WO2025145015A1

WO2025145015A1 - Sourcing, extracting, organizing and publishing content and digital rules for consumption by service engines for producing resources

Info

Publication number: WO2025145015A1
Application number: PCT/US2024/062073
Authority: WO
Inventors: Venkatesan Subramanian; Sayeed REZA; David LINGERFELT; Manoj Nataraj SESHAN
Original assignee: Avalara Inc
Current assignee: Avalara Inc
Priority date: 2023-12-29
Filing date: 2024-12-27
Publication date: 2025-07-03
Anticipated expiration: 2026-06-29

Abstract

Disclosed are systems and methods for automated sourcing, extracting, organizing and publishing content and digital rules for consumption by service engines. A content management platform electronically crawls multiple remote data sites concurrently over a network to obtain current data. The platform detects changes from previous data and determines, using a machine learning model, whether detected changes are relevant for updating content or digital rules for producing resources associated with relationship instances. The platform extracts portions of relevant data and associates them with aspects of digital rules. The platform organizes the data by updating databases with new versions of rules based on the associations, enabling multiple service engines to concurrently consume updated rules. The platform also translates relevant portions from narrative text into machine-readable code consumable via an API by service engines for producing resources associated with relationship instances.

Description

SOURCING, EXTRACTING, ORGANIZING AND PUBLISHING CONTENT AND DIGITAL RULES FOR CONSUMPTION BY SERVICE ENGINES FOR PRODUCING RESOURCES

Technical Field

[0001] The technical field relates to computer networks, and particularly to the technology of automated networked systems for sourcing, extracting, organizing and publishing content and digital rules for consumption by sendee engines for producing resources.

BRIEF SUMMARY

[0002] The present description gives instances of Online Software Platforms (OSPs) that are operated by online sen ice providers, for the benefit of clients of the online sendee providers. [0003] In embodiments, an OSP receives data from the client from its operations. The data is often provided in a single dataset. The data is associated with a domain taken from a plurality of domains. The dataset includes data representing relationship instances between the client of the OSP and one or more secondary entities. One or more data sites of a plurality of data sites are associated with or controlled by one or more respective domains which are authorities for generating content and/or rules regarding producing resources associated w ith the relationship instances associated with the one or more respective domains.

[0004] The OSP then applies digital rules based on the generated content and/or rules to the dataset including data representing the relationship instances to produce resources associated with the relationship instances.

[0005] In some embodiments, a content management platform component (CMP) that is part of or accessible by the OSP automatically sources, extracts, organizes and publishes content and/or digital rules from remote data sites for consumption by service engines of the OSP for producing resources. One or more of such operations may be performed by using a machine learning model trained for performing the respective operation. As part of the training, a user may validate determinations made by the machine learning model of hich data to source, extract, organize and publish at one or more of the sourcing, extracting, organizing and publishing stages.

[0006] An advantage and/or benefit may be that computer system efficiency is increased and computing resources are saved by a user not needing to manually read and analyze millions of pages of content form remote data sites to determine which is relevant for produces resources in an accurate and timely manner for the relationship instances represented by the datasets received bv the OSP from the clients.

BRIEF DESCRIPTION OF DRAWINGS

[0007] FIG. 1 is a block diagram of an example content management platform that is part of or accessible by an OSP and shows elements and relationships for explaining embodiments, according to a non-limiting example embodiment.

[0008] FIG. 2 is a block diagram illustrating an OSP in communication with an example content management platform component and multiple computer systems for processing entity data, according to a non-limiting example embodiment.

[0009] FIG. 3 is a block diagram illustrating an example online software platform in communication with an example content management platform component and remote data sites, showing both architectural and data processing elements, according to a non-limiting example embodiment.

[0010] FIG. 4 is a diagram showing details and aspects of different types of possible embodiments of digital resource rules, according to a non-limiting example embodiment.

[0011] FIG. 5 is a diagram showing sample digital resource rules juxtaposed with decision boxes of a flowchart portion of a sample method for rule recognition, according to a non-limiting example embodiment.

[0012] FIG. 6 is a flowchart illustrating a sample method for a content sourcing stage implemented by the content management platform, according to a non-limiting example embodiment.

[0013] FIG. 7 is a flowchart illustrating a sample method for a data extraction stage implemented by the content management platform, according to a non-limiting example embodiment.

[0014] FIG. 8 is a flowchart illustrating a sample method for a data organization stage implemented by the content management platform, according to a non-limiting example embodiment.

[0015] FIG. 9 is a flowchart illustrating a sample method for a content publication stage implemented by the content management platform, according to a non-limiting example embodiment.

[0016] FIG. 10 is a flowchart illustrating a sample method for an administrative content portal implemented by the content management platform, according to a non-limiting example embodiment. [0017] FIG. 11 is a flowchart illustrating a sample method for Al driven classification code assignment implemented by the content management platform, according to a non-limiting example embodiment.

[0018] FIG. 12 is a block diagram illustrating an example OSP in communication with an example content management platform component and tax authority for tax-related services, according to a non-limiting example embodiment.

[0019] FIG. 13 is a block diagram illustrating an example OSP in communication with an example content management platform component and multiple tax authorities, according to a non-limiting example embodiment.

[0020] FIG. 14 is a diagram showing details and aspects of different types of possible embodiments of digital tax rules, according to a non-limiting example embodiment.

[0021] FIGS. 15-33 are sample views of User Interfaces displayed by the content management platform showing an example workflow for content sourcing, extracting, organizing and publishing operations, according to a non-limiting example embodiment.

DETAILED DESCRIPTION

[0022] FIG. 1 is a block diagram of an example content management platform component that is part of or accessible by an OSP and shows elements and relationships for explaining embodiments.

[0023] At stage 1 162. the content management platform 160. which may be part of or accessible by an OSP, sources content for producing resources associated with relationship instances of clients of the OSP and/or digital rules for producing resources associated with such relationship instances. For example, the content management platform 160 electronically crawls a plurality of remote data sites concurrently over a computer network. Each data site of the plurality of data sites is a source of data selected by the content management platform 160 as potentially relevant for updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances.

[0024] Concurrently, for each data site of the plurality of data sites, the content management platform 160 electronically obtains current data from the data site while electronically crawling the data site. Based on the current data, the content management platform 160 electronically detects a particular change from previous data from the data site. The content management platform 160 then electronically determines, using a machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances. The current data may include, but is not limited to: textual data, images, audio data, video, machine code, metadata, multimedia data, etc.

[0025] The content management platform 160 then electronically performs one or more actions to facilitate producing resources associated with relationship instances based on the determining, using the machine learning model, whether the detected particular change is relevant. Such actions may include those represented by or performed in stages 2, 3 and 4 shown in FIG. 1. [0026] The content management platform 160 may receive input (e.g., by a user of The content management platform 160) indicating whether previous detected changes in data from one or more of the of the plurality of data sites are relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances.

[0027] The content management platform 160 may train the machine learning model for determining whether detected changes are relevant based on the input indicating whether the previous detected changes in data are relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances. The content management platform 160 may then electronically determine, using the trained machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances.

[0028] At stage 2 164, the content management platform 160 electronically extracts one or more particular portions of the current data in response to determining, using the machine learning model for determining whether detected changes are relevant, that the detected particular change is relevant. The content management platform 160 then electronically associates, using a machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, the one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances.

[0029] The content management platform 160 then electronically stores the association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

[0030] At stage 3 166, the content management platform 160 electronically organizes, based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances, relevant portions of the current data for consumption by one or more service engines for producing resources associated with relationship instances. For example, this may include identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances. The content management platform 160 then updates the electronic database with an updated version of the one or more digital rules based on the stored association. This enables a plurality of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances, thereby increasing efficiency of the computerized system by avoiding providing the needed data to each of the service engines in a serial and manual operation.

[0031] At stage 4 168, the content management platform 160 electronically translates, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by the one or more service engines for producing resources associated with relationship instances (i. e. , publishes such data in a format for consumption by the API). The machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources associated with relationship instances.

[0032] FIG. 2 is a block diagram illustrating an OSP 298 in communication with an example content management platform (CMP) component (such as content management platform 260). Content management platform 260 is a particular example of content management platform 160 as integrated into an example technical environment via an electronic communication pathway to an example service engine 283 of the OSP 298 and via an electronic communication pathway to an example domain 281, according to various embodiments described herein. Content management platform 260 may include one or more computer systems. In various embodiments, content management platform 260 may be in electronic communication with multiple different service engines of the OSP 298 and/or of other OSPs and in electronic communication with multiple different domains. Also, in some embodiments, content management platform 260 maybe part of the OSP 298. [0033] In FIG. 2. a sample computer system 295 according to embodiments is shown. The computer system 295 has one or more processors 294 and a memory 230. The memory 230 stores programs 231 and data 238. An element of the data 238 is a resource 279 that is produced as described later in this document. The one or more processors 294 and the memory 230 of the computer system 295 thus implement a service engine 283.

[0034] The computer system 295 may be located in “the cloud.'’ In fact, the computer system 295 may optionally be implemented as part of OSP 298. The computer system 295 may be configured to perform one or more predefined services, for example via operations of the service engine 283. Such services may be searches, determinations, computations, verifications, notifications, the transmission of specialized information, including data that effectuates payments, the generation and transmission of documents, the online accessing of other systems to effect registrations, and so on, including what is described in this document. Producing the resource 279 may be part of one of these services or that which is provided as part of one of these services. Such sendees may be provided in the form of Software as a Service (SaaS). As such, the OSP 298 may be an online sen ice provider.

[0035] A computer system 282 may be part of a domain 281. The domain 281 may be an organization.

[0036] A user 292 may be standalone. The user 292 may use a computer system 290 that has a screen 291, on which User Interfaces (UIs) may be shown. In embodiments, the user 292 and the computer system 290 are considered part of a primary entity 293, which may be an organization, an institution, and so on. In such instances, the user 292 may be an agent of the primary entity 293, and even within a physical site of the primary entity 293, although that is not necessary. In embodiments, the computer system 290 or other device of the user 292 may be client devices for the computer system 295. The user 292 or the primary entity 293 may be clients for the OSP 298. For instance, the user 292 may log into the computer system 295 by using credentials, such as a user name, a password, a token, and so on.

[0037] The computer system 290 may access the computer system 282 via a communications network 288, such as the internet. In particular, the entities and associated systems of FIG. 2 may communicate via physical and logical channels of the communications network 288. Accordingly, from certain perspectives, the domain 281 is in the cloud.

[0038] The computer system 290 may access the computer system 295 via a communications network 288, such as the internet. Accordingly, from certain perspectives, the OSP 298 is in the cloud. [0039] In some instances, the computer system 295 may access the computer system 282 on behalf of the primary entity’ 293.

[0040] Accessing, downloading and/or uploading, and so on may be permitted among these computer systems. Such may be performed, for instance, with manually uploading files, like spreadsheet files, etc. Such may also be performed automatically as shown in the example of FIG. 2, with systems exchanging requests and responses.

[0041] Moreover, in some embodiments, data from the computer system 290 and/or from the computer system 295 may be stored in an Online Processing Facility (OPF) 289 that may run software applications, perform operations, and so on. In such embodiments, requests and responses may be exchanged ith the OPF 289, downloading or uploading may involve the OPF 289, and so on. In such embodiments, the computer system 290 and any devices of the OPF 289 may be considered to be remote devices, at least from the perspective of the computer system 295.

[0042] In embodiments, the user 292 and/or the primary entity 293 have instances of relationships with secondary entities. Only one such secondary entity 296 is shown. The secondary entity 296 may be an organization, a person, and so on. In some embodiments, the secondary entity 296 has a device 232, which may be an electronic device such as a cellphone, tablet, laptop, computer system and so on. The device 232 may have a screen 233. In this example, the primary entity 293 has a relationship instance 297 with the secondary entity 296. In fact, the secondary entity 296 may have used a device such as the device 232 to create the relationship instance 297. The primary entity 293 and/or the secondary entity 296 may be referred to as simply entities.

[0043] In some instances, the user 292 and/or the primary entity 293 obtain data about one or more secondary entities, for example as necessary for conducting the relationship instances with them. The obtained data may be about attributes of the entities, or of the relationship instances. [0044] In embodiments, the computer system 295 receives one or more datasets. A sample received dataset 235 is shown. The dataset 235 may be received by the computer system 295 in a number of ways. In some embodiments, one or more requests may be received by the computer system 295 via a network. The received one or more requests may carry payloads. In such embodiments, the one or more payloads may be parsed by the computer system 295 to extract the dataset.

[0045] In embodiments, the dataset 235 has parameters that may also be called dataset parameters. At least some of the dataset parameters have respective values that may also be called dataset values. The dataset values may be numerical, alphanumeric, Boolean, and so on. as needed for what the parameters characterize. For example, the value of an identity parameter ID may indicate an identity’ of the dataset 235. so as to differentiate it from other such datasets. At least one of the dataset values may characterize an attribute of a certain one of the entities 293 and 296, as indicated by correspondence arrows 299. For instance, a parameter DI may have the value of a name of the certain entity, a parameter D2 may have a value of relevant data of the entity, and so on. Plus, an optional dataset parameter Bl may have a numerical base value. The base value Bl may be for an aspect of the dataset, and so on. The aspect of the dataset may be the aspect of a value that characterizes the attribute, an aspect of the reason that the dataset was created in the first place, an aspect of the relationship instance 297, and so on. The dataset 235 may further have additional dataset parameters, as indicated by the horizontal dot-dot-dot in the right side of the dataset 235. In this document the dot-dot-dot, whether horizontal or vertical, means ‘'potentially more of’ what it is shown together with. In embodiments, the dataset values characterize attributes of both the primary entity’ 293 and the secondary entity’ 296, but that is not required.

[0046] In embodiments, the computer system 295 may identify a domain for the dataset. In this example, the domain 281 is identified this way. The identification may be directly by the primary entity 293. Or, this identifying may be performed responsive to the characterized attribute. In particular, the identification may be by one or more associations of the characterized attribute with domains, properties of domains, lists of what domains include, and so on. Sometimes the characterized attribute may even be the name of a domain, such as a component of a mailing address. The identified domain may be associated with the certain entity’, which may be the primary entity 293 or the secondary entity’ 296.

[0047] In embodiments, the computer system 295 produces a resource for the dataset 235, such as the resource 279. The produced resource may be a document, a determination, a computational result, etc., made, created or prepared for the user 292, and/or the primary entity’ 293, and/or the secondary entity 296, etc. As such, in some embodiments, the resource is produced by processing and/or a computation. In some embodiments, therefore, the resource is produced on the basis of a characterized attribute of the primary entity 293 and/or the secondary entity 296.

[0048] As seen above, the computer system 290, the computer system 295, and possibly also the OPF 289 may exchange requests and responses. Such may be implemented with a number of different architectures. Two sample such architectures are now described with reference to the computer systems 290 and 295 only. [0049] FIG. 3 is a block diagram illustrating an example online software platform (OSP) 398 in communication with an example content management platform component (such as content management platform (CMP) 360. Content management platform 360 is a particular example of content management platform 260 as integrated into an example technical environment via a an electronic communication pathway to an example service engine 383 of the OSP 398 and via a an electronic communication pathway to an example plurality of remote data sites 362. according to various embodiments described herein. In an example embodiment, the plurality of remote data sites 362 may include domain 281 of FIG. 2. In various embodiments, content management platform 360 may be in electronic communication with multiple different sendee engines of the OSP 398 and/or of other OSPs. Also, in some embodiments, content management platform 360 may be part of the OSP 398.

