Detailed Action
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 response to claims filed on 05/08/2025.
Claims 1-20 are pending; claim 1 is independent.
Claim Objections
Claims 3 and 11 are objected to for the following informalities.
There is insufficient antecedent basis for “the input prompt” in claim 3.
There is insufficient antecedent basis for “the corresponding sub-prompt” in claim 11.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 102 that forms the basis for all 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.
Claims 1-12, 16-17 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sen et al., Pub. No.: US 2025/0252319 A1 (Sen).
Sen discloses:
Claim 1. A method implemented by one or more processors, the method comprising:
receiving an input query that is generated based on user interface input at a client device; (¶¶ 6-7, “Questions may be a simple one query question, or a more complex question that include multiple parts or requires an explanation of the answer… When the question or request is more complex…the question may be decomposed…The decomposing may include separating the question into multiple parts for a better understanding and to allow focus on each part of the question separately for producing the best results. Prompt design…which is a technique to select the right words and guide the LLM in generating high quality results may also be used”, ¶¶ 41, 101-105, “a question is received...the question may be a simple question with a single ask or query that may be inputted by a user in an input area on a user interface…. the question may be a multipart or complex question that may require multiple answers or answers that are based on multiple factors…the results obtained from analyzing the content and/or context of the question may be used to determine which ELLM to use”)
decomposing the input query, decomposing the input query comprising processing the input query, using a first generative model and/or a second generative model, to determine: (a received question is analyzed and each part of a multipart question is processed by selected ELLM and LLMs: ¶¶ 6-7, ¶ 33, “The selected combination of ELLMs and LLMs are used to process a question inputted by a user. Answers obtained by processing the question via the combination of ELLMs and LLMs are blended and used as an input into an ensemble model to obtain a final answer”; ¶¶ 102-110, “the question may be a multipart or complex question that may require multiple answers or answers that are based on multiple factors…the control circuitry 428 and/or 420 may analyze the content and/or context of the question….the control circuitry 428 and/or 420 may use the results from the analysis to determine which ELLM has been trained with data that is relevant to the content and/or context of the question…Analyzing the question may…involve using natural language processing (NLP) techniques…NLP techniques may also be used in conjunction with other tools, such as sentiment analysis tools, to capture the sentiment and mood of the user when asking the question…The question may also be analyzed using artificial intelligence (AI) and machine learning (ML) engines that executive AI and ML algorithms. Such engines and algorithms may provide recommendations based on the question, such as what the question is really seeking, what class or subclass the question fall under, or what enterprise departments are more relevant to the content and context of the question… Since context and content of the question received may apply to more than one topic and as such to more than on ELLM, any ELLMs that is available for use and connected to the topic, either the entire question or a portion of the question, may be included in a set of the narrowed n ELLMS”)
a plurality of sub-queries, and for each of the sub-queries, one or more corresponding tools to utilize in processing the sub-query; (see above, “NLP techniques may also be used in conjunction with other tools…Since context and content of the question received may apply to more than one topic and as such to more than on ELLM, any ELLMs that is available for use and connected to the topic, either the entire question or a portion of the question, may be included in a set of the narrowed n ELLMS”)
for each of the sub-queries:
processing the sub-query, using the one or more corresponding tools for the subquery, to generate one or more corresponding sub-query responses; (¶¶ 127-128, “the control circuitry…may obtain answers from all the ELLMs and/or LLMs as part of the sequence and strategy deployed… the control circuitry 428 and/or 420 may blend the answers obtained at block 970. The blending process, in one embodiment, may include selecting portions of answers from different ELLMs and/or LLMs and combining them such that make logical sense)
generating an initial comprehensive response to the input query, generating the initial comprehensive response comprising processing, using the first generative model, the second generative model, or a third generative model, the one or more corresponding sub-query responses for each of the sub-queries; (¶¶ 127-128, “the control circuitry…may obtain answers from all the ELLMs and/or LLMs as part of the sequence and strategy deployed… the control circuitry 428 and/or 420 may blend the answers obtained at block 970. The blending process, in one embodiment, may include selecting portions of answers from different ELLMs and/or LLMs and combining them such that make logical sense)
determining whether the initial comprehensive response is responsive to the input query, determining whether the initial comprehensive response is responsive to the input query comprising:
processing, using the first generative model, the second generative model, the third generative model, or a fourth generative model, the input query and the initial comprehensive response to generate a critique response that indicates whether the initial comprehensive response is responsive to the input query; and (¶¶ 130-131, generated responses are analyzed by an ensemble model for selecting a golden response: “the control circuitry…may input the blended answer into an ensemble model…the ensemble model may be a ruled-based model that has its own rules on how to determine the better answer…the control circuitry…may obtain a golden answer from the ensemble model and display it to the user from whom the question was received”)
determining, based on the critique response, whether the initial comprehensive response is responsive to the input query; (¶¶ 130-131, generated responses are analyzed by an ensemble model for selecting a golden response: “the control circuitry…may input the blended answer into an ensemble model…the ensemble model may be a ruled-based model that has its own rules on how to determine the better answer…the control circuitry…may obtain a golden answer from the ensemble model and display it to the user from whom the question was received”)
in response to determining that the initial comprehensive response is responsive to the input query: causing the initial comprehensive response to be rendered at the client device as responsive to the input query; and (¶¶ 130-131, generated responses are analyzed by an ensemble model for selecting a golden response: “the control circuitry…may input the blended answer into an ensemble model…the ensemble model may be a ruled-based model that has its own rules on how to determine the better answer…the control circuitry…may obtain a golden answer from the ensemble model and display it to the user from whom the question was received”)
in response to determining that the initial comprehensive response is not responsive to the input query: generating a refined comprehensive response that is based on a further sub-query response, the further sub-query response being generated based on the critique response; and causing the refined comprehensive response to be rendered at the client device as responsive to the input query. (This portion of the claim is not implemented because it has been determined that “the initial comprehensive response is responsive to the input query”)
Claim 2. The method of claim 1, wherein generating the refined comprehensive response, that is based on the further sub-query response, comprises: determining, based on the critique response, a further sub-query and one or more further tools to utilize in processing the further sub-query; processing the further sub-query, using the one or more further tools for the further subquery, to generate one or more further sub-query responses; generating the refined comprehensive response based on processing, using the first generative model, the second generative model, or the third generative model, the one or more further subquery responses and the initial comprehensive response or the one or more corresponding sub-query responses for each of the sub-queries. (This claim is not implementable because it has been determined that “the initial comprehensive response is responsive to the input query”)
Claim 3. The method of claim 2, further comprising:
determining whether the refined comprehensive response is responsive to the input query, determining whether the refined comprehensive response is responsive to the input query comprising: processing, using the first generative model, the second generative model, the third generative model, or the fourth generative model, the input query and the refined comprehensive response to generate an additional critique response that indicates whether the refined comprehensive response is responsive to the input query; and determining, based on the critique response, whether the refined comprehensive response is responsive to the input query; wherein causing the refined comprehensive response to be rendered at the client device as responsive to the input prompt is in response to determining that the refined comprehensive response is responsive to the input query. (Claim 2 is not implemented)
Claim 4. The method of claim 2, wherein the critique response directly indicates one or both of the further sub-query and the one or more further tools to utilize in processing the further sub-query. (Claim 2 is not implemented)
Claim 5. The method of claim 2, further comprising: determining, based on processing the critique response, the further sub-query and the one or more further tools to utilize in processing the further sub-query. (Claim 2 is not implemented)
Claim 6. The method of claim 1, wherein processing, using the first generative model, the second generative model, or the third generative model, the input query and the initial comprehensive response to generate the critique response that indicates whether the initial comprehensive response is responsive to the input query further comprises:
processing, using the first generative model, the second generative model, or the third generative model, and along with the input query and the initial comprehensive response: each of the sub-queries, each of the corresponding tools utilized in processing the sub-queries, and/or each of the corresponding sub-query responses. (see claim 1, ¶¶ 130-131, “the control circuitry…may input the blended answer into an ensemble model…the ensemble model may be a ruled-based model that has its own rules on how to determine the better answer…the control circuitry…may obtain a golden answer from the ensemble model and display it to the user from whom the question was received”)
Claim 7. The method of claim 1, further comprising:
determining whether to provide an LLM-only response to the input query in lieu of a comprehensive response; wherein generating the initial comprehensive response is performed responsive to determining to not provide the LLM-only response to the input query. (¶ 116, a simple question is answered using an LLM: “A user may be willing to live with an average or low level of accuracy for simple questions, such as a middle school math problem or for writing a thank you letter and desire a high level of accuracy for critical problems. For example, if the question posed to the LLM is desiring to seek a solution that would impact a company's sales, a job prospect, debugging of a bug in a critical software, then the user may desire a higher level of accuracy and be willing to pay for the higher level of accuracy. Since accuracy may relate to computing power, e.g., a higher level of accuracy for a complex problem requiring higher usage of computational resources and thereby incurring more costs, the user may reserve seeking a higher level of accuracy for more important and critical tasks. Accordingly, in an example where accuracy parameters are described and the question is presented to LLMs such as ChatGPT™, Bard™, Llama™, Bing chat™, Claude™, and Jasper™, the system may narrow the selection of the LLMs based on the accuracy parameters”)
Claim 8. The method of claim 7, further comprising:
prior to generating the initial comprehensive response: generating the LLM-only response based on processing, in a single LLM pass, an LLM prompt that is based on the input query; wherein determining whether to provide the LLM-only response to the input query in lieu of the comprehensive response comprises processing the LLM-only response. (a simple question is answered using an LLM while answering a complex question requires using and blending multiple LLMs generated answers: ¶ 116, “A user may be willing to live with an average or low level of accuracy for simple questions, such as a middle school math problem or for writing a thank you letter and desire a high level of accuracy for critical problems. For example, if the question posed to the LLM is desiring to seek a solution that would impact a company's sales, a job prospect, debugging of a bug in a critical software, then the user may desire a higher level of accuracy and be willing to pay for the higher level of accuracy. Since accuracy may relate to computing power, e.g., a higher level of accuracy for a complex problem requiring higher usage of computational resources and thereby incurring more costs, the user may reserve seeking a higher level of accuracy for more important and critical tasks. Accordingly, in an example where accuracy parameters are described and the question is presented to LLMs such as ChatGPT™, Bard™, Llama™, Bing chat™, Claude™, and Jasper™, the system may narrow the selection of the LLMs based on the accuracy parameters”; ¶¶ 127- 128, “the control circuitry 428 and/or 420 may obtain answers from all the ELLMs and/or LLMs…The blending process, in one embodiment, may include selecting portions of answers from different ELLMs and/or LLMs and combining them such that make logical sense”)
Claim 9. The method of claim 7, wherein processing the LLM-only response in determining whether to provide the LLM-only response to the input query in lieu of the comprehensive response comprises:
processing, using the first generative model, the second generative model, the third generative model, or the fourth generative model, the input query and the LLM-only response to generate an initial critique response that indicates whether the LLM-only response is responsive to the input query; and determining, based on the initial critique response, whether to provide the LLM-only response to the input query in lieu of the comprehensive response. (A generated result is evaluated for refining the result by selecting LLMs: : ¶ 123, “the control circuitry 428 and/or 420 may simultaneously feed the question received to a plurality of ELLMs and LLMs, use the results obtained from the ELLMs and LLMs, and refine the results by feeding them into a second set of ELLMs and LLMs to obtain a more refined answer”, ¶¶ 130-131, “The ensemble model may continuously learn from the parameters as they are added and the continuous learning may allow the ensemble model to further refine its ability to make predictions of the next work and accordingly determine a better answer for the question”)
Claim 10. The method of claim 9, wherein processing, using the first generative model, the second generative model, the third generative model, or the fourth generative model, the input query and the LLM-only response to generate the initial critique response further comprises:
processing, using the first generative model, the second generative model, the third generative model, or the fourth generative model, and along with the input query and the LLM-only response: each of the sub-queries, and/or each of the corresponding tools utilized in processing the sub-queries. (A generated result is evaluated for refining the result by selecting LLMs: : ¶ 123, “the control circuitry 428 and/or 420 may simultaneously feed the question received to a plurality of ELLMs and LLMs, use the results obtained from the ELLMs and LLMs, and refine the results by feeding them into a second set of ELLMs and LLMs to obtain a more refined answer”, ¶¶ 130-131, “The ensemble model may continuously learn from the parameters as they are added and the continuous learning may allow the ensemble model to further refine its ability to make predictions of the next work and accordingly determine a better answer for the question”)
Claim 11. The method of claim 1, further comprising:
prior to processing each of the sub-queries to generate the corresponding sub-prompt responses:
causing a prompt to be rendered, at the client device, that characterizes the plurality of sub-queries; and determining that affirmative user interface input is received responsive to the prompt; wherein processing each of the sub-queries to generate the corresponding sub-prompt responses is contingent on receiving the affirmative user interface input. (Kim, wherein a user query is divided into suggested questions and wherein a prompt is generated for the user select a suggested question: ¶ 332, “in some implementations, the natural language user input is divided into multiple sub-parts or portions, each portion used to generate a separate prompt. …In some cases, natural language processing is performed on the user input to identify potentially divisible requests that may be serviced using separate prompts…the multiple requests or prompts are dependent such that the result of one prompt is used to generate another prompt” and ¶¶ 369-371, “FIG. 31A depicts an example graphical user interface 3100 a that includes an initial user-generated request message 3102 including a natural language user input provided to a chat interface of a messaging platform…the interface 3100 b includes a response 3120 that includes a computed completeness score 3233 based on the user input of the initial request message 3120…If the user…selecting one or more of the proposed questions, the sequence may be repeated until a satisfactory question completeness score 3122 is achieved. In some embodiments, the system generates the series of suggested questions or proposed queries 3120 regardless of the completeness score such that the user can review additional questions even when the initial request may be predicted to be sufficiently complete to obtain a successful resolution”)
Claim 12. The method of claim 11, wherein the prompt further characterizes the corresponding tools for the plurality of sub-queries. (Kim, wherein, ¶ 332, “in some implementations, the natural language user input is divided into multiple sub-parts or portions, each portion used to generate a separate prompt. …In some cases, natural language processing is performed on the user input to identify potentially divisible requests that may be serviced using separate prompts…the multiple requests or prompts are dependent such that the result of one prompt is used to generate another prompt” and wherein ¶ 372, “the automated chat service may conduct a search of a knowledge base or other content store using the original user input or a modified user input resulting from an exchange similar to as described above with respect to FIG. 31B” suggests characterizing a corresponding tool for multiple sub-parts or portions)
Claim 16. The method of claim 1, wherein the plurality of sub-queries include a first sub-query and a second sub-query that is distinct from the first sub-query and wherein the corresponding tools include a first tool to utilize in processing the first sub-query and a second tool, that is distinct from the first tool, to utilize in processing the second sub-query. (¶¶ 102-110, tools are used for processing different question parts “Analyzing the question may…involve using natural language processing (NLP) techniques…NLP techniques may also be used in conjunction with other tools, such as sentiment analysis tools, to capture the sentiment and mood of the user when asking the question…The question may also be analyzed using artificial intelligence (AI) and machine learning (ML) engines that executive AI and ML algorithms. Such engines and algorithms may provide recommendations based on the question, such as what the question is really seeking, what class or subclass the question fall under, or what enterprise departments are more relevant to the content and context of the question… Since context and content of the question received may apply to more than one topic and as such to more than on ELLM, any ELLMs that is available for use and connected to the topic, either the entire question or a portion of the question, may be included in a set of the narrowed n ELLMS”)
Claim 17. The method of claim 1, wherein the plurality of sub-queries include a first sub-query and a second sub-query that is distinct from the first sub-query and that is conditioned on the corresponding sub-query response generated based on the first sub-query. (¶¶ 122-125, answer generated by an LLM is provided to another LLM for geniting answers: “the control circuitry 428 and/or 420 may determine which ELLMs and LLMs, from the set of n ELLMs and LLMs, to use first and then use the results from such ELLMs and LLMs to feed into a second set of ELLMs and LLMs, from the set of n ELLMs and LLMs”)
Claim 20. The method of claim 1, further comprising:
determining, based on one or more properties of the client device, to generate a comprehensive response to the input query; wherein generating the initial comprehensive response is performed responsive to determining to generate the comprehensive response. (¶ 51, wherein “Computing device 418 may receive the displays generated by the remote server and may display the content of the displays locally via display 434. This way, the processing of the instructions is performed remotely (e.g., by server 402) while the resulting displays, such as the display windows described elsewhere herein, are provided locally on computing device 418” suggests that generating results is provided based on the display provided to the computing device)
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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 13-15 and 18-19 are rejected under 35 U.S.C. 103(a) as being unpatentable over Sen as applied to above claims, in view of Kim et al., Pub. No.: US 2025/0007870 A1 (Kim).
Claim 13. The method of claim 1, further comprising: prior to or while processing each of the sub-queries to generate the corresponding subquery responses: causing a notification to be rendered, at the client device, that characterizes that there will be a time delay before a comprehensive response is provided.
Sen did not specially disclose the above feature but Kim discloses the feature in Fig. 31A wherein “Hang tight as I search the knowledge base for relevant content and help find the answer. This can sometimes take a few seconds” suggests providing a time delay notification to the user.
It would have been obvious before the effective filling date of the claimed invention to a person having ordinary skill in the art to combine the applied references for disclosing prior to or while processing each of the sub-queries to generate the corresponding subquery responses: causing a notification to be rendