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 .
Claim(s) 1-20 are pending for examination. Claim(s) 1, 2, 4-8, 11, 12, 14-17, and 20 have been amended. Claim(s) 1, 11, and 20 are independent claims. This action is Non-Final.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/29/2026 has been entered.
Response to Arguments
Applicant's arguments filed 6/29/2026 with respect to the 35 U.S.C. 103 rejection have been fully considered but they are not persuasive.
Applicant Argues: In response to this rejection, Applicants have amended independent claims 1, 11, and 20 in order to positively and meaningfully tie the subject matter to a particular apparatus and specific structural components.
Specifically, independent claims 1, 11, and 20 similarly recite, in part:
[...]
Independent claims 1, 11, and 20 are not directed to merely evaluating compliance but rather include the above-recited features for generating and tracing the knowledge graph so as to find the knowledge graph traversal path including the first target node, which has the highest semantic similarity from the query vector and the one or more current nodes. These recited features recite a specific technological solution for navigating complex graph structures.
Further, building the ontology, generating the knowledge graph from parsed text entities, filtering target nodes by removing candidate target nodes, and tracing through every hierarchical level based on metadata cannot be practically performed in human mind aided by pen and paper.
Furthermore, the above-recited features of independent claims 1, 11, and 20 involve technological improvements to data organization and retrieval rather than to business-method cases. Thus, independent claims 1, 11, and 20 are believed to be patent eligible.
Even assuming, arguendo, that independent claims 1, 11, and 20 recites abstract idea, the ordered combination of the above-recited features provides significantly more than the alleged abstract idea.
Therefore, in view of the above, Applicant respectfully submits that independent claims 1, 11, and 20 are patent eligible.
Claims 2-10 depend from independent claim 1, and claims 12-19 depend from independent claim 11. Therefore, Applicant respectfully submits that these dependent claims are also patent eligible for at least the same reasons set forth above with respect to independent claims 1 and 11.
Accordingly, Applicant respectfully requests that the rejections of claims 1-20 under 35 U.S.C. § 101 be withdrawn.
Examiner’s Response: The examiner respectfully disagrees. The claims are in fact to “evaluating compliance.” The examiner respectfully notes that such a concept is directed towards “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they recite “commercial interactions" in the form of business relations. The examiner respectfully notes that “evaluating compliance but rather include the above-recited features for generating and tracing the knowledge graph so as to find the knowledge graph traversal path including the first target node, which has the highest semantic similarity from the query vector and the one or more current nodes” is noted to be part of the abstract idea, as noted in the 35 U.S.C. 101 rejection noted below. Further, these claimed features relate to Applicant’s Specification, ⁋[0019] – “...a dynamic compliance knowledge graph provides functionality to map a user’s current status to the dynamic compliance knowledge graph and to provide users with a metric indicating the user’s level of compliance with regulatory laws and codes.” Thus, the claim does in fact relate to a form of a business relation. Thus, these features would fall under the abstract idea itself, and, thus would not provide a specific technological solution for navigating complex graph structures. The examiner respectfully notes that the claims are directed towards “Certain Methods of Organizing Human Activity.”
Further, the human mind with pen and paper is capable of “navigating complex graph structures” and is further capable of “building the ontology, generating the knowledge graph from parsed text entities, filtering target nodes by removing candidate target nodes, and tracing through every hierarchical level based on metadata.” The examiner respectfully notes that the claims are directed towards “Mental Processes.”
Further, the examiner disagrees with respect to the argument directed “technological improvements to data organization and retrieval” as this is an a result from the high level “apply it” use of a processor and media. The examiner respectfully notes that these features a processor and media to “organize and retrieve data (i.e., part of the abstract idea)” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, such features are noted to be mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, there are no meaningful limitations that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Therefore, the examiner finds this argument not persuasive.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: claim(s) 1-20 are directed to a machine, process, and/or manufacture. Therefore, the claims are directed to statutory subject matter under Step 1 (Step 1: YES). See MPEP 2106.03.