[0050] FIG. 3 shows some of the elements described in FIG. 2. As such, explanations from one of these two diagrams may apply also to the other.

[0051] FIG. 3 is a diagram showing sample aspects of embodiments. A thick horizontal line 315 separates this diagram, although not completely or rigorously. Above the line 315 are shown elements with emphasis mostly on entities, components, their relationships, and their interactions. Below the line 315 are shown elements with emphasis mostly on processing of data that takes place often within one or more of the components that are shown above the line 315. [0052] Above the line 315, a sample computer system 395 according to embodiments is shown. The computer system 395 has one or more processors 394 and a memory 330. The memory 330 stores programs 331 and data 338. The one or more processors 394 and the memory 330 of the computer system 395 thus implement a sendee engine 383.

[0053] The computer system 395 may be located in “the cloud.'’ In fact, the computer system 395 may optionally be implemented as part of an Online Software Platform (OSP) 398. The computer system 395 may be configured to perform one or more predefined services, for example via operations of the sendee engine 383. Such services may be searches, determinations, computations, verifications, notifications, the transmission of specialized information, including data that effectuates payments, the generation and transmission of documents, the online accessing of other systems to effect registrations, and so on, including what is described in this document. Such sendees may be provided in the form of Software as a Service (SaaS). As such, the OSP 398 may be an online service provider.

[0054] A user 392 may be standalone. The user 392 may use a computer system 390 that has a screen 391. on which User Interfaces (UIs) may be shown. In embodiments, the user 392 and the computer system 390 are considered part of a primary entity 393, which may be an organization, an institution, and so on. In such instances, the user 392 may be an agent of the primary entity 393. and even within a physical site of the primary entity 393, although that is not necessary⁷. In embodiments, the computer system 390 or other device of the user 392 may be client devices for the computer system 395. The user 392 or the primary entity 393 may be clients for the OSP 398. For instance, the user 392 may log into the computer system 395 by using credentials, such as a user name, a password, a token, and so on.

[0055] The computer system 390 may access the computer system 395 via a communications network 388, such as the internet. In particular, the entities and associated systems of FIG. 3 may communicate via physical and logical channels of the communications network 388. For example, information may be communicated as data using the Internet Protocol (IP) suite over a packet-switched network such as the Internet or other packet-switched network, which may be included as part of the communications network 388. The communications network 388 may include many different ty pes of computer networks and communication media, including those used by various different physical and logical channels of communication, now known or later developed. Non-limiting media and communication channel examples include one or more, or any operable combination of: fiber optic systems, satellite systems, cable systems, microwave systems, Asynchronous Transfer Mode (“ATM”) systems, frame relay systems, satellite systems. Radio Frequency (“RF”) systems, telephone systems, cellular systems, other wireless systems, and the Internet. In various embodiments the communications network 388 may be or include any type of network, such as a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), or the internet. Accordingly, from certain perspectives, the OSP 398 is in the cloud.

[0056] Accessing, downloading and/or uploading, and so on may be permitted among these computer systems. Such may be performed, for instance, with manually uploading files, like spreadsheet files, etc. Such may also be performed automatically as shown in the example of FIG. 3, with systems exchanging requests and responses.

[0057] Moreover, in some embodiments, data from the computer system 390 and/or from the computer system 395 may be stored in an Online Processing Facility (OPF) 389 that may run software applications, perform operations, and so on. In such embodiments, requests and responses may be exchanged with the OPF 389, downloading or uploading may involve the OPF 389, and so on. In such embodiments, the computer system 390 and any devices of the OPF 389 may be considered to be remote devices, at least from the perspective of the computer system

[0058] In embodiments, the user 392 and/or the primary' entity' 393 have instances of relationships with secondary entities. Only one such secondary entity 396 is shown. The secondary entity 396 may be an organization, a person, and so on. In some embodiments, the secondary entity 396 has a device 332, which may be an electronic device such as a cellphone, tablet, laptop, computer system and so on. The device 332 may have a screen 333. In this example, the primary entity’ 393 has a relationship instance 397 with the secondary entity 396. In fact, the secondary’ entity 396 may have used a device such as the device 332 to create the relationship instance 397. The primary entity 393 and/or the secondary^ entity' 396 may' be referred to as simply entities. One of these entities may have one or more attributes. Such an attribute of such an entity' may be any one of its name, ty pe of entity', a physical or geographical location such as an address, a contact information element, an affiliation, a characterization of another entity', a characterization by another entity', an association or relationship wi th another entity⁷ (general or specific instances), an asset of the entity⁷, a declaration by or on behalf of the entity, a specific domain that the entity' belongs in a context of multiple domains that are defined in terms of the above, and so on.

[0059] In some instances, the user 392 and/or the primary entity 393 obtain data about one or more secondary entities, for example as necessary' for conducting the relationship instances with them. The obtained data may be about attributes of the entities, or of the relationship instances. [0060] In embodiments, the computer system 395 receives one or more datasets. A sample received dataset 335 is shown below the line 315. The dataset 335 may be received by the computer system 395 in a number of ways. In some embodiments, one or more requests may be received by the computer system 395 via a network. In this example, a request 384 is received by the computer system 395 via the communications network 388. The request 384 has been transmitted by the remote computer system 390. The received one or more requests may carry payloads. In this example, the request 384 carries a payload 334. In such embodiments, the one or more payloads may be parsed by the computer system 395 to extract the dataset. In this example, the payload 334 may be parsed by the computer system 395 to extract the dataset 335. In this example the single payload 334 encodes the entire dataset 335, but that is not required. In fact, a dataset may be received from the payloads of multiple requests. In such cases, a single payload may encode only a portion of the dataset. And, of course, the payload of a single request may encode multiple datasets. Additional computers may be involved with the communications network 388, some beyond the control of the user 392 or of the OSP 398, and some within such control. [0061] In embodiments, the dataset 335 has parameters that may also be called dataset parameters. At least some of the dataset parameters have respective values that may also be called dataset values. The dataset values may be numerical, alphanumeric, Boolean, and so on, as needed for what the parameters characterize. For example, the value of an identity parameter ID may indicate an identity of the dataset 335, so as to differentiate it from other such datasets. At least one of the dataset values may characterize an attribute of a certain one of the entities 393 and 396, as indicated by correspondence arrows 399. For instance, a parameter DI may have the value of a name of the certain entity, a parameter D2 may have a value of relevant data of the entity⁷, and so on. Plus, an optional dataset parameter Bl may have a numerical base value. The base value Bl may be for an aspect of the dataset, and so on. The aspect of the dataset may be the aspect of a value that characterizes the attribute, an aspect of the reason that the dataset was created in the first place, an aspect of the relationship instance 397, and so on. The dataset 335 may further have additional dataset parameters, as indicated by the horizontal dot-dot-dot in the right side of the dataset 335. In this document the dot-dot-dot, whether horizontal or vertical, means "‘potentially more of' what it is shown together with. In embodiments, the dataset values characterize attributes of both the primary entity 393 and the secondary entity' 396, but that is not required.

[0062] In embodiments, the computer system 395 may identity', responsive to the characterized attribute, a domain for the dataset. This identifying may be performed by one or more associations of the characterized attribute with domains, properties of domains, lists of what domains include, and so on. Sometimes the characterized attribute may even be the name of a domain, such as a component of a mailing address. The identified domain may be associated with the certain entity', which may be the primary entity 393 or the secondary entity' 396.

[0063] In embodiments, digital resource rules 370 are provided for use by the OSP 398. In the example of this diagram, only one sample digital resource rule is shown explicitly, namely rule D R RULE4 374, while other such rules are indicated by the vertical dot-dot-dots. These rules 370 are digital in that they are implemented for use by software. For example, these rules 370 may be implemented within the programs 331 and/or the data 338. The data portion of these rules 370 may alternately be stored in memories, local or in other places that may be accessed by the computer system 395, such as content management platform 360. The storing may be in the form of a spreadsheet, a database, etc. One or more digital rules may be provided for a domain. Different sets of rules may be provided for different domains. [0064] In embodiments, the computer system 395 may access the stored digital resource rules 370 of the domain that was identified. This accessing may be performed responsive to the computer system 395 receiving one or more datasets, such as the dataset 335.

[0065] Then the computer system 395 may select a certain one of the accessed digital resource rules 370. In this example, the rule D_R_RULE4 374 is thus selected as the certain digital resource rule. The computer system 395 may thus select the certain rule D_R_RULE4 374 responsive to one or more of the dataset values of the dataset parameters of the dataset 335, as per the arrows 371 . The selection of this particular rule is indicated also by the fact that an arrow 378 begins from that rule. The arrow 378 is described in more detail later in this document. The selected rule may be associated with the identified domain. In fact, the whole set of these rules 370 may be associated with the identified domain, while other sets (not shown) may be associated with different domains.

[0066] Then the computer system 395 may produce a resource for the dataset 335, such as the resource 379. The computer system 395 may thus produce the resource by applying the certain digital resource rule, which was previously selected, responsive to at least one of the dataset values of the dataset parameters of the dataset 335. In the example of FIG. 3, the resource 379 is produced for the dataset 335 by the computer system 395 applying the certain digital resource rule D_R_RULE4 374, as indicated by the arrow 378. The impact of the dataset 335 in producing the resource 379 is indicated by at least one of the arrows 371.

[0067] The produced resource may be a document, a determination, a computational result, etc., made, created or prepared for the user 392, and/or the primary entity 393, and/or the secondary entity 396, etc. As such, in some embodiments, the resource is produced by processing and/or a computation. In some embodiments, therefore, the resource is produced on the basis of a characterized attribute of the primary entity 393 and/or the secondary entity 396.

[0068] The resource may be produced in a number of ways. For instance, at least one of the dataset values may be a numerical base value, e.g. Bl, as mentioned above. In such cases, applying the certain digital resource rule may include performing a mathematical operation on the base value Bl. For example, applying the certain digital resource rule may include multiplying the numerical base value Bl with a number indicated by the certain digital resource rule. Examples of small such numbers include 0.015, 0.03, 0.05, and so on, but the numbers need not be small or only positive. Such a number may be indicated directly by the certain rule, or be stored in a place indicated by the certain rule, or by the dataset 335, and so on.

[0069] In some embodiments two or more digital main rules may be applied to produce the resource. For example, the computer system 395 may select, responsive to one or more of the dataset values, another one of the accessed digital resource rules 370. These one or more dataset values may be the same as. or different than, the one or more dataset values responsive to which the first selected rule was selected. In such embodiments, the resource may be produced by the computer system 395 also applying the other selected digital resource rule to at least one of the dataset values. For instance, where the base value Bl is used, applying the first selected rule may include multiplying the numerical base value Bl with a first number indicated by the first selected rule, so as to compute a first product. In addition, applying the second selected rule may include multiplying the numerical base value Bl with a second number indicated by the second selected rule, so as to compute a second product. And, a value of the resource may be produced by summing the first product and the second product.

[0070] In embodiments, a notification may be caused to be transmitted, e.g. via the communications network 388, by the computer system 395. In the example of FIG. 3, a notification 336 may be caused to be transmitted by the computer system 395, for example as an answer or other response to the received dataset 335.

[0071] The notification may be about an aspect of the resource, and possibly not about the whole resource. Or, the notification may be about the whole resource. That is why the resource 379 is not depicted in FIG. 3 as being entirely with the notification 336. In particular, the notification 336 may inform about the aspect of the resource 379, namely that it has been determined, or where it may be found, or what it is, or a portion of its content, or a value of it, or a statistic of the value, or a rounded version of the value, and so on. Of course, the planning should be such that the recipient of the notification 336 is able to parse what it is being provided, use it properly, and so on.

[0072] The notification 336 may be transmitted to one of an output device and another device. The output device may be the screen of a local user or a remote user. The notification 336 may thus cause a desired image, message, or other such notification to appear on the screen, such as within a Graphical User Interface (GUI) and so on. The other device may be the remote device, from which the dataset 335 was received, as in the example of FIG. 3. In particular, the computer system 395 may cause the notification 336 to be communicated by being encoded as a payload 337. which is carried by a response 387. The response 387 may be transmitted via the communications network 388 responsive to the received request 384. The response 387 may be transmitted to the computer system 390, or to the OPF 389, and so on. As such, the other device may be the computer system 390, or the OPF 389, or the screen 391 of the user 392, and so on. In this example the single payload 337 encodes the entire notification 336, but that is not required. Similarly with what is written above about encoding datasets in payloads, the notification 336 instead may be provided via two or more payloads, or in other cases the notification 336 and at least one other notification may be included in the same single payload. Along with the aspect of the resource 379, it may be advantageous to embed in the payload 337 the identity parameter (ID) and/or one or more parameters of the dataset 335. This will help the recipient correlate the response 387 to the request 384, and therefore match the received aspect of the resource 379 as the answer or other response to the appropriate dataset.

[0073] As seen above, the computer system 390, the computer system 395. and possibly also the OPF 389 may exchange requests and responses. Such may be implemented with a number of different architectures. Two sample such architectures are now described with reference to the computer systems 390 and 395 only.

[0074] In one such architecture, a device remote to the service engine 383, such as the computer system 390, may have a certain application (not shown) and a connector (not shown) that is a plugin that sits on top of that certain application. The connector may be able to fetch from the remote device the details required for the service desired from the OSP 398, form an object or payload (e.g. 334), and then send or push a request (e.g. 384) that carries the payload to the service engine 383 via a service call. The service engine 383 may receive the request with its payload. The service engine 383 may then access the digital resource rules 370, find the appropriate one(s) of them, and apply it or them to the payload to produce the requested resource 379. The service engine 383 may then form a payload (e.g. 337) that includes an aspect of the resource 379. and then push. send, or otherwise cause to be transmitted a response (e.g. 387) that carries the payload it formed to the connector. The connector receives the response, reads its payload, and forwards that payload to the certain application.

[0075] An alternative such architecture uses Representational State Transfer (REST) Application Programming Interfaces (APIs). REST or RESTful API design is designed to take advantage of existing protocols. While REST may be used over nearly any protocol, it usually takes advantage of Hyper Text Transfer Protocol (HTTP) w hen used for Web APIs. In such an alternative architecture, a device remote to the sendee engine 383, such as the computer system 390, may have a particular application (not shown). In addition, the computer system 395 implements a REST API (not shown). This alternative architecture enables the primary entity 393 to directly consume a REST API from their particular application, without using a connector. The particular application of the remote device may be able to fetch internally from the remote device the details required for the service desired from the OSP 398, and thus send or push the request 384 to the REST API. In turn, the REST API talks in the background to the service engine 383. Again, the service engine 383 determines the requested resource 379, and sends an aspect of it back to the REST API. In turn, the REST API sends the response 387 that has the payload 337 to the particular application.

[0076] The digital resource rules 370 include the rule D R RULE4 374 that is eventually selected and applied. In some embodiments, the rules 370 are implemented by simple rules. A simple rule has a single condition (“P”), and a single consequent (“Q”). As a result of an initial search, then, the digital resource rule D_R_RULE4 374 is selected, and then its consequent is applied to produce the resource.

[0077] In some embodiments, the rules 370 further include additional digital resource rules that select that digital resource rule D R RULE4 374 in the first place, for ultimately applying it. In such embodiments, the digital resource rules 370 may be implemented as simple rules or as complex rules. Complex rules may have more than one condition, and/or more than one consequence. Complex rules may be implemented as individual single rules with complex coding. Alternatively, a complex rule may be implemented in part by more than one simpler individual rules, which may have hierarchical relationships among them, e.g. from one rule’s application or execution leading to another, and so on. As a result of the initial search, then, rules are found which, when applied, select that certain rule in the first place.

