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 Office Action is in reply to communication filed on 04/25/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/16/2025 was filed after the mailing date of the original application filed on 04/25/2024. 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 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, 5-7, 10, 11, 15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over TUNKELANG et al. (hereinafter TUN), US 20250217863 A1, in view of Dolph, US 20250173541 A1
Regarding claim 1, TUN teaches the invention substantially as claimed. Tun discloses: A system for generating a prompt for use with a language model system comprising a first Large Language Model (LLM), the system comprising:
one or more processors; a memory storage device storing instructions thereon, which, when executed by the one or more processors (figs. 1 and 7; par. 0039-0047), cause the system to perform operations comprising:
receiving a query (par. 0047-0050);
receiving a set of search results for the query, the set of search results generated by a search engine (par. 0047-0053 and 0092);
generating a plurality of intent indicators based on the query and the set of search results (0074-0083 and 0091-0095);
causing presentation of a user interface (UI) with a prompt builder UI component, the prompt builder UI component presenting (not expressly taught), as user-selectable, a subset of the plurality of intent indicators (presenting UI 0040-0049, intent specifically application interface, fig. 1, but user-selectable intent not exactly taught);
detecting a user-selection of at least one intent indicator via the prompt builder UI component (not expressly taught);
with a pretrained machine learning model, generating as output the prompt for use with the language model system, using as input to the pretrained machine learning model at least the query and the at least one intent indicator (par. 0034-0035, 0063-0070, and 0081-0095); and
causing presentation of the prompt for use with the language model system via the prompt builder UI component along with a user interface element (Generate prompt user pretrained model; 0074-0083 and 0091-0095);
which, when selected, will cause the prompt to be provided as input to the language model system (0034, 0047, 0063 and 0082).
Clearly, TUN, the primary prior art teaches receiving a user query, generating a prompt for a large language model using a pretrainined machine learning model, and presenting the generated prompt for submission to the language model. However, TUN fails to disclose generating multiple intent indicators from search results and presenting those intent indicators as selectable interface elements for user selection before prompt generations. These features are well-known in the art as evidenced Dolph.
In the same field of endeavor, Dolph teaches AI assistant and generating engine (par. 0064-0067, 0113-0117, and 0193-0219); User interaction with AI assistant and prompt generation (par. 0166-0177, and 0193-0219), user request and interaction with generated prompts (0166-0177, and 0193-0219), and AI assistant services request using generated prompts and selected AI models (0193-0220).
Accordingly, it would have been obvious for an ordinary skill in the art at the time of the invention to modify the intent-determination system of TUN with the prompt-generation architecture of Dolph. TUN already determines user intent from search queries and search-results information using trained machine-learning models. Dolph teaches generating AI prompts based on identified knowledge categories, prompt templates, prompt sets, and AI assistant interfaces.
One of ordinary skilled would have recognized that using TUN’s inferred intent as input to Dolph’s prompt would predictably improve the quality and contextual relevance of prompts presented to a large language model. The combination would have enhanced efficacy by generating prompts tailored to inferred user intent, effectiveness by improving the accuracy and relevance of LLM interactions, and user agency by allowing users to rev view, select, and execute generated prompts through an interactive prompt-building interface.
The proposed modification merely combines known techniques according to their establishes functions to obtain the predictable result of improving prompt generation and language-model interaction, consistent with KSR Int’l Co, v, Teleflex Inc. 550 U.S. 398 (2007). By this rationale, claim 1 is rejected.
Regarding claims 5-7, 10, 11, 15, 17, and 20, the combination TUN-Dolph teaches:
5. (Original) The system of claim 1, wherein, subsequent to causing presentation of the prompt for use with the language model system via the prompt builder UI component, the instructions cause the system to perform additional operations comprising: causing presentation of additional intent indicators within the prompt builder UI component, the additional intent indicators being hierarchically subordinate to the at least one intent indicator for which a user-selection was previously detected; and detecting a user-selection of at least one additional intent indicator from the presented additional intent indicators; and invoking the pretrained machine learning model a second time to generate an updated prompt that incorporates the at least one additional intent indicator (TUN, par. 0082).
6. (Original) The system of claim 1, wherein the pretrained machine learning model that generates the prompt for use with the language model system has been trained by: utilizing a dataset comprising a plurality of training data instances, each instance including a user query, a corresponding set of user-selected intent indicators, and a resulting detailed prompt, wherein the training data is employed to train the pretrained machine learning model to discern patterns and associations between user queries, the selected intent indicators, and the detailed prompts, such that the model learns to generate prompts that are contextually relevant and specific to a query and one or more selected intent indicators (TUN, 0067, 0069 and 0082).
