DETAILED ACTION
This non-final Office action is responsive to Applicant’s election filed October 6, 2025. Applicant has elected Group I without traverse. Claims 25-40 stand as withdrawn. Claims 1, 3-5, and 7-24 are examined below (including the incorporated claim amendments made on October 6, 2025, which cancelled claims 2 and 6 and amended claims 1, 3, 5, 7-8, and 23-24).
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-5, and 7-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1, 3-5, and 7-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to “systems and methods for extracting data with context, and more specifically to generating and using enterprise knowledge graphs comprising conceptual, structural, and behavioral knowledge associated with the enterprise” (Spec: ¶ 1) without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 1, 3-5, and 7-22), Apparatus (claim 23), Article of Manufacture (claim 24)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 1, 23, 24] A method/operations for generating a knowledge graph, the method/operations comprising:
receiving first input data comprising a first set of data components related to one or more entities from one or more data sources;
determining based upon the first input data, a second set of data components, wherein the second set of data components comprises a first derived component derived based on a first processing operation performed using the first input data;
identifying one or more relationships between the first set of data components and the second set of data components; and
generating a knowledge graph comprising a plurality of nodes, wherein a first node of the knowledge graph represents a first respective data component of the first set of data components and a second node of the knowledge graph represents a second respective data component of the second set of data components, and wherein the first node is associated with the second node, the association defined by one or more of the identified one or more relationships;
determining a third set of data components, wherein the third set of data components comprises the result of a second processing operation performed using the generated knowledge graph; and
incorporating the third set of data components into the generated knowledge graph.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can gather data components from data sources, determine other relevant data components, identify relationships among the data components, generate a knowledge graph, determine additional data components, incorporate the additional data components into a knowledge graph, etc. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process may be related to business operations analysis and sales analysis (as evidenced by the specific type of data defined in claim 9), which (under its broadest reasonable interpretation) is an example of business relations and sales activities (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
Choosing which data to incorporate into a knowledge graph based on limited information from nodes (as recited in the claims) is an example of filtering content. MPEP § 2106.04(a)(2)(II)(C) cites the following as an example of managing personal behavior, i.e., organizing human activity: “filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis).” MPEP § 2106.04(a)(2)(III)(D) cites the following as an example of a mental process: “An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356.”
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 1 uses one or more processors to perform various operations of the claim.
Claim 23 recites a system for generating a knowledge graph, the system comprising one or more processors configured to cause the system to perform the recited operations. Claim 23 also uses one or more processors to perform various operations of the claim.
Claim 24 recites a non-transitory computer readable storage medium storing instructions for generating a knowledge graph, the instructions configured to be executed by a system comprising one or more processors to cause the system to perform the recited operations. Claim 23 also uses one or more processors to perform various operations of the claim.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 133-140).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claim 3] wherein the first derived component comprises an entity risk profile associated with a first entity, the entity risk profile determined based on the first input data.
[Claim 4] wherein the entity risk profile is constructed based on any one or more of structural knowledge associated with the first entity, conceptual knowledge associated with the first entity, and behavioral knowledge associated with the first entity.
[Claim 5] wherein the first processing operation comprises a financial audit operation performed using the first input data.
[Claim 7] wherein the second processing operation is different from the first processing operation.
[Claim 8] determining an insight from the generated knowledge graph, wherein the insight is based on nodes representing the first set of data components, the second set of data components, and the third set of data components.
[Claim 9] wherein the first set of data components comprises any one or more of: financial statements, sales orders, subsidiary entity lists, supplier lists, customer lists, employee lists, competitor lists, patent filings, trademark filings, social media posts, purchase orders, sales orders, bills of lading, bank statements, general ledger records, inventory lists, invoices, shipment records, accounts receivable records, accounts payable records, social media posts, and SEC filings.
[Claim 10] determining an insight from the generated knowledge graph, wherein the insight is based on nodes representing the first set of data components and the second set of data components.
[Claim 11] wherein the first input data comprises data of one or more data modalities, the one or more data modalities comprising an unstructured data modality, a semi-structured data modality, and a structured data modality.
[Claim 12] wherein the one or more relationships comprise a one-to-one mapping of all or a subset of all of the first set of data components and the second set of data components.
[Claim 13] wherein the one or more relationships comprise a one-to-many mapping of all or a subset of all of the data components of the first set of data components and the second set of data components.
[Claim 14] wherein the one or more relationships comprise a many-to-one mapping of all or a subset of all of the data components of the first set of data components and the second set of data components.
[Claim 15] wherein the one or more relationships comprise a many-to-many mapping of all or a subset of all of the data components of the first set of data components and the second set of data components.
[Claim 16] wherein the first node of the knowledge graph refers to one or more of structural knowledge associated with a first entity of the one or more entities, conceptual knowledge associated with the first entity, and behavioral knowledge associated with the first entity.
[Claim 17] wherein the conceptual knowledge associated with the first entity comprises taxonomies and ontologies associated with the first entity.
[Claim 18] wherein the structural knowledge associated with the first entity comprises a legal structure of one or more of the first entity and one or more entities related to the first entity.
[Claim 19] wherein the behavioral knowledge associated with the first entity comprises one or more business processes associated with the first entity.
[Claim 20] wherein an entity of the one or more entities is any one of an individual, a business entity, or a government entity.