[0078] Content management platform 360 component automatically sources, extracts, organizes and publishes content and/or digital resource rules 370 based on data obtained from remote data sites 379 for consumption by one or more service engines OSP 398 (such as service engine 383 ) for producing resources, such as resource 379. One or more of such operations may be performed by using a machine learning model trained for performing the respective operation. As part of the training, a user may validate determinations made by the machine learning model of which data to source, extract, organize and publish at one or more of the sourcing, extracting, organizing and publishing stages. Various types, implementations and combinations of machine learning models and techniques may be used in various embodiments. Such machine learning models and techniques may include or use, but are not limited to supervised learning, unsupervised learning, semi-supervised learning reinforcement learning, transfer learning, selfsupervised learning, evolutionary’ algorithms and labeled data.

[0079] In supervised learning, the algorithm is trained on a labeled dataset, where the input data is paired with corresponding output labels. Supervised learning may be used for classification and regression tasks, such as image recognition, speech recognition, and predicting numerical values. In various embodiments, the CMP 360 obtains images and recognizes the images as potentially relevant for updating one or more of: content for producing resources associated with relationship instances and digital mles for producing resources associated with relationship instances, and then may extract data from the images. Input indicating validation or rejection of the images and/or data extracted therefrom as relevant may be used in a feedback loop to the CMP 360 and/or machine learning model of the CMP as training data for the CMP 360 and/or machine learning model of the CMP to improve future determinations of which images and data extracted therefrom may be potentially relevant. Unsupervised learning involves training an algorithm on an unlabeled dataset, and the algorithm must discover patterns and relationships within the data on its own. Applications of unsupervised learning may include clustering, dimensionality reduction, and association rule learning. Examples include clustering rules and topic modeling. Semi-supervised learning combines elements of both supervised and unsupervised learning. The algorithm is trained on a dataset with both labeled and unlabeled data. Semi-supervised learning is useful when obtaining a fully labeled dataset is expensive or time-consuming. Semi-supervised learning may be applied in scenarios where limited labeled data is available. Reinforcement learning involves an agent that learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or punishments based on its actions. Transfer learning involves training a model on one task and then using the knowledge gained to improve performance on a related task. Transfer learning is useful when labeled data for a specific task is limited. Pre-trained models may be fine-tuned for specific applications. Self-supervised learning is a type of unsupervised learning where the algorithm generates its own labels from the input data. Examples include word embeddings and contrastive learning. Evolutionary algorithms are inspired by the process of natural selection. Evolutionary algorithms involve generating a population of potential solutions and iteratively selecting, recombining, and mutating them to optimize for a specific objective. Evolutionary’ algorithms may be applied in optimization problems, design automation, and evolving neural network architectures. These learning techniques are not mutually exclusive, and they may be combined to perform the machine learning described herein.

[0080] Labeled data is a component in supervised machine learning. In supervised learning, an algorithm is trained on a dataset that includes both input features and corresponding output labels. The labeled data serves as a set of examples from which the algorithm leams to make predictions or classifications. Here's how labeled data is used in the machine learning process: [0081] During the training phase, the algorithm is exposed to a labeled dataset, which consists of input-output pairs. The input features represent the characteristics of the data, and the output labels indicate the correct or desired prediction or classification. Such a labeled dataset may be provided to the content management platform 360 herein via content management platform 360 receiving input indicating whether previous detected changes in data from one or more of the of the plurality of remote data sites 362 are relevant to updating one or more of: content for producing resources associated with relationship instances and digital resource rules 370 for producing resources associated with relationship instances. The algorithm uses this labeled data to learn the patterns, relationships, and dependencies between the input features and the output labels. The goal is to develop a model that may generalize well to unseen data. The algorithm builds a model based on the patterns it identifies in the labeled training data. The model is essentially a mathematical representation of the relationships between input features and output labels.

[0082] The selection of the model architecture may vary and various algorithms (such as decision trees, neural networks, or support vector machines) may be used. After the model is trained, it is evaluated using a separate set of labeled data called the validation or test set. This set was not used during the training phase and serves as an independent benchmark to assess the model’s performance on new, unseen examples. Such a test set may also be provided to the content management platform 360 herein via content management platform 360 receiving input indicating whether previous detected changes in data from one or more of the of the plurality of remote data sites 362 are relevant to updating one or more of: content for producing resources associated with relationship instances and digital resource rules 370 for producing resources associated with relationship instances. The model's predictions are compared to the actual labels in the test set, and metrics such as accuracy, precision, recall, and Fl score are computed to measure the model's effectiveness. For example, as part of the training, a user may validate determinations made by the machine learning model of which data to source, extract, organize and publish at one or more of the sourcing, extracting, organizing and publishing stages performed by content management platform 360. In various embodiments, the CMP 160 obtains data and/or detects changes to data, and then recognizes the data (or the detected changes to the data) as potentially relevant for updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances. Input indicating validation or rejection of the data or the detected changes to the data as relevant may be used in a feedback loop to the CMP 160 and/or machine learning model of the CMP as training data for the CMP 160 and/or machine learning model of the CMP to improve future determinations of which data (or which changes to such data) may be potentially relevant.

[0083] Once the model is trained and evaluated, it may be used to make predictions on new, unlabeled data. The model applies the learned patterns to make predictions or classifications based on the input features. For example, the machine learning model may automatically predict what content and rules obtained from remote data sites 362 is relevant for updating one or more of: content for producing resources associated with relationship instances and digital resource rules 370 for producing resources associated with relationship instances and, based on such predictions, sources, extracts, organizes and publishes content and/or digital rules from remote data sites 362 for consumption by service engine 383 of the OSP 398 for producing resources. Overall, the output of the model is used by content management platform 360 for decisionmaking, automation, or any other application relevant to updating one or more of: content for producing resources associated with relationship instances and digital resource rules 370 for producing resources associated with relationship instances. The availability' of high-quality' labeled data is a key factor in the success of supervised learning models. In some cases, preexisting labeled datasets are used, and in others, manual labeling may be performed. Techniques such as transfer learning and semi-supervised learning may also be employed when labeled data is limited.

[0084] FIG. 4 shows details and aspects of different types of possible embodiments of the digital resource rules of FIG. 3.

[0085] Referring now to FIG. 4, a dataset 435 may be as descnbed for the dataset 335 of FIG. 3. In addition, a set 470 of digital resource rules is an example of digital resource rules, such as the digital resource rules 370 of FIG. 3.

[0086] Similarly with FIG. 3, in FIG. 4 a resource 479 may be produced according to an arrow 478. The resource 479 may be as the resource 379, at least an aspect of which may be reported by the notification 336, and so on.

[0087] The set 470 of digital resource rules includes different subsets, into which the individual rules belong. In addition, there may be hierarchical relationships among rules of different subsets, and/or of types. Often only one of these individual rules is eventually selected and applied, yvhile one or more of them may have been used for selecting it. The certain rule that is eventually selected may be a rule in any one of subsets of the set 470 of digital resource rules, and none is shown as such in the example of FIG. 4.

[0088] In the example of FIG. 4. the set 470 includes a subset 480 of domain-selecting rules. The set 470 also includes subsets 472. 473, 474. .... each for digital resource rules for sample domains A, B, C, ... respectively. A domain for yvhich a subset of resource rules is thus provided may be associated with the primary' entity 393 of FIG. 3, another domain may be associated with the secondary entity 396, and so on.

[0089] In many embodiments, one of the domain-selecting rules of the subset 480 may be used to select which domain’s rules should be applied. Then the certain one of the digital resource rule(s) may be selected from the digital resource rules of the selected domain. Then the resource 479 may be produced by applying the selected certain digital resource rule(s) to at least one of the dataset values of the dataset parameters of the dataset 435.

[0090] In this example, the subset 480 of domain-selecting rules includes rules D S RULEl 481, D_S_RULE2 482, D_S_RULE3 483, ... . One of these rules may be selected and used when more than one domain may be considered as eligible for its rules to apply. The rules of the subset 480. however, might not be necessary for embodiments where a single domain is considered or implied for one or more, or all of, the relationship instances. This may happen, for example, when it is known in advance that the primary entity 393 and every possible secondary entity are both associated with the same domain. Or, when it is planned that digital resource rules of only one domain will be considered, while any rules of any other domain will not be considered and will be disregarded.

[0091] Resource rules for individual domains are now described. Such rules need not be the same for each domain, or of the same type for each domain. The sample subset 472 of resource rules for domain A is now described in more detail. Its description may be similar for subsets for other domains, such as the subsets 473, 474, ... .

[0092] The subset 472 of resource rules includes different types of rules. In this example, the subset 472 includes precedence rules 420, main rules 430, and override rules 440. In this example, the precedence rules 420 include rules P_RULE1 421, P_RULE2 422, P_RULE3 423, ... . The main rules 430 include rules M RULEl 431, M RULE2 432. M RULE3 433, ... . The override rules 440 include rules O_RULE1 441 , O_RULE2 442, O_RULE3 443, ... .

[0093] In embodiments, one of the main rules 430 may ordinarily be selected as the certain digital resource rule, which in FIG. 3 is shown as rule D_R_RULE4 374. In other words, the certain digital resource rule may be, say. the main rule M_RULE2 432.

[0094] In this example, although not always required, the different types of rules within the subset 472 further have different hierarchies among them.

[0095] For a first instance, one of the precedence rules 420 may indicate which one of the main rules 430 is to be selected, as generally indicated by an arrow 429. Or, the one of the precedence rules 420 that does apply may itself be the eventually selected certain digital resource rule, instead of indicating any one of the main rules 430.

[0096] For a second instance, even w hen one of the main rules 430 is thus indicated, one of the override rules 440 may still override the indication, as generally indicated by an arrow 449. In such cases, the one of the rules 440 that overrides may be the eventually selected certain digital resource rule, instead of one of the main rules 430. Or, one of the rules 440 overrides by indicating yet a different one of the main rules 430 to be selected instead, and so on.

[0097] In FIG. 4, sample arrows 471 A, 471B and 471D begin from the dataset 435. These arrows suggest possible paths of the eventual selection of the certain rule, for ultimately producing the resource 479. These arrows are more detailed versions of the arrows 371 of FIG. 3. They are examples of possible arrows, and not all of them are necessarily used in every such determination.

[0098] According to the arrow 471 A, the subset 472 is indicated. So, at least one of the rules of the subset 472 may initially be indicated as the certain rule, e.g. from one or more values of the parameters of the dataset 435. The initially indicated rule may be the finally certain rule, or another intermediate rule which, in turn, will be used to select that certain rule.

[0099] According to the arrow 47 IB, at least one of the domain-selecting mles of the subset 480 may be invoked, from one or more values of the parameters of the dataset 435.

[0100] According to an arrow 471C, the one of the rules of subset 480 that was invoked by the arrow 47 IB was the rule D_S_RULE2 482. And, the arrow 471C further indicates that the invoked rule points to the subset 472, instead of to the subsets 473, 474, ... . As such, the subset 472 of resource rules should be used for selecting the certain rule. This example has the same result, but from a different path, as the sample arrow 471 A.

[0101] The arrow 47 ID is drawn to indicate that one or more of the values of the parameters of the dataset 435 are received and processed by the finally selected certain rule, for producing the resource 479.

[0102] FIG. 5 is provided to demonstrate how any one of the sets of rules described in FIG. 3 and/or of FIG. 4 may be searched iteratively. In particular, FIG. 5 shows sample digital resource rules such as those of FIG. 3 and/or of FIG. 4, and juxtaposes them with decision boxes of a flowchart portion of a sample method for recognizing that conditions of a certain digital resource rule may be met for its consequent to be applied, all according to embodiments of the present disclosure, which is an improvement in automated computerized systems.

[0103] Referring now also to FIG. 5, digital resource rules 570 are shown, which may be as the digital resource rules 370 or of the set 470. The digital resource rules 570 include the shown D R RULE3 573, D R RULE4 574 and D R RULE5 575, plus others according to the dotdot-dots. In embodiments, some of the digital resource rules may be expressed in the form of a logical “if-then” statement, such as: “if P then Q”. In such statements, the “if’ part, represented by the “P”, is called the condition, and the “then” part, represented by the “Q”, is called the consequent. Therefore, at least some of the digital resource rules include respective conditions and respective consequents that are associated with the respective conditions. And, for a certain digital main rule, if its certain condition P is met, then its certain consequent Q is what happens or becomes applied. In this example, the digital resource rules D R RULE3 573, D R RULE4 574, and D_R_RULE5 575, include respective conditions CN3, CN4, CN5. They also include consequents CT3, CT4, CT5 that are associated with the respective conditions CN3, CN4, CN5, respectively.

[0104] In addition. FIG. 5 shows a flowchart portion 500. In particular, individual ones of the digital resource rules 570 are shown juxtaposed with individual ones of respective decision operations of the flowchart portion 500, according to two-way juxtaposition arrows 579.

[0105] In embodiments, therefore, the operation of identifying an applicable digital rule is performed by recognizing, by the computer system 395. that a certain condition of a certain one of the accessed digital resource rules is met by one or more of the values of the parameters of the dataset. An example of such an operation is shown by the flow chart portion 500. In particular, according to successive decision operations 583, 584, 585, it is determined whether or not conditions CN3, CN4, CN5 are met by at least one of the values of a parameter of the dataset, respectively. If the answer to all is NO, then execution may proceed to the next decision operation. If the answer is YES then, according to operations 593, 594, 595, it is further determined that the respective consequents CT3, CT4, CT5 are to be applied. A consequent that is to be applied may be, for example, flagged as TRUE, executed on the spot, and so on. Then execution may proceed to the next decision operation in the flowchart portion 500 as is shown, or exit from it.

[0106] From what was mentioned in connection with FIG. 3, the certain D R RULE4 574 was thus identified. With reference to FIG. 5, the identification may have happened at the operation 584, at which time it was recognized that condition CN4 was met by a value of a parameter of the dataset 335. This made: the condition CN4 be the certain condition, the digital main rule D R RULE4 574 be the certain digital main rule, and the consequent CT4 be the certain consequent. Therefore, according to operation 594, the consequent CT4 is what happens or becomes applied, as part of applying the rule.

[0107] A number of examples are possible for how to recognize that a certain condition of a certain digital rule is met by at least one of the values of a parameter of the dataset. For instance, the certain condition may define a boundary⁷ of a region that is within a space. The region may be geometric, and even be within a larger space. For example, the region may be geographic, within the space of a city, a county, a state, a country, a continent or the earth. The boundary⁷ of the region may be defined in terms of numbers according to a coordinate system within the space. In the example of geography, the boundary may be defined in terms of groups of longitude and latitude coordinates. In such embodiments, the certain condition may be met responsive to the characterized attribute of the dataset being in the space and within the boundary of the region instead of outside the boundary. For instance, the attribute may be a location of the entity, and the one or more values of the parameters of the dataset 335 that characterize the location may be one or more numbers or an address, or longitude and latitude. The condition may be met depending on how the one or more values compare with the boundary. For example, the comparison may reveal that the location is in the region instead of outside the region. The comparison may be made by rendering the characterized attribute in units comparable to those of the boundary. For example, the characterized attribute may be an address that is rendered into longitude and latitude coordinates, and so on.