Prong 1, Step 2A: claim 1, and for similar claim(s) 11 and 20, taken as representative, recites at least the following limitations that recite an abstract idea:
receive a
generate a knowledge graph from text within the
parsing the text into constitute elements and performing
creating nodes based on one or more parsed text entities within the
creating edges based on relationships between the nodes within the
building ontology from the parsed text; and
generating the knowledge graph based on the nodes and the edges, wherein the ontology defines hierarchical structure of the knowledge graph identifying the hierarchical levels;
identifying one or more current nodes within the knowledge graph by mapping a current status of the manufacturing process to the knowledge graph;
receive a query regarding compliance to the
embedding the query into a query vector
represent each node of the knowledge graph as a node vector;
calculate a metric between each node vector to and query vector including at least one cosine similarity, an L1 norm, or an L2 norm; and
generate one or more knowledge graph traversal paths to one or more target nodes based on metrics, wherein the one or more target nodes correspond to desired statuses identified based on the query, wherein one or more knowledge graph traversal paths are generated by:
selecting a first target node based on a highest semantic similarity between the query vector and the node vectors and based on a metric between the first target node and the one or more current nodes,
filtering additional target nodes using the first target node as a guardrail by removing candidate target nodes among the additional target nodes, which have a sematic similarity relative to the first target node that fails to satisfy a threshold, and
generating each knowledge graph traversal path including the first target node by tracing back through every hierarchical level identified by the metadata associated with the first target node.
The above limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that they recite "commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations. The broadest reasonable interpretation of these limitations for claim 1, and for similar claim(s) 11 and 20, includes receive a file including a compliance file, which includes one or more levels of compliance processes in a hierarchical order; generate a knowledge graph from text within the file by: parsing the text into constitute elements and performing preprocessing on the parsed text; creating nodes based on one or more parsed text entities within the file, wherein each node includes metadata including hierarchal information identifying hierarchical levels of the file, to which the node belongs; creating edges based on relationships between the nodes within the file, including relationships including by entity extraction, relation extraction, and ontology building, building ontology from the parsed text; and generating the knowledge graph based on the nodes and the edges, wherein the ontology defines hierarchical structure of the knowledge graph identifying the hierarchical levels; identifying one or more current nodes within the knowledge graph by mapping a current status of the manufacturing process to the knowledge graph; receive a query regarding compliance to the file; embedding the query into a query vector; represent each node of the knowledge graph as a node vector; calculate a metric between each node vector to and query vector including at least one cosine similarity, an L1 norm, or an L2 norm; and generate one or more knowledge graph traversal paths to one or more target nodes based on metrics, wherein the one or more target nodes correspond to desired statuses identified based on the query, wherein one or more knowledge graph traversal paths are generated by: selecting a first target node based on a highest semantic similarity between the query vector and the node vectors and based on a metric between the first target node and the one or more current nodes, filtering additional target nodes using the first target node as a guardrail by removing candidate target nodes among the additional target nodes, which have a sematic similarity relative to the first target node that fails to satisfy a threshold, and generating each knowledge graph traversal path including the first target node by tracing back through every hierarchical level identified by the metadata associated with the first target node, thus, claim 1, and similar claim(s) 11and 20, falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they recite “commercial interactions" in the form of business relations.