[Claim 21] receiving second input data related to the one or more entities from the one or more data sources;
identifying one or more relationships between the second input data and a node of the generated knowledge graph; and
updating the knowledge graph by incorporating the second input data, wherein incorporating the second input data comprises associating the second input data with the node of the generated knowledge graph based on the identified one or more relationships between the second input data and the node of the generated knowledge graph.
[Claim 22] wherein the first input data comprises a first set of rules associated with a structure of a first entity and a second set of rules associated with a process of the first entity.
The dependent claims further present details of the abstract ideas identified in regard to the independent claims.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can gather data components from data sources, determine other relevant data components, identify relationships among the data components, generate a knowledge graph, determine additional data components, incorporate the additional data components into a knowledge graph, etc. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process may be related to business operations analysis and sales analysis (as evidenced by the specific type of data defined in claim 9), which (under its broadest reasonable interpretation) is an example of business relations and sales activities (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
Choosing which data to incorporate into a knowledge graph based on limited information from nodes (as recited in the claims) is an example of filtering content. MPEP § 2106.04(a)(2)(II)(C) cites the following as an example of managing personal behavior, i.e., organizing human activity: “filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis).” MPEP § 2106.04(a)(2)(III)(D) cites the following as an example of a mental process: “An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356.”
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims include the additional elements of their independent claims.
Claim 1 uses one or more processors to perform various operations of the claim.
Claim 23 recites a system for generating a knowledge graph, the system comprising one or more processors configured to cause the system to perform the recited operations. Claim 23 also uses one or more processors to perform various operations of the claim.
Claim 24 recites a non-transitory computer readable storage medium storing instructions for generating a knowledge graph, the instructions configured to be executed by a system comprising one or more processors to cause the system to perform the recited operations. Claim 23 also uses one or more processors to perform various operations of the claim.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 133-140).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3-5, and 7-24 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Crabtree et al. (US 2021/0173711).
[Claim 1] Crabtree discloses a method for generating a knowledge graph (¶ 63 – “FIG. 4 is a weighted directed graph diagram showing an exemplary temporospatial enriched knowledge graph 400 and its possible use in creating a value chain disruption risk and optimization plan. The temporospatial enriched knowledge graph (TEKG) 400 is created from a plurality of data sources and data types including, but not limited to public, private, synthetic, structured, unstructured, inferred, proprietary, and individual data. The TEKG 400 may be used to map the integrated value chain of a company, industry, region, or given sufficient time for data ingestion, the global integrated value chain of all industries. Consequently, the knowledge graph it creates may be massive, comprised of billions of vertices and edges representing and capturing the complex, interwoven, dynamic nature of integrated value chain relationships between and among entities within the value chain. The knowledge graph contained within this diagram is a very simplified snapshot of a knowledge graph for illustrative purposes only.“), the method comprising:
receiving, by one or more processors (¶¶ 6, 78-81 – The invention is implemented using one or more processors.), first input data comprising a first set of data components related to one or more entities from one or more data sources (¶ 56 – “End users and entities may submit a risk query 320 to the system which initiates the process of generating a risk profile and optimization plan 340. A risk query 320 may be a general query where the end user or entity is wanting an overall disruption risk assessment, or it may be a targeted risk query capturing disruption risk in a specified industry, location, type of disruption, time, and many other searchable characteristics. The system 310 captures relationships between entities and is linked to a DCG-based ingest system which captures metadata around data provenance and model provenance for a variety of information sources 330. The ability to handle data provenance and metadata tracking is of prime importance (given restrictions on usage of different data under HIPAA, CPRA, GDPR, etc.) when creating the best overall data set, which may be partially common and partially distinct for different use cases. Information sources 330 may include private, public, structured or unstructured, synthetic, and alternative data relating to tangible and intangible goods or services.”; ¶ 63 – “The temporospatial enriched knowledge graph (TEKG) 400 is created from a plurality of data sources and data types including, but not limited to public, private, synthetic, structured, unstructured, inferred, proprietary, and individual data…Once a risk query is presented to the integrated value chain risk profiling and optimization platform 310, entity-centric behavioral analysis tools in combination with the subject of the risk query are used to identify subsets of the overall knowledge graph that pertain to the risk query and perform risk analysis and characterization. The TEKG 400 of this example is the result of an interested party, Company X 410, conducting a risk query about its own value chain. Interested parties may include, investors, potential clients or business partners, investigators, regulators, etc.”);
determining, by the one or more processors (¶¶ 6, 78-81 – The invention is implemented using one or more processors.), based upon the first input data, a second set of data components, wherein the second set of data components comprises a first derived component derived based on a first processing operation performed using the first input data (¶¶ 63-70 – Multiple additional sets of data (data components) may be inferred as being related to a query regarding a company of interest. Inferred data may be gleaned from the nodes and edges of a knowledge graph and other data sources to provide more insights into the original query, including queries seeking more information about a particular entity or entities.; ¶ 59 – “The simulation engine 314 may be used to simulate and model what-if scenarios for value chain disruptions and possible optimizations to mitigate the disruption risks. The simulation risk disruption results may include information such as the likelihood of a disruption event occurring, the type of risk(s) caused by the event, the financial impact to the business or entity, the time to recover, and potential optimization outcomes. This list of disruption results is non-exhaustive and merely a simple representation of the many insights into value chain disruption risks that can be found using simulation and modeling. The simulation disruption results are sent to the risk optimization engine where the disruption results are used to produce an entity specific risk profile and optimization plan 340.”);
identifying, by the one or more processors (¶¶ 6, 78-81 – The invention is implemented using one or more processors.), one or more relationships between the first set of data components and the second set of data components (¶¶ 63-70 – Multiple additional sets of data (data components) may be inferred as being related to a query regarding a company of interest. Inferred data may be gleaned from the nodes and edges of a knowledge graph and other data sources to provide more insights into the original query, including queries seeking more information about a particular entity or entities.); and
generating a knowledge graph comprising a plurality of nodes, wherein a first node of the knowledge graph represents a first respective data component of the first set of data components and a second node of the knowledge graph represents a second respective data component of the second set of data components, and wherein the first node is associated with the second node, the association defined by one or more of the identified one or more relationships (¶¶ 63-70 – Multiple additional sets of data (data components) may be inferred as being related to a query regarding a company of interest. Inferred data may be gleaned from the nodes and edges of a knowledge graph and other data sources to provide more insights into the original query, including queries seeking more information about a particular entity or entities.; fig. 4, ¶ 63 – “FIG. 4 is a weighted directed graph diagram showing an exemplary temporospatial enriched knowledge graph 400 and its possible use in creating a value chain disruption risk and optimization plan. The temporospatial enriched knowledge graph (TEKG) 400 is created from a plurality of data sources and data types including, but not limited to public, private, synthetic, structured, unstructured, inferred, proprietary, and individual data. The TEKG 400 may be used to map the integrated value chain of a company, industry, region, or given sufficient time for data ingestion, the global integrated value chain of all industries. Consequently, the knowledge graph it creates may be massive, comprised of billions of vertices and edges representing and capturing the complex, interwoven, dynamic nature of integrated value chain relationships between and among entities within the value chain. The knowledge graph contained within this diagram is a very simplified snapshot of a knowledge graph for illustrative purposes only. Once a risk query is presented to the integrated value chain risk profiling and optimization platform 310, entity-centric behavioral analysis tools in combination with the subject of the risk query are used to identify subsets of the overall knowledge graph that pertain to the risk query and perform risk analysis and characterization. The TEKG 400 of this example is the result of an interested party, Company X 410, conducting a risk query about its own value chain. Interested parties may include, investors, potential clients or business partners, investigators, regulators, etc.”);
determining a third set of data components, wherein the third set of data components comprises the result of a second processing operation performed using the generated knowledge graph (¶¶ 63-70 – Multiple additional sets of data (data components) may be inferred as being related to a query regarding a company of interest. Inferred data may be gleaned from the nodes and edges of a knowledge graph and other data sources to provide more insights into the original query, including queries seeking more information about a particular entity or entities.; fig. 4, ¶ 63 – “FIG. 4 is a weighted directed graph diagram showing an exemplary temporospatial enriched knowledge graph 400 and its possible use in creating a value chain disruption risk and optimization plan. The temporospatial enriched knowledge graph (TEKG) 400 is created from a plurality of data sources and data types including, but not limited to public, private, synthetic, structured, unstructured, inferred, proprietary, and individual data. The TEKG 400 may be used to map the integrated value chain of a company, industry, region, or given sufficient time for data ingestion, the global integrated value chain of all industries. Consequently, the knowledge graph it creates may be massive, comprised of billions of vertices and edges representing and capturing the complex, interwoven, dynamic nature of integrated value chain relationships between and among entities within the value chain. The knowledge graph contained within this diagram is a very simplified snapshot of a knowledge graph for illustrative purposes only. Once a risk query is presented to the integrated value chain risk profiling and optimization platform 310, entity-centric behavioral analysis tools in combination with the subject of the risk query are used to identify subsets of the overall knowledge graph that pertain to the risk query and perform risk analysis and characterization. The TEKG 400 of this example is the result of an interested party, Company X 410, conducting a risk query about its own value chain. Interested parties may include, investors, potential clients or business partners, investigators, regulators, etc.” Multiple layers of data components are presented in the knowledge graph.); and
incorporating the third set of data components into the generated knowledge graph (¶¶ 63-70 – Multiple additional sets of data (data components) may be inferred as being related to a query regarding a company of interest. Inferred data may be gleaned from the nodes and edges of a knowledge graph and other data sources to provide more insights into the original query, including queries seeking more information about a particular entity or entities.; fig. 4, ¶ 63 – “FIG. 4 is a weighted directed graph diagram showing an exemplary temporospatial enriched knowledge graph 400 and its possible use in creating a value chain disruption risk and optimization plan. The temporospatial enriched knowledge graph (TEKG) 400 is created from a plurality of data sources and data types including, but not limited to public, private, synthetic, structured, unstructured, inferred, proprietary, and individual data. The TEKG 400 may be used to map the integrated value chain of a company, industry, region, or given sufficient time for data ingestion, the global integrated value chain of all industries. Consequently, the knowledge graph it creates may be massive, comprised of billions of vertices and edges representing and capturing the complex, interwoven, dynamic nature of integrated value chain relationships between and among entities within the value chain. The knowledge graph contained within this diagram is a very simplified snapshot of a knowledge graph for illustrative purposes only. Once a risk query is presented to the integrated value chain risk profiling and optimization platform 310, entity-centric behavioral analysis tools in combination with the subject of the risk query are used to identify subsets of the overall knowledge graph that pertain to the risk query and perform risk analysis and characterization. The TEKG 400 of this example is the result of an interested party, Company X 410, conducting a risk query about its own value chain. Interested parties may include, investors, potential clients or business partners, investigators, regulators, etc.” Multiple layers of data components are presented in the knowledge graph and in subsets of the knowledge graph.).