[0108] FIG. 6 is a flowchart for illustrating sample method 600 for a sourcing stage implemented by the content management platform 160 according to embodiments.

[0109] At 610, the system crawls a plurality of remote data sites.

[0110] At 612, the system obtains current data from the data site while electronically crawling the data site.

[OHl] At 614, the system detects a particular change from previous data from the data site. [0112] At 616, the system determines, using a machine learning model, whether the detected particular change is relevant.

[0113] At 618. the system performs one or more actions to facilitate producing resources based on the determination.

[0114] FIG. 7 is a flowchart for illustrating sample method 700 for a data extraction stage implemented by the content management platform 160 according to embodiments.

[0115] At 710, the system extracts one or more particular portions of current data in response to determining that a detected particular change is relevant.

[0116] At 712, the system associates, using a machine learning model, the one or more particular portions of the current data with a particular aspect of one or more digital rules.

[0117] At 714, the system stores the association.

[0118] FIG. 8 is a flowchart for illustrating sample method 800 for a data organization stage implemented by the content management platform 160 according to embodiments.

[0119] At 810, the system Access a stored association of one or more particular portions of current data with a particular aspect of one or more digital rules

[0120] At 812. the system organizes, based on the stored association, relevant portions of the cunent data for consumption by one or more service engines. [0121] At 814, the system identifies an electronic database associated with the one or more digital rules.

[0122] At 816, the system updates the electronic database with an updated version of the one or more digital rules based on the stored association.

[0123] FIG. 9 is a flowchart for illustrating sample method 900 for a content publication stage implemented by the content management platform 160 according to embodiments.

[0124] At 910. the system accesses a stored association of one or more particular portions of current data with a particular aspect of one or more digital rules.

[0125] At 912, the system electronically translates, based on the stored association, the one or more particular portions of the current data from narrative textual content into machine readable computer code that is able to be electronically consumed via an API by one or more service engines.

[0126] FIG. 10 is a flowchart for illustrating sample method 1000 for an administrative content portal for according to embodiments.

[0127] At 1010, the system provides an administrative content portal to a plurality of remote data sites for updating one or more of content and rules.

[0128] At 1012, the system receives from the plurality of remote data sites, current data including updates.

[0129] At 1014, the system integrates an update received from the remote data site into an electronic database associated with the remote data site.

[0130] At 1016, the system receives from the remote data site a request for data.

[0131] At 1018, the system transmits to the remote data site the update integrated into the electronic database associated with the remote data site.

[0132] FIG. 11 is a flowchart for illustrating sample method 1100 for Al driven classification code assignment system according to embodiments.

[0133] At 1110, the system receives a training set of data from one or more online marketplaces [0134] At 1112, the system trains, using the training set of data, a machine learning model for assigning classification codes to products.

[0135] At 1114, the system receives product information identifying a particular product.

[0136] At 1116, the system assigns a classification code to the particular product using the machine learning model.

[0137] Below are descriptions of more detailed embodiments and examples of the methods 600, 700, 800. 900, 1000 and 1100 above. [0138] In some aspects, the techniques described herein relate to a method, including: electronically crawling a plurality of remote data sites concurrently over a computer network, in which each data site of the plurality of data sites is a source of data selected as potentially relevant for updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; concurrently, for each data site of the plurality of data sites: electronically obtaining cunent data from the data site while electronically crawling the data site; based on the current data, electronically detecting a particular change from previous data from the data site; and electronically determining, using a machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; and electronically performing one or more actions to facilitate producing resources associated with relationship instances based on the determining, using the machine learning model, whether the detected particular change is relevant.

[0139] In some aspects, the techniques described herein relate to a method in which the electronically determining, using the machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant includes: receiving input indicating whether previous detected changes in data from one or more of the of the plurality of data sites are relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; training the machine learning model for determining whether detected changes are relevant based on the input indicating whether the previous detected changes in data are relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; and electronically determining, using the trained machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances.

[0140] In some aspects, the techniques described herein relate to a method in which the input indicating whether previous detected changes in data are relevant is provided by a user of the machine learning model.

[0141] In some aspects, the techniques described herein relate to a method in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances includes: electronically extracting one or more particular portions of the cunent data in response to determining, using the machine learning model for determining whether detected changes are relevant, that the detected particular change is relevant; electronically associating, using a machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, the one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically storing the association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

[0142] In some aspects, the techniques described herein relate to a method in which the electronically associating includes: receiving input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; training the machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances based on the input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; electronically determining, using the trained machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, whether the one or more particular portions of the current data should be associated with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

[0143] In some aspects, the techniques described herein relate to a method, in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically organizing, based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances, relevant portions of the current data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

[0144] In some aspects, the techniques described herein relate to a method in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by the one or more sendee engines for producing resources associated with relationship instances, in which the machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources associated with relationship instances.

[0145] In some aspects, the techniques described herein relate to a method, in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more sendee engines for producing resources associated with relationship instances.

[0146] In some aspects, the techniques described herein relate to a method, further including: in response to electronically determining that the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances, electronically generating, using a generative artificial intelligence (Al) model, a textual summary that provides a narrative explaining why the detected particular change may be relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; receiving, based on the generated textual summary, input form a user indicating whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; and training the machine learning model for determining whether detected changes in data obtained from the plurality of data sites are relevant based on the received input.

[0147] In some aspects, the techniques described herein relate to a method in which the data sites are web sites and the electronically obtaining current data includes electronically scraping data from web sites. [0148] In some aspects, the techniques described herein relate to a method in which one or more data sites of the plurality of data sites are associated with or controlled by one or more respective domains which are authorities for generating content or rules regarding producing resources associated with relationship instances associated with the one or more respective domains.

[0149] In some aspects, the techniques described herein relate to a system, including: at least one processor; and a memory coupled to the at least one processor, the memory storing instructions that, when executed by the at least one processor, cause the system to perform operations including one or more of the operations described herein.

[0150] In some aspects, the techniques described herein relate to a non-transitory computer- readable storage medium having computer-executable instructions stored thereon that, when executed by at least one processor, cause a system to perform operations including one or more of the operations described herein.

[0151] In some aspects, the techniques described herein relate to a method, including: electronically extracting one or more particular portions of current data in response to determining, using a machine learning model for determining whether detected changes are relevant, that a detected particular change represented by the current data is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; electronically associating, using a machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, the one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically storing the association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances. [0152] In some aspects, the techniques described herein relate to a method in which the electronically associating includes: receiving input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; training the machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances based on the input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; and electronically determining, using the trained machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, whether the one or more particular portions of the current data should be associated with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

[0153] In some aspects, the techniques described herein relate to a method, further including: electronically performing one or more actions to facilitate producing resources associated with relationship instances based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

[0154] In some aspects, the techniques described herein relate to a method in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances includes: electronically organizing, based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances, relevant portions of the current data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

[0155] In some aspects, the techniques described herein relate to a method in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by the one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents the updated version of the one or more digital rules used by the one or more service engines in order to produce resources.

[0156] In some aspects, the techniques described herein relate to a method, further including: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more service engines for producing resources associated with relationship instances. [0157] In some aspects, the techniques described herein relate to a system, including: at least one processor; and a memory coupled to the at least one processor, the memory storing instructions that, when executed by the at least one processor, cause the system to perform operations including one or more of the operations described herein.

[0158] In some aspects, the techniques described herein relate to a non-transitory computer- readable storage medium having computer-executable instructions stored thereon that, when executed by at least one processor, cause a system to perform operations including one or more of the operations described herein.

[0159] In some aspects, the techniques described herein relate to a method, including: electronically accessing a stored association of one or more particular portions of current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; electronically organizing, based on the stored association, relevant portions of the current data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

[0160] In some aspects, the techniques described herein relate to a method, further including: electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents the updated version of the one or more digital rules used by the one or more sendee engines in order to produce resources.

[0161] In some aspects, the techniques described herein relate to a method, further including: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more service engines for producing resources associated with relationship instances. [0162] In some aspects, the techniques described herein relate to a system, including: at least one processor; and a memory coupled to the at least one processor, the memory storing instructions that, when executed by the at least one processor, cause the system to perform operations including one or more of the operations described herein.

[0163] In some aspects, the techniques described herein relate to a non-transitory computer- readable storage medium having computer-executable instructions stored thereon that, when executed by at least one processor, cause a system to perform operations including one or more of the operations described herein.

[0164] In some aspects, the techniques described herein relate to a method, including: electronically accessing a stored association of one or more particular portions of current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources.

[0165] In some aspects, the techniques described herein relate to a method, further including: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more service engines for producing resources associated with relationship instances.

[0166] In some aspects, the techniques described herein relate to a system, including: at least one processor; and a memory coupled to the at least one processor, the memory storing instructions that, when executed by the at least one processor, cause the system to perform operations including one or more of the operations described herein.

[0167] In some aspects, the techniques described herein relate to a non-transitory computer- readable storage medium having computer-executable instructions stored thereon that, when executed by at least one processor, cause a system to perform operations including one or more of the operations described herein.

[0168] In some aspects, the techniques described herein relate to a method, including: electronically providing an administrative content portal to a plurality of remote data sites concurrently over a computer network for the plurality of remote data sites to electronically update one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances: and in response to providing the administrative content portal, receiving over a computer network from the plurality of remote data sites, current data including updates to one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; for each remote data site of the plurality of remote data sites: electronically integrating an update received from the remote data site into an electronic database associated with the remote data site; electronically receiving from the remote data site a request for data regarding one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances from the electronic database associated with the remote data site; and in response to the request, electronically transmitting to the remote data site the update integrated into the electronic database associated with the remote data site, thereby enabling the remote data site to display the update integrated into the electronic database associated with the remote data site. [0169] In some aspects, the techniques described herein relate to a method, further including: electronically accessing a stored association of one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; electronically organizing, based on the stored association, relevant portions of the current data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality⁷ of sen ice engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

[0170] In some aspects, the techniques described herein relate to a method, further including: electronically accessing a stored association of one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources.

[0171] In some aspects, the techniques described herein relate to a system, including: at least one processor; and a memory coupled to the at least one processor, the memory storing instructions that, when executed by the at least one processor, cause the system to perform operations including one or more of the operations described herein.

[0172] In some aspects, the techniques described herein relate to a non-transitory computer- readable storage medium having computer-executable instructions stored thereon that, when executed by at least one processor, cause a system to perform operations including one or more of the operations described herein.

[0173] In some aspects, the techniques described herein relate to a method, including: electronically receiving a training set of data from one or more online marketplaces in which the training set of data includes, for each product of a plurality of products, product information identifying the product and an associated classification codes correlated with the product information identifying the product; electronically training, using the training set of data, a machine learning model for assigning classification codes to products; electronically receiving product information identifying a particular product; and electronically assigning a classification code to the particular product using the machine learning model.

[0174] In some embodiments, the content management platform such and electronically assigning a classification code to the particular product may use such assigning of classification codes when electronically crawling a plurality of remote data sites concurrently over a computer network to detect changes in product information, rule changes regarding such products, new products and other changes in data and determine whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances.

[0175] In some aspects, the techniques described herein relate to a method in which the classification code is a Harmonized Commodity Description and Coding System (HS) code. [0176] In some aspects, the techniques described herein relate to a method in which the product information identifying the product includes one or more of: a product identification code, a product serial number, a product name, a Universal Product Code (UPC), an Amazon Standard Identification Number (ASIN), an International Article Number, a European Article Number (EAN), an image of the product, and a description of the product. In various embodiments, the CMP 160 obtains images and recognizes the images as potentially relevant (e.g., such as by recognizing product identification information represented by the image) for updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances. Input indicating validation or rejection of the images or data represented in the images as relevant may be used in a feedback loop to the CMP 160 and/or machine learning model of the CMP as training data for the CMP 160 and/or machine learning model of the CMP to improve future relevant object identification and determinations of which images may be potentially relevant.

[0177] In some aspects, the techniques described herein relate to a method in which the electronically receiving a training set of data includes electronically receiving the training set of data as part of producing respective resources associated with respective relationship instances involving each product of the plurality of products.

[0178] In some aspects, the techniques described herein relate to a method, further including: electronically providing the assignment of the classification code to the particular product to be electronically consumed via an Application Programming Interface (API) by one or more service engines to enable the one or more service engines to produce resources associated with respective relationship instances involving the particular product.

OPERATIONAL EXAMPLES - USE CASES

[0179] The above-mentioned embodiments have one or more uses. Aspects presented below may be implemented as was described above for similar aspects. Some, but not all of these aspects even have reference numerals that are similar to the above, for ease of explanation. [0180] Operational examples and sample use cases are possible where the attribute of an entity in a dataset is any one of the entity’s name, type of entity, a physical location such as an address, a contact information element, an affiliation, a characterization of another entity, a characterization by another entity, an association or relationship with another entity (general or specific instances), an asset of the entity, a declaration by or on behalf of the entity, and so on. Different resources may be produced in such instances, and so on.

[0181] FIG. 12 is a block diagram illustrating an example OSP 1298 in communication with an example content management platform component (such as content management platform (CMP) 1260). Content management platform 1260 is a particular example of content management platform 160 as integrated into an example technical environment via a an electronic communication pathway to an example tax engine 1283 of the OSP 1298 and via a an electronic communication pathway to an example tax authority 1281, according to various embodiments described herein. Content management platform 1260 may include one or more computer systems. In various embodiments, content management platform 1260 may be in electronic communication with multiple different tax service engines of the OSP 1298 (e g., tax engines for computing taxes, registering companies with tax authorities, preparing and filing tax returns electronically, computing taxes for cross border sales, generating and/or capturing tax exemption certifications, etc.) and/or of other OSPs and in electronic communication with multiple different tax authorities. Also, in some embodiments, content management platform 1260 may be part of the OSP 1298. As there are millions of different tax compliance rules and related data, which are constantly changing, ensuring the tax engine, tax engine 1283, has instant access to updated and accurate tax compliance rules and related data poses a significant technical problem for computerized tax computation systems worldwide. Content management platform 1260 solves such technical problems by employing machine learning techniques to automatically source such data from tax authorities, such as tax authority 1281, detect relevant changes in such data, and then extract, organize and publish such data in a format for consumption by one or more tax service engines for computing tax and performing other services associated with buysell transactions as described herein.

[0182] FIG. 12 is a diagram for an operational example where a buy-sell transaction 1297 is a use case of the relationship instance 297. The buy-sell transaction 1297 is conducted between a primary entity 1293, which is a seller, and a secondary entity 1296, which is a buyer. The transaction 1297 is therefore a buy-sell transaction between them, for instance for a physical item, but it may be a non-physical item such as a digital item, a specific right, and so on. A tax obligation 1279 often arises from the buy-sell transaction 1297 — in particular a sales and/or use tax must be paid by either the primary entity seller 1293 or the secondary entity buyer 1296, and/or documents must be prepared and filed, such as tax returns, compliance forms, etc. A computation of the tax obligation 1279 is a use case of producing the resource 279.

[0183] It will be recognized that aspects of FIG. 12 have similarities with aspects of FIG. 2. Portions of such aspects may be implemented as described for analogous aspects of FIG. 2. In particular, a computer system 1295 is shown, which is used to help customers, such as a user 1292, with tax compliance. For instance, the user 1292 may log into the computer system 1295 by using credentials, such as a user name, a password, a token, and so on. Further in this example, the computer system 1295 is part of an OSP 1298 that is implemented as a Software as a Service (SaaS) provider, for being accessed by the user 1292 online. As such, the OSP 1298 may be an online service provider for clients. Alternately, the functionality' of the computer system 1295 may be provided locally to a user.

