DETAILED ACTION
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 .
This action is made final.
Claims 1-18 are pending. Claims 1, 14 and 17 are independent claims.
Response to Arguments
The objections to claims 5 (applicant is correct that the objection should have been applied to claim 6), 14 and 17 are withdrawn.
With respect to the 35 U.S.C. 101 rejections of the previous office action, the applicant’s arguments have been fully considered but they are not persuasive. The applicant argues that the invention as claimed reflects a technical benefit, reciting limitations:
a) generating an initial entity component of the knowledge graph using underlying stored or streamed data, the entity component including a plurality of entity nodes and one or more relationship edges comprising connections between the entities
b) storing the entity component in a computer memory; -- and particularly points out –
c) associating at least a first requirement with a particular one of the one or more of the relationship edges, the at least one requirement defining an iterative or recursive functional description ("function") of the relationship edge to which it is associated, wherein the function defines a dependency of the relationship upon conditions, parameters and other factors and wherein each iteration or recursion of the requirement defines a dimension of the knowledge graph and such iteration or recursion proceeds without human intervention until all conditions, parameters and other factors that determine a state of the particular one of the particular one or more relationships edges included in the graph.
The applicant argues that limitations a, b, and especially c provide a technical benefit of “improving the representation of complex relationships in knowledge graph data models and topologies”. The examiner argues that limitation c is mostly a series of abstract ideas (with some insignificant extra-solution activity) and not an additional element capable of integrating the abstract idea(s) into a practical application (see MPEP 2106.05(a), ¶6). No additional elements are provided that can integrate the abstract ideas into a practical application, as described in the 101 rejection below.
With respect to the 35 U.S.C. 103 rejections of the previous office action, the applicant’s arguments have been fully considered but they are not persuasive. The applicant argues that the combination of Lopez Garcia and Zheng fails to teach the limitations of claim 1 – in specific: and wherein each iteration or recursion of the requirement defines a dimension of the knowledge graph and such iteration or recursion proceeds without human intervention until all conditions, parameters and other factors that determine a state of the particular one of the particular one or more relationships edges included in the graph. The examiner argues that Zheng does teach this limitation. The recited portion of Zheng – pg. 389, ¶2, Otherwise, we split the nested triple to get the corresponding subject, predicate and object, recursively execute insertToDB on subject and object, and return the id of the triple in the table as the subject or object of the previous recursion to implement the insertion of the nested triple, and fig. 2 – defines an automatic, recursive functional requirement that proceeds until base level triples are extracted (“all conditions, parameters, and other factors” are extracted, i.e., included in the graph that define a graph edge). The applicant further argues that the iterations of the algorithm in Zheng “do not define a dimension of the knowledge graph”. The examiner argues that the broadest reasonable interpretation of “define a dimension of the knowledge graph” includes the definition of hierarchical relationships within the knowledge graph, as seen in Zheng. Additionally, the limitation does not recite defining a new dimension of the knowledge graph and can also be interpreted as further defining an existing dimension in a knowledge graph.
Applicant also argues that Zheng does not disclose or suggest “arranging all conditions/factors that affect all of the nodes/edges of knowledge graph in distinct dimensions”; however, the examiner argues that this requirement is not reflected in claim 1 or its dependents.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. Claim 1 is directed to a method (Step 1: YES).
Step 2A prong 1: Does the claim recite a judicial exception? Claim 1 recites: A computer-implemented method of providing a multi-dimensional knowledge graph that includes entities and relationships between entities, the method… comprising: generating an initial entity component of the knowledge graph… the entity component including a plurality of entity nodes and one or more relationship edges comprising connections between the entities (creating an entity component with nodes and edges can be performed in the human mind with the aid of pen and paper and is a mental process)… associating at least a first requirement with a particular one of the one or more of the relationship edges, the at least one requirement defining an iterative or recursive functional description (“function”) of the relationship edge to which it is associated, wherein the function defines a dependency of the relationship upon conditions, parameters and other factors (associating an edge of the entity component with a recursive or iterative function definition is a mental process) and wherein each iteration or recursion of the requirement defines a dimension of the knowledge graph and such iteration or recursion proceeds… until all conditions, parameters and other factors that determine a state of the particular one of the particular one or more relationships edges included in the graph (expanding the knowledge graph by repeatedly performing or calculating the iterative or recursive function is a mental process and/or mathematical calculation)... These steps can be performed mentally or are mathematical calculations (Step 2A prong 1: YES).
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, considered individually and in combination, integrate the judicial exception into a practical application? Claim 1 recites: executed by a processing unit of a computing system… using underlying stored or streamed data… storing the entity component in a computer memory… iteration/recursion without human intervention… and storing all associated requirements included in the knowledge graph for the particular one of the one or more relationship edges in the computer memory. Storing entity components and requirements produced by the method is insignificant extra-solution activity of data storage that does not add a meaningful limitation to the process of knowledge graph creation. Using stored or streamed data is also insignificant extra-solution activity of data gathering that does not add a meaningful limitation to the process of knowledge graph creation. Executing the method with a processing unit of a computing system and specifying that the iteration or recursions happens without human intervention are recited at a high level of generality, i.e., as a generic computer performing generic computer functions (Step 2A prong 2: NO).
Step 2B: These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since they since they only amount to data storing without significantly more (MPEP 2106.05(g)) or provide nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)). These limitations, taken either alone or in combination, fail to provide an inventive concept (Step 2B: NO). Thus, the claim is not patent eligible.
Regarding claims 2-13, they recite limitations which further narrow the abstract idea by specifying more details of the mental and mathematical process that occurs (Claim 2, performing automatic updates is mere instructions to implement an abstract idea, updating a knowledge graph, on a generic computer, recited at a high level of generality; Claim 3, displaying the knowledge graph is extra-solution activity of data outputting; Claim 4, the variety of functions described can be performed either mentally, or are mathematical calculations; Claim 5, logical and rule-based relationships based on logical operators like “or”, “and”, “xor”, etc. are mental processes; Claim 6, learning the function with a machine learning system is an additional element recited at a high level of generality, i.e., an attempt to apply the abstract idea to a technological field (machine learning) and generically applied to a computer; Claim 7, using a neural-network based architecture is an additional element recited at a high level of generality, i.e., an attempt to apply the abstract idea to a technological field and generically applied to a computer; Claim 8, using a variety of neural network types is an additional element recited at a high level of generality, i.e., an attempt to apply the abstract idea to a technological field and generically applied to a computer; Claim 9, inputting historical data, previous knowledge graphs, and a new knowledge graph is insignificant extra-solution activity of data gathering, and approximating the function by matching information in the inputted data is a mental process; Claim 10, describing the knowledge graph as having to do with financial occurrences and how they relate to regulations is limiting the field of use without significantly more; Claim 11, describing at least a first requirement as a function to determine if a financial occurrence is in compliance with regulations is limiting the field of use without significantly more; Claim 12, determining the status of a compliance-related relationship in the graph by calculating the function associated with that relationship is a mental process or mathematical calculation, and detecting whether the financial occurrence is in compliance using the knowledge graph as a whole is a series of mental processes and/or mathematical calculations, i.e., calculating the relevant set of relationships to determine a final compliance outcome; Claim 13, describing the various function inputs is limiting the field of use without significantly more).
Regarding claim 14,
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. Claim 14 is directed to a method (Step 1: YES).
Step 2A prong 1: Does the claim recite a judicial exception? Claim 14 recites: A computer-implemented method of determining a status of a relationship in a multi-dimensional knowledge graph that includes entities and relationships between entities, the method… comprising: generating an initial entity component of the knowledge graph… the entity component including a plurality of entity nodes and one or more relationship edges comprising connections between the entities (creating an entity component with nodes and edges by analyzing data can be performed in the human mind with the aid of pen and paper and is a mental process)… associating at least a first requirement with a particular one of the one or more of the relationship edges, the at least one requirement defining an iterative or recursive functional description (“function”) of the relationship edge to which it is associated, wherein the function defines a dependency of the relationship upon conditions, parameters and other factors (associating an edge of the entity component with a recursive or iterative function definition is a mental process) and wherein each iteration or recursion of the requirement defines a dimension of the knowledge graph and such iteration or recursion proceeds… until all conditions, parameters and other factors that determine a state of the particular one of the particular one or more relationships edges are included in the graph (expanding the knowledge graph by repeatedly performing or calculating the iterative or recursive function is a mental process and/or mathematical calculation)… locating the particular relationship in the knowledge graph; and… determining the status of the relationship by ascertaining all of the requirements associated with the relationship and calculating all of the functions (locating the relationship in the graph is a mental process and determining its status is a mental process or series of mathematical calculations)… These steps can be performed mentally or are mathematical calculations (Step 2A prong 1: YES).
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, considered individually and in combination, integrate the judicial exception into a practical application? Claim 14 recites: executed by a processing unit of a computing system… using underlying stored or streamed data… storing the entity component in a computer memory… without human intervention… storing all associated requirements included in the knowledge graph for the particular one of the one or more relationship edges in the computer memory… receiving a query to determine the status of the particular relationship in the knowledge graph; and automatically…without human intervention. Storing entity components and requirements produced by the method is insignificant extra-solution activity of data storage that does not add a meaningful limitation to the process of knowledge graph creation. Using stored and streamed data is also insignificant extra-solution activity of data gathering that does not add a meaningful limitation to the process of knowledge graph creation. Executing the method with a processing unit of a computing system and specifying that processing happens automatically or without human intervention are recited at a high level of generality, i.e., as a generic computer performing generic computer functions. Receiving a query to determine the status of a relationship in the graph is insignificant extra-solution activity of data gathering that does not add a meaningful limitation to the process of determining compliance with the knowledge graph (Step 2A prong 2: NO).
Step 2B: These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since they since they only amount to data storing without significantly more (MPEP 2106.05(g)) or provide nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)). These limitations, taken either alone or in combination, fail to provide an inventive concept (Step 2B: NO). Thus, the claim is not patent eligible.
Regarding claims 15 and 16, they recite similar limitations to claims 4 and 5 and are rejected on the same grounds – see above.
Regarding claim 17:
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. Claim 17 is directed to a method (Step 1: YES).
Step 2A prong 1: Does the claim recite a judicial exception? Claim 17 recites: A computer-implemented method of determining whether a financial transaction, event, action or entity is in compliance with a regulation, performed using a multi-dimensional knowledge graph that includes entities and relationships between entities, the method… comprising: generating an initial entity component of the knowledge graph using underlying stored or streamed data, the entity component including a plurality of entity nodes and one or more relationship edges comprising connections between the entities (creating an entity component with nodes and edges by analyzing data can be performed in the human mind with the aid of pen and paper and is a mental process)… associating at least a first requirement with a particular one of the one or more of the relationship edges, the at least one requirement defining an iterative or recursive functional description (“function”) of the relationship edge to which it is associated, wherein the function defines a dependency of the relationship upon conditions, parameters and other factors (associating an edge of the entity component with a recursive or iterative function definition is a mental process) and wherein each iteration or recursion of the requirement defines a dimension of the knowledge graph and such iteration or recursion proceeds… until all conditions, parameters and other factors that determine a state of the particular one of the particular one or more relationships edges are included in the graph (expanding the knowledge graph by repeatedly performing or calculating the iterative or recursive function is a mental process and/or mathematical calculation)… and… determining the status of the relationship by ascertaining all of the connections and associated functions connected to the relationship and calculating all of the recursively defined functions (determining the relationships status by ascertaining relevant functions and connections and calculating the functions is a mixture of mental processes and mathematical calculations) These steps can be performed mentally or are mathematical calculations (Step 2A prong 1: YES).
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, considered individually and in combination, integrate the judicial exception into a practical application? Claim 17 recites: executed by a processing unit of a computing system… wherein the plurality of entity nodes include financial transactions, events, actions or participants, and wherein relationships between the financial transactions relate to compliance of the financial transactions, event, actions or entities with regulations; storing the entity component in a computer memory… without human intervention… storing all associated requirements included in the knowledge graph for the particular one of the one or more relationship edges in the computer memory… querying the knowledge graph to ascertain the compliance status of a relationship present in the knowledge graph… automatically…. Executing the method using a processing unit of a computing system and specifying that steps occur automatically/without human intervention are recited at a high level of generality, i.e., as a generic computer performing generic computer functions. Specifying that the entity nodes include financial transactions, events, actions or participants, and that the relationships have a compliance status is an additional element specifying a field of use without significantly more. Storing entity components and requirements produced by the method and querying the knowledge graph to determine the status of a relationship are insignificant extra-solution activities of storing results or data gathering, respectively, that do not add a meaningful limitation to the process of determining compliance with the knowledge graph (Step 2A prong 2: NO).
Step 2B: These elements are recited at such a high level of generality that they fail to integrate the abstract idea into a practical application, since they since they only amount to data storing without significantly more (MPEP 2106.05(g)) or provide nothing more than mere instructions to implement an abstract idea on a generic computer (MPEP 2106.05(f)). These limitations, taken either alone or in combination, fail to provide an inventive concept (Step 2B: NO). Thus, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 3, 4 and 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lopez Garcia et al. (US 20200357001 A1), herein Lopez Garcia, in view of Zheng et al. (“A Novel Conditional Knowledge Graph Representation and Construction”, 2021), herein Zheng.
Regarding claim 1, Lopez Garcia teaches: A computer-implemented method of providing a multi-dimensional knowledge graph that includes entities and relationships between entities, the method, executed by a processing unit of a computing system, comprising: generating an initial entity component of the knowledge graph using underlying stored or streamed data, the entity component including a plurality of entity nodes and one or more relationship edges comprising connections between the entities (¶73, the knowledge graph (“KG”) may be a graph-theoretic knowledge representation one or more model entities and attribute values as nodes, and relationships and attributes (e.g., conditions) as labeled, directed edges); storing the entity component in a computer memory (¶50, Program/utility 40, having a set (at least one) of program modules 42, may be stored in system memory); associating at least a first requirement with a particular one of the one or more of the relationship edges, the at least one requirement defining an… description (“function”) of the relationship edge to which it is associated, wherein the function defines a dependency of the relationship upon conditions, parameters and other factors (¶84, use (as input) the knowledge graphs 506 representing one or more operational rules – and – ¶91, The operational rule (e.g., a policy benefit rule) in this example includes a conditions (and corresponding values) that a claim needs to fulfill to be valid, which would then receive a compliance tag)… and storing all associated requirements included in the knowledge graph for the particular one of the one or more relationship edges in the computer memory (¶50, Program/utility 40, having a set (at least one) of program modules 42, may be stored in system memory).
Lopez Garcia fails to explicitly teach: defining an iterative or recursive functional description… wherein each iteration or recursion of the requirement defines a dimension of the knowledge graph and such iteration or recursion proceeds without human intervention until all conditions, parameters and other factors that determine a state of the particular one of the particular one or more relationships edges included in the graph.
However, in the same field of endeavor, Zheng teaches: defining an iterative or recursive functional description… wherein each iteration or recursion of the requirement defines a dimension of the knowledge graph and such iteration or recursion proceeds without human intervention until all conditions, parameters and other factors that determine a state of the particular one of the particular one or more relationships edges included in the graph (pg. 389, ¶2, Otherwise, we split the nested triple to get the corresponding subject, predicate and object, recursively execute insertToDB on subject and object, and return the id of the triple in the table as the subject or object of the previous recursion to implement the insertion of the nested triple – the results of this recursive process can be seen in the graph on pg. 386, fig. 2).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to recursively define knowledge graph dimensions as disclosed by Zheng in the method disclosed by Lopez Garcia to represent hierarchical relationships within data (pg. 385, represent entity-level and triple-level semantic relations simultaneously).
Regarding claim 3, Lopez Garcia fails to teach: The method of claim 1, further comprising displaying the knowledge graph such that the different dimensions, representing different levels of recursion, are presented as spatially separated to thereby provide a visual representation of the levels of recursion.
However, in the same field of endeavor, Zheng teaches: further comprising displaying the knowledge graph such that the different dimensions, representing different levels of recursion, are presented as spatially separated to thereby provide a visual representation of the levels of recursion (pg. 386, fig. 2, the graph has a nested structure created as a result of different levels of recursion).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to display the knowledge graph with spatially separated dimensions as disclosed by Zheng in the method disclosed by Lopez Garcia to intuitively represent data (pg. 386, Figure 2 illustrates our proposed conditional knowledge graph representation more intuitively).
Regarding claim 4, Lopez Garcia fails to teach: The method of claim 1, wherein the function includes one or more of: a) logical and rule-based relationships; b) multivariate functions; c) temporal functions; d) vector functions; e) stochastic functions.; f) deterministic functions; g) static functions and h) hybrid functions.
However, in the same field of endeavor, Zheng teaches: wherein the function includes one or more of: a) logical and rule-based relationships; b) multivariate functions; c) temporal functions; d) vector functions; e) stochastic functions.; f) deterministic functions; g) static functions and h) hybrid functions (pg. 386, fig. 2, logical and rule-based relationships like “if” and “or” and temporal functions like “during” or “for more than 15 minutes”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use logical and rule-based relationships and temporal functions as disclosed by Zheng in the method disclosed by Lopez Garcia to accurately describe data (¶2, To capture and formalize the conditional semantic information accurately).
Regarding claim 5, Lopez Garcia fails to teach: The method of claim 4, wherein the logical and rule-base relationships include one or more of: a) OR; b) AND; c) XOR; d) XNOR; e) NAND; f) NOR; g) negation (NOT); and h) if/then conditional statements including if then else and if then only.
However, in the same field of endeavor, Zheng teaches: wherein the logical and rule-base relationships include one or more of: a) OR; b) AND; c) XOR; d) XNOR; e) NAND; f) NOR; g) negation (NOT); and h) if/then conditional statements including if then else and if then only (pg. 386, fig. 2, logical and rule-based relationships like “if” and “or”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use logical and rule-based relationships like OR and IF conditional statements as disclosed by Zheng in the method disclosed by Lopez Garcia to accurately describe data (¶2, To capture and formalize the conditional semantic information accurately).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lopez Garcia in view of Zheng as applied to claim 1 above, and further in view of Kumar (US 20220180215 A1).
Regarding claim 2, Lopez Garcia in view of Zheng fails to explicitly teach: The method of claim 1, further comprising automatically updating a status of the entities, relationships and one or more function based on underlying data.
However, in the same field of endeavor, Kumar teaches: further comprising automatically updating a status of the entities, relationships and one or more function based on underlying data (¶32, Knowledge graph is configured to automatically create new relationships and update strength of existing relationships when a new document or entity is added).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to automatically update the knowledge graph as disclosed by Kumar in the graph creation method disclosed by Lopez Garcia to achieve human-like learning (fig. 2, Mimicking Human Brain).
Claim(s) 6 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lopez Garcia in view of Zheng as applied to claim 1 above, and further in view of Malynin et al. (US 20210271965 A1), herein Malynin.
Regarding claim 6, Lopez Garcia in view of Zheng fails to teach: The method of claim 1, wherein the function is learned using a machine learning system.
However, in the same field of endeavor, Malynin teaches: wherein the function is learned using a machine learning system (¶37, generates a calculation graph neural network from a knowledge graph or portion of a knowledge graph defining a function).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to learn a function with a machine learning system as disclosed by Malynin in the method disclosed by Lopez Garcia in view of Zheng to model non-linear interactions in functions (¶21, allows complicated functions that are unable to be described using linear functions may be optimized dynamically).
Regarding claim 7, Lopez Garcia fails to teach: The method of claim 6, wherein the machine learning system includes a neural-network based architecture.
However, in the same field of endeavor, Malynin teaches: wherein the machine learning system includes a neural-network based architecture (¶37, generates a calculation graph neural network from a knowledge graph or portion of a knowledge graph defining a function).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to learn a function with a neural network based machine learning system as disclosed by Malynin in the method disclosed by Lopez Garcia to model non-linear interactions in functions (¶21, allows complicated functions that are unable to be described using linear functions may be optimized dynamically).
Claim(s) 8-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lopez Garcia in view of Zheng and Malynin as applied to claim 7 above, and further in view of Huettner et al. (US 20220148679 A1), herein Huettner.
Regarding claim 8, Lopez Garcia in view of Zheng and Malynin fails to teach: The method of claim 7, wherein the neural-network based architecture includes one or more sub-components including one or more of a convolutional neural network, a multi-layer perceptron, a recurrent neural network, and a Boltzmann network.
However, in the same field of endeavor, Huettner teaches: wherein the neural-network based architecture includes one or more sub-components including one or more of a convolutional neural network, a multi-layer perceptron, a recurrent neural network, and a Boltzmann network (¶44, Deep learning ML computer models use neural networks, such as multi-layer perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), Boltzmann machines, autoencoders, and the like).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use one or more of a variety of neural network types as disclosed by Huettner in the method disclosed by Lopez Garcia in view of Zheng, Malynin and Huettner to learn complex features from data (¶44, to progressively extract higher-level features from raw input data).
Regarding claim 9, Lopez Garcia further teaches: The method of claim 8, further comprising: inputting historical data, entity-related information, previously generated knowledge graphs into the machine learning system; inputting a new knowledge graph invoking the machine learning system to approximate a relationship function; and approximating the function, using the machine learning system, by a process of matching information in the new knowledge graph with information provided in the historical data, entity-related information, previously generated knowledge graphs (¶68, The machine learning component 470 may learn those of the one or more policies or conditions from the knowledge graph that identify the operational data as being non-compliant operational data from historical data, user feedback, one or more non-compliant operational rules, or a combination thereof – operational rules can be in the form of knowledge graphs, as described in ¶63, determine the set of operational rules as knowledge graphs).
Regarding claim 10, Lopez Garcia further teaches: The method of claim 1, wherein the plurality of entity nodes include financial transactions, events, actions or entities, and wherein relationships between the financial transactions relate to compliance of the financial transactions, event, actions or participants with regulations (¶3, Also, many businesses and organizations, such as financial institutions, employing the use of computing systems and online data must ensure operations, practices, and/or procedures are in compliance with… legal regulations – and – ¶21, The knowledge may be analyzed, interpreted, and/or learned to create operational rules (e.g., business benefit rules) that may (automatically) determine a compliance and/or non-compliance to legal or policy constraints of an enterprise's operations – the operations and procedures of a financial institution encompass financial transactions).
Regarding claim 11, Lopez Garcia further teaches: The method of claim 10, wherein at least the first requirement from the one or more of the relationships defines a function (¶76, The output of non-compliance operational rule generator 510 is one or more operational rules (e.g., represented as knowledge graph 506) – the claimed knowledge graph includes at least one iteratively or recursive requirement which defines a dimension of the knowledge graph, which itself can be another graph) that determines whether the financial transactions, events, actions or entities are in compliance with regulations (¶76, operational rule… that describe non-compliance, as in block 2B).
Regarding claim 12, Lopez Garcia further teaches: The method of claim 10, further comprising: determining a status of a compliance-related relationship between the entities of the knowledge graph by calculating the function of the function associated with the relationship edge; detecting whether at least one of the financial transactions, event, action or entity is in compliance based on the determined status of the relationships of the knowledge graph (¶76, The output of non-compliance operational rule generator 510 is one or more operational rules (e.g., represented as knowledge graph 506) that describe non-compliance, as in block 2B – the output of the operational rule, i.e., a compliance-related relationship, is used to detect if the input to the rule is non-compliant).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lopez Garcia in view of Zheng as applied to claim 12 above, and further in view of Hassanzadeh et al. (US 20220414661 A1), herein Hassanzadeh.
Regarding claim 13, Lopez Garcia in view of Zheng fails to teach: The method of claim 12, wherein the function is calculated based on conditions including one or more of: a time or date, a geographical location, and a citizenship of one or more of the financial transactions, events, actions or entities.
However, in the same field of endeavor, Hassanzadeh teaches: wherein the function is calculated based on conditions including one or more of: a time or date, a geographical location, and a citizenship of one or more of the financial transactions, events, actions or entities (¶89, the input financial data indicates a transaction history, a billing address, an available credit line, a last transaction location, a transaction time, an amount of transactions during a threshold time period, or a combination thereof).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to automatically search and evaluate portions of the knowledge graph as disclosed by Beller in the method disclosed by Lopez Garcia in view of Zheng to provide desired information to users (¶31, returns the results of the search as query results 126 to requestor 120)
Claim(s) 14 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lopez Garcia in view of Zheng and Beller et al. (US 20200311565 A1), herein Beller.
Regarding claim 14, Lopez Garcia teaches: A computer-implemented method of determining a status of a relationship in a multi-dimensional knowledge graph that includes entities and relationships between entities, the method, executed by a processing unit of a computing system, comprising: generating an initial entity component of the knowledge graph using underlying stored or streamed data, the entity component including a plurality of entity nodes and one or more relationship edges comprising connections between the entities (¶73, the knowledge graph (“KG”) may be a graph-theoretic knowledge representation one or more model entities and attribute values as nodes, and relationships and attributes (e.g., conditions) as labeled, directed edges); storing the entity component in a computer memory (¶50, Program/utility 40, having a set (at least one) of program modules 42, may be stored in system memory); associating at least a first requirement with a particular one of the one or more of the relationship edges, the at least one requirement defining an… functional description (“function”) of the relationship edge to which it is associated, wherein the function defines a dependency of the relationship upon conditions, parameters and other factors (¶84, use (as input) the knowledge graphs 506 representing one or more operational rules – and – ¶91, The operational rule (e.g., a policy benefit rule) in this example includes a conditions (and corresponding values) that a claim needs to fulfill to be valid, which would then receive a compliance tag)… storing all associated requirements included in the knowledge graph for the particular one of the one or more relationship edges in the computer memory (¶50, Program/utility 40, having a set (at least one) of program modules 42, may be stored in system memory).
Lopez Garcia fails to teach: defining an iterative or recursive functional description… and wherein each iteration or recursion of the requirement defines a dimension of the knowledge graph and such iteration or recursion proceeds without human intervention until all conditions, parameters and other factors that determine a state of the particular one of the particular one or more relationships edges are included in the graph;
However, in the same field of endeavor, Zheng teaches: defining an iterative or recursive functional description… and wherein each iteration or recursion of the requirement defines a dimension of the knowledge graph and such iteration or recursion proceeds without human intervention until all conditions, parameters and other factors that determine a state of the particular one of the particular one or more relationships edges are included in the graph (pg. 389, ¶2, Otherwise, we split the nested triple to get the corresponding subject, predicate and object, recursively execute insertToDB on subject and object, and return the id of the triple in the table as the subject or object of the previous recursion to implement the insertion of the nested triple).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to recursively define knowledge graph dimensions as disclosed by Zheng in the method disclosed by Lopez Garcia to capture hierarchical relations within data (pg. 385, represent entity-level and triple-level semantic relations simultaneously).
Lopez Garcia in view of Zheng fails to teach: receiving a query to determine the status of the particular relationship in the knowledge graph; locating the particular relationship in the knowledge graph; and automatically determining the status of the relationship by ascertaining all of the requirements associated with the relationship and calculating all of the functions, without human intervention.
However, in the same field of endeavor, Beller teaches: receiving a query to determine the status of the particular relationship in the knowledge graph; locating the particular relationship in the knowledge graph; and automatically determining the status of the relationship by ascertaining all of the requirements associated with the relationship and calculating all of the functions, without human intervention (¶42, in response to receiving a knowledge graph query identifying multiple entities, may search knowledge graph 114 for mentions of each of the entities and also evaluate the relationships indicated by the mentions of each of the entities).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to automatically search and evaluate portions of the knowledge graph when queried, as disclosed by Beller, in the method disclosed by Lopez Garcia in view of Zheng to provide desired information to users (¶31, returns the results of the search as query results 126 to requestor 120).
Regarding claim 15, it recites similar limitations to claim 4 and is rejected on the same grounds – see above.
Regarding claim 16, it recites similar limitations to claim 5 and is rejected on the same grounds—see above.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lopez Garcia in view of Zheng and Beller.
Regarding claim 17, Lopez Garcia teaches: A computer-implemented method of determining whether a financial transaction, event, action or entity is in compliance with a regulation (¶76, The output of non-compliance operational rule generator 510 is one or more operational rules (e.g., represented as knowledge graph 506) that describe non-compliance, as in block 2B), performed using a multi-dimensional knowledge graph that includes entities and relationships between entities (¶73, the knowledge graph (“KG”) may be a graph-theoretic knowledge representation one or more model entities and attribute values as nodes, and relationships and attributes (e.g., conditions) as labeled, directed edges), the method, executed by a processing unit of a computing system, comprising: generating an initial entity component of the knowledge graph using underlying stored or streamed data (¶68, the knowledge graph that identify the operational data as being non-compliant operational data from historical data, user feedback, one or more non-compliant operational rules, or a combination thereof), the entity component including a plurality of entity nodes and one or more relationship edges comprising connections between the entities wherein the plurality of entity nodes include financial transactions, events, actions or participants (¶3, Also, many businesses and organizations, such as financial institutions, employing the use of computing systems and online data must ensure operations, practices, and/or procedures are in compliance – and – ¶21, a knowledge graph representing policy regulations and structured or semi-structured enterprise's operational data (e.g., a claim) – an insurance claim payout, for example, is a financial transaction), and wherein relationships between the financial transactions relate to compliance of the financial transactions, event, actions or entities with regulations (¶84, knowledge graphs 506 representing one or more operational rules – and – ¶91, The operational rule… includes… conditions (and corresponding values) that a claim needs to fulfill to be valid, which would then receive a compliance tag); storing the entity component in a computer memory (¶50, Program/utility 40, having a set (at least one) of program modules 42, may be stored in system memory); associating at least a first requirement with a particular one of the one or more of the relationship edges, the at least one requirement defining an… functional description (“function”) of the relationship edge to which it is associated, wherein the function defines a dependency of the relationship upon conditions, parameters and other factors (¶91, The operational rule… includes… conditions (and corresponding values) that a claim needs to fulfill to be valid, which would then receive a compliance tag)… storing all associated requirements included in the knowledge graph for the particular one of the one or more relationship edges in the computer memory (¶50, Program/utility 40, having a set (at least one) of program modules 42, may be stored in system memory)…
Lopez Garcia fails to teach: defining an iterative or recursive functional description… and wherein each iteration or recursion of the requirement defines a dimension of the knowledge graph and such iteration or recursion proceeds without human intervention until all conditions, parameters and other factors that determine a state of the particular one of the particular one or more relationships edges are included in the graph.
However, in the same field of endeavor, Zheng teaches: defining an iterative or recursive functional description… and wherein each iteration or recursion of the requirement defines a dimension of the knowledge graph and such iteration or recursion proceeds without human intervention until all conditions, parameters and other factors that determine a state of the particular one of the particular one or more relationships edges are included in the graph.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to recursively define knowledge graph dimensions as disclosed by Zheng in the method disclosed by Lopez Garcia to define hierarchical relations within data (pg. 385, represent entity-level and triple-level semantic relations simultaneously).
Lopez Garcia in view of Zheng fails to teach: querying the knowledge graph to ascertain the compliance status of a relationship present in the knowledge graph; and automatically determining the status of the relationship by ascertaining all of the connections and associated functions connected to the relationship and calculating all of the recursively defined functions.
However, in the same field of endeavor, Beller teaches: querying the knowledge graph to ascertain the compliance status of a relationship present in the knowledge graph; and automatically determining the status of the relationship by ascertaining all of the connections and associated functions connected to the relationship and calculating all of the recursively defined functions (¶42, in response to receiving a knowledge graph query identifying multiple entities, may search knowledge graph 114 for mentions of each of the entities and also evaluate the relationships indicated by the mentions of each of the entities).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to automatically search and evaluate portions of the knowledge graph when queried as disclosed by Beller in the method disclosed by Lopez Garcia in view of Zheng to provide desired information to users (¶31, returns the results of the search as query results 126 to requestor 120).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lopez Garcia in view of Zheng and Beller as applied to claim 17 above, and further in view of Hassanzadeh.
Regarding claim 18, it recites similar limitations to claim 13 and is rejected on the same grounds – see above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HARRISON C KIM/ Examiner, Art Unit 2145
/CESAR B PAULA/ Supervisory Patent Examiner, Art Unit 2145