DETAILED ACTION
Introduction
1. This office action is in response to Applicant's submission filed on 06/28/2024. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are currently pending and examined below.
Drawings
2. The drawings filed on 06/28/2024 have been accepted and considered by the Examiner.
Information Disclosure Statement
3. The Information Statement (IDS) filed on 10/22/2025 has been accepted/considered and is in compliance with the provisions of 37 CFR 1.97.
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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 as being nothing more than an abstract idea. As an example, regarding claim 1, the limitations of obtaining question-answer pairs, processing them by: modifying them, identifying content portions within them, scoring them and generating new question-answer pairs from the aforementioned processing fall under the category of mental processes. These steps are drafted at a high level of generality without tying it to a specific technological improvement. More specifically, these steps can be performed in the mind of a human being with at most the aid of a pen and paper but for the recitation of generic computer components, and thus it falls within the -Mental Processes- grouping of abstract ideas. Accordingly, this claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the
recitation of a device, a system (including a RAG system), processor, AI model and/or a computer readable medium merely read to generalized computer components, based upon the claim interpretation wherein the structure is interpreted using the specification.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using generalized computer components to generate, extract, determine, and generate, amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is therefore not patent eligible.
Claims 2-8, only provide certain details of the mental processes outlined above, such receiving a subsequent query, training the RAG system, identifying subsets of query variations, mixing/mutating tokens and processing user feedback etc. These are all steps which themselves can also be accomplished by a human being with at most the aid of a pen and paper and hence also do not amount to significantly more than the judicial exception. Claims 8-14, are apparatus claims corresponding to method claims 8-14 and hence are also rejected at least for the reasons outlined above. Claims 15-20, are computer readable medium (CRM) claims corresponding to method claims 8-14 and hence are also rejected at least for the reasons outlined above.
Claim Rejections - 35 USC § 102
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 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 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 –
(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.
5. Claims 1-7, 9-13 and 15-20 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Larson (U.S. Patent Application Publication # 2025/0328565 A1).
With regards to claim 1, Larson teaches a computer-implemented method, executed on a computing device, comprising processing a plurality of query-answer pairs associated with a generative artificial intelligence (AI) model (Para 27, teaches a user of a scientific instrument who can have inquiries regarding how the scientific instrument should be operated, maintained, serviced, or troubleshot. In various aspects, such inquiries can be automatically answered by leveraging generative artificial intelligence. In particular, such inquiries can be automatically answered by leveraging retrieval augmented generative artificial intelligence or RAG-AI. This RAG-AI can involve a large language model or LLM);
generating a first set of query variations from the plurality of query-answer pairs using a genetic algorithm (Para 36, teaches RAG-AI boosting via composition of adjacent context-tagged text blocks via iterative graph-walking and embedding-change comparison);
identifying a plurality of content portions associated with the first set of query variations using a Retrieval Augmentation Generation (RAG) system (Para 36, teaches RAG-AI boosting via composition of adjacent context-tagged text blocks via iterative graph-walking and embedding-change comparison);
determining a fitness score associated with each of the query variations of the first set of query variations using the plurality of content portions (Para 36, further teaches text block re-ranking based on synthesized responses to chain-of-thought prompts. Para 39, further teaches that in order to reconcile or compare these differently-discovered context-tagged text blocks, a re-ranker can be implemented to assign to each discovered or found context-tagged text block a relevance score showing how relevant or irrelevant a respective context-tagged text block is to the given natural language question);
and generating a plurality of query variation-answer pairs by generating a second set of query variations from the first set of query variations using the genetic algorithm and the fitness scores associated with each of the first set of query variations (Para 36, further teaches a repository or database of document-graphs, each document-graph comprising respective context-tagged text blocks and prompt augmentation for identifier emphasis. Para 98, teaches generating a unified prompt, by concatenating those multiple top or most-relevant context-tagged text blocks with the plain text question).
With regards to claim 2, Larson teaches the computer-implemented method of claim 1, further comprising processing a subsequent query (Para 106, provides examples of multiple subsequent queries);
and providing an answer to the subsequent query from a semantic cache using a query variation-answer pair from the plurality of query variation-answer pairs (Para 98, teaches a document-graph can represent the semantic or substantive structure and content of a respective technical document. For any potentially-relevant context-tagged text block, the second logic can involve generating a composed context-tagged text block, via iterative graph-walking and embedding-change comparison with respect to that potentially-relevant context-tagged text block).
With regards to claim 3, Larson teaches the computer-implemented method of claim 1, further comprising training the RAG system using the plurality of query variation-answer pairs (Para 214, teaches that when it is desired to train the re-ranker, the training input can be any suitable pair of training texts e.g., a training question and a training text block; a training question and a set of training chain-of-thought responses, and the ground-truth annotation can be whatever correct or accurate relevance score that is known or deemed to correspond to the training input).
With regards to claim 4, Larson teaches the computer-implemented method of claim 1, wherein generating the first set of query variations includes tokenizing a plurality of queries from the plurality of query-answer pairs into a plurality of tokens (Para 28, teaches breaking up lengthy technical documents into smaller, discrete blocks of text e.g., into individual pages, sections, paragraphs, or passages so as to comply with token limits of the LLM).
With regards to claim 5, Larson teaches the computer-implemented method of claim 1, wherein generating the plurality of query variation-answer pairs includes selecting a subset of the first set of query variations using the fitness score associated with each of the query variations of the first set of query variations (Para 72, teaches that the search component can compute a respective relevance score for each potentially-relevant context-tagged text block. In various aspects, the search component can identify a subset of the plurality of potentially-relevant context-tagged text blocks that are actually relevant to the plain text question, based on those relevance scores).
With regards to claim 6, Larson teaches the computer-implemented method of claim 5, wherein generating the plurality of query variation-answer pairs includes mixing tokens from the subset of the plurality of candidate tokens to generate the second set of query variations (Para 168, teaches that the search component can concatenate, aggregate, combine, or otherwise compose that nearest or most adjacent text block with the specific text block, with all of the contextual information e.g., non-leaf nodes, that is upstream of the specific text block, and with all of the contextual information that is upstream of that nearest or most adjacent text block. Such concatenation can be referred to as the composed context-tagged text block. Furthermore, the search component can generate an embedding for the composed context-tagged text block, by passing the composed context-tagged text block through the encoder portion).
With regards to claim 7, Larson teaches the computer-implemented method of claim 5, wherein generating the plurality of query variation-answer pairs includes mutating randomly selected tokens from the subset of the plurality of candidate tokens to generate the second set of query variations (Para 168, further teaches that the search component can generate a composed context-tagged text block, by iteratively performing graph-walking and embedding-change comparison with respect to the potentially-relevant context-tagged text block. More specifically, the potentially-relevant context-tagged text block can include one leaf node that represents a specific text block within the document-graph. The search component can traverse or walk along the edges of the document-graph so as to identify whichever other text block e.g., other leaf node, within the document-graph is nearest or most adjacent to that specific text block e.g., is separated from that specific text block by a minimum number of intervening levels, branches, or nodes. In case two or more other text blocks are equidistant from the specific text block, the search component can select randomly from among those two or more other text blocks).
With regards to claims 9-13, these are system claims for the corresponding method claims 1-7. These two sets of claims are related as method and apparatus of using the same, with each claimed system element's function corresponding to the claimed method step. Accordingly, claims 9-13 are similarly rejected under the same rationale as applied above with respect to method claims 1-7.
With regards to claims 15-20, these are CRM claims for the corresponding method claims 1-7. These two sets of claims are related as method and CRM of using the same, with each claimed CRM element's function corresponding to the claimed method step. Accordingly, claims 15-20 are similarly rejected under the same rationale as applied above with respect to method claims 1-7.
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 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.
6. Claims 8 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Larson in view of Durg (U.S. Patent Application Publication # 2025/0173330 A1).
With regards to claim 8, Larson may not explicitly detail the limitation comprising processing user feedback associated with the query-answer pairs. However, Durg teaches this (See para 33).
Larson and Durg can be considered as analogous art as they belong to a similar field of endeavor in retrieval augmented generation systems (See Durg, para 40). It would thus have been obvious to one having ordinary skill in the art to advantageously combine the teachings of Durg (Use of user feedback for question-answer pairs) with those of Larson (Use of RAG-Ai system for question-answer boosting) so as to fine-tune one or more of the plurality of LLM models (Durg, para 33).
With regards to claim 14, this is a system claim for the corresponding method claim 8. These two claims are related as method and apparatus of using the same, with each claimed system element's function corresponding to the claimed method step. Accordingly, claim 14 are similarly rejected under the same rationale as applied above with respect to method claim 8.
Conclusion
7. The following prior art, made of record but not relied upon, is considered pertinent to applicant's disclosure: Niu (U.S. Patent Application Publication # 2025/0103592 A1), Kislal (U.S. Patent Application Publication # 2024/0012842 A1). These references are also included in the PTO-892 form attached with this office action.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to NEERAJ SHARMA whose contact information is given below. The examiner can normally be reached on Monday to Friday 8 am to 5 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Louis-Desir can be reached on 571-272-7799 (Direct Phone). The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
/NEERAJ SHARMA/
Primary Examiner, Art Unit 2659
571-270-5487 (Direct Phone)
571-270-6487 (Direct Fax)
neeraj.sharma@uspto.gov (Direct Email)