[0184] The user 1292 may be standalone. The user 1292 may use a computer system 1290 that has a screen 1291. In embodiments, the user 1292 and the computer system 1290 are considered part of the primary entity seller 1293, which is also known as primary entity seller 1293. The primary entity seller 1293 may be a business, such as a seller of items, a reseller, a buyer, a service business, and so on. In such instances, the user 1292 may be an employee, a contractor, or otherwise an agent of the primary entity' seller 1293.

[0185] The secondary' entity buyer 1296 may be an organization, a person, and so on. The secondary’ entity buyer 1296 has a device 1232 with a screen 1233. The secondary entity buyer 1296 may have used a device such as the device 1232 for the buy-sell transaction 1297.

[0186] The buy-sell transaction 1297 may involve an operation, such as an exchange of data to form an agreement. This operation may be performed in person, or over a network 1288, which may be as described elsewhere for communications networks, etc. In such cases the primary entity seller 1293 may even be an online seller, but that is not necessary. The buy-sell transaction 1297 will have data that is known to the primary entity seller 1293, similarly with w hat w as described by the relationship instance 297 of FIG. 2.

[0187] In a number of instances, the user 1292 and/or the primary entity seller 1293 use software applications to manage their business activities, such as sales, resource management, production, inventory management, delivery, billing, and so on. The user 1292 and/or the primary entity seller 1293 may further use accounting applications to manage purchase orders, sales invoices, refunds, payroll, accounts payable, accounts receivable, and so on. Such software applications, and more, may be used locally by the user 1292. or from an Online Processing Facility (OPF) 1289 that has been engaged for this purpose by the user 1292 and/or the primary entity seller 1293. The OPF 1289 may be analogous to the OPF 289. In such use cases, the OPF 1289 may be a Mobile Payments system, a Point Of Sale (POS) system, an Accounting application, an Enterprise Resource Planning (ERP) provider, an e-commerce provider, an electronic marketplace, a Customer Relationship Management (CRM) system, and so on.

[0188] Businesses have tax obligations to various tax authorities of respective tax jurisdictions. It is often challenging to even determine what taxes are owed and to whom, because the underlying statutes and tax rules and guidance issued by the tax authorities are very' complex. There are various types of tax, such as sales tax, use tax, excise tax, value-added tax, and issues about cross-border taxation including customs and duties, and many more. Some types of tax are industry specific. Each type of tax has its own set of rules. Additionally, statutes, tax rules, and rates change often, and new' tax rules are continuously added. Compliance becomes further complicated when a taxing authority offers a temporary tax holiday, during which certain taxes are waived. [0189] Tax jurisdictions are defined mainly by geography. Businesses have tax obligations to various tax authorities within the respective tax jurisdictions. There are various tax authorities, such as that of a group of countries, of a single country, of a state, of a county, of a municipality, of a city, of a local district such as a local transit district and so on. So, for example, when a business sells items in transactions that may be taxed by a tax authority, the business may have the tax obligations to the tax authority. These obligations include requiring the business to: a) register itself with the tax authority’s taxing agency, b) set up internal processes for collecting sales tax in accordance with the sales tax rules of the tax authority, c) maintain records of the sales transactions and of the collected sales tax in the event of a subsequent audit by the taxing agency, d) periodically prepare a form (“tax return”) that includes an accurate determination of the amount of the money owed to the tax authority as sales tax based on the sales transactions, e) file the tax return with the tax authority by a deadline determined by the tax authority’, and f) pay (“remit”) that amount of money to the tax authority. In such cases, the filing and payment frequency and deadlines are determined by the tax authority.

[0190] A challenge for businesses is that the above-mentioned software applications generally cannot provide tax information that is accurate enough for the businesses to be tax compliant with all the relevant tax authorities. The lack of accuracy may manifest itself as errors in the amounts determined to be owed as taxes to the various tax authorities, and it is plain not good to have such errors. For example, businesses that sell products and services have risks whether they over-estimate or under-estimate the sales tax due from a sale transaction. On the one hand, if a seller over-estimates the sales tax due, then the seller collects more sales tax from the buyers than was due. Of course, the seller may not keep this surplus sales tax, but instead must pay it to the tax authorities — if the seller cannot refund it to the buyers. If a buyer later learns that they paid unnecessarily more sales tax than was due, the seller risks at least harm to their reputation. Sometimes the buyer will have the option to ask the state for a refund of the excess tax by sending an explanation and the receipt, but that is often not done as it is too cumbersome for the amounts of money involved. On the other hand, if a seller under-estimates the sales tax due, then the seller collects less sales tax from the buyers, and therefore pays less sales tax to the authonties than was actually due. That is an underpayment of sales tax that will likely be discovered later, if the tax authority audits the seller. Then the seller will be required to pay the difference, plus fines and/or late fees, because ignorance of the law is not an excuse. Further, one should note that sales taxes may be considered trust-fund taxes, meaning that the management of a company may be held personally liable for the unpaid sales tax. [0191] For sales in particular, making correct determinations for sales and use tax is even more complex, and therefore difficult. There are a number of factors that contribute to the complexity. [0192] First, some state and local tax authorities have origin-based tax rules, while others have destination-based tax rules. Accordingly, a sales tax may be charged from the seller’s location, meaning according to the rules of the tax authority of the seller, or from the buyer’s location, meaning according to the rules of the tax authority of the buyer.

[0193] Second, the various tax authorities assess different, i.e., non-uniform, percentage rates of the sales price as sales tax, for the purchase and sale of items that involve their various tax jurisdictions. These tax jurisdictions include various states, counties, cities, municipalities, special taxing jurisdictions, and so on. As the United States switched, largely but not completely, from primarily origin-based sales tax to destination-based tax, the number of tax jurisdictions rapidly multiplied, and the incentives for local governments to implement new and varied tax rules and ever smaller jurisdictions multiplied. As such, there are over 10,000 different tax jurisdictions in the US, with many partially overlapping. Their sizes vary from as large as many square miles to as small as a single building. In parallel, tens of thousands of tax rules and tax rates have been developed.

[0194] Third, in some instances no sales tax is due at all because of the type of item sold. For example, in 2018 selling cowboy boots was exempt from sales tax in Texas, but not in New York. This non-uniformity gives rise to numerous individual taxability rules related to various products and services across different tax jurisdictions.

[0195] Fourth, in some instances no sales tax is due at all because of who the individual buyer is, and/or what the purchase is for. For example, certain entities are exempt from paying sales tax on their purchases, as long as they properly create and sign an exemption certificate and give it to the seller for each purchase made. Entities that are entitled to such exemptions may include wholesalers, resellers, non-profit charities, educational institutions, etc. Of course, who can be exempt is not exactly the same in each tax jurisdiction. And, even when an entity is entitled to be exempt, different tax jurisdictions may have different requirements for the certificate of exemption to be issued and/or remain valid. And, certificates of exemption may expire after some time, and may need to be renewed or reissued.

[0196] Fifth, it may be hard to determine which tax authorities a seller owes sales tax to. A seller may start with tax jurisdictions that it has a physical presence in, such as a main office, a distribution center or warehouse, an employee working remotely, and so on. Such ties with a tax jurisdiction establish the so-called physical nexus. However, a tax authority such as a state or even a city may set its own nexus rules for when a business is considered to be '‘engaged in business’' with it, and therefore that business is subject to registration and collection of sales taxes. These nexus rules may include different types of nexus, such as affiliate nexus, clickthrough nexus, cookie nexus, economic nexus with thresholds, and so on. For instance, due to economic nexus, a remote seller may owe sales tax for sales made in the jurisdiction that are a) above a set threshold volume, and/or b) above a set threshold number of sales transactions.

[0197] The economic nexus mentioned above may be even more complicated. Even where a seller might not have reached any of the thresholds for economic nexus, a number of states are promulgating marketplace facilitator laws that sometimes use such thresholds. According to such laws, intermediaries that are characterized as marketplace facilitators per laws of the state may have an obligation, instead of the seller, to collect sales tax on behalf of their sellers, and remit it to the state. The situation becomes even more complex when a seller sells directly to a state, and also via such an intermediary.

[0198] To help with such complex determinations, the computer system 1295 may be specialized for tax compliance. For instance, the computer system 1295 thus implements a tax engine 1283 to make the determinations of tax obligations. The tax engine 1283 may be as described for the service engine 283.

[0199] For a specific determination of a tax obligation, the computer system 1295 may receive one or more datasets. A sample received dataset 1235 is shown below line 1299. The dataset 1235 has parameters that may also be called dataset parameters, some of which may have respective dataset values, and may be otherwise examples of what was described for the dataset 335 of FIG. 3. In this example, the computer system 1290 transmits to the OSP 1298 a request that includes a payload, and the dataset 1235 is received by the computer system 1295 parsing the received payload. In this example the single payload encodes the entire dataset 1235, but that is not required, as mentioned above.

[0200] In this example, the dataset 1235 has been received because it is desired to determine any tax obligations arising from the buy-sell transaction 1297. As such, the sample received dataset 1235 has dataset parameters with values that characterize attributes of the buy-sell transaction 1297. as indicated by a correspondence arrow 1299. Accordingly, in this example the sample received dataset 1235 has a parameter ID with a value for an identity of the dataset 1235 and/or the transaction 1297. The dataset 1235 also has a parameter PE with a value for the name of the primary entity seller 1293 or the user 1292, which may be the seller making sales transactions, some perhaps online. The dataset 1235 further has an optional parameter PD with a value for relevant data of the primary entity seller 1293 or the user 1292, such as an address, place(s) of business, prior nexus determinations with various tax jurisdictions, and so on. The parameter PD is optional because it may be possible to look up its value from the parameter PE. The dataset 1235 also has a parameter SE with a value for the name of the secondary entity buyer 1296, which may be the buyer. The dataset 1235 further has a parameter SD with a value for relevant data of the secondary entity buyer 1296, entity-driven exemption status, and so on. In some instances, the parameter SD is optional, similarly with the parameter PD. The dataset 1235 has a parameter B2 with a numerical value for the sale price of the item sold. The dataset 1235 may further have additional dataset parameters, as indicated by the dot-dot-dot in the right side of the dataset 1235. These parameters may characterize further attributes, such as what item was sold, for example by a Stock Keeping Unit (SKU), how many units of the item were sold in the buysell transaction 1297, a date and possibly also time of the buy-sell transaction 1297, and so on. [0201] Then the computer system 1295 may produce the tax obligation 1279, which is akin to producing the resource 279 of FIG. 2. The tax obligation 1279 is due to the tax authority 1281. In some instances, the tax obligation 1279 is fulfilled by the user 1292 using the computer system 1290 to access the computer system 1282. In some instances, the tax obligation 1279 is fulfilled by the computer system 1295 accessing the computer system 1282 on behalf of the user 1292.

[0202] In the online implementation of FIG. 12, the OSP 1298 has a database 1294 for storing entity data. This entity data may be inputted by the user 1292, and/or caused to be downloaded or uploaded by the user 1292 from the computer system 1290 or from the OPF 1289, or extracted from the computer system 1290 or from the OPF 1289, and so on. In other implementations, a simpler memory configuration may suffice for storing the entity' data.

[0203] Digital tax content 1286 is further implemented within the OSP 1298. The digital tax content 1286 may be a utility that stores digital tax rules for use by the tax engine 1283. As part of managing the digital tax content 1286, there may be continuous updates of the digital tax rules, by inputs gleaned from the tax authority 1281. Updating may be performed by humans, or by computers, and so on. The tax authority may include tax jurisdictions such as a country, a state, a county, a city, a municipality, etc., and may correspond to domains discussed earlier in this document.

[0204] FIG. 13 is a block diagram illustrating an example OSP 1398 in communication with an example content management platform component (such as content management platform (CMP) 1360). Content management platform 1360 is a particular example of content management platform 360 as integrated into an example technical environment via a an electronic communication pathway to an example tax engine 1383 of the OSP 1398 and via a an electronic communication pathway to an example plurality of tax authorities 1380, according to various embodiments described herein. In various embodiments, content management platform 1360 may be in electronic communication with multiple different tax service engines of the OSP 1398 and/or of other OSPs. Also, in some embodiments, content management platform 1360 may be part of the OSP 1398.

[0205] FIG. 13 repeats some of the elements described in FIG. 12. As such, explanations from one of these two diagrams may apply also to the other. It is just that FIG. 13 has a different emphasis than FIG. 12.

[0206] FIG. 13 is a diagram for an operational example where a buy-sell transaction 1397 is a use case of the relationship instance 397. The transaction 1397 is conducted between a primary entity 1393, which is a seller, and a secondary entity 1396, which is a buyer. The transaction 1397 is therefore a buy-sell transaction between them, for instance for a physical item, but it may be a non-physical item such as a digital item, a specific right, and so on. A tax obligation 1379 often arises from the buy-sell transaction 1397 — in particular a sales and/or use tax must be paid by either the primary entity seller 1393 or the secondary entity' buyer 1396. A computation of the tax obligation 1379 is a use case of producing the resource 379.

[0207] It will be recognized that aspects of FIG. 13 have similarities with aspects of FIG. 3. Portions of such aspects may be implemented as described for analogous aspects of FIG. 3. In particular, a thick horizontal line 1315 separates FIG. 13, although not completely or rigorously. Above the line 1315 are shown elements with emphasis mostly on entities, components, their relationships, and their interactions, while below the line 1315 are shown elements with emphasis mostly on processing of data that takes place often within one or more of the components that are shown above the line 1315.

[0208] Above the line 1315, a computer system 1395 is shown, which is used to help customers, such as a user 1392, with tax compliance. For instance, the user 1392 may log into the computer system 1395 by using credentials, such as a user name, a password, a token, and so on. Further in this example, the computer system 1395 is part of an OSP 1398 that is implemented as a Softw are as a Service (SaaS) provider, for being accessed by the user 1392 online. As such, the OSP 1398 may be an online service provider for clients. Alternately, the functionality of the computer system 1395 may be provided locally to a user.

[0209] The user 1392 may be standalone. The user 1392 may use a computer system 1390 that has a screen 1391. In embodiments, the user 1392 and the computer system 1390 are considered part of the primary entity seller 1393, which is also known as primary entity seller 1393. The primary entity seller 1393 may be a business, such as a seller of items, a reseller, a buyer, a service business, and so on. In such instances, the user 1392 may be an employee, a contractor, or otherwise an agent of the entity 1393.

[0210] The secondary entity buyer 1396 may be an organization, a person, and so on. The buyer 1396 has a device 1332 with a screen 1333. The secondary entity buyer 1396 may have used a device such as the device 1332 for the buy-sell transaction 1397.

[0211] The buy-sell transaction 1397 may involve an operation, such as an exchange of data to form an agreement. This operation may be performed in person, or over a network 1388, which may be as described elsewhere for communications networks, etc. In such cases the primary entity⁷ seller 1393 may even be an online seller, but that is not necessary'. The buy-sell transaction 1397 will have data that is known to the primary entity' seller 1393, similarly with what was described by the relationship instance 397 of FIG. 3.

