# US 2026/0065019 A1 · Leveraging generative artificial intelligence agents to facilitate user-centric, goal-based evaluations of applications > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0065019 A1 - Document ID: US20260065019A1 - Status: Published application - Kind: A1 - Application number: 18/821,766 - Filed: 2024-08-30 - Published: 2026-03-05 - Earliest claimed: 2024-08-30 - Assignees: Intuit Inc. - Applicants: Intuit Inc. - Inventors: Manas Kumar MUKHERJEE - Web page: https://patentgrep.com/patent/US20260065019A1 ## CPC - G06N 3/045: Combinations of networks - G06N 3/0475: Generative networks - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Aspects of the present disclosure relate to evaluating performance of a generative machine learning model. Embodiments include using a plurality of generative machine learning models to generate evaluation questions, wherein each of the generative machine learning models is configured to use a given persona for generating one or more of the evaluation questions. Embodiments further include providing the evaluation questions as input to a target application. Embodiments further include generating an indication of a level of performance of the target application based on evaluating an answer generated in response to a question of the evaluation questions. ## Claims ### Claim 1 A method of evaluating performance of a generative machine learning model, comprising: using a plurality of generative machine learning models to generate evaluation questions, wherein each of the generative machine learning models is configured to use a given persona for generating one or more of the evaluation questions; providing the evaluation questions as input to a target application; and generating an indication of a level of performance of the target application based on evaluating an answer generated in response to a question of the evaluation questions. ### Claim 2 (depends on 1) The method of claim 1, wherein the given persona comprises a level of proficiency in a given language. ### Claim 3 (depends on 1) The method of claim 1, wherein the given persona comprises a sentiment for a question. ### Claim 4 (depends on 1) The method of claim 1, wherein the given persona comprises a level of ambiguity for a question. ### Claim 5 (depends on 1) The method of claim 1, further comprising using one or more of the plurality of generative machine learning models to generate a correct answer to an evaluation question. ### Claim 6 (depends on 5) The method of claim 5, wherein the evaluating is based on comparing the answer generated by the target application to the correct answer. ### Claim 7 (depends on 1) The method of claim 1, wherein the evaluation questions are based on questions submitted by users associated with a particular domain. ### Claim 8 (depends on 7) The method of claim 7, wherein a correct answer to a question of the evaluation questions is based on an answer submitted by a user associated with the particular domain. ### Claim 9 (depends on 1) The method of claim 1, further comprising using the plurality of generative machine learning models to generate one or more follow-up evaluation questions based on the answer generated by the target application. ### Claim 10 (depends on 1) The method of claim 1, wherein using the plurality of generative machine learning models to generate the evaluation questions comprises submitting an application programming interface (API) call to a particular domain and generating the evaluation questions based on information retrieved via the API call. ### Claim 11 A system for evaluating performance of a generative machine learning model, comprising: one or more processors; and a memory comprising instructions that, when executed by the one or more processors, cause the system to: use a plurality of generative machine learning models to generate evaluation questions, wherein each of the generative machine learning models is configured to use a given persona for generating one or more of the evaluation questions; provide the evaluation questions as input to a target application; and generate an indication of a level of performance of the target application based on evaluating an answer generated in response to a question of the evaluation questions. ### Claim 12 (depends on 11) The system of claim 11, wherein the given persona comprises a level of proficiency in a given language. ### Claim 13 (depends on 11) The system of claim 11, wherein the given persona comprises a sentiment for a question. ### Claim 14 (depends on 11) The system of claim 11, wherein the given persona comprises a level of ambiguity for a question. ### Claim 15 (depends on 11) The system of claim 11, wherein the instructions further cause the system to use one or more of the plurality of generative machine learning models to generate a correct answer to an evaluation question. ### Claim 16 (depends on 15) The system of claim 15, wherein the evaluating is based on comparing the answer generated by the target application to the correct answer. ### Claim 17 (depends on 11) The system of claim 11, wherein the evaluation questions are based on questions submitted by users associated with a particular domain. ### Claim 18 (depends on 17) The system of claim 17, wherein a correct answer to a question of the evaluation questions is based on an answer submitted by a user associated with the particular domain. ### Claim 19 (depends on 11) The system of claim 11, wherein the instructions further cause the system to use the plurality of generative machine learning models to generate one or more follow-up evaluation questions based on the answer generated by the target application. ### Claim 20 (depends on 11) The system of claim 11, wherein using the plurality of generative machine learning models to generate the evaluation questions comprises submitting an application programming interface (API) call to a particular domain and generating the evaluation questions based on information retrieved via the API call.