DETAILED ACTION
The Action is responsive to Applicant’s Application filed July 15, 2025.
Please note claims 1-20 are pending.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged.
Drawings
The drawings, filed July 15, 2025 are considered in compliance with 37 CFR 1.81 and accepted.
Information Disclosure Statement
The information disclosure statements filed July 15, 2025 are in compliance with 37 CFR 1.97(c) and therein have been considered. Its corresponding PTO-1449 has been electronically signed as attached.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 5-6, 8, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bayless et al. (US Pub. No. 2025/0112878) further in view of Sen et al. (US Pub. No. 2019/0228068).
Regarding claim 1, Bayless teaches a method of restricting operation of a copilot to a domain of competence of the copilot, comprising:
‘building a knowledge graph of a corpus of documents, the knowledge graph comprising a plurality of vertices representing language tokens or concepts derived from the corpus of documents and edges representing relationships between respective pairs of the vertices’ as building a knowledge graph from a large corpus of text data where the nodes represent concepts and edges indicate relationships represented between the concepts (¶0019-20 45)
‘on a visualization of the knowledge graph, removing one or more portions of the knowledge graph to obtain a pruned graph representation of the copilot's domain of competence’ as performing knowledge graph modification to remove answers from the knowledge graph (¶0091) and curating domain-specific knowledge graphs (¶0028)
‘configuring the copilot to provide a dataflow leading from a client input to a corresponding output’ as configuring the chatbot to provide provenance data for the answers to a user query (¶0026)
Bayless fails to explicitly teach:
‘subsequently, at a qualification microservice within the copilot:
receiving, for testing, data outputted from another microservice within the copilot’
‘testing the data for conformance with the pruned graph representation’
‘excluding one or more non-conforming portions of the data from the dataflow leading to the corresponding output’
Sen teaches:
‘subsequently, at a qualification microservice within the copilot:
receiving, for testing, data outputted from another microservice within the copilot’ as multiple dialog states and processing components that cooperate to process user queries and connecting information produced during one dialog state to transition to subsequent dialog states (¶0018-23)
‘testing the data for conformance with the pruned graph representation’ as creating one or more dialog states for each determined dialog intention that represents a sub-graph (¶0039-40)
‘excluding one or more non-conforming portions of the data from the dataflow leading to the corresponding output’ as creating one or more connecting dialog states between pairs of dialog states based on a user question to derive to a corresponding answer (¶0041)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Sen’s would have allowed Bayless’ to create automata to improve chatbots understanding of natural language actions (¶0018)
Regarding claim 5, Sen teaches ‘wherein the received data is obtained directly or indirectly from a retrieval microservice of the copilot, and the method further comprises forwarding conforming portions of the data toward a core microservice of the copilot’ as multiple dialog states and processing components that cooperate to process user queries and connecting information produced during one dialog state to transition to subsequent dialog states (¶0018-23)
Regarding claim 6, Sen teaches ‘wherein the received data is obtained directly or indirectly from a core microservice of the copilot, and the method further comprises forwarding conforming portions of the data toward an evaluation microservice of the copilot.’ (¶0018-23)
Regarding claim 8, Bayless teaches one or more computer-readable media storing instructions which, when executed by one or more hardware processors, cause the one or more hardware processors to perform first and second operations, the first operations comprising:
‘building a knowledge graph of a corpus of documents’ as building a knowledge graph from a large corpus of text data where the nodes represent concepts and edges indicate relationships represented between the concepts (¶0019-20 45)
‘on a visualization of the knowledge graph, removing one or more portions of the knowledge graph to obtain a pruned graph representation of a domain of competence of a copilot’ as performing knowledge graph modification to remove answers from the knowledge graph (¶0091) and curating domain-specific knowledge graphs (¶0028)
‘wherein the copilot comprises a network of microservices configured to provide a dataflow leading from a client input to a corresponding output’ as configuring the chatbot to provide provenance data for the answers to a user query (¶0026)