[0212] In a number of instances, the user 1392 and/or the entity 1393 use software applications to manage their business activities, such as sales, resource management, production, inventory management, delivery⁷, billing, and so on. The user 1392 and/or the primary⁷ entity seller 1393 may further use accounting applications to manage purchase orders, sales invoices, refunds, payroll, accounts payable, accounts receivable, and so on. Such software applications, and more, may be used locally by⁷ the user 1392, or from an Online Processing Facility (OPF) 1389 that has been engaged for this purpose by the user 1392 and/or the primary⁷ entity⁷ seller 1393. The OPF 1389 may be analogous to the OPF 389. In such use cases, the OPF 1389 may be a Mobile Payments system, a Point Of Sale (POS) system, an Accounting application, an Enterprise Resource Planning (ERP) provider, an e-commerce provider, an electronic marketplace, a Customer Relationship Management (CRM) system, and so on.

[0213] Businesses have tax obligations to various tax authorities of respective tax jurisdictions. It is often challenging to even determine what taxes are owed and to whom, because the underlying statutes and tax rules and guidance issued by the tax authorities are very⁷ complex. There are various types of tax, such as sales tax, use tax, excise tax, value-added tax, and issues about cross-border taxation including customs and duties, and many more. Some types of tax are industry specific. Each type of tax has its own set of rules. Additionally, statutes, tax rules, and rates change often, and new tax rules are continuously added. Compliance becomes further complicated when a taxing authority⁷ offers a temporary⁷ tax holiday, during which certain taxes are waived.

[0214] To help with such complex determinations, the computer system 1395 may be specialized for tax compliance. The computer system 1395 may have one or more processors and memory, for example as was described for the computer system 395 of FIG. 3. The computer system 1395 thus implements a tax engine 1383 to make the determinations of tax obligations. The tax engine 1383 may be as described for the service engine 383.

[0215] The computer system 1395 may further store locally entity data, i.e. data of user 1392 and/or of primary entity seller 1393, either of which/whom may be a customer, and/or a seller or a buyer in a sales transaction. The entity data may include profile data of the customer, and transaction data from which a determination of a tax obligation is desired. In the online implementation of FIG. 13, the OSP 1398 has a database 1394 for storing the entity data. This entity data may be inputted by the user 1392, and/or caused to be downloaded or uploaded by the user 1392 from the computer system 1390 or from the OPF 1389, or extracted from the computer system 1390 or from the OPF 1389, and so on. In other implementations, a simpler memory configuration may suffice for storing the entity data.

[0216] A digital tax content 1386 is further implemented within the OSP 1398. The digital tax content 1386 may be a utility’ that stores digital tax rules 1370 for use by the tax engine 1383. As part of managing the digital tax content 1386, there may be continuous updates of the digital tax rules, by inputs gleaned from a set 1380 of different tax authorities 1381, 1382. ... . Updating may be performed by humans, or by computers, and so on. As mentioned above, the number of the different tax authorities in the set 1380 may be very large. In such use cases, tax jurisdictions such as a country', a state, a county, a city, a municipality, etc. correspond to domains discussed earlier in this document.

[0217] For a specific determination of a tax obligation, the computer system 1395 may receive one or more datasets. A sample received dataset 1335 is shown below line 1315. The dataset 1335 has parameters that may also be called dataset parameters, some of which may have respective dataset values, and may be otherwise examples of what was described for the dataset 335 of FIG. 3. In this example, the computer system 1390 transmits a request 1384 that includes a payload 1334, and the dataset 1335 is received by the computer system 1395 parsing the received payload 1334. In this example the single payload 1334 encodes the entire dataset 1335, but that is not required, as mentioned above.

[0218] The digital tax rules 1370 are digital in that they are implemented for use by software, similarly with these rules 370. The digital tax rules 1370 may be created so as to accommodate legal tax rules that the set 1380 of different tax authorities 1381, 1382 ... promulgate to apply within the boundaries of their tax jurisdictions. In the example of this diagram, only one sample digital tax rule is shown explicitly, namely rule T_RULE4 1374. In this diagram, all other such rules are indicated by the vertical dot-dot-dots. [0219] Then the computer system 1395 may select a certain one of the digital tax rules 1370. In this example, the rule T_RULE4 1374 is thus selected. The selection of this particular rule is indicated also by the fact that an arrow 1378 begins from that rule. The arrow 1378 is similar to the arrow 378.

[0220] The computer system 1395 may thus select the certain rule T RULE4 1374 responsive to one or more of the dataset values of the dataset parameters of the dataset 1335. The impact of the dataset 1335 in the selection is indicated by at least some of the arrows 1371. similarly with the arrow s 371. For example, it may be recognized that a condition of the digital tax rule T_RULE4 1374 is met by one or more of the values of the dataset parameters of the dataset 1335. For instance, it may be further determined that, at the time of the sale, the secondary entity buyer 1396 is located within the boundaries of a tax jurisdiction, that the primary’ entity seller 1393 has nexus with that tax jurisdiction, and that there is no tax holiday.

[0221] As such, the computer system 1395 may produce the tax obligation 1379, which is akin to producing the resource 379 of FIG. 3. The tax obligation 1379 may be produced by the computer system 1395 applying the certain digital tax rule T RULE4 1374, as indicated by the arrow 1378. The impact of the dataset 1335 in producing the tax obligation 1379 is indicated by at least one of the arrows 1371. In this example, the identified certain digital tax rule T_RULE4 1374 may specify that a sales tax is due, that the amount is to be determined by a multiplication of the sale price of the value of the parameter B2 by a specific rate, the tax return form that needs to be prepared and filed, a date by which it needs to be filed, and so on.

[0222] The computer system 1395 may then cause a notification 1336 to be transmitted. In the example of FIG. 13, the notification 1336 is caused to be transmitted by the computer system 1395 as an answer to the received dataset 1335. The notification 1336 may be about an aspect of the tax obligation 1379, similarly with the notification 336 of FIG. 3. For instance, the notification 1336 may inform that the tax obligation 1379 has been determined, where it may be found, what it is, or at least a portion or a statistic of its content, and so on.

[0223] The notification 1336 may be transmitted to one of an output device and another device that may be the remote device, from which the dataset 1335 w as received. The output device may be the screen of a local user or a remote user. The notification 1336 may thus cause a desired image to appear on the screen, such as within a Graphical User Interface (GUI) and so on. The other device may be a remote device, as in this example. In particular, the computer system 1395 causes the notification 1336 to be communicated by being encoded as a payload 1337. which is carried by a response 1387. The response 1387 may be transmitted via a communications network 1388 responsive to the received request 1384. The communications network 1388 may be as described for the communications network 388, even the same network. The response 1387 may be transmitted to the computer system 1390, or to the OPF 1389, and so on. As such, the other device may be the computer system 1390, or a device of the OPF 1389, or the screen 1391 of the user 1392, and so on. In this example the single payload 1337 encodes the entire notification 1336, but that is not required, similarly with what is written above about encoding datasets in payloads. Of course, along with the aspect of the tax obligation 1379. it is advantageous to embed in the payload 1337 the ID parameter and/or one or additional more parameters of the dataset 1335. This will help the recipient correlate the response 1387 that they receive to their request 1384, and therefore match the received aspect of the tax obligation 1379 as the answer to the transmitted dataset 1335.

[0224] The digital tax rules 1370 may be implemented or organized in different ways. For example, these digital tax rules 1370 may have applicability⁷ conditions that relate to geographical boundaries, effective dates with possible temporary exceptions, item classification into categories, differently -treated parties, and so on, for determining where and when a certain digital tax rule is to be selected and applied, to determine the tax obligation 1379. These conditions may be expressed as logical conditions with ranges, dates, other data, and so on. Values of the dataset parameters of the dataset 1335 may be iteratively tested against these logical conditions according to arrows 1371. In such cases, the applicable tax rules may indicate how to compute one or more tax obligations, such as to indicate different types of taxes that are due, rules, rates, exemption requirements, reporting requirements, remittance requirements, the actual amounts of tax obligations, etc.

[0225] As with the digital resource rules 370, the digital tax rules 1370 may also be complex.

While a certain one of these digital resource rules is eventually selected and applied to determine the tax obligation, more than one of them may be used for selecting that certain one.

[0226] FIG. 14 is a diagram showing details and aspects of different types of possible embodiments of the digital tax rules of the use cases of FIG. 13.

[0227] Referring now to FIG. 14, a dataset 1435 may be as described for the dataset 1335 of FIG. 13. In addition, a set 1470 of digital tax rules is an example of the digital tax rules 1370 of FIG. 13. A tax obligation 1479 may be produced according to the arrow 1478. The tax obligation 1479 may be as described for the tax obligation 1379. And it will be further recognized that FIG. 14 has many similarities with FIG. 4. This is intentional, so that portions of the explanations for FIG. 4 also apply to FIG. 14.

[0228] The set 1470 of digital tax rules shows examples for digital tax rules, such as the digital tax rules 1370 of FIG. 13. The set 1470 of digital tax rules includes different subsets, into which the individual rules belong. In addition, there may be hierarchical relationships among rules of different subsets, and/or of t pes. In this example, the set 1470 includes a subset 1480 of tax authority selecting rules. The set 1470 also includes subsets 1472, 1473, 1474, ... , each for digital tax rules for tax authorities A, B, C, ... respectively. A tax authority for which a subset of tax rules is thus provided may be associated with the primary entity seller 1393, another tax authority may be associated with the secondary entity buyer 1396, and so on. In cases where digital tax rules are provided only for the tax authority A. the tax obligation 1479 will be determined by starting from an arrow 1471 A.

[0229] The subset 1480 includes rules 1481, 1482, 1483, ... . The subset 1480 may be invoked, e.g. per an arrow 1471B, when multiple jurisdictions are candidates. These rules may select which tax jurisdiction’s rules will be applied, e.g. per an arrow 1471C. For instance, the rules of the subset 1480 may be used to resolve whether a sales tax determination will be origin-based or destination-based. This may depend on appropriate rules of the tax jurisdictions themselves. Then the rules of the subset 1480 may point to the digital tax rules of one or more tax authorities whose legal tax rules must be followed. For instance, and as mentioned above, a buy-sell transaction may be burdened by a sales tax from the tax jurisdictions of a state and of a city. These may invoke, respectively, the subsets 1472 and 1473.

[0230] Digital tax rules for individual tax authorities are now⁷ described. Such rules need not be the same for each tax authority, or of the same type for each domain. The sample subset 1472 of digital tax rules for tax authority A is now described in more detail. Its description may be similar for subsets for other domains, such as the subsets 1473, 1474, ... .

[0231] The subset 1472 includes different types of rules. In this example, the subset 1472 includes tax precedence rules 1420, tax computation rules 1430, and tax override rules 1440. In this example, the tax precedence rules 1420 include rules 1421, 1422. 1423, ... . The tax computation rules 1430 include rules 1431, 1432, 1433, ... . The tax override rules 1440 include rulesl441, 1442, 1443, ... .

[0232] In embodiments, one of the tax computation rules 1430 may ordinarily be selected as the certain digital tax rule, which in FIG. 13 is shown as rule T RULE4 1374. For instance, it may specify a percentage tax rate for the sales tax. The tax obligation 1479 may be the percentage rate. Or, the purchase price (base value of the parameter B2) may be further learned from the dataset 1435, e.g. per an arrow 1471D, and the tax obligation 1479 may be the sales tax amount, produced by multiplying the percentage rate by the purchase price.

[0233] In addition, although not always required, the different types of rules within the subset 1472 further have different hierarchies among them. [0234] For a first instance, one of the tax precedence rules 1420 may indicate which one of the tax computation rules 1430 is to be selected, as generally indicated by an arrow 1429. As an example, one of the tax precedence rules 1420 may decide the taxability of a specific item indicated in the dataset 1435. Such a tax precedence rule may implement, therefore, an item classification task. The answer may be no sales tax, or different sales tax depending on different categories. For instance, bagels may be taxed differently depending on whether or not they are sold with utensils, based on whether or not they are pre-sliced when sold, and so on. Then the precedence rule may indicate which one of the tax computation rules 1430 is the appropriate one to use for the computation of the sales tax. As another example, one of the tax precedence rules 1420 may indicate that there is a temporary sales tax holiday in a tax jurisdiction on the day of the transaction, in which case the sales tax for the transaction 1397 of FIG. 13 will be zero, and the tax obligation will be computed accordingly. As one more example, one of the tax precedence rules 1420 may indicate that there is no economic nexus for this transaction which, alone or in combination with other nexus determinations, may determine that no sales tax will be imposed.

[0235] For a second instance, even when one of the tax computation rules 1430 is thus indicated, one of the tax override rules 1440 may still override the indication, as generally indicated by an arrow 1449. As an example, one of the tax override rules 1440 may indicate that a party⁷ is exempt from paying sales tax because certain conditions are met, for instance if they have a valid and current exemption certificate. As another example, rules for implementing cases where the sales tax computation is overridden and no sales tax is due, such as with a tax holiday or lack of economic nexus, may instead be implemented as the tax override rules 1440.

[0236] FIG. 15 through FIG. 33 are sample views of User Interfaces (UIs) for content management platform 1360 shown on a screen of computer system 1395. computer system 1295, computer system 295 and/or computer system 395, or an applicable remote device connected to such computer systems. The UIs show, in chronological order, an example workflow of a user viewing the content sourcing results and initiating the extracting, organizing and publishing operations of content management platform 1360 that extract, organize and publish content and/or tax rules from remote data sites (e.g.. web sites) for consumption by service engines of the OSP 1398 for the computation of taxes and electronic performance of other tax compliance services described herein by the OSP 1398. In various embodiments, the CMP 1360 obtains data and/or detects changes to data, and then recognizes the data (or the detected changes to the data) as potentially relevant for updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances. Input received via the UIs shown in FIG. 15 through FIG. 33 indicating validation or rejection of the data or the detected changes to the data as relevant may be used in a feedback loop to the CMP 160 and/or machine learning model of the CMP as training data for the CMP 1 0 and/or machine learning model of the CMP to improve future determinations of which data (or which changes to such data) may be potentially relevant. In various embodiments, such data may include images and the determination made by the system of whether such data or changes in data are relevant may include object recognition to extract relevant data or identify objects in the images (e.g., to identify relevant products, product codes, etc ). Such a feedback loop thus improves the object recognition and extraction of relevant image data to detect which images and data extracted therefrom is relevant.

[0237] FIG. 15 is a sample view of UI 1500 for the content management platform component, such as content management platform 1360, displayed on a screen 1591 that displays an interactive content management platform dashboard. UI 1500 enables the user to view active projects, sourcing activities and sourcing tasks regarding various sources (e.g., remote data sites) from which content management platform 1360 has received (e.g., via automated web site scraping) potentially relevant data for updating one or more of: content for producing resources (e.g., tax amounts) associated with relationship instances (e.g., transactions) and digital rules for producing resources associated with relationship instances.

[0238] FIG. 16 is a sample view of UI 1600 for the content management platform component, such as content management platform 1360. displayed on a screen 1691 that displays an inbox of interactive content management platform sourcing tasks for a particular user. For example, UI 1600 may be displayed in response to the user selecting the “review my sourcing tasks” interactive UI element on UI 1500.

[0239] FIG. 17 is a sample view of UI 1700 for the content management platform component, such as content management platform 1360, displayed on a screen 1791 that displays interactive content management platform elements to review source document changes. For example, UI 1700 may be displayed in response to the user selecting the “Get started” interactive UI element on UI 1600 for the first remote data site listed on UI 1600.

