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 .
This action is in response to the communication filed on February 11, 2025.
Claims 1-16 are pending in this action.
Claim Rejections - 35 USC § 102
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 –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 5, 9-11, 15, and 16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tan et al. (User Modeling in the Era of Large Language Models: Current Research and Future Directions).
As per claim 1, Tan discloses, an information processing apparatus comprising
circuitry configured to:
receive an input of question data (Fig. 4);
in response to receiving the input of the question data, output, to a large-scale language model, a prompt requesting generation of answer data to the question data based on user-specific information (Section 5. Approach to LLM-UM); and
output, to a terminal apparatus, output answer data that is output from the large-scale language model in response to an input of the prompt (Section 5.1.1 Common Generative Reasoner).
As per claim 5, Tan discloses, wherein the circuitry is further configured to output, to the large-scale language model, another prompt including the output answer data and requesting revision of the output answer data (Section 5.1.6. Chatbot).
As per claim 9, Tan discloses, wherein the circuitry is configured to output, to the large-scale language model, the prompt requesting generation of answer data based on the user-specific information and information that the large-scale language model has learned (Abstract).
As per claim 10, Tan discloses, wherein in a case where the user-specific information does not include answer data associated with the question data, the circuitry is configured to output, to the large-scale language model, the prompt requesting generation of answer data based on the information that the large-scale language model has learned (Section 2.2. Large Language Model).
As per claim 11, Tan discloses, wherein in response to receiving the input of the question data, the circuitry is configured to generate the prompt requesting generation of answer data to the question data (Section 5.1.6. Chatbot).
As per claim 15 and 16, they are analyzed and thus rejected for the same reasons set forth in the rejection of claim 1, because the corresponding claims have similar limitations.
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.
Claim(s) 2-4 and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tan et al. (User Modeling in the Era of Large Language Models: Current Research and Future Directions) as applied to claim 1 above, and further in view of Shimono et al. (US 2025/0173521).
As per claim 2, Tan discloses, wherein in a case where the circuitry acquires, (Section 5.1.1 Common Generative Reasoner, Combining non-tune and fine-tune paradigms can improve LLM performance). Tan does not explicitly disclose a first memory and a second memory, however Shimono discloses, a first memory and a second memory as claimed (Paragraphs 0004-0006 and 0077-0078).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Tan by including a first memory and a second memory as taught by Shimono for the advantage of combining non-tune and fine-tune paradigms can improve LLM performance (Section 5.1.1 Common Generative Reasoner).
As per claim 3, Tan discloses, wherein the circuitry is further configured to acquire the user-specific information being a search result of searching the second memory using the query that is output from the large-scale language model in response to the input of the prompt, as the user-specific information to be included in the prompt requesting generation of answer data (Section 5.1.1 Common Generative Reasoner).
As per claim 4, Tan discloses, wherein in a case where the circuitry does not acquire the conversation history information, the circuitry is configured to acquire the user-specific information being a search result of the second memory using the question data, as the user-specific information to be included in the prompt requesting generation of answer data (Section 5.1.1 Common Generative Reasoner).
As per claim 12, Tan discloses, wherein the user-specific information are classified by a plurality of specified items and (Section 5.1.1 Common Generative Reasoner).
As per claim 13, Tan discloses, wherein the user-specific information are classified by a plurality of specified items and question data to obtain the user-specific information to be included in the prompt (Section 5.1.1 Common Generative Reasoner).
Tan does not explicitly disclose, stored in a second memory, however Shimono discloses, stored in a second memory as claimed (Paragraphs 0004-0006 and 0077-0078).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Tan by including a first memory and a second memory as taught by Shimono for the advantage of combining non-tune and fine-tune paradigms can improve LLM performance (Section 5.1.1 Common Generative Reasoner).
As per claim 14, Tan does not explicitly disclose, but Shimono discloses, the terminal apparatus configured to communicate with the information processing apparatus, the terminal apparatus including: a display to display the question data and the output answer data in association (Paragraph 0061).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Tan by including a display as taught by Shimono for the advantage of convenience of the user to see question and answer on a display.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tan et al. (User Modeling in the Era of Large Language Models: Current Research and Future Directions) as applied to claim 1 above, and further in view of Hamilton, II et al. (US 2020/0097606).
As per claim 6, Tan does not explicitly disclose, but Hamilton discloses, wherein the circuitry is further configured to, in response to receiving an input of priority information associated with the user-specific information, store the priority information and the user-specific information in association in a memory, and the circuitry is configured to output the prompt including the priority information associated with the user-specific information and requesting generation of answer data based on the priority information (Paragraphs 0045-0065).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Tan by including generation of answer data based on the priority as taught by Shimono for the advantage of relate generally to an interactive user interface configured to provide a user with information content items that are prioritized specifically for the user (Paragraph 0001).
Claim(s) 7 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tan et al. (User Modeling in the Era of Large Language Models: Current Research and Future Directions) as applied to claim 1 above, and further in view of Makunugu et al. (Provided by applicant).
As per claim 7, Tan does not expclitly disclose, but Makunugu discloses, wherein the circuitry is configured to transmit a search request based on the question data to a first memory storing question and answer information associating registered question data and registered answer data, and in a case where the question and answer information does not include registered answer data associated with the question data, the circuitry is configured to output the prompt requesting generation of answer data to the large-scale language model (Chapter 09 Retrieval-Augmented Generation (RAG)).
As per claim 8, Tan does not expclitly disclose, but Makunugu discloses, wherein the circuitry is configured to transmit a search request based on the question data to a first memory storing question and answer information associating registered question data and registered answer data, and in a case where the question and answer information includes registered answer data associated with the question data, the circuitry is configured to output the prompt requesting generation of answer data to the large-scale language model, in response to a request for generation of answer data using the large-scale language model (Chapter 10, Utilizing Knowledge Data Stores).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Tan by including question and answer does not include registered answer data as taught by Makunugu for the advantage of enhances the quality of responses by supplementing the knowledge possessed by LLM (What is RAG?).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Orr et al. (US 10,049,663) discloses, intelligent automated assistant for media exploration.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Abul K. Azad whose telephone number is (571) 272-7599. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Bhavesh Mehta, can be reached at (571) 272-7453.
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Commissioner for Patents
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September 22, 2026
/ABUL K AZAD/Primary Examiner, Art Unit 2656
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