[Claim 3] Crabtree discloses wherein the first derived component comprises an entity risk profile associated with a first entity, the entity risk profile determined based on the first input data (¶¶ 63-70 – Multiple additional sets of data (data components) may be inferred as being related to a query regarding a company of interest. Inferred data may be gleaned from the nodes and edges of a knowledge graph and other data sources to provide more insights into the original query, including queries seeking more information about a particular entity or entities.; ¶ 63 – “The temporospatial enriched knowledge graph (TEKG) 400 is created from a plurality of data sources and data types including, but not limited to public, private, synthetic, structured, unstructured, inferred, proprietary, and individual data…Once a risk query is presented to the integrated value chain risk profiling and optimization platform 310, entity-centric behavioral analysis tools in combination with the subject of the risk query are used to identify subsets of the overall knowledge graph that pertain to the risk query and perform risk analysis and characterization. The TEKG 400 of this example is the result of an interested party, Company X 410, conducting a risk query about its own value chain.”).
[Claim 4] Crabtree discloses wherein the entity risk profile is constructed based on any one or more of structural knowledge associated with the first entity, conceptual knowledge associated with the first entity, and behavioral knowledge associated with the first entity (While not limited to such interpretations in the claims due to the breadth of each term, it is noted that Applicant’s Specification states, “Conceptual knowledge can include taxonomies and ontologies associated with the entity, structural knowledge can include the legal structure of the entity and related entities, and behavioral knowledge can include business processes associated with the entity.” (Spec: ¶ 4); Example(s) of at least behavioral knowledge: ¶ 59 – “The simulation and modeling engine 314 may access the stored value chain data and the temporospatial knowledge graph to perform a variety of entity-centric behavioral analyses, ad hoc analytics, and visualizations at scale in regards value chain disruptions and event forecasting. The simulation and modeling engine 314 may leverage public and private information on small or non-public business both real and/or simulated and/or synthetically generated via tools like Snorkel or a generative adversarial network (GAN) to find insight into the temporospatial knowledge graph. The mapping of individuals into the integrated value chain is becoming increasingly important, especially when considering the effects of demand shocks at the consumer and business levels are increasingly important in global economic output forecasting. This requires that demand shocks and supply shocks must be considered when modeling, especially for events such as economic and political uncertainty, or more recently Covid-19. The simulation engine 314 may be used to simulate and model what-if scenarios for value chain disruptions and possible optimizations to mitigate the disruption risks. The simulation risk disruption results may include information such as the likelihood of a disruption event occurring, the type of risk(s) caused by the event, the financial impact to the business or entity, the time to recover, and potential optimization outcomes. This list of disruption results is non-exhaustive and merely a simple representation of the many insights into value chain disruption risks that can be found using simulation and modeling. The simulation disruption results are sent to the risk optimization engine where the disruption results are used to produce an entity specific risk profile and optimization plan 340.”; Example(s) of at least conceptual knowledge: ¶ 53 – “Specialized purpose libraries may include but are not limited to financial markets functions libraries 251, Monte-Carlo risk routines 252, numeric analysis libraries 253, deep learning libraries 254, contract manipulation functions 255, money handling functions 256, Monte-Carlo search libraries 257, and quant approach securities routines 258. Pre-existing deep learning routines including information theory statistics engine 259 may also be used. The invention may also make use of other libraries and capabilities that are known to those skilled in the art as instrumental in the regulated trade of items of worth.” Based on a broadest reasonable interpretation, the use of libraries is interpreted as an example of a use of taxonomies.; ¶¶ 56-58 – Data related to the risk query flows through an ontology engine.; Example(s) of at least structural knowledge and conceptual knowledge: ¶ 61 – “The system 310 may optionally include an asset registry manager 313 and/or a legal document engine. The asset registry manager 313 comprises an asset registry database, a provenance manager, an ontology manager, and an interoperability manager. The asset registry manager 313 is responsible for unified contextualization of computing resources by identifying and storing provenance metadata and allows for sharing of assets between systems. The asset registry may be used by the system 310 to store information about intangible goods such as software, software bill of materials, computing resources such as workflows and processes, software components, intellectual property, among other things. Detailed information about the asset registry manager 313 is contained in U.S. patent application Ser. No. 16/915,176, which is incorporated herein by reference. A legal document engine may further provide information about intangible objects such as political, financial, environmental, and trade regulations as well as contracts between entities and individuals. A legal document engine is able to scan legal documents to identify facts using a combination of natural language (NLP) and semantic processing. This provides rich context surrounding value chain behavior, consumer demand, and geopolitical constraints. Detailed information about the legal document engine is contained in U.S. patent application Ser. No. 16/654,309, which is incorporated herein by reference.”; ¶ 62 – “If a risk query was initiated by a mask manufacturing company that produced N-95 masks, the knowledge graph generated would include vertices and edges derived from public/legal discourse as well as proposed governmental legislation that a potential law requiring all citizens to wear a mask may be imminent.”).
[Claim 5] Crabtree discloses wherein the first processing operation comprises a financial audit operation performed using the first input data (¶ 59 – “The simulation engine 314 may be used to simulate and model what-if scenarios for value chain disruptions and possible optimizations to mitigate the disruption risks. The simulation risk disruption results may include information such as the likelihood of a disruption event occurring, the type of risk(s) caused by the event, the financial impact to the business or entity, the time to recover, and potential optimization outcomes. This list of disruption results is non-exhaustive and merely a simple representation of the many insights into value chain disruption risks that can be found using simulation and modeling. The simulation disruption results are sent to the risk optimization engine where the disruption results are used to produce an entity specific risk profile and optimization plan 340.”; ¶ 61 – “A legal document engine may further provide information about intangible objects such as political, financial, environmental, and trade regulations as well as contracts between entities and individuals.”; ¶ 65 – “The TEKG 400 generated by the risk query by Company X 410 also includes information about the company that may not directly be tied to value chain processes. For example, the knowledge graph could identify that Company X 410 has partnered with 435 retail Company Z 413 which may mean that Company X 410 produces an exclusive line of laptops just for Company Z 413 or has some kind of promotional or marketing contract with that retailer. Other types of information about Company X 410 found within the TEKG 400 may include the type of industry (domain) of which Company X 410 is a part, financial information 430, 431 such as net income, operating cashflow, capital expenditures, etc., insurance information such as the Insurance Provider 436, hedged risks, private reinsurance partners, and insurance policy coverage and adjustments. Additionally, the TEKG 400 may include information about the country 424 in which Company X 410 is located such as the country's global economic rank 423, its trade regulations such as import/export tariffs 425, its environmental regulations 426, its political climate, its trade agreements with other nations, its population and their spending/saving habits, etc.”).
[Claim 7] Crabtree discloses wherein the second processing operation is different from the first processing operation (¶ 59 – “The simulation engine 314 may be used to simulate and model what-if scenarios for value chain disruptions and possible optimizations to mitigate the disruption risks. The simulation risk disruption results may include information such as the likelihood of a disruption event occurring, the type of risk(s) caused by the event, the financial impact to the business or entity, the time to recover, and potential optimization outcomes. This list of disruption results is non-exhaustive and merely a simple representation of the many insights into value chain disruption risks that can be found using simulation and modeling. The simulation disruption results are sent to the risk optimization engine where the disruption results are used to produce an entity specific risk profile and optimization plan 340.”; ¶ 61 – “A legal document engine may further provide information about intangible objects such as political, financial, environmental, and trade regulations as well as contracts between entities and individuals.”; ¶ 65 – “The TEKG 400 generated by the risk query by Company X 410 also includes information about the company that may not directly be tied to value chain processes. For example, the knowledge graph could identify that Company X 410 has partnered with 435 retail Company Z 413 which may mean that Company X 410 produces an exclusive line of laptops just for Company Z 413 or has some kind of promotional or marketing contract with that retailer. Other types of information about Company X 410 found within the TEKG 400 may include the type of industry (domain) of which Company X 410 is a part, financial information 430, 431 such as net income, operating cashflow, capital expenditures, etc., insurance information such as the Insurance Provider 436, hedged risks, private reinsurance partners, and insurance policy coverage and adjustments. Additionally, the TEKG 400 may include information about the country 424 in which Company X 410 is located such as the country's global economic rank 423, its trade regulations such as import/export tariffs 425, its environmental regulations 426, its political climate, its trade agreements with other nations, its population and their spending/saving habits, etc.” Various operations, including operations with data relevant to each other, may be performed.).
[Claim 8] Crabtree discloses determining an insight from the generated knowledge graph, wherein the insight is based on nodes representing the first set of data components, the second set of data components, and the third set of data components (¶¶ 63-70 – Multiple additional sets of data (data components) may be inferred as being related to a query regarding a company of interest. Inferred data may be gleaned from the nodes and edges of a knowledge graph and other data sources to provide more insights into the original query, including queries seeking more information about a particular entity or entities.; ¶¶ 63-73 – Various insights may be gleaned from the generated knowledge graphs, subgraphs, and various data components.).
[Claim 9] Crabtree discloses wherein the first set of data components comprises any one or more of: financial statements, sales orders, subsidiary entity lists, supplier lists, customer lists, employee lists, competitor lists, patent filings, trademark filings, social media posts, purchase orders, sales orders, bills of lading, bank statements, general ledger records, inventory lists, invoices, shipment records, accounts receivable records, accounts payable records, social media posts, and SEC filings (¶ 67 – “As another example, the system may be configured to show the inferred risk as differing diameters of vertices (node). According to risk query by the user, the system may have determined that considering temporal and public sentiment that global rank 423 of the country 424 in which Company X 410 is located in, is more significant to the risk query constraints than is an export tariff 425 and that environmental regulations 426 are more significant than the tariff. A multitude of configurations are optionally available and more may be added with relative ease. The temporospatial knowledge graph 400 also provides context into whether vertices and edges are formed from inferred algorithms or observed 440 through reliable data points. Ingesting unstructured data such as websites, blogs, news articles, and social media 428 as well as proprietary data such as customer relations data 429 may be used to understand sentiment 427 and potential disruption risks of Company X 410.”; ¶ 70 – “For example, using Company X's 410 sales data and Country B's 419 governmental economic data relating to extreme weather events, an optimization plan may suggest Company X 410 keep surplus inventory of 50,000 laptops in the event that an extreme weather event disrupts the entire country where the suppliers operate.”).
[Claim 10] Crabtree discloses determining an insight from the generated knowledge graph, wherein the insight is based on nodes representing the first set of data components and the second set of data components (¶¶ 63-73 – Various insights may be gleaned from the generated knowledge graphs, subgraphs, and various data components.).
[Claim 11] Crabtree discloses wherein the first input data comprises data of one or more data modalities, the one or more data modalities comprising an unstructured data modality, a semi-structured data modality, and a structured data modality (¶ 56 – “Information sources 330 may include private, public, structured or unstructured, synthetic, and alternative data relating to tangible and intangible goods or services.”).
[Claim 12] Crabtree discloses wherein the one or more relationships comprise a one-to-one mapping of all or a subset of all of the first set of data components and the second set of data components (¶ 31 – “This can leverage traditional data sources such as entity-to-entity trade flow data…” “Entity-to-entity” is an example of a one-to-one mapping.).
[Claim 13] Crabtree discloses wherein the one or more relationships comprise a one-to-many mapping of all or a subset of all of the data components of the first set of data components and the second set of data components (¶ 69 – “As an example, behavioral analysis of Company X's 410 mapped integrated value chain may identify a cluster 450 within the map as a potential locational disruption risk. The cluster 450 encompasses the Tier 1 417 and Tier 2 418 suppliers that Company X 410 relies on to enable their manufacturing processes as well as the country 419 that both suppliers operate in and the country's associated data such as various forms of regulations, location, trade agreements, political climate, and other such information.” The relationship between Company X and Tier 1 and Tier 2 suppliers is an example of a one-to-many mapping and a many-to-one mapping since one mapping is the inverse of the other.).
[Claim 14] Crabtree discloses wherein the one or more relationships comprise a many-to-one mapping of all or a subset of all of the data components of the first set of data components and the second set of data components (¶ 69 – “As an example, behavioral analysis of Company X's 410 mapped integrated value chain may identify a cluster 450 within the map as a potential locational disruption risk. The cluster 450 encompasses the Tier 1 417 and Tier 2 418 suppliers that Company X 410 relies on to enable their manufacturing processes as well as the country 419 that both suppliers operate in and the country's associated data such as various forms of regulations, location, trade agreements, political climate, and other such information.” The relationship between Company X and Tier 1 and Tier 2 suppliers is an example of a one-to-many mapping and a many-to-one mapping since one mapping is the inverse of the other.).
[Claim 15] Crabtree discloses wherein the one or more relationships comprise a many-to-many mapping of all or a subset of all of the data components of the first set of data components and the second set of data components (¶ 69 – “As an example, behavioral analysis of Company X's 410 mapped integrated value chain may identify a cluster 450 within the map as a potential locational disruption risk. The cluster 450 encompasses the Tier 1 417 and Tier 2 418 suppliers that Company X 410 relies on to enable their manufacturing processes as well as the country 419 that both suppliers operate in and the country's associated data such as various forms of regulations, location, trade agreements, political climate, and other such information.” Comparing a cluster of supplies based on “various forms of regulations, location, trade agreements, political climate, and other such information” associated with a country is an example of a many-to-many mapping.).
[Claim 16] Crabtree discloses wherein the first node of the knowledge graph refers to one or more of structural knowledge associated with a first entity of the one or more entities, conceptual knowledge associated with the first entity, and behavioral knowledge associated with the first entity (While not limited to such interpretations in the claims due to the breadth of each term, it is noted that Applicant’s Specification states, “Conceptual knowledge can include taxonomies and ontologies associated with the entity, structural knowledge can include the legal structure of the entity and related entities, and behavioral knowledge can include business processes associated with the entity.” (Spec: ¶ 4); Example(s) of at least behavioral knowledge: ¶ 59 – “The simulation and modeling engine 314 may access the stored value chain data and the temporospatial knowledge graph to perform a variety of entity-centric behavioral analyses, ad hoc analytics, and visualizations at scale in regards value chain disruptions and event forecasting. The simulation and modeling engine 314 may leverage public and private information on small or non-public business both real and/or simulated and/or synthetically generated via tools like Snorkel or a generative adversarial network (GAN) to find insight into the temporospatial knowledge graph. The mapping of individuals into the integrated value chain is becoming increasingly important, especially when considering the effects of demand shocks at the consumer and business levels are increasingly important in global economic output forecasting. This requires that demand shocks and supply shocks must be considered when modeling, especially for events such as economic and political uncertainty, or more recently Covid-19. The simulation engine 314 may be used to simulate and model what-if scenarios for value chain disruptions and possible optimizations to mitigate the disruption risks. The simulation risk disruption results may include information such as the likelihood of a disruption event occurring, the type of risk(s) caused by the event, the financial impact to the business or entity, the time to recover, and potential optimization outcomes. This list of disruption results is non-exhaustive and merely a simple representation of the many insights into value chain disruption risks that can be found using simulation and modeling. The simulation disruption results are sent to the risk optimization engine where the disruption results are used to produce an entity specific risk profile and optimization plan 340.”; Example(s) of at least conceptual knowledge: ¶ 53 – “Specialized purpose libraries may include but are not limited to financial markets functions libraries 251, Monte-Carlo risk routines 252, numeric analysis libraries 253, deep learning libraries 254, contract manipulation functions 255, money handling functions 256, Monte-Carlo search libraries 257, and quant approach securities routines 258. Pre-existing deep learning routines including information theory statistics engine 259 may also be used. The invention may also make use of other libraries and capabilities that are known to those skilled in the art as instrumental in the regulated trade of items of worth.” Based on a broadest reasonable interpretation, the use of libraries is interpreted as an example of a use of taxonomies.; ¶¶ 56-58 – Data related to the risk query flows through an ontology engine.; Example(s) of at least structural knowledge and conceptual knowledge: ¶ 61 – “The system 310 may optionally include an asset registry manager 313 and/or a legal document engine. The asset registry manager 313 comprises an asset registry database, a provenance manager, an ontology manager, and an interoperability manager. The asset registry manager 313 is responsible for unified contextualization of computing resources by identifying and storing provenance metadata and allows for sharing of assets between systems. The asset registry may be used by the system 310 to store information about intangible goods such as software, software bill of materials, computing resources such as workflows and processes, software components, intellectual property, among other things. Detailed information about the asset registry manager 313 is contained in U.S. patent application Ser. No. 16/915,176, which is incorporated herein by reference. A legal document engine may further provide information about intangible objects such as political, financial, environmental, and trade regulations as well as contracts between entities and individuals. A legal document engine is able to scan legal documents to identify facts using a combination of natural language (NLP) and semantic processing. This provides rich context surrounding value chain behavior, consumer demand, and geopolitical constraints. Detailed information about the legal document engine is contained in U.S. patent application Ser. No. 16/654,309, which is incorporated herein by reference.”; ¶ 62 – “If a risk query was initiated by a mask manufacturing company that produced N-95 masks, the knowledge graph generated would include vertices and edges derived from public/legal discourse as well as proposed governmental legislation that a potential law requiring all citizens to wear a mask may be imminent.”).
[Claim 17] Crabtree discloses wherein the conceptual knowledge associated with the first entity comprises taxonomies and ontologies associated with the first entity (¶ 53 – “Specialized purpose libraries may include but are not limited to financial markets functions libraries 251, Monte-Carlo risk routines 252, numeric analysis libraries 253, deep learning libraries 254, contract manipulation functions 255, money handling functions 256, Monte-Carlo search libraries 257, and quant approach securities routines 258. Pre-existing deep learning routines including information theory statistics engine 259 may also be used. The invention may also make use of other libraries and capabilities that are known to those skilled in the art as instrumental in the regulated trade of items of worth.” Based on a broadest reasonable interpretation, the use of libraries is interpreted as an example of a use of taxonomies.; ¶¶ 56-58 – Data related to the risk query flows through an ontology engine.).
[Claim 18] Crabtree discloses wherein the structural knowledge associated with the first entity comprises a legal structure of one or more of the first entity and one or more entities related to the first entity (¶ 61 – “The system 310 may optionally include an asset registry manager 313 and/or a legal document engine. The asset registry manager 313 comprises an asset registry database, a provenance manager, an ontology manager, and an interoperability manager. The asset registry manager 313 is responsible for unified contextualization of computing resources by identifying and storing provenance metadata and allows for sharing of assets between systems. The asset registry may be used by the system 310 to store information about intangible goods such as software, software bill of materials, computing resources such as workflows and processes, software components, intellectual property, among other things. Detailed information about the asset registry manager 313 is contained in U.S. patent application Ser. No. 16/915,176, which is incorporated herein by reference. A legal document engine may further provide information about intangible objects such as political, financial, environmental, and trade regulations as well as contracts between entities and individuals. A legal document engine is able to scan legal documents to identify facts using a combination of natural language (NLP) and semantic processing. This provides rich context surrounding value chain behavior, consumer demand, and geopolitical constraints. Detailed information about the legal document engine is contained in U.S. patent application Ser. No. 16/654,309, which is incorporated herein by reference.”; ¶ 62 – “If a risk query was initiated by a mask manufacturing company that produced N-95 masks, the knowledge graph generated would include vertices and edges derived from public/legal discourse as well as proposed governmental legislation that a potential law requiring all citizens to wear a mask may be imminent.”).
[Claim 19] Crabtree discloses wherein the behavioral knowledge associated with the first entity comprises one or more business processes associated with the first entity (¶ 59 – “The simulation and modeling engine 314 may access the stored value chain data and the temporospatial knowledge graph to perform a variety of entity-centric behavioral analyses, ad hoc analytics, and visualizations at scale in regards value chain disruptions and event forecasting. The simulation and modeling engine 314 may leverage public and private information on small or non-public business both real and/or simulated and/or synthetically generated via tools like Snorkel or a generative adversarial network (GAN) to find insight into the temporospatial knowledge graph. The mapping of individuals into the integrated value chain is becoming increasingly important, especially when considering the effects of demand shocks at the consumer and business levels are increasingly important in global economic output forecasting. This requires that demand shocks and supply shocks must be considered when modeling, especially for events such as economic and political uncertainty, or more recently Covid-19. The simulation engine 314 may be used to simulate and model what-if scenarios for value chain disruptions and possible optimizations to mitigate the disruption risks. The simulation risk disruption results may include information such as the likelihood of a disruption event occurring, the type of risk(s) caused by the event, the financial impact to the business or entity, the time to recover, and potential optimization outcomes. This list of disruption results is non-exhaustive and merely a simple representation of the many insights into value chain disruption risks that can be found using simulation and modeling. The simulation disruption results are sent to the risk optimization engine where the disruption results are used to produce an entity specific risk profile and optimization plan 340.”).
[Claim 20] Crabtree discloses wherein an entity of the one or more entities is any one of an individual, a business entity, or a government entity (¶ 57 – “The system 310 may be used to ingest alternative data and grow an increasingly active corpora of information about individuals and their links to business entities and one another to build a knowledge base.”; ¶ 59 – “The mapping of individuals into the integrated value chain is becoming increasingly important, especially when considering the effects of demand shocks at the consumer and business levels are increasingly important in global economic output forecasting.”; ¶ 63 – “The TEKG 400 of this example is the result of an interested party, Company X 410, conducting a risk query about its own value chain. Interested parties may include, investors, potential clients or business partners, investigators, regulators, etc.”).
[Claim 21] Crabtree discloses:
receiving second input data related to the one or more entities from the one or more data sources (¶ 60 – “After the integrated value chain ontological databases have been created and/or updated, a directed computational graph module 155 utilizing the ontologies generates an advanced temporospatial knowledge graph.”; ¶¶ 63-70 – Multiple additional sets of data (data components) may be inferred as being related to a query regarding a company of interest. Inferred data may be gleaned from the nodes and edges of a knowledge graph and other data sources to provide more insights into the original query, including queries seeking more information about a particular entity or entities.; ¶¶ 63-73 – Various insights may be gleaned from the generated knowledge graphs, subgraphs, and various data components.);
identifying one or more relationships between the second input data and a node of the generated knowledge graph (¶¶ 63-70 – Multiple additional sets of data (data components) may be inferred as being related to a query regarding a company of interest. Inferred data may be gleaned from the nodes and edges of a knowledge graph and other data sources to provide more insights into the original query, including queries seeking more information about a particular entity or entities.; ¶¶ 63-73 – Various insights may be gleaned from the generated knowledge graphs, subgraphs, and various data components.); and
updating the knowledge graph by incorporating the second input data, wherein incorporating the second input data comprises associating the second input data with the node of the generated knowledge graph based on the identified one or more relationships between the second input data and the node of the generated knowledge graph (¶ 60 – “After the integrated value chain ontological databases have been created and/or updated, a directed computational graph module 155 utilizing the ontologies generates an advanced temporospatial knowledge graph.”; ¶¶ 63-70 – Multiple additional sets of data (data components) may be inferred as being related to a query regarding a company of interest. Inferred data may be gleaned from the nodes and edges of a knowledge graph and other data sources to provide more insights into the original query, including queries seeking more information about a particular entity or entities.; ¶¶ 63-73 – Various insights may be gleaned from the generated knowledge graphs, subgraphs, and various data components.).
[Claim 22] Crabtree discloses wherein the first input data comprises a first set of rules associated with a structure of a first entity and a second set of rules associated with a process of the first entity (¶ 41 – “Value chain” or “integrated value chain” are used herein interchangeably to mean a system of organizations, people, activities, information, and resources involved in supplying a product or service to a consumer. Value activities often involve the transformation of natural resources, raw materials, and components into a finished product that is delivered to the end customer. Additionally, the integrated value chain as mapped by the disclosed system comprises information regarding ecological, biological, and political regulations governing natural resources and international trade, information about tangible and intangible goods such as digital value chain for software and other intellectual property assets, components, computing resources, and software bill of materials and specification. Furthermore, the value chain may include information about banking and payment partners in regards to money laundering, fraud, economic activity indices, and consumer behavior such as purchasing tendencies and consumer reviews.”; ¶ 61 – “The system 310 may optionally include an asset registry manager 313 and/or a legal document engine. The asset registry manager 313 comprises an asset registry database, a provenance manager, an ontology manager, and an interoperability manager. The asset registry manager 313 is responsible for unified contextualization of computing resources by identifying and storing provenance metadata and allows for sharing of assets between systems. The asset registry may be used by the system 310 to store information about intangible goods such as software, software bill of materials, computing resources such as workflows and processes, software components, intellectual property, among other things. Detailed information about the asset registry manager 313 is contained in U.S. patent application Ser. No. 16/915,176, which is incorporated herein by reference. A legal document engine may further provide information about intangible objects such as political, financial, environmental, and trade regulations as well as contracts between entities and individuals. A legal document engine is able to scan legal documents to identify facts using a combination of natural language (NLP) and semantic processing. This provides rich context surrounding value chain behavior, consumer demand, and geopolitical constraints. Detailed information about the legal document engine is contained in U.S. patent application Ser. No. 16/654,309, which is incorporated herein by reference.”; ¶ 62 – “If a risk query was initiated by a mask manufacturing company that produced N-95 masks, the knowledge graph generated would include vertices and edges derived from public/legal discourse as well as proposed governmental legislation that a potential law requiring all citizens to wear a mask may be imminent.”; ¶ 65 – “Additionally, the TEKG 400 may include information about the country 424 in which Company X 410 is located such as the country's global economic rank 423, its trade regulations such as import/export tariffs 425, its environmental regulations 426, its political climate, its trade agreements with other nations, its population and their spending/saving habits, etc.”; ¶¶ 67, 69 – Regulations based on country.).
[Claim 23] Claim 23 recites limitations already addressed by the rejection of claim 1 above; therefore, the same rejection applies. Furthermore, Crabtree discloses a system for generating a knowledge graph, the system comprising one or more processors configured to cause the system to perform the disclosed operations (Crabtree: ¶¶ 6, 78-90).
[Claim 24] Claim 24 recites limitations already addressed by the rejection of claim 1 above; therefore, the same rejection applies. Furthermore, Crabtree discloses a non-transitory computer readable storage medium storing instructions for generating a knowledge graph, the instructions configured to be executed by a system comprising one or more processors to cause the system to perform the disclosed operations (Crabtree: ¶¶ 6, 78-90).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mukherjee (US 2021/0256025) – Generates knowledge graphs.
Zhu et al. (US 2022/0269936) – Generates financial knowledge graphs.
Ding et al. (US 2022/0198146) – Gleans information from knowledge graphs.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A