DETAILED ACTION
This is in response to the application filed on 04/09/2025 in which claims 1-20 are preserved for examination; of which claims 1, 15, and 20 are in independent forms.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/29/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter of abstract ideas.
Step 1:
Claims 1-20 are directed to a method/system/storage which is one of the statutory categories of invention.
Step 2A:
Prong 1:
Claims 1, 15, and 20 are directed to an abstract idea without significantly more.
The claims recite the steps of:
generating a conversation representation of the conversation history record; [recited at a high level of generality and based on broadest and reasonable interpretation of the claim, it involves the concepts of observation, evaluation and/or judgement which could be practically performed in the human mind. A person can mentally and manually generate a representation (e.g., a question or query) for a conversation history]
generating an embedding corresponding to the conversation representation thereby forming a representation embedding; [recited at a high level of generality and based on broadest and reasonable interpretation of the claim, it involves the concepts of observation, evaluation and/or judgement which could be practically performed in the human mind. A person can mentally and manually generate a digital representation (i.e., embedding) for the representation (e.g., a question or query) of the conversation history. It also involves mathematical concepts]
for a plurality of passages within one or more documents in a knowledge base, determining a set of passages relevant to the conversation representation, each passage having a relevance to the conversation representation based on a computed similarity of the representation embedding to an embedding corresponding to the passage; [recited at a high level of generality and based on broadest and reasonable interpretation of the claim, it involves the concepts of observation, evaluation and/or judgement which could be practically performed in the human mind.]
determining a number of N documents in the knowledge base having passages in the set of passages that are most relevant to the conversation representation, the of number N documents comprising at least one document; [recited at a high level of generality and based on broadest and reasonable interpretation of the claim, it involves the concepts of observation, evaluation and/or judgement which could be practically performed in the human mind.]
for each document of the N documents, determining a document-passage grouping comprising an indication of the document and an indication of each passage within the document that is in the set of passages, thereby forming N relevant document-passage groupings; [recited at a high level of generality and based on broadest and reasonable interpretation of the claim, it involves the concepts of observation, evaluation and/or judgement which could be practically performed in the human mind.]
programmatically generate an answer-generation input instruction for a language model to cause the language model to produce an answer output, the answer-generation input instruction generated based at least on the conversation representation, at least a portion of the n relevant document-passage groupings, and an answer-format instruction that instructs the language model to include, in the answer output, at least a first citation corresponding to at least a first portion of the answer output that is generated using a first passage from the document-passage groupings, the first citation indicating the first passage and a first document that includes the first passage; [recited at a high level of generality and based on broadest and reasonable interpretation of the claim, it involves the concepts of observation, evaluation and/or judgement which could be practically performed in the human mind. A person can mentally and manually generate an input instruction for a logical algorithm (i.e., a model) including data such as representation, document information, and the output format]
receiving the answer output from the language model in response to providing the answer-generation input instruction to the language model. [recited at a high level of generality and based on broadest and reasonable interpretation of the claim, it involves the concepts of observation, evaluation and/or judgement which could be practically performed in the human mind. The model or algorithm output the result]
The above-mentioned steps are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, nothing in the claim element precludes the step from practically being performed in a human mind or with pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
Prong 2:
This judicial exception is not integrated into a practical application. Claims 1, 15, and 20 recite the additional step of “accessing a conversation history record” and “causing a representation of the answer output to be presented, as an answer representation, via a user interface (UI) of a computing device” which could be considered as insignificant extra solution activities of data gathering and data output. See MPEP 2106.04(d) and 2106.05(g).
Furthermore, claims 15 and 20 recite a processor, memory, and/or storage media so generically that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
Accordingly, these additional elements 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. See MPEP 2106.04(d) and 2106.05(g).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claims 1, 15, and 20 recite the additional step of “accessing a conversation history record” and “causing a representation of the answer output to be presented, as an answer representation, via a user interface (UI) of a computing device” which could be considered as a well-understood, conventional, and routine activities of data gathering and data output. See MPEP 2106.04(d) and 2106.05(g).
Furthermore, claims 15 and 20 recite a processor, memory, and/or storage
media so generically that they represent no more than mere instructions to apply the judicial exception on a computer. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
Therefore, the claims are not patent eligible.