[0240] FIG. 18 is a sample view of UI 1800 for the content management platform component, such as content management platform 1360, displayed on a screen 1891 that displays interactive content management platform elements to review source document changes to a specific document. Shown side-by-side is the original document and the changed document with portions that have been changed highlighted for the user. In an example embodiment, UI 1800 may be displayed in response to the user selecting the “View difference” interactive UI element on UI 1700 for the first remote source document listed on UI 1700.

[0241] FIG. 19 is a sample view of UI 1900 for the content management platform component, such as content management platform 1360, displayed on a screen 1991 that displays a summary of a specific document to which changes were made. For example, UI 1900 may be displayed in response to the user selecting the “View a summary of the document before deciding” interactive UI element on UI 1800.

[0242] FIG. 20 is a sample view of UI 2000 for the content management platform component, such as content management platform 1360, displayed on a screen 2091 that displays interactive UI elements for the user to accept findings regarding the changes to the document that were detected by the content management platform component. For example, such findings may include, but are not limited to: date change discovered, authority typejurisdiction, change type, link to the relevant law, change effective date(s), applicable regulation section affected, task information, etc.). The user may then edit and accept such findings and accept the changes to the document as valid, relevant and/or applicable via UI 2000. In an example embodiment, UI 2000 may be displayed in response to the user selecting the “Does this document have valid changes” interactive UI element on UI 1800 or UI 1900.

[0243] FIG. 21 is a sample view of UI 2100 for the content management platform component, such as content management platform 1360, displayed on a screen 2191 that displays interactive UI elements for a content management platform ticket builder for further processing of the valid document change by the content management platform system. In an example embodiment, UI 2100 may be displayed in response to the user selecting the “Save” interactive UI element on UI 2000 to save the valid changes information for the particular document that was detected as having been changed.

[0244] FIG. 22 is a sample view of UI 2200 for the content management platform component, such as content management platform 1360, displayed on a screen 2291 that displays interactive UI elements for a user to create a task regarding a particular change to a specific document (e.g., adding a note for the particular change). In an example embodiment, UI 2200 may be displayed in response to the user selecting the “Add note” interactive UI element on UI 2100.

[0245] FIG. 23 is a sample view of UI 2300 for the content management platform component, such as content management platform 1360, displayed on a screen 2391 that displays interactive UI elements for a user to submit a task regarding a particular change to a specific document. In an example embodiment. UI 2300 may be displayed in response to the user selecting the “Save” interactive UI element on UI 2200. [0246] FIG. 24 is a sample view of UI 2400 for the content management platform component, such as content management platform 1360. displayed on a screen 2491 that displays interactive UI elements for submitting findings after one or more tasks have been submitted regarding a particular changes to one or more specific documents. Shown is an interactive list of documents identified by source name and source link for which changes have been detected and respective status indicators indicating whether the changes for that document have been validated by the user and are ready to submit. In an example embodiment. UI 2400 may be displayed in response to the user selecting the “Submit” interactive UI element on UI 2300.

[0247] FIG. 25 is a sample view of UI 2500 for the content management platform component, such as content management platform 1360, displayed on a screen 2591 that displays interactive UI elements for reviewing changes to one or more specific documents. In an example embodiment, UI 2500 may be displayed in response to or after the user selecting the “View difference” interactive UI element on UI 2400 for the second document listed identified by “WA City of Bridgeport Local Sales Tax Change” source name on UI 2400.

[0248] FIG. 26 is a sample view of UI 2600 for the content management platform component, such as content management platform 1360, displayed on a screen 2691 that displays interactive UI elements for submitting findings after one or more tasks have been submitted regarding a particular changes to additional specific documents. Shown is an interactive list of documents identified by source name and source link for which changes have been detected and respective status indicators indicating w hether the changes for that document have been validated by the user and are ready to submit. In an example embodiment, UI 2600 showing changes have been validated for the second document listed identified by the “WA City of Bridgeport Local Sales Tax Change” source name may be displayed in response to the user selecting the “Submit” interactive UI element on UI 2500.

[0249] FIG. 27 is a sample view of UI 2700 for the content management platform component, such as content management platform 1360, displayed on a screen 2791 that displays interactive UI elements for viewing content management task inboxes for a particular user. Shown is an interactive list of tasks identified by task name and task type. In an example embodiment, UI 2700 may be displayed in response to the user selecting the “Content Management” interactive UI element on UI 1600.

[0250] FIG. 28 is a sample view of UI 2800 for the content management platform component, such as content management platform 1360, displayed on a screen 2891 that displays interactive UI elements for viewing content management details for a particular content management task. For example, UI 2800 interactively displays and enables the user to review and validate the entity ty pe and product/service ty pe database items the machine learning model of the content management platform selected as being applicable to and/or affected by the detected change. This functionality is provided via displaying interactive review sub-tasks for the detected document change. In an example embodiment, UI 2800 may be displayed in response to the user selecting the “Get started” interactive UI element on UI 2700 for the first item listed in the content management inbox shown on UI 2700.

[0251] FIG. 29 is a sample view of UI 2900 for the content management platform component, such as content management platform 1360, displayed on a screen 2991 that displays interactive UI elements for reviewing corresponding database changes related to a particular detected document change. In an example embodiment, UI 2900 may be displayed in response to the user selecting the “Begin review” interactive UI element on UI 2800 for the first sub-task listed in the content management inbox shown on UI 2800.

[0252] FIG. 30 is a sample view of UI 3000 for the content management platform component, such as content management platform 1360, displayed on a screen 3091 that displays interactive UI elements for editing corresponding database changes related to a particular detected document change. In an example embodiment, UI 3091 may be displayed in response to the user selecting the “Edit” interactive UI element on UI 2900.

[0253] FIG. 31 is a sample view of UI 3100 for the content management platform component, such as content management platform 1360, displayed on a screen 3191 that displays interactive UI elements for verifying and publishing corresponding database changes related to a particular detected document change. In an example embodiment, UI 3100 may be displayed in response to the user selecting the “Save” interactive UI element on UI 3000.

[0254] FIG. 32 is a sample view of UI 3200 for the content management platform component, such as content management platform 1360. displayed on a screen 3291 that displays a notice that the content management platform is verifying and preparing for publication corresponding database changes related to a particular detected document change. For example, this verifying and preparing for publication may include the content management platform organizing and publishing such data in a format for automated consumption by one or more tax service engines for computing tax and performing other services associated with buy-sell transactions as described herein. In an example embodiment, UI 3200 may be displayed in response to the user selecting the “Verify⁷ and publish” interactive UI element on UI 3100.

[0255] FIG. 33 is a sample view of UI 3200 for the content management platform component, such as content management platform 1360, displayed on a screen 3391 that displays details of a digital tax rule that is the result of the content management platform verifying and preparing for publication corresponding database changes related to a particular detected document change. The content management platform 1360 has translated one or more particular portions of the data corresponding to the detected change in the document associated with a particular aspect of one or more digital rules from narrative textual content into machine readable computer code (e.g., JSON code) that is able to be electronically consumed via an Application Programming Interface (API) by the one or more service engines for producing resources (e.g., tax amounts or other tax related services) associated with buy-sell transactions. LU 3200 displays the actual code of the updated digital rule in a code window as well as entities affected by or related to the change (e.g., taxability rule, etc.), as well as a download option to download the code. In an example embodiment, UI 3300 may be displayed in response to the user selecting the ‘‘Verify and publish" interactive UI element on UI 3100 and after the notice in UI 3200 indicates the publishing process is complete.

[0256] Software aspects of embodiments may include instructions for processors, methods of operation, datasets, interfaces, user interfaces (UIs), applications, Application Programming Interfaces (APIs), connectors, and the like.

[0257] Software aspects or modules of embodiments may be hosted on any suitable machine, anyw ere. For example, such software aspects may be hosted on a computer system, a desktop computer, an on-location server, a machine that is located remotely to where other processes are executed, such as in the cloud or on the premises of a provider, a memory of such, and so on. The software may be accessible by a user via a browser, a UI. an API, etc. Depending on where hosted, some software components or modules may be considered a client, etc.

[0258] Types of embodiments include at least:

[0259] new systems, machines, devices, computers, portable or not, mobile telephones, etc. configured to perform the above operations, run the above-mentioned software, implement the above-mentioned methods, etc.;

[0260] new software that may be additionally implemented in existing systems, machines, devices, computers, portable or not, mobile telephones, etc. ;

[0261] new user interface appearances and sequences that may be additionally implemented in existing systems, machines, devices, computers, portable or not, mobile telephones, etc., while performing the above-mentioned operations;

[0262] new storage media that store instructions w hich, when read and executed by one or more processors of systems, machine, devices, computers, portable or not, mobile telephones, etc., result in the actions/operations described above to be performed, user interfaces to appear to users, etc.; and [0263] new methods, operations, functions, processes, acts and methods implemented by systems, machines, devices, computers, portable or not, mobile telephones, etc.

[0264] Importantly, although the operational and or functional descriptions of this document are understandable by the human mind, they are not abstract ideas of the operations/functions divorced from computational implementation of those operations/functions. Rather, the operations/functions represent a specification for complex computational machines or other means. As discussed in detail elsewhere in this document, each time the operational/functional language must be read in its proper technological context, i.e., as concrete specifications for physical implementations. Far from being understood as an abstract idea, it may be recognized that a functional/operational technical description as a humanly-understandable representation of one or more almost unimaginably complex and time sequenced hardware instantiations.

[0265] Moreover, the methods, algorithms, operations, functions and acts described in this document are not necessarily inherently associated with any particular logic device or other apparatus. Rather, they are advantageously implemented by programs for use by any of the devices or systems described in this document. These algorithms are not necessarily purely mathematical, and are configured to address challenges particular to the problem solved, as will be apparent to a person skilled in the art.

[0266] This detailed description may include flowcharts, display images, algorithms, and symbolic representations of program operations within at least one computer readable medium. An economy may be achieved in that a single set of flowcharts may be used to describe both programs, and also methods. So, while flowcharts describe methods in terms of boxes, they may also concurrently describe programs.

[0267] In the methods described above, each operation may be performed as an affirmative step of doing, or causing to happen, what is written that may take place. Such doing or causing to happen may be by the whole system or device, or just one or more components of it. In addition, the order of operations is not constrained to what is shown, and different orders may be possible according to different embodiments. Moreover, in certain embodiments, new operations may be added, or individual operations may be modified or deleted. The added operations may be, for example, from what is mentioned while primarily describing a different system, apparatus, device or method.

[0268] This description includes one or more examples, but that does not limit how the invention may be practiced. Indeed, examples or embodiments of the invention may be practiced according to what is described, or yet differently, and also in conjunction with other present or future technologies. Other embodiments include combinations and sub-combinations of features described or shown in the drawings herein, including for example, embodiments that are equivalent to: providing or applying a feature in a different order than in a described embodiment, extracting an individual feature from one embodiment and inserting such feature into another embodiment; removing one or more features from an embodiment; or both removing one or more features from an embodiment and adding one or more features extracted from one or more other embodiments, while providing the advantages of the features incorporated in such combinations and sub-combinations. As used in this paragraph, feature or features may refer to the structures and/or functions of an apparatus, article of manufacture or system, and/or the steps, acts, or modalities of a method.

[0269] A person skilled in the art will be able to practice the present invention in view of this description, which is to be taken as a whole. Details have been included to provide a thorough understanding. In other instances, well-known aspects have not been described, in order to not obscure unnecessarily the present invention. Plus, any reference to any prior art in this description is not, and should not be taken as, an acknowledgement or any form of suggestion that this prior art forms parts of the common general knowledge in any country.

Claims

1. A method, including: electronically crawling a plurality of remote data sites concurrently over a computer network, in which each data site of the plurality of data sites is a source of data selected as potentially relevant for updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; concurrently, for each data site of the plurality of data sites: electronically obtaining current data from the data site while electronically crawling the data site; based on the current data, electronically detecting a particular change from previous data from the data site; and electronically determining, using a machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances: and electronically performing one or more actions to facilitate producing resources associated with relationship instances based on the determining, using the machine learning model, whether the detected particular change is relevant.

2. The method of claim 1 in which the electronically determining, using the machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant includes: receiving input indicating whether previous detected changes in data from one or more of the of the plurality of data sites are relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; training the machine learning model for determining whether detected changes are relevant based on the input indicating whether the previous detected changes in data are relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances: and electronically determining, using the trained machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances.

3. The method of claim 2 in which the input indicating whether previous detected changes in data are relevant is provided by a user of the machine learning model.

4. The method of claim 1 in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances includes: electronically extracting one or more particular portions of the current data in response to determining, using the machine learning model for determining whether detected changes are relevant, that the detected particular change is relevant; electronically associating, using a machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, the one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically storing the association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

5. The method of claim 4 in which the electronically associating includes: receiving input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; training the machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances based on the input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; electronically determining, using the trained machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, whether the one or more particular portions of the current data should be associated with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

6. The method of claim 4, in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically organizing, based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances, relevant portions of the current data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

7. The method of claim 6 in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by the one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources associated with relationship instances.

8. The method of claim 7, in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more service engines for producing resources associated with relationship instances.

9. The method of claim 1, further including: in response to electronically determining that the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances, electronically generating, using a generative artificial intelligence (Al) model, a textual summary that provides a narrative explaining why the detected particular change may be relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances: receiving, based on the generated textual summary', input form a user indicating whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; and training the machine learning model for determining whether detected changes in data obtained from the plurality of data sites are relevant based on the received input.

10. The method of claim 1 in which the data sites are web sites and the electronically obtaining current data includes electronically scraping data from web sites.

11. The method of claim 1 in which one or more data sites of the plurality of data sites are associated with or controlled by one or more respective domains which are authorities for generating content or rules regarding producing resources associated with relationship instances associated with the one or more respective domains.

12. A system, including: at least one processor; and a memory' coupled to the at least one processor, the memory' storing instructions that, when executed by the at least one processor, cause the system to perform operations, the operations including: electronically crawling a plurality of remote data sites concurrently over a computer network, in which each data site of the plurality of data sites is a source of data selected as potentially relevant for updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; concurrently', for each data site of the plurality of data sites: electronically obtaining current data from the data site while electronically crawling the data site; based on the current data, electronically detecting a particular change from previous data from the data site; and electronically determining, using a machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; and electronically performing one or more actions to facilitate producing resources associated with relationship instances based on the determining, using the machine learning model, whether the detected particular change is relevant.

13. The system of claim 12 in which the electronically determining, using the machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant includes: receiving input indicating whether previous detected changes in data from one or more of the of the plurality of data sites are relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; training the machine learning model for determining whether detected changes are relevant based on the input indicating whether the previous detected changes in data are relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances: and electronically determining, using the trained machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances.

14. The system of claim 13 in which the input indicating whether previous detected changes in data are relevant is provided by a user of the machine learning model.

15. The system of claim 12 in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances includes: electronically extracting one or more particular portions of the current data in response to determining, using the machine learning model for determining whether detected changes are relevant, that the detected particular change is relevant; electronically associating, using a machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, the one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically storing the association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

16. The system of claim 15 in which the electronically associating includes: receiving input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; training the machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances based on the input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; electronically determining, using the trained machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, whether the one or more particular portions of the current data should be associated with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

17. The system of claim 15, in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically organizing, based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances, relevant portions of the current data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

18. The system of claim 17 in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by the one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources associated with relationship instances.

19. The system of claim 18, in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more service engines for producing resources associated with relationship instances.

