US20190038934A1

US20190038934A1 - Cognitive advisory system of structured assessments through iot sensors

Info

Publication number: US20190038934A1
Application number: US15/668,432
Authority: US
Inventors: Krisha Banerjee; Ya Bin Dang; Radha M. De; Jian Wang
Original assignee: International Business Machines Corp
Current assignee: International Business Machines Corp
Priority date: 2017-08-03
Filing date: 2017-08-03
Publication date: 2019-02-07
Also published as: US20190038936A1

Abstract

A method, computer system, and a computer program product for structured assessments is provided. The present invention may include initializing a structured assessment. The present invention may also include receiving a plurality of signup data from a user based on the initialized structured assessment. The present invention may then include collecting a plurality of user data from an IoT device based on the received plurality of signup data. The present invention may further include processing the collected plurality of user data. The present invention may also include generating a feedback report based on the processed plurality of user data, and outputting the generated feedback report to the user.

Description

BACKGROUND

The present invention relates generally to the field of computing, and more particularly to structured assessments utilizing a cloud service and Internet of Things (IoT) sensors.
Given the development of the IoT, there are many internet connected devices with a wide range of capabilities. Some devices may have the ability to determine the pressure used to squeeze a ball, or determine the direction, velocity or speed at which a ball is thrown. Sensory data collected by an internet connected device can be utilized to test a person's capabilities in a controlled and structured environment. IoT enabled devices may be utilized to test sensory motor development, visual motor integration, motor coordination, visual perception, gross and fine motor coordination, or cognitive capability. Specific use cases may include testing the cognitive capability of the elderly, identifying weaknesses in members of a sports team, or conducting visual or motor examinations of law enforcement officers or members of the military.

SUMMARY

Embodiments of the present invention disclose a method, computer system, and a computer program product for structured assessments. The present invention may include initializing a structured assessment. The present invention may also include receiving a plurality of signup data from a user based on the initialized structured assessment. The present invention may then include collecting a plurality of user data from an IoT device based on the received plurality of signup data. The present invention may further include processing the collected plurality of user data. The present invention may also include generating a feedback report based on the processed plurality of user data, and outputting the generated feedback report to the user.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. The various features of the drawings are not to scale as the illustrations are for clarity in facilitating one skilled in the art in understanding the invention in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to at least one embodiment;

FIG. 2 is an operational flowchart illustrating a process for structured assessments according to at least one embodiment;

FIG. 3 is an exemplary illustration of IoT devices which utilize a cloud service and IoT sensors according to at least one embodiment;

FIG. 4 is an exemplary illustration of a feedback report generated by the structured assessments program according to at least one embodiment;

FIG. 5 is a block diagram of internal and external components of computers and servers depicted in FIG. 1 according to at least one embodiment;

FIG. 6 is a block diagram of an illustrative cloud computing environment including the computer system depicted in FIG. 1, in accordance with an embodiment of the present disclosure; and

FIG. 7 is a block diagram of functional layers of the illustrative cloud computing environment of FIG. 6, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosed herein; however, it can be understood that the disclosed embodiments are merely illustrative of the claimed structures and methods that may be embodied in various forms. This invention may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of this invention to those skilled in the art. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments.
The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
The following described exemplary embodiments provide a system, method and program product for structured assessments utilizing a cloud service and IoT sensors. As such, the present embodiment has the capacity to improve the technical field of structured assessments by utilizing sensory data from IoT devices and applying the sensory data to specific assessments. More specifically, an assessment may first be initialized. Program users may then be permitted to sign up to participate in the assessment using their IoT enabled device. The user may use the IoT enabled device in accordance with prescribed guidelines and any data generated from the user's use of the IoT enabled device may be collected and stored by the structured assessments program. Thereafter, the collected data may be analyzed, the user's results may be benchmarked against the results of other users utilizing the service and/or against a preconfigured guideline, and a feedback report may be outputted to the user. The outcome of the assessment may also be outputted to the assessment coordinator (e.g., a doctor or specialist) and may be accompanied by a recommendation or action plan for the given user.
As described previously, there are many IoT devices with a wide range of capabilities. Some devices may have the ability to determine the pressure used to squeeze a ball, or determine the direction, velocity or speed at which a ball is thrown. IoT devices may have the ability to collect sensory data which may be utilized to test a person's capabilities in a controlled and structured environment. Among many other things, IoT enabled devices may be utilized to test sensory motor development, visual motor integration, motor coordination, visual perception, gross and fine motor coordination, or cognitive capability. Specific use cases may include testing the cognitive capability of the elderly, identifying weaknesses in members of a sports team, or conducting visual or motor examinations of law enforcement officers or members of the military. However, mere use of an IoT device may not measure the user's ability against other users of the same IoT device.
Therefore, it may be advantageous to facilitate structured assessments by utilizing a private or public cloud service and sensory data collected by IoT devices. The IoT devices may be aligned with the assessment being performed, and any collected data may be utilized to benchmark a user's results against the results of other users utilizing the service or against a preconfigured guideline. A feedback report may be outputted to the user. The outcome of the assessment may also be outputted to the assessment coordinator and may be accompanied by a recommendation or action plan.
According to at least one embodiment, the structured assessments program may be useful in leveraging IoT sensors embedded in devices so that when a user utilizes the device, data may be collected which may contain critical information regarding the user's abilities and behaviors (e.g., health data). Based on the collected data, a benchmark or assessment model may be constructed to measure and evaluate the user's embedded data and advise the user of any deviations from predefined guidelines as early as possible so that any necessary action or treatment may be taken by the user (e.g., see a doctor). Thereafter, the user may provide feedback (e.g., indicate in the program that the user was sick with a fever when his physical aptitude appeared lower than predefined guidelines) so that the assessment model may be improved upon as needed.
According to at least one embodiment, an assessment coordinator may utilize the service to create an assessment. The assessment may evaluate, among other things, motor development, visual motor integration, motor coordination, visual perception, and gross and fine motor coordination. As part of the assessment setup, the assessment coordinator may define the IoT devices that may be aligned with the assessment and how any generated sensory data may be utilized to score and rank a user who completes the assessment.
The present embodiment may permit a user to sign up to utilize the service and select any relevant assessments. The service may be utilized to perform the assessments in a public environment (e.g., where a user may sign up for the assessment on their own, utilize any authorized IoT device to complete the assessment, and may receive benchmarked assessment results against those of other users) or a private environment (e.g., in conjunction with a sports team or military training center). Some IoT devices may be supported by more than one controlled assessment, in which case users may sign up for several assessments and may receive a separate report for each completed assessment.
As the IoT devices are used, the generated data may be collected and associated with a user and assessment. The computer program may continue to monitor the user's IoT device activity over a period of time or within a specified assessment window. If the assessment is conducted within a private environment, the user may need to complete the assessment within a specific time frame, while a public environment may not have the same time restrictions.
The present embodiment may further include a method for scoring and ranking a user's assessment results and benchmarking the user's performance against other users participating in the same assessment. A feedback report may be outputted to the user and may also be outputted to the assessment coordinator with a recommendation or action plan based on the user's assessment results.