NOTE: Arguendo, even if the function of using a “language model” in to take input instruction, process data, and output a result is not considered to be a as using a logical algorithm, it is considered to be an extra solution and/or well-understood, convention, and routine activity. At that level of generality, the claims do no more than describe desired function or outcome, without providing limiting details that confine the claimed to a practical solution to an identified problem. The feature of using models to take user’s instruction as input and output an answer is extra-solution activity the central idea of claims. An invocation to use such an old technology in the manner it is intended to be used for its ordinary purpose is both generic and well-understood and conventional activity. They do not describe any particular improvement in the manner of computer functions.
Moreover, the feature of using a machine learning function to process data is a conventional and well-understood function in the art (See for example Koudas et al., US 2009/0319518, paragraph 130) which is simply appending well-understood, routine, conventional activities previously known to the industry, specified at high level of generality to the general exception (See MPEP 2106.05(d)). Thus, the claimed additional elements individually and in combination do not amount significantly more than abstract idea.
Regarding dependent claims 2-14 and 16-19,
the dependent claims also lack additional elements that sufficient to integrate the judicial exception into a practical application or amount to significantly more than abstract idea found in the independent claims.
The dependent claims 2, 7, 11-14, 16, and 18 further recite the additional step for determining, identifying, updating, including and non-functional descriptive material that could be performed mentally failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea.
The dependent claims 3, 8, 9, and 17 recite non-functional descriptive material failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea.
The dependent claims 4 and 5 involve mathematical concepts failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea.
The dependent claim 6 and 19 additional steps for generating and/or determining that could be performed mentally failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea. The claims further recite generic computer functions of accessing, receiving, and storing which are considered to be insignificant extra solution and/or well-understood routine computer routines of receiving and storing data failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea.
The dependent claim 10 additional steps for presenting data on UI are considered to be insignificant extra solution and/or well-understood routine computer routines of receiving and storing data failing to integrate the judicial exception into a practical application or to amount significantly to more than abstract idea.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shao et al., CN 118113836 disclosing a large language model question answering method and device based on knowledge retrieval enhancement. The method comprises the following steps: pre-constructing a vector knowledge base; obtaining the current query question, and associating the current query question with the related historical query question, generating a plurality of enhanced query questions; respectively obtaining the enhanced query problem and the feature vector of the corresponding key word; inquiring the corresponding knowledge vector in the vector knowledge base according to the enhanced inquiry problem characteristic vector and/or the corresponding keyword characteristic vector to obtain the document paragraph corresponding to the knowledge vector as the reference knowledge; according to the current inquiry question, the related history inquiry question and the reference knowledge, generating the question instruction and inputting to the large language model to obtain the corresponding answer. The application solution introduces external knowledge to enhance the question answering ability of the large language model, and improves the accuracy of question answering by precisely matching and searching related knowledge points and optimizing the context continuity in the multi-round query.
Zhang et al., CN 117056471 disclosing a knowledge base construction method and question-answer dialogue method and system based on generated large language model, according to the question-answer dialogue service target requirement, establishing knowledge base name and vector database table storage structure for storing knowledge base document data in knowledge base, and writing the knowledge base metadata information into the vector database; obtaining the text information in the document file corresponding to the question-answer session service target requirement; dividing the text according to the text dividing length and the text terminator to obtain multiple text blocks corresponding to the text information; using the pre-training large language model to extract the text feature embedding vector corresponding to each text block, and writing the text block metadata information and the text feature embedding vector corresponding to the text block into the library table storage structure in the vector database, the data stored in the vector database is used as the knowledge base needed by the question and answer dialogue service target demand, the text dialogue is realized by constructing the private knowledge base and performing understanding and summarizing to the user question and knowledge base content based on the generated large language model, so as to improve the user experience.
Kumbi et al., US 2025/0103822 disclosing a methoc for generating, validating, and augmenting question-answer pairs using generative AI are provided. An online interaction server accesses a set of digital content available at a set of designated network locations. The online interaction server further trains a pre-trained large language model (LLM) using the set of digital content to obtain a customized LLM. The online interaction server generates a set of question-answer pairs based on the set of digital content using the customized LLM and validates the set of question-answer pairs by determining if an answer in a question-answer pair is derived from the set of digital content. The online interaction server also selects a digital asset to augment an answer in a validated question-answer pair based on a semantic similarity between the validated question-answer pair and the digital asset.
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/Hares Jami/ Primary Examiner, Art Unit 2164
01/30/2026