20. The system of claim 12, in which the operations further include: in response to electronically determining that the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances, electronically generating, using a generative artificial intelligence (Al) model, a textual summary that provides a narrative explaining why the detected particular change may be relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances: receiving, based on the generated textual summary', input form a user indicating whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; and training the machine learning model for determining whether detected changes in data obtained from the plurality of data sites are relevant based on the received input.

21. The system of claim 12 in which the data sites are web sites and the electronically obtaining current data includes electronically scraping data from web sites.

22. The system of claim 12 in which one or more data sites of the plurality of data sites are associated with or controlled by one or more respective domains which are authorities for generating content or rules regarding producing resources associated with relationship instances associated with the one or more respective domains.

23. A non-transitory computer-readable storage medium having computerexecutable instructions stored thereon that, when executed by at least one processor, cause a system to perform operations, the operations including: electronically crawling a plurality of remote data sites concurrently over a computer network, in which each data site of the plurality of data sites is a source of data selected as potentially relevant for updating one or more of content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; concurrently, for each data site of the plurality of data sites: electronically obtaining current data from the data site while electronically crawling the data site; based on the current data, electronically detecting a particular change from previous data from the data site; and electronically determining, using a machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; and electronically performing one or more actions to facilitate producing resources associated with relationship instances based on the determining, using the machine learning model, whether the detected particular change is relevant.

24. The non-transitory computer-readable storage medium of claim 23 in which the electronically determining, using the machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant includes: receiving input indicating whether previous detected changes in data from one or more of the of the plurality of data sites are relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; training the machine learning model for determining whether detected changes are relevant based on the input indicating whether the previous detected changes in data are relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; and electronically determining, using the trained machine learning model for determining whether detected changes are relevant, whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances.

25. The non-transilory computer-readable storage medium of claim 24 in which the input indicating whether previous detected changes in data are relevant is provided by a user of the machine learning model.

26. The non-transitory computer-readable storage medium of claim 23 in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances includes: electronically extracting one or more particular portions of the current data in response to determining, using the machine learning model for determining whether detected changes are relevant, that the detected particular change is relevant; electronically associating, using a machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, the one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically storing the association of the one or more particular portions of the cunent data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

27. The non-transitory computer-readable storage medium of claim 26 in which the electronically associating includes: receiving input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; training the machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances based on the input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; electronically determining, using the trained machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, whether the one or more particular portions of the current data should be associated with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

28. The non-transitory computer-readable storage medium of claim 26, in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically organizing, based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances, relevant portions of the current data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

29. The non-transitory computer-readable storage medium of claim 28 in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by the one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources associated with relationship instances.

30. The non-transitory computer-readable storage medium of claim 29, in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more service engines for producing resources associated with relationship instances.

31. The non-transitory' computer-readable storage medium of claim 23, in which the operations further include: in response to electronically determining that the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances, electronically generating, using a generative artificial intelligence (Al) model, a textual summary that provides a narrative explaining why the detected particular change may be relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; receiving, based on the generated textual summary', input form a user indicating whether the detected particular change is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; and training the machine learning model for determining whether detected changes in data obtained from the plurality of data sites are relevant based on the received input.

32. The non-transitory' computer-readable storage medium of claim 23 in which the data sites are web sites and the electronically obtaining current data includes electronically scraping data from web sites.

33. The non-transitory computer-readable storage medium of claim 23 in which one or more data sites of the plurality of data sites are associated with or controlled by one or more respective domains which are authorities for generating content or rules regarding producing resources associated with relationship instances associated with the one or more respective domains.

34. A method, including: electronically extracting one or more particular portions of current data in response to determining, using a machine learning model for determining whether detected changes are relevant, that a detected particular change represented by the current data is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances: electronically associating, using a machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, the one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically storing the association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

35. The method of claim 34 in which the electronically associating includes: receiving input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; training the machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances based on the input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; and electronically determining, using the trained machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, whether the one or more particular portions of the current data should be associated with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

36. The method of claim 34, further including: electronically performing one or more actions to facilitate producing resources associated with relationship instances based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

37. The method of claim 36 in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances includes: electronically organizing, based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances, relevant portions of the current data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

38. The method of claim 37 in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by the one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents the updated version of the one or more digital rules used by the one or more service engines in order to produce resources.

39. The method of claim 38, further including: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more service engines for producing resources associated with relationship instances.

40. A system, including: at least one processor; and a memory coupled to the at least one processor, the memory⁷ storing instructions that, when executed by the at least one processor, cause the system to perform operations, the operations including: electronically extracting one or more particular portions of current data in response to determining, using a machine learning model for determining whether detected changes are relevant, that a detected particular change represented by the current data is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; electronically associating, using a machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, the one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically storing the association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

41. The system of claim 40 in which the electronically associating includes: receiving input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; training the machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances based on the input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; and electronically determining, using the trained machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, whether the one or more particular portions of the current data should be associated with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

42. The system of claim 40, in which the operations further include: electronically performing one or more actions to facilitate producing resources associated with relationship instances based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

43. The system of claim 42 in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances includes: electronically organizing, based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances, relevant portions of the current data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

44. The system of claim 43 in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by the one or more service engines for producing resources associated w ith relationship instances, in which the machine readable computer code represents the updated version of the one or more digital rules used by the one or more service engines in order to produce resources.

45. The system of claim 44, in which the operations further include: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more service engines for producing resources associated with relationship instances.

46. A non-transitory computer-readable storage medium having computerexecutable instructions stored thereon that, when executed by at least one processor, cause a system to perform operations, the operations including: electronically extracting one or more particular portions of current data in response to determining, using a machine learning model for determining whether detected changes are relevant, that a detected particular change represented by the current data is relevant to updating one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; electronically associating, using a machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, the one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically storing the association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

47. The non-transitory' computer-readable storage medium of claim 46 in which the electronically associating includes: receiving input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; training the machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances based on the input indicating whether previous associations of portions of data with aspects of the one or more digital rules for producing resources associated with relationship instances are valid; and electronically determining, using the trained machine learning model for associating portions of data with aspects of one or more digital rules for producing resources associated with relationship instances, whether the one or more particular portions of the current data should be associated with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

48. The non-transitory computer-readable storage medium of claim 46, in which the operations further include: electronically performing one or more actions to facilitate producing resources associated with relationship instances based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances.

49. The non-transitory computer-readable storage medium of claim 48 in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances includes: electronically organizing, based on the stored association of the one or more particular portions of the current data with the particular aspect of the one or more digital rules for producing resources associated with relationship instances, relevant portions of the current data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

50. The non-transitory computer-readable storage medium of claim 49 in which the electronically performing one or more actions to facilitate producing resources associated with relationship instances further includes: electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by the one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents the updated version of the one or more digital rules used by the one or more service engines in order to produce resources.

51. The non-transitory computer-readable storage medium of claim 50, in which the operations further include: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more service engines for producing resources associated with relationship instances.

52. A method, including: electronically accessing a stored association of one or more particular portions of current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources.

53. The method of claim 52, further including: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more service engines for producing resources associated with relationship instances.

54. A system, including: at least one processor; and a memory coupled to the at least one processor, the memory⁷ storing instructions that, when executed by the at least one processor, cause the system to perform operations, the operations including: electronically accessing a stored association of one or more particular portions of current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (APT) by one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources.

55. The system of claim 54, in which the operations further include: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more service engines for producing resources associated with relationship instances.

56. A non-transitory computer-readable storage medium having computerexecutable instructions stored thereon that, when executed by at least one processor, cause a system to perform operations, the operations including: electronically accessing a stored association of one or more particular portions of current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources.

57. The non-transitory computer-readable storage medium of claim 56, in which the operations further include: electronically verifying, in a test environment, the machine readable computer code operates correctly with the one or more service engines for producing resources associated with relationship instances.

58. A method, including: electronically providing an administrative content portal to a plurality of remote data sites concurrently over a computer network for the plurality of remote data sites to electronically update one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; in response to providing the administrative content portal, receiving over a computer network from the plurality of remote data sites, current data including updates to one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; and for each remote data site of the plurality of remote data sites: electronically integrating an update received from the remote data site into an electronic database associated with the remote data site; electronically receiving from the remote data site a request for data regarding one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances from the electronic database associated with the remote data site; and in response to the request, electronically transmitting to the remote data site the update integrated into the electronic database associated with the remote data site, thereby enabling the remote data site to display the update integrated into the electronic database associated with the remote data site.

59. The method of claim 58, further including: electronically accessing a stored association of one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically organizing, based on the stored association, relevant portions of the cunent data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality' of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

60. The method of claim 58, further including: electronically accessing a stored association of one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated yvith relationship instances; and electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources.

61 . A system, including: at least one processor; and a memory' coupled to the at least one processor, the memory' storing instructions that, when executed by the at least one processor, cause the system to perform operations, the operations including: electronically providing an administrative content portal to a plurality of remote data sites concurrently over a computer network for the plurality of remote data sites to electronically update one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; in response to providing the administrative content portal, receiving over a computer network from the plurality⁷ of remote data sites, current data including updates to one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; and for each remote data site of the plurality of remote data sites: electronically integrating an update received from the remote data site into an electronic database associated with the remote data site; electronically receiving from the remote data site a request for data regarding one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances from the electronic database associated with the remote data site; and in response to the request, electronically transmitting to the remote data site the update integrated into the electronic database associated with the remote data site, thereby enabling the remote data site to display the update integrated into the electronic database associated with the remote data site.

62. The system of claim 61, in which the operations further include: electronically⁷ accessing a stored association of one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically organizing, based on the stored association, relevant portions of the current data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

63. The system of claim 61, in which the operations further include: electronically accessing a stored association of one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources.

64. A non-transitory computer-readable storage medium having computerexecutable instructions stored thereon that, when executed by at least one processor, cause a system to perform operations, the operations including: electronically providing an administrative content portal to a plurality of remote data sites concurrently over a computer network for the plurality of remote data sites to electronically update one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; in response to providing the administrative content portal, receiving over a computer network from the plurality of remote data sites, current data including updates to one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances; and for each remote data site of the plurality of remote data sites: electronically integrating an update received from the remote data site into an electronic database associated with the remote data site; electronically receiving from the remote data site a request for data regarding one or more of: content for producing resources associated with relationship instances and digital rules for producing resources associated with relationship instances from the electronic database associated with the remote data site; and in response to the request, electronically transmitting to the remote data site the update integrated into the electronic database associated with the remote data site, thereby enabling the remote data site to display the update integrated into the electronic database associated with the remote data site.

65. The non-transitory computer-readable storage medium of claim 64, in which the operations further include: electronically accessing a stored association of one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically organizing, based on the stored association, relevant portions of the cunent data for consumption by one or more service engines for producing resources associated with relationship instances by at least: identifying an electronic database associated with the one or more digital rules for producing resources associated with relationship instances; and updating the electronic database with an updated version of the one or more digital rules based on the stored association, thereby enabling a plurality of service engines for producing resources associated with relationship instances that have access to the identified database to concurrently consume the updated version of the one or more digital rules to produce resources based on applying the updated version of the one or more digital rules to a set of respective applicable relationship instances.

66. The non-transitory computer-readable storage medium of claim 64, in which the operations further include: electronically accessing a stored association of one or more particular portions of the current data with a particular aspect of one or more digital rules for producing resources associated with relationship instances; and electronically translating, based on the stored association, the one or more particular portions of the current data associated with the particular aspect of one or more digital rules from narrative textual content into machine readable computer code that is able to be electronically consumed via an Application Programming Interface (API) by one or more service engines for producing resources associated with relationship instances, in which the machine readable computer code represents an updated version of the one or more digital rules used by the one or more service engines in order to produce resources.

67. A method, including: electronically receiving a training set of data from one or more online marketplaces in which the training set of data includes, for each product of a plurality of products, product information identifying the product and an associated classification codes correlated with the product information identifying the product; electronically training, using the training set of data, a machine learning model for assigning classification codes to products; electronically receiving product information identifying a particular product; and electronically assigning a classification code to the particular product using the machine learning model.

68. The method of claim 67 in which the classification code is a Harmonized Commodity Description and Coding System (HS) code.

69. The method of claim 67 in which the product information identifying the product includes one or more of: a product identification code, a product serial number, a product name, a Universal Product Code (UPC), an Amazon Standard Identification Number (ASIN), an International Article Number, a European Article Number (EAN), an image of the product, and a description of the product.

70. The method of claim 67 in which the electronically receiving a training set of data includes electronically receiving the training set of data as part of producing respective resources associated with respective relationship instances involving each product of the plurality of products.

71. The method of claim 67, further including: electronically providing the assignment of the classification code to the particular product to be electronically consumed via an Application Programming Interface (API) by one or more service engines to enable the one or more service engines to produce resources associated with respective relationship instances involving the particular product.

72. A system, including: at least one processor; and a memory coupled to the at least one processor, the memory storing instructions that, when executed by the at least one processor, cause the system to perform operations, the operations including: electronically receiving a training set of data from one or more online marketplaces in which the training set of data includes, for each product of a plurality of products, product information identifying the product and an associated classification codes correlated with the product information identifying the product; electronically training, using the training set of data, a machine learning model for assigning classification codes to products; electronically receiving product information identifying a particular product; and electronically assigning a classification code to the particular product using the machine learning model.

73. The system of claim 72 in which the classification code is a Harmonized Commodify Description and Coding System (HS) code.

74. The system of claim 72 in which the product information identify ing the product includes one or more of: a product identification code, a product serial number, a product name, a Universal Product Code (UPC), an Amazon Standard Identification Number (ASIN), an International Article Number, a European Article Number (EAN), an image of the product, and a description of the product.

75. The system of claim 72 in which the electronically receiving a training set of data includes electronically receiving the training set of data as part of producing respective resources associated with respective relationship instances involving each product of the plurality⁷ of products.

76. The system of claim 72, in which the operations further include: electronically providing the assignment of the classification code to the particular product to be electronically consumed via an Application Programming Interface (API) by one or more service engines to enable the one or more service engines to produce resources associated with respective relationship instances involving the particular product.

77. A non-transitory computer-readable storage medium having computerexecutable instructions stored thereon that, when executed by at least one processor, cause a system to perform operations, the operations including: electronically receiving a training set of data from one or more online marketplaces in which the training set of data includes, for each product of a plurality of products, product information identifying the product and an associated classification codes correlated with the product information identifying the product; electronically training, using the training set of data, a machine learning model for assigning classification codes to products; electronically receiving product information identifying a particular product; and electronically assigning a classification code to the particular product using the machine learning model.

78. The non-transitory computer-readable storage medium of claim 77 in which the classification code is a Harmonized Commodity Description and Coding System (HS) code.

79. The non-transitory computer-readable storage medium of claim 77 in which the product information identify ing the product includes one or more of: a product identification code, a product serial number, a product name, a Universal Product Code (UPC), an Amazon Standard Identification Number (ASIN), an International Article Number, a European Article Number (E AN), an image of the product, and a description of the product.

80. The non-transitory computer-readable storage medium of claim 77 in which the electronically receiving a training set of data includes electronically receiving the training set of data as part of producing respective resources associated with respective relationship instances involving each product of the plurality of products.

81. The non-transitory computer-readable storage medium of claim 77, in which the operations further include: electronically providing the assignment of the classification code to the particular product to be electronically consumed via an Application Programming Interface (API) by one or more service engines to enable the one or more service engines to produce resources associated with respective relationship instances involving the particular product.