The above limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(III), in that they recite as concepts performed in the human mind, including observations, evaluations, judgments, and opinions. That is, other than reciting for claim 1, and for similar claim(s) 11 and 20, i.e., system with processor and computer-readable media and further a digital file and use of natural language processing and an embedding model; nothing in these claim element(s) precludes the step(s) from practically being performed in the mind. For example, the broadest reasonable interpretation of these limitations for claim 1, and for similar claim(s) 11 and 20, includes receive a file including a compliance file, which includes one or more levels of compliance processes in a hierarchical order; generate a knowledge graph from text within the file by: parsing the text into constitute elements and performing preprocessing on the parsed text; creating nodes based on one or more parsed text entities within the file, wherein each node includes metadata including hierarchal information identifying hierarchical levels of the file, to which the node belongs; creating edges based on relationships between the nodes within the file, including relationships including by entity extraction, relation extraction, and ontology building, building ontology from the parsed text; and generating the knowledge graph based on the nodes and the edges, wherein the ontology defines hierarchical structure of the knowledge graph identifying the hierarchical levels; identifying one or more current nodes within the knowledge graph by mapping a current status of the manufacturing process to the knowledge graph; receive a query regarding compliance to the file; embedding the query into a query vector; represent each node of the knowledge graph as a node vector; calculate a metric between each node vector to and query vector including at least one cosine similarity, an L1 norm, or an L2 norm; and generate one or more knowledge graph traversal paths to one or more target nodes based on metrics, wherein the one or more target nodes correspond to desired statuses identified based on the query, wherein one or more knowledge graph traversal paths are generated by: selecting a first target node based on a highest semantic similarity between the query vector and the node vectors and based on a metric between the first target node and the one or more current nodes, filtering additional target nodes using the first target node as a guardrail by removing candidate target nodes among the additional target nodes, which have a sematic similarity relative to the first target node that fails to satisfy a threshold, and generating each knowledge graph traversal path including the first target node by tracing back through every hierarchical level identified by the metadata associated with the first target node, which, encompass steps that a user can manually perform in the human mind or by a human using a pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas.
Accordingly, these claims recite an abstract idea. (Prong 1, Step 2A: YES). The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Prong 2, Step 2A: Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Claim 1, and for similar claim(s) 11 and 20, recite i.e., system with processor and computer-readable media and further a digital file and use of natural language processing and an embedding model. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of claim 1, and for similar claim(s) 11 and 20 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). See MPEP 2106.04(d).
Since claim 1, and for similar claim(s) 11 and 20 recites an abstract idea and fails to integrate the abstract idea into a practical application, claim 1, and for similar claim(s) 11 and 20 is “directed to” an abstract idea under Step 2A (Step 2A: YES). See MPEP 2106.04(d).
Step 2B: The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of for claim 1, and for similar claim(s) 11 and 20, i.e., system with processor and computer-readable media and further a digital file and use of natural language processing and an embedding model; thus, amounts to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in claim 1, and for similar claim(s) 11 and 20 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO). See MPEP 2106.05.
Accordingly, under the Subject Matter Eligibility test, claim 1, and for similar claim(s) 11 and 20 is ineligible.
Regarding Claims 2-10 and 12-19, claims 2-10 and 12-19 further defines the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above w/ respect to “commercial interactions” or “legal interactions” include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations i.e., further features related to a dynamic compliance knowledge graph and/or further recite “Mental Processes” as the claims recite further concepts that can be performed in the human mind, including observations, evaluations, judgments, and opinions. These dependent claim does not include any additional elements that integrate the abstract idea into a practical application; as such elements are recited at a high level of generality such that it amounts not more than mere instructions to apply the exception using a generic computer component. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do no not amount to significantly more than the abstract idea itself. Thus, the aforementioned claims are not patent-eligible.
Reasons For No Prior Art Rejection
Upon review of the evidence at hand, it is hereby concluded that the evidence obtained and made of record, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of applicant’s invention as the noted features amount to more than a predictable use of elements in the prior art.
The closest prior art of record noted below:
Brecque (US 11,087,219 B1) discusses a semantic document generation system is described. The semantic document is composed of document details, people and meta-data. The semantic document is self-aware of the information it contains. The semantic document's structure and terms are governed by legal, logical and party related rules. A semantic contract can be created from a semantic document generation system. The semantic document generation system receives an indication of a type of a document to be generated and plurality of terms for the document from a plurality of sources. The terms are converted into triples. A plurality of rules governing the terms of the document is applied to the triples to generate a knowledge graph and determine whether terms from the different parties are compatible. The terms are determined to be compatible in a case where the plurality of rules governing terms of the document is satisfied. If at least one set of terms is non-compatible, the system reconciles the non-compatible terms in the generated knowledge graph until all the terms are compatible, and generates the document based at least on the reconciled knowledge graph. (Abstract).
Kaur et al (US 2023/0237512 A1) discusses a method and a system for automatically processing financial documents to generate knowledge graphs that convey information relating to entities of interest and relationships between those entities are provided. The method includes: receiving a document; extracting raw text included in the document; identifying, based on the extracted raw text, a set of entities that are named in the document; determining respective relationship information that corresponds to respective pairs of entities; constructing a knowledge graph that illustrates respective relationships among the respective pairs of entities; and outputting the knowledge graph. The determination of the respective relationship information may be performed by applying an artificial intelligence (AI) algorithm that is trained by using historical data that relates to the set of entities.. (Abstract)
Kurshan (US 2024/0054320 A1) discusses a method of providing a multi-dimensional knowledge graph including entities and relationships between entities comprises generating an initial entity component of the knowledge graph using underlying data including a plurality of entity nodes and one or more relationship edges that connect the entities, storing the entity component in computer memory, associating at least a first requirement with a one the relationship edges that defines an iterative or recursive functional description (“function”) of the relationship edge, wherein the function defines a dependency of the relationship upon conditions, parameters and other factors, each iteration or recursion of the requirement defining a dimension of the knowledge graph. The recursion proceeds until all conditions, parameters and other factors that determine a state of the relationships edge is included in the graph. The associated requirements included in the knowledge graph for the relationship edges are then store in computer memory. (Abstract).
However, regarding claim 1, and for similar claim(s) 11 and 20, the prior art of record as cited within this Office Action, nor those cited, in as additional references on the PTO-892, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the features of:
A computer system for implementing a dynamic compliance knowledge graph associated with a manufacture process, the computer comprising:
one or more processors; and
one or more computer-readable media having stored thereon executable instructions that when executed by the one or more processors configure the computer system to:
receive a digital file including a compliance file, which includes one or more levels of compliance processes in a hierarchical order;
generate a knowledge graph from text within the digital file by:
parsing the text into constitute elements and performing natural language preprocessing on the parsed text;
creating nodes based on one or more parsed text entities within the digital file, wherein each node includes metadata including hierarchal information identifying hierarchical levels of the digital file, to which the node belongs;
creating edges based on relationships between the nodes within the digital file, including relationships including by entity extraction, relation extraction, and ontology building,
building ontology from the parsed text; and
generating the knowledge graph based on the nodes and the edges, wherein the ontology defines hierarchical structure of the knowledge graph identifying the hierarchical levels;
identifying one or more current nodes within the knowledge graph by mapping a current status of the manufacturing process to the knowledge graph;
receive a query regarding compliance to the digital file;
embedding the query into a query vector using an embedding model;
represent each node of the knowledge graph as a node vector;
calculate a metric between each node vector to and query vector including at least one cosine similarity, an L1 norm, or an L2 norm; and
generate one or more knowledge graph traversal paths to one or more target nodes based on metrics, wherein the one or more target nodes correspond to desired statuses identified based on the query, wherein one or more knowledge graph traversal paths are generated by:
selecting a first target node based on a highest semantic similarity between the query vector and the node vectors and based on a metric between the first target node and the one or more current nodes,
filtering additional target nodes using the first target node as a guardrail by removing candidate target nodes among the additional target nodes, which have a sematic similarity relative to the first target node that fails to satisfy a threshold, and
generating each knowledge graph traversal path including the first target node by tracing back through every hierarchical level identified by the metadata associated with the first target node.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASFAND M SHEIKH whose telephone number is (571)272-1466. The examiner can normally be reached Mon-Fri: 7a-3p (MDT).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JESSICA LEMIEUX can be reached at (571)270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ASFAND M SHEIKH/Primary Examiner, Art Unit 3626
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