Bayless fails to explicitly teach:
‘wherein the second operations cause a qualification microservice of the copilot to use the pruned graph representation to restrict operation of the copilot to the copilot's domain of competence, the second operations comprising:
testing data received by the qualification microservice for conformance with the pruned graph representation’
‘excluding one or more non-conforming portions of the data from the dataflow leading to the corresponding output’
Sen teaches:
‘wherein the second operations cause a qualification microservice of the copilot to use the pruned graph representation to restrict operation of the copilot to the copilot's domain of competence, the second operations comprising:
testing data received by the qualification microservice for conformance with the pruned graph representation’ as multiple dialog states and processing components that cooperate to process user queries and connecting information produced during one dialog state to transition to subsequent dialog states (¶0018-23) and creating one or more dialog states for each determined dialog intention that represents a sub-graph (¶0039-40)
‘excluding one or more non-conforming portions of the data from the dataflow leading to the corresponding output’ as creating one or more connecting dialog states between pairs of dialog states based on a user question to derive to a corresponding answer (¶0041)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Sen’s would have allowed Bayless’ to create automata to improve chatbots understanding of natural language actions (¶0018)
Regarding claim 12, Bayless teaches ‘wherein the removing is performed interactively’ as performing knowledge graph modification to remove answers from the knowledge graph (¶0091)
Claim(s) 2-3, 7, 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bayless et al. (US Pub. No. 2025/0112878) Sen et al. (US Pub. No. 2019/0228068) further in view of Marin et al. (US Pub. No. 2019/0034780)
Regarding claim 2, Bayless and Sen fail to explicitly teach ‘wherein the knowledge graph is built based on vector representations of the corpus of documents.’
Marin teaches ‘wherein the knowledge graph is built based on vector representations of the corpus of documents’ as precomputed vector representation of entities (¶0083)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Marin’s would have allowed Bayless and Sen’s to improve environments of tasks execution including searching (¶0004)
Regarding claim 3, Marin teaches ‘wherein the pruned graph representation comprises vector representations derived, after the removing, from the knowledge graph’ as mapping whole or part of a knowledge graph to vectors (¶0083)
Regarding claim 7, Marin teaches ‘wherein the data is in two or more of: an audio mode, an image mode, a numerical mode, or a text mode’ as processing data in the form of text, voice, touch, visual, etc. (¶0051)
Regarding claim 9, Bayless and Sen fail to explicitly teach ‘wherein the knowledge graph is built based on vector representations of the corpus of documents.’
Marin teaches ‘wherein the knowledge graph is built based on vector representations of the corpus of documents’ as precomputed vector representation of entities (¶0083)
It would have been obvious to one of ordinary skill in the art at the time that the present invention was effectively filed to modify the teachings of the cited references because Marin’s would have allowed Bayless and Sen’s to improve environments of tasks execution including searching (¶0004)
Regarding claim 10, Marin teaches ‘wherein the pruned graph representation comprises vector representations derived, after the removing, from the knowledge graph’ as mapping whole or part of a knowledge graph to vectors (¶0083)
Allowable Subject Matter
Claims 4, 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claims 13-20 are allowed.
While the cited references contain subject matter directed towards knowledge graph generation and pruning, neither Bayless, Sen or Marin, alone or in combination, fairly teach or suggest determining conformance based on tested data lying within an envelope defined based on the vertices of the knowledge graph remaining after the removing or a distance measure between the tested data and a vertex of the knowledge graph being less than or equal to a predetermined threshold.
Examiner’s Note
Examiner has cited particular columns/paragraphs and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as “Applicants believe no new matter has been introduced” may be deemed insufficient.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VAN OBERLY whose telephone number is (571)272-7025. The examiner can normally be reached Monday - Friday, 7:30am-4pm MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at (571) 272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VAN H OBERLY/Primary Examiner, Art Unit 2166
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