Referring to FIG. 1, an exemplary networked computer environment 100 in accordance with one embodiment is depicted. The networked computer environment 100 may include a computer 102 with a processor 104 and a data storage device 106 that is enabled to run a software program 108 and a structured assessments program 110 a. The networked computer environment 100 may also include a server 112 that is enabled to run a structured assessments program 110 b that may interact with a database 114 and a communication network 116. The networked computer environment 100 may include a plurality of computers 102 and servers 112, only one of which is shown. The communication network 116 may include various types of communication networks, such as a wide area network (WAN), local area network (LAN), a telecommunication network, a wireless network, a public switched network and/or a satellite network. It should be appreciated that FIG. 1 provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.
The client computer 102 may communicate with the server computer 112 via the communications network 116. The communications network 116 may include connections, such as wire, wireless communication links, or fiber optic cables. As will be discussed with reference to FIG. 5, server computer 112 may include internal components 902 a and external components 904 a, respectively, and client computer 102 may include internal components 902 b and external components 904 b, respectively. Server computer 112 may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS). Server 112 may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud. Client computer 102 may be, for example, a mobile device, a telephone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, or any type of computing devices capable of running a program, accessing a network, and accessing a database 114. According to various implementations of the present embodiment, the structured assessments program 110 a, 110 b may interact with a database 114 that may be embedded in various storage devices, such as, but not limited to a computer/mobile device 102, a networked server 112, or a cloud storage service.
According to the present embodiment, a user using a client computer 102 or a server computer 112 may use the structured assessments program 110 a, 110 b (respectively) to monitor the usage of connected IoT devices in connection with a structured assessment, to benchmark, score and rank assessment results, and to provide a recommendation or action plan based on assessment results. The structured assessments method is explained in more detail below with respect to FIGS. 2-4.
Referring now to FIG. 2, an operational flowchart illustrating the exemplary structured assessments process 200 used by the structured assessments program 110 a and 110 b according to at least one embodiment is depicted.
At 202 the test setup is received from the assessment coordinator. Setting up of a test (i.e., a structured assessment) may include an assessment coordinator (e.g., an expert in the testing field or a doctor) running the structured assessments program 110 a, 110 b on a client computer 102 and defining a behavior model, key performance indicator(s), and behavior validation rules within the structured assessments program 110 a, 110 b. The test setup may also include the assessment coordinator defining the IoT toys or devices within the structured assessments program 110 a, 110 b that may be aligned with the test and indicating how sensory data collected by the IoT toys or devices may be utilized to score and rank the user's performance. For example, a physical therapist may use the structured assessments program 110 a, 110 b to configure a program which seeks to test and identify any existing issues with the therapist's youth patients' upper body physical strength. The physical therapist defines a behavior model (i.e., age appropriate physical norms) and relevant devices (i.e., an IoT enabled tension ball and basketball) within the structured assessments program 110 a, 110 b.
A key performance indicator (KPI), as indicated above, may be a quantitative metric used to measure how effectively a user is meeting a predefined guideline configured by the assessment coordinator during setup of the structured assessments program 110 a, 110 b. For example, if the assessment coordinator configures a test to measure a child's aptitude for throwing a ball, the KPI metrics may be the speed of the ball once thrown, the distance that the ball travels, and the angle that the ball is thrown at.
Next, at 204, user signup is received. The user may indicate an interest in participating in a test by selecting the test from a list of available tests in the interface of the structured assessments program 110 a, 110 b on a client computer 102. Once the user selects a test, the user may select an IoT device (e.g., toy) that the user may use to participate in the test from a list of compatible IoT devices in the interface of the structured assessments program 110 a, 110 b on a client computer 102. The user may link the selected IoT device to the selected test by logging in to the selected IoT device through the interface of the structured assessments program 110 a, 110 b on a client computer 102. The structured assessments program 110 a, 110 b may be utilized to perform the selected test in a public environment (e.g., where a user may sign up for the assessment on their own, utilize any authorized IoT device to complete the assessment, and may receive benchmarked assessment results against those of other users) or in a private environment (e.g., in conjunction with a sports team or military training center). Certain IoT devices may align with multiple tests, enabling users to utilize the device to participate in more than one test at a time by using the same generated data for each test but receiving a separate report for each.