7. (Original) The system of claim 1, wherein the memory storage device is storing instructions thereon, which, when executed by the one or more processors, cause the system to perform additional operations comprising: evaluating the query to ascertain whether to initiate search assistance experience provided by the language model system; and upon a determination that the search assistance experience is to be initiated, embedding the prompt builder UI component within a search results page that includes the set of search results from the search engine (TUN, 0047).
10. (Original) The system of claim 1, wherein the pretrained machine learning model that generates the prompt for use with the language model system is a Sequence-to-Sequence (Seq2Seq) model, trained to convert an input sequence comprising the user query and the selected intent indicators into an output sequence forming the detailed prompt (TUN, par. 0077).
11. (Original) A computer-implemented method for generating a prompt for use with a language model system comprising a first Large Language Model (LLM), the method comprising: receiving a query; receiving a set of search results for the query, the set of search results generated by a search engine; generating a plurality of intent indicators based on the query and the set of search results; causing presentation of a user interface (UI) with a prompt builder UI component, the prompt builder UI component presenting, as user-selectable, a subset of the plurality of intent indicators; detecting a user-selection of at least one intent indicator via the prompt builder UI component; with a pretrained machine learning model, generating as output the prompt for use with the language model system, using as input to the pretrained machine learning model at least the query and the at least one intent indicator; and causing presentation of the prompt for use with the language model system via the prompt builder UI component along with a user interface element which, when selected, will cause the prompt to be provided as input to the language model system [See TUN, (par. 0034-0035, 0047-0053, 0063-0070, and 0081-0095) -- see also Dolph, (par. 0064-0067, 0113-0117), and 0193-0219; User interaction with AI assistant and prompt generation (par. 0166-0177, and 0193-0219), user request and interaction with generated prompts (0166-0177, and 0193-0219), and AI assistant services request using generated prompts and selected AI models (0193-0220)]. The same motivation and reason to combine used for the rejection of claim 1 is also valid for this claim. By this rationale, claim 11 is rejected.
15. (Original) The method of claim 11, wherein, subsequent to causing presentation of the prompt for use with the language model system via the prompt builder UI component, the instructions cause the system to perform additional operations comprising: causing presentation of additional intent indicators within the prompt builder UI component, the additional intent indicators being hierarchically subordinate to the at least one intent indicator for which a user-selection was previously detected; and detecting a user-selection of at least one additional intent indicator from the presented additional intent indicators; and invoking the pretrained machine learning model a second time to generate an updated prompt that incorporates the at least one additional intent indicator (TUN, 0067, 0069 and 0082).
17. (Original) The method of claim 11, wherein the memory storage device is storing instructions thereon, which, when executed by the one or more processors, cause the system to perform additional operations comprising: evaluating the query to ascertain whether to initiate search assistance experience provided by the language model system; and upon a determination that the search assistance experience is to be initiated, embedding the prompt builder UI component within a search results page that includes the set of search results from the search engine (TUN, 0047).
20. (Original) A memory storage device storing instructions thereon, which, when executed by the one or more processors, cause the system to perform operations comprising: receiving a query; receiving a set of search results for the query, the set of search results generated by a search engine; generating a plurality of intent indicators based on the query and the set of search results; causing presentation of a user interface (UI) with a prompt builder UI component, the prompt builder UI component presenting, as user-selectable, a subset of the plurality of intent indicators; detecting a user-selection of at least one intent indicator via the prompt builder UI component; with a pretrained machine learning model, generating as output the prompt for use with the language model system, using as input to the pretrained machine learning model at least the query and the at least one intent indicator; and causing presentation of the prompt for use with the language model system via the prompt builder UI component along with a user interface element which, when selected, will cause the prompt to be provided as input to the language model system [See TUN, (par. 0034-0035, 0047-0053, 0063-0070, and 0081-0095) -- see also Dolph, (par. 0064-0067, 0113-0117), and 0193-0219; User interaction with AI assistant and prompt generation (par. 0166-0177, and 0193-0219), user request and interaction with generated prompts (0166-0177, and 0193-0219), and AI assistant services request using generated prompts and selected AI models (0193-0220)]. The same motivation and reason to combine used for the rejection of claim 1 is also valid for this claim. By this rationale, claim 11 is rejected.
Allowable Subject Matter
Claims 2-4, 8, 9, 12-14, 16, 18, and 19 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.
CONCLUSION
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jude Jean-Gilles whose telephone number is 571-272-3914. The examiner can normally be reached on Mon-Fri, from 9:00AM-5:00PM.
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/JUDE JEAN GILLES/Primary Examiner, Art Unit 2459
July 15, 2026
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