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 .
The instant office action having application number 19/257467, filed on July 2, 2025, has claims 1-6 and 9-10 pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/02/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 10 is objected to because of the following informalities:
Claim 10, line 1, recites “A computer-readable storage medium that non-transitory”. Examiner respectfully suggest to modify the writing to -- A non-transitory computer-readable storage medium--. Appropriate correction is required.
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 1-6 and 9-10 are rejected under 35 U.S.C. 101 because the claim invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 2A-Prong One:
Claim 1, recites “A response system comprising: a task executing section that accepts a request including text data from a user; an evaluation information acquiring section that acquires, from a predetermined database, evaluation information about each of a plurality of language models; and a recommendation information generating section that causes a language model to select a recommended language model for generating a response to the request from the plurality of language models on a basis of the request, and the evaluation information, wherein the task executing section causes the recommended language model to generate a response to the request.” These limitations describe collecting information, analyzing information, evaluating alternatives, making a recommendation, and providing a recommendation.
The claimed subject matter therefore recites a method of organizing and evaluating information that can be performed in the human mind or with pen and paper. For example a human advisor could receive a user’s request, review performance ratings of several available experts, select the expert deemed most suitable based upon the request and ratings, and direct the selected expert to provide a response.
Accordingly, claim 1 recites the abstract idea of:
Mental process, including observation, evaluation, judgment, and decision making; and
Certain methods of organizing human activity, including managing and collecting tasks among available recourses based on evaluation criteria.
Step 2A, Prong Two
The claim does not integrate the judicial exception into a practical application.
The additional elements recited beyond the abstract idea include:
A task executing section;
An evaluation information acquiring section;
A recommendation information generating section;
A database
A plurality of language models.
These elements merely implement the abstract decision making process using generic computer components and generic artificial intelligence models.
The claim does not recite:
An improvement to computer functionality;
An improvement to language model architecture;
An improvement to database technology
The language models are used merely as tools for carrying out the abstract process of evaluating alternatives and selecting a recommended model. The claim does not specify any particular model architecture, training technique, inference mechanism, or technological improvement.
Accordingly, the claim does not integrate the judicial exception into a practical application.
Step 2B:
The claim does not include additional elements that amount to significantly more than the abstract idea.
The additional elements recite only generic computer implementation:
Receiving user requests;
Accessing a database;
Retrieving stored evaluation information;
Executing software component;
Selecting one model from multiple available models; and
Generating a response using the selected model.
These are well-understood, routine; and conventional computer functions previously known in the art.
The claim merely automates the abstract process of evaluating candidate responders and assigning a task to a selected responder. The use of generic language model as decision making tools does not add a technological improvement or inventive concept.
The claim therefore amount to no more than instructions to apply the abstract idea using generic computer technology.
Accordingly claim 1 is rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claims 9 and 10 are rejected under the same rationale as claim 1 above.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim recites the additional limitations of “an intention information generating section that causes a language model to generate, on a basis of the request, intention information for being input to the first-language model for a purpose of supplementing the request, wherein the task executing section inputs at least part of the intention information to the language model, and causes the language model to generate the first-response”, which is further elaborating on the abstract idea, and therefore it does not amount to significantly more. Same rationale applies to claims 4, 5, and 8.
Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claim 1 is rejected under 35 U.S.C. 101 because the claims recite "A system comprising” however the claim's limitations do not include any physical structure to perform the steps recited in the claim, furthermore the claim fails to disclose a physical article or object associated with the claimed system. These claims lack the necessary physical articles or objects to constitute a machine or a manufacture within the meaning of 35 USC 101. They are clearly not a series of steps or acts to be a process nor are they a combination of chemical compounds to be a composition of matter. As such, they fail to fall within a statutory category. They are, at best, functional descriptive material per se.
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.
Claims 1-6 and 9-10 are rejected under Kuperman et al (US 12236193 B1) (hereinafter Kuperman) in view of Qadrud-Din et al. (US 11995411 B1) (hereinafter Qadrud-Din.
