging, either alone or in combination with other information collected by monitoring systems 808, the platform may determine and classify what brain modules, operations, systems, and/or functions are employed during the undertaking of a set of tasks or activities, such as ones involving software interaction 1500, physical process observations 1510, or a combination thereof.
  • fMRI functional magnetic resonance imaging
  • EEG electroencephalogram
  • CT computed tomography
  • other brain imaging systems have improved to the point that patterns of brain activity can be recognized in real time and temporally associated with other information, such behaviors, stimulus information, environmental condition data, gestures, eye movements, and other information, such that via functional imaging, either alone or in combination with other information collected by monitoring systems 808, the platform may determine
  • This classification may assist in selection and/or configuration of a set of artificial intelligence solutions, such as from an artificial intelligence store 3504, that includes a similar set of capabilities and/or functions to the set of modules and functions of the human brain when undertaking an activity, such as for the initial configuration of a robotic process automation (RPA) system 1442 that automates a task performed by an expert human.
  • the platform may include a system that takes input from a functional imaging system to configure, optionally automatically based on matching of attributes between one or more biological systems, such as brain systems, and one or more artificial intelligence systems, a set of artificial intelligence capabilities for a robotic process automation system.
  • Selection and configuration may further comprise selection of inputs to robotic process automation and/or artificial intelligence that are configured at least in part based on functional imaging of the brain while workers undertake tasks, such as selection of visual inputs (such as images from cameras) where vision systems of the brain are highly activated, selection of acoustic inputs where auditory systems of the brain are highly activated, selection of chemical inputs (such as chemical sensors) where olfactory systems of the brain are highly activated, or the like.
  • a biologically aware robotic process automation system may be improved by having initial configuration, or iterative improvement, be guided, either automatically or under developer control, by imaging-derived information collected as workers perform expert tasks that may benefit from automation.
  • FIG. 27 additional details of an embodiment of the platform 604 are provided, in particular relating to elements of the adaptive intelligence layer 614 that facilitate improved edge intelligence, including the adaptive edge compute management system 1400 and the edge intelligence system 1420.
  • These elements provide a set of systems that adaptively manage “edge” computation, storage and processing, such as by varying storage locations for data and processing locations (e.g., optimized by Al) between on-device storage, local systems, in the network and in the cloud.
  • These elements enable facilitation of a dynamic definition by a user, such as a developer, operator, or host of the platform 102, of what constitutes the “edge” for purposes of a given application.
  • edge computing capabilities can be defined and deployed to operate on the local area network of an environment, in peer-to-peer networks of devices, or on computing capabilities of local value chain entities 652.
  • tasks may be intelligently load balanced based on a current context (e.g., network availability, latency, congestion, and the like) and, in an example, one type of data may be prioritized for processing, or one workflow prioritized over another workflow, and the like.
  • a current context e.g., network availability, latency, congestion, and the like
  • edge computing capabilities can be disposed in the network, such as for caching frequently used data at locations that improve input/output performance, reduce latency, or the like.
  • edge computing operations are enabled, under control of a developer or operator, or optionally determined automatically, such as by an expert system or automation system, such as based on detected network conditions for an environment, for a financial entity 652, or for a network as a whole.
  • edge intelligence 1420 enables adaptation of edge computation (including where computation occurs within various available networking resources, how networking occurs (such as by protocol selection), where data storage occurs, and the like) that is multi-application aware, such as accounting for QoS, latency requirements, congestion, and cost as understood and prioritized based on awareness of the requirements, the prioritization, and the value (including ROI, yield, and cost information, such as costs of failure) of edge computation capabilities across more than one application, including any combinations and subsets of the applications 630 described herein or in the documents incorporated herein by reference.
  • the platform 604 may employ a micro-services architecture with the various data handling layers 608, a set of network connectivity facilities 642 (which may include or connect to a set of interfaces 702 of various layers of the platform 604), a set of adaptive intelligence facilities or adaptive intelligent systems 1160, a set of data storage facilities or systems 624, and a set of monitoring facilities or systems 808.
  • the platform 604 may support a set of applications 614 (including processes, workflows, activities, events, use cases and applications) for enabling an enterprise to manage a set of value chain network entities 652, such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • applications 614 including processes, workflows, activities, events, use cases and applications
  • a set of value chain network entities 652 such as from a point of origin to a point of customer use of a product 1510, which may be an intelligent product.
  • the platform 604 may include a unified set of adaptive edge computing and other edge intelligence systems 1420 that provide coordinated edge computation and other edge intelligence 1420 capabilities for a set of multiple applications 630 of various types, such as a set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510 and enterprise resource management applications 1520 that monitor and/or manage a value chain network and a set of value chain network entities 652.
  • a set of supply chain management applications 21004 such as a set of supply chain management applications 21004, demand management applications 1502, intelligent product applications 1510 and enterprise resource management applications 1520 that monitor and/or manage a value chain network and a set of value chain network entities 652.
  • edge intelligence capabilities of the systems and methods described herein may include, but are not limited to, on-premise edge devices and resources, such as local area network resources, and network edge devices, such as those deployed at the edge of a cellular network or within a peripheral data center, both of which may deploy edge intelligence, as described herein, to, for example, carry out intelligent processing tasks at these edge locations before transferring data or other matter, to the primary or core cellular network command or central data center.
  • on-premise edge devices and resources such as local area network resources
  • network edge devices such as those deployed at the edge of a cellular network or within a peripheral data center, both of which may deploy edge intelligence, as described herein, to, for example, carry out intelligent processing tasks at these edge locations before transferring data or other matter, to the primary or core cellular network command or central data center.
  • an information technology system may include: a cloud-based management platform with a micro-services architecture, a set of interfaces, network connectivity facilities, adaptive intelligence facilities, data storage facilities, and monitoring facilities that are coordinated for monitoring and management of a set of value chain network entities; a set of applications for enabling an enterprise to manage a set of value chain network entities from a point of origin to a point of customer use; and a unified set of adaptive edge computing systems that provide coordinated edge computation for a set of applications of at least two types from among a set of demand management applications, a set of supply chain applications, a set of intelligent product applications and a set of enterprise resource management applications for a category of goods.
  • the adaptive edge computing and other edge intelligence systems 1420 may thus provide, in embodiments, intelligence for monitoring, managing, controlling, or otherwise handling a wide range of facilities, devices, systems, environments, and assets, such as supply chain infrastructure facilities 1560 and other value chain network entities 652 that are involved as a product 1510 travels from a point of origin through distribution and retail channels to an environment where it is used by a customer.
  • This unification may provide a number of advantages, including improved monitoring, improved remote control, improved autonomy, improved prediction, improved classification, improved visualization and insight, improved visibility, and others.
  • coordinated intelligence may include, but is not limited to, analytics and processing for monitoring data streams, as described herein, for the purposes of classification, prediction or some other type of analytic modeling.
  • coordinated intelligence methods and systems may be applied in an automated manner in which differing combinations of intelligence assets are applied.
  • the coordinated intelligence system may monitor signals coming from machinery deployed in the environment.
  • the coordinated intelligence system may classify, predict or perform some other intelligent analytics, in combination, for the purpose of, for example, determining a state of a machine, such as a machine in a deteriorated state, in an at- risk state, or some other state.
  • the determination of a state may cause a control system to alter a control regime, for example, slowing or shutting down a machine that is in a deteriorating state.
  • the coordinated intelligence system may coordinate across multiple entities of a value chain, supply chain and the like. For example, the monitoring of the deteriorating machine in the industrial environment may simultaneously occur with analytics related to parts suppliers and availability, product supply and inventory predictions, or some other coordinated intelligence operation.
  • the adaptive edge computing and other edge intelligence systems 1420 may be adapted over time, such as by learning on outcomes 1040 or other operations of the other adaptive intelligent systems 614, such as to determine which elements collected and/or processed by the adaptive edge computing and other edge intelligence systems 1420 should be made available to which applications 630, what elements and/or content provide the most benefit, what data should be stored or cached for immediate retrieval, what data can be discarded versus saved, what data is most beneficial to support adaptive intelligent systems 614, and for other uses.
  • the unified set of adaptive edge computing systems that provide coordinated edge computation include a wide range of systems, such as classification systems 1610 (such as image classification systems, object type recognition systems, and others), video processing systems 1612 (such as video compression systems), signal processing systems 1614 (such as analog-to-digital transformation systems, digital-to- analog transformation systems, RF filtering systems, analog signal processing systems, multiplexing systems, statistical signal processing systems, signal filtering systems, natural language processing systems, sound processing systems, ultrasound processing systems, and many others), data processing systems 1630 (such as data filtering systems, data integration systems, data extraction systems, data loading systems, data transformation systems, point cloud processing systems, data normalization systems, data cleansing system, data deduplication systems, graph-based data storage systems, object-oriented data storage systems, and others), predictive systems 1620 (such as motion prediction systems, output prediction systems, activity prediction systems, fault prediction systems, failure prediction systems, accident prediction systems, event predictions systems, event prediction systems, and many others), configuration systems 1630 (such as protocol
  • the interface is a user interface for a command center dashboard by which an enterprise orchestrates a set of value chain entities related to a type of product.

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Abstract

Un système peut comprendre un circuit de commande robotique configuré pour commander une ou plusieurs fonctions robotiques d'un robot. Un système peut comprendre une pluralité de capteurs configurés pour collecter des données. Un système peut comprendre un circuit d'analyse de gouvernance configuré pour analyser les données et sélectionner une ou plusieurs structures de gouvernance sur la base des données analysées. Un système peut comprendre un circuit de modèle de gouvernance configuré pour générer un modèle qui applique la ou les structures de gouvernance pour déterminer une ou plusieurs actions de gouvernance, le circuit de commande robotique étant configuré pour commander la ou les fonctions robotiques conformément à l'action ou aux actions de gouvernance, le circuit de commande robotique, le circuit d'analyse de gouvernance et le circuit de modèle de gouvernance étant intégrés sur un seul substrat.
PCT/US2024/044898 2023-08-31 2024-08-30 Systèmes, procédés, kits et appareils pour puces spécialisées pour couches d'intelligence robotique Pending WO2025050067A1 (fr)

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