For example, the user is a four-year-old child with questionable motor impairment whose parents elect for him to participate in the physical therapist's test described above. The child's issues, if existing and undetected, may develop into difficulties with writing and dressing. The hope is to identify the issue by registering the child for a test which can diagnose and possibly treat the child's issue in advance of any progression. As such, the child's parents purchase an IoT enabled basketball for his use in the test and select the child's preferred IoT enabled device on the client computer 102 interface of the structured assessments program 110 a, 110 b. The child's parents initialize the basketball with the child's information, including the child's name, age, gender, and any other data requested during the basketball's setup. The initialization of the IoT device and the selection and linking of the device within the structured assessments program 110 a, 110 b constitutes user signup.
The IoT device may be shared between one or more users by programming and reprogramming the embedded sensors with different personal information (e.g., name, age, gender), which may enable the one or more users to use the same device to perform the same test, if so desired. For example, if an IoT toy equipped with an embedded sensor is purchased by one family and subsequently sold to another family, the purchasing family may reprogram the IoT toy to reflect the personal information of the new user. Both users may use the toy to participate in the same test.
Next, at 206, user data is collected. Data collection may be done in real time even though data processing may be done at a later time. Collected data may be stored in real time and thereafter retrieved to perform data processing and analysis. Collected data may be stored locally on the IoT device, if such storage is permitted, in a data repository such as database 114, or may be stored remotely on a cloud storage device by utilizing a Bluetooth or Wi-Fi connection to transmit the data over the communication network 116. Any data collected by the IoT device prior to the user's test signup may remain on the IoT device and may not be accessed by the structured assessments program 110 a, 110 b. For example, if the child plays with the IoT enabled basketball, as described above, prior to signing up for the physical therapist's test, the data collected by the basketball will remain on the device and will not be considered in any assessment done by the structured assessments program 110 a, 110 b.
The structured assessments program 110 a, 110 b may collect data from the relevant IoT device and may associate the data with the given user and the selected test. The program may monitor the activity over a period of time or within a specified window. If, for example, the test is private or instructor led, users may need to complete the test within a specified time frame. This is compared to a public test whereby users may benchmark their performance against other users and no structured time requirement may be implemented.
Next, at 208, the collected data is processed. Data processing may include noise reduction, wherein unexplained variants in the collected data may be removed and not considered for analysis by the structured assessments program 110 a, 110 b. Noise reduction may be based on explanations for abnormalities given by the user (e.g., an indication within the structured assessments program 110 a, 110 b that the child was sick with a fever when his physical aptitude appeared lower than predefined guidelines), so that the assessment model may not be tainted with inaccurate data which provides no meaningful explanation for the user's actions. Noise reduction may also be based on digital and signal processing techniques, including, for example, applying a filter to remove unwanted components or features from a given dataset. An applied filter may plot and identify meaningful aspects of a signal and eliminate unwanted or noisy signal aspects.
Data processing may include permitting the user to provide cognitive feedback within the structured assessments program 110 a, 110 b to benchmark the user's data and evolve the predefined benchmark. Cognitive feedback may permit the structured assessments program 110 a, 110 b to modify the behavior model and behavior validation rule based on the user's data by comparing the user's data to the predefined norms and updating the norm to reflect the user's actual results. If the feedback received by the user is qualitative, comprising a description of the user's data, the behavior model and behavior validation rule may not be updated, but a label may be given to those portions of data described by the user's qualitative feedback. For example, a label may indicate that the child was sick with a fever when the data was collected. The label may thereafter be saved as descriptive feedback relative to a particular KPI, so that if the behavior repeats itself, the structured assessments program 110 a, 110 b may associate the user's behavior with the appropriate portion of data. The generated labels may further be used in the feedback report, as discussed below.
Benchmarking a user's results may be done after the structured assessments program 110 a, 110 b has removed any noise from the data and has recognized typical behaviors of the given user. To benchmark data, the structured assessments program 110 a, 110 b may calculate the value of each KPI by determining the average of all values for that KPI and may compare, score, and rank the calculated KPI values by comparing the calculated KPI values to predefined guidelines (i.e., behavior models). The benchmarking process may measure a user's data against behavior models created by experts or doctors in the same field as the structured assessment or against other users performing the same test. For example, if a child throws an IoT enabled ball into the air, the structured assessments program 110 a, 110 b may collect the speed and distance of the ball in an effort to measure the child's gross motor skills. The predefined guideline or behavior validation model configured by the physical therapist (e.g., assessment coordinator) indicates that the child should be throwing the ball at 10 meters per second. In this instance, the child's calculated speed is found to be less than the benchmarked rule and is noted as being abnormal.
Thereafter, at 210, feedback is outputted to the user. The feedback may be related to observations made based on the user's results and may include an appropriate recommendation or action plan. For each KPI analyzed, a feedback report may be generated which may indicate whether the observed behavior falls within normal or abnormal ranges. Labels generated by the structured assessments program 110 a, 110 b based on a user's behavior, as discussed previously at 208, may be provided with the feedback report to assist the user in determining whether performance with respect to a particular KPI fits within prescribed norms. The feedback may be outputted in written format in the interface of the structured assessments program 110 a, 110 b or via a portable document format (PDF), among other formats. For example, a feedback report may be provided to the parent of a child participating in a test to advise the parent of the child's abnormal behavior. By providing the report to a parent, the structured assessments program 110 a, 110 b notifies an individual with knowledge of the child's detected issue so that the child may receive assistance. The feedback report and action plan are discussed in more detail below with respect to FIG. 4.
In response to given feedback, the user may calibrate the benchmark or validation model by providing a response to the outputted feedback. The administered test may then repeat by collecting additional data at 206, processing the collected data at 208, and outputting feedback at 210.
Referring now to FIG. 3, an exemplary illustration of IoT devices which utilize a cloud service and IoT sensors is depicted according to at least one embodiment. The networked IoT devices 300 includes IoT devices 302 a-e transmitting data over a communication network 116. The IoT devices 302 a-e may include any internet connected device which is embedded with electronics, software, sensors, etc., and a means of network connectivity whereby the IoT devices 302 a-e may remotely collect and exchange data between other internet connected devices and systems.
Referring now to FIG. 4, an exemplary illustration of a feedback report and action plan 400 generated by the structured assessments program 110 a, 110 b is depicted according to at least one embodiment. The feedback report and action plan 400 includes a table with a test column 402 representing the selected test chosen by the user, a scaled score column 404 representing the user's scaled performance score based on the performance of other users or on predefined guidelines configured by the assessment coordinator, a percentile rank column 406 representing the user's percentile rank based on the scaled score column 404 and the performance of other users or on predefined guidelines configured by the assessment coordinator, a rating column 408 representing the user's assigned rating based on the user's performance, and an action plan column 410 representing a course of remedial action provided by the assessment coordinator. The action plan column 410 may include a recommendation in accordance with the prescribed guidelines configured by the assessment coordinator and based on the user's performance in the selected test.
The feedback report and action plan 400 also includes a composite score column 412 representing the categories of tests the user participated in, a composite scaled score column 414 representing the total scaled score of all tests participated in by the user in each category, a composite percentile rank column 416 representing the overall percentile rank of the user based on the composite scaled score column 414, and a composite rating column 418 representing the user's assigned rating based on the composite scaled score column 414 and the composite percentile rank column 416. The composite scores may also provide an action plan for the user if an overall recommendation is provided by the assessment coordinator.
It may be appreciated that FIGS. 2-4 provide only an illustration of one embodiment and do not imply any limitations with regard to how different embodiments may be implemented. Many modifications to the depicted embodiment(s) may be made based on design and implementation requirements.
FIG. 5 is a block diagram 900 of internal and external components of computers depicted in FIG. 1 in accordance with an illustrative embodiment of the present invention. It should be appreciated that FIG. 5 provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.
Data processing system 902, 904 is representative of any electronic device capable of executing machine-readable program instructions. Data processing system 902, 904 may be representative of a smart phone, a computer system, PDA, or other electronic devices. Examples of computing systems, environments, and/or configurations that may represented by data processing system 902, 904 include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, network PCs, minicomputer systems, and distributed cloud computing environments that include any of the above systems or devices.
User client computer 102 and network server 112 may include respective sets of internal components 902 a, b and external components 904 a, b illustrated in FIG. 5. Each of the sets of internal components 902 a, b includes one or more processors 906, one or more computer-readable RAMs 908 and one or more computer-readable ROMs 910 on one or more buses 912, and one or more operating systems 914 and one or more computer-readable tangible storage devices 916. The one or more operating systems 914, the software program 108 and the structured assessments program 110 a in client computer 102, and the structured assessments program 110 b in network server 112, may be stored on one or more computer-readable tangible storage devices 916 for execution by one or more processors 906 via one or more RAMs 908 (which typically include cache memory). In the embodiment illustrated in FIG. 5, each of the computer-readable tangible storage devices 916 is a magnetic disk storage device of an internal hard drive. Alternatively, each of the computer-readable tangible storage devices 916 is a semiconductor storage device such as ROM 910, EPROM, flash memory or any other computer-readable tangible storage device that can store a computer program and digital information.
Each set of internal components 902 a, b also includes a R/W drive or interface 918 to read from and write to one or more portable computer-readable tangible storage devices 920 such as a CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk or semiconductor storage device. A software program, such as the software program 108 and the structured assessments program 110 a and 110 b can be stored on one or more of the respective portable computer-readable tangible storage devices 920, read via the respective R/W drive or interface 918, and loaded into the respective hard drive 916.
Each set of internal components 902 a, b may also include network adapters (or switch port cards) or interfaces 922 such as a TCP/IP adapter cards, wireless wi-fi interface cards, or 3G or 4G wireless interface cards or other wired or wireless communication links. The software program 108 and the structured assessments program 110 a in client computer 102 and the structured assessments program 110 b in network server computer 112 can be downloaded from an external computer (e.g., server) via a network (for example, the Internet, a local area network or other, wide area network) and respective network adapters or interfaces 922. From the network adapters (or switch port adaptors) or interfaces 922, the software program 108 and the structured assessments program 110 a in client computer 102 and the structured assessments program 110 b in network server computer 112 are loaded into the respective hard drive 916. The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.
Each of the sets of external components 904 a, b can include a computer display monitor 924, a keyboard 926, and a computer mouse 928. External components 904 a, b can also include touch screens, virtual keyboards, touch pads, pointing devices, and other human interface devices. Each of the sets of internal components 902 a, b also includes device drivers 930 to interface to computer display monitor 924, keyboard 926, and computer mouse 928. The device drivers 930, R/W drive or interface 918, and network adapter or interface 922 comprise hardware and software (stored in storage device 916 and/or ROM 910).
It is understood in advance that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.
Characteristics are as Follows:
On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.
Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.
Service Models are as Follows:
Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Deployment Models are as Follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes.
Referring now to FIG. 6, illustrative cloud computing environment 1000 is depicted. As shown, cloud computing environment 1000 comprises one or more cloud computing nodes 100 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 1000A, desktop computer 1000B, laptop computer 1000C, and/or automobile computer system 1000N may communicate. Nodes 100 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 1000 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 1000A-N shown in FIG. 6 are intended to be illustrative only and that computing nodes 100 and cloud computing environment 1000 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).
Referring now to FIG. 7, a set of functional abstraction layers 1100 provided by cloud computing environment 1000 is shown. It should be understood in advance that the components, layers, and functions shown in FIG. 7 are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:
Hardware and software layer 1102 includes hardware and software components. Examples of hardware components include: mainframes 1104; RISC (Reduced Instruction Set Computer) architecture based servers 1106; servers 1108; blade servers 1110; storage devices 1112; and networks and networking components 1114. In some embodiments, software components include network application server software 1116 and database software 1118.
Virtualization layer 1120 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 1122; virtual storage 1124; virtual networks 1126, including virtual private networks; virtual applications and operating systems 1128; and virtual clients 1130.
In one example, management layer 1132 may provide the functions described below. Resource provisioning 1134 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 1136 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 1138 provides access to the cloud computing environment for consumers and system administrators. Service level management 1140 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 1142 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
Workloads layer 1144 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 1146; software development and lifecycle management 1148; virtual classroom education delivery 1150; data analytics processing 1152; transaction processing 1154; and structured assessments 1156. A structured assessments program 110 a, 110 b provides a way to facilitate structured assessments by utilizing a private or public cloud service and IoT sensors to conduct specialized tests, benchmark user results, and provide a recommendation or action plan given predefined expert guidelines.
The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

What is claimed is:

1. A method for structured assessments, the method comprising:

initializing a structured assessment;

receiving a plurality of signup data from a user based on the initialized structured assessment;

collecting a plurality of user data from an Internet of Things (IoT) device based on the received plurality of signup data;

processing the collected plurality of user data;

generating a feedback report based on the processed plurality of user data; and

outputting the generated feedback report to the user.

2. The method of claim 1, wherein initializing the structured assessment further comprises:

defining at least one assessment component;

defining the IoT device that is associated with the structured assessment; and

denoting a benchmarking process by which the collected plurality of user data will be utilized to score and rank a user's performance.

3. The method of claim 2, wherein the at least one assessment component is selected from the group consisting of one or more behavior models, one or more key performance indicators and one or more behavior validation rules.

4. The method of claim 2, wherein receiving the plurality of signup data from the user based on the initialized structured assessment further comprises:

selecting the structured assessment from a list of available structured assessments; and

choosing and linking the IoT device to be used to the selected structured assessment.

5. The method of claim 4, wherein collecting the user's data further comprises:

retrieving data from the chosen IoT device or a cloud server; and

associating the retrieved data with the user and the selected structured assessment.

6. The method of claim 5, wherein processing the collected user data further comprises:

removing a plurality of noise from the collected data;

receiving a plurality of cognitive feedback from the user; and

benchmarking the collected data.

7. The method of claim 6, wherein the generated feedback report indicates whether the collected data falls within a predefined norm.

8. A computer system for structured assessments, comprising:

one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:

initializing a structured assessment;

processing the collected plurality of user data;

generating a feedback report based on the processed plurality of user data; and

outputting the generated feedback report to the user.

9. The computer system of claim 8, wherein initializing the structured assessment further comprises:

defining at least one assessment component;

defining the IoT device that is associated with the structured assessment; and

10. The computer system of claim 9, wherein the at least one assessment component is selected from the group consisting of one or more behavior models, one or more key performance indicators and one or more behavior validation rules.

11. The computer system of claim 9, wherein receiving the plurality of signup data from the user based on the initialized structured assessment further comprises:

12. The computer system of claim 11, wherein collecting the user's data further comprises:

retrieving data from the chosen IoT device or a cloud server; and

13. The computer system of claim 12, wherein processing the collected user data further comprises:

removing a plurality of noise from the collected data;

receiving a plurality of cognitive feedback from the user; and

benchmarking the collected data.

14. The computer system of claim 13, wherein the generated feedback report indicates whether the collected data falls within a predefined norm.

15. A computer program product for structured assessments, comprising:

one or more computer-readable storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor to cause the processor to perform a method comprising:

initializing a structured assessment;

processing the collected plurality of user data;

generating a feedback report based on the processed plurality of user data; and

outputting the generated feedback report to the user.

16. The computer program product of claim 15, wherein initializing the structured assessment further comprises:

defining at least one assessment component;

defining the IoT device that is associated with the structured assessment; and

17. The computer program product of claim 16, wherein the at least one assessment component is selected from the group consisting of one or more behavior models, one or more key performance indicators and one or more behavior validation rules.

18. The computer program product of claim 16, wherein receiving the plurality of signup data from the user based on the initialized structured assessment further comprises:

19. The computer program product of claim 18, wherein collecting the user's data further comprises:

retrieving data from the chosen IoT device or a cloud server; and

20. The computer program product of claim 19, wherein processing the collected user data further comprises:

removing a plurality of noise from the collected data;

receiving a plurality of cognitive feedback from the user; and

benchmarking the collected data.