As per claim 1, Kuperman discloses a task executing section that accepts a request including text data from a user [FIGS. 2A and 2B illustrate examples outputs of two different LLMs based on a same prompt, “why is the sky blue?”, col. 4, line 61]; and a recommendation information generating section that causes a language model to select a recommended language model for generating a response to the request from the plurality of language models on a basis of the request [The routing module 450 is configured to apply the classification model 430 and similarity model 440 and select an LLM based on the result of the classification model 430 and/or similarity model 440, col. 9, line 12], and the evaluation information, wherein the task executing section causes the recommended language model to generate a response to the request [the routing module 450 is configured to send the selected LLM to a corresponding application as a recommendation. In some embodiments, the routing model 450 is configured to send route the prompt or updated prompt to the selected LLM, causing the selected LLM to generate a response, and pass the response to the corresponding application., col. 9, line 16]. However Kuperman does not disclose an evaluation information acquiring section that acquires, from a predetermined database, evaluation information about each of a plurality of language models. On the other hand, Qadrud-Din discloses an evaluation information acquiring section that acquires, from a predetermined database, evaluation information about each of a plurality of language models [the text generation model 276 may be a large language model. The text generation model 276 may be trained to predict successive words in a sentence. It may be capable of performing functions such as generating correspondence, summarizing text, and/or evaluating search results. The text generation model 276 may be pre-trained using many gigabytes of input text and may include billions or trillions of parameters, col. 6, line 25]. Therefore it would have been obvious for one having ordinary skill in the computer art before the filing date of application to combine the teaching of receiving the user request and evaluating candidate language model, selecting an appropriate and generating a response with the teaching of Qadrud-Din by evaluating model performance characteristics and model evaluation information. The motivation for doing so would be to use evaluation information regarding multiple language models to improve model selection decisions.
As per claim 2, Qadrud-Din discloses an intention information generating section that causes a language model to generate, on a basis of the request, intention information for being input to the first-language model for a purpose of supplementing the request, wherein the task executing section inputs at least part of the intention information to the language model, and causes the language model to generate the first-response [The large language model's response is then parsed and potentially used to trigger additional analysis, such as one or more database searches, one or more additional prompts sent back to the large language model, and/or a response returned to a client machine., col. 3, line 8].
As per claim 3, Qadrud-Din discloses wherein the intention information generating section generates the intention information including information about a type of the request on a basis of context of the text data [The particular types of input text included in the request may depend in significant part on the type of request, col. 9, line 47].
As per claim 4, Qadrud-Din discloses wherein the intention information generating section includes a search engine that searches for information in a predetermined network in cooperation with the language model, and causes the language model and the search engine to generate the intention information [A search is conducted base on a search query. Then, the search results are provided to an artificial intelligence system. The artificial intelligence system then further processes the search results to produce an answer based on those search results. In this context, a large language model may be used to determine the search query, apply one or more filters and/or tags, and/or synthesize potentially many different types of search, col. 3, line 16].
As per claim 5, Qadrud-Din discloses an evaluating section that causes the language model to output an evaluation score of the first response generated by the language model using the request, the response, and the intention information as an input, wherein the recommendation information generating section generating recommendation information including a message to present the recommended language model to the user by taking into account the evaluation score [relevance scores for portions of text included in a document are determined based on a comparison with a natural language criterion. A subset of the text portions are selected at 104 based on the relevance scores. In some embodiments, one or more of the relevance scores may be determined via a machine learning model, such as a bi-encoder and/or a cross-encoder. Alternatively, or additionally, one or more of the relevance scores may be determined based on communication with a remote text generation modeling system, col. 5, line 21].
As per claim 6, Qadrud-Din discloses wherein the evaluating section supplies the request and the evaluation score corresponding to the request to the database for accumulating the evaluation information [the automated evaluation of text against criteria specified in natural language. According to various embodiments, one or more operations may be performed via a large language model capable of evaluating and generating natural language text. A policy including one or more criteria may be specified in natural language. A document may be divided into a set of clauses. These clauses may then be evaluated for relevance against the one or more criteria. Clauses deemed relevant may then be individually evaluated for compliance against the relevant criteria, col. 2, line 21].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOOSHA ARJOMANDI whose telephone number is (571)272-9784. The examiner can normally be reached on (571)272-9784.
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June 24, 2026
/NOOSHA ARJOMANDI/Primary Examiner, Art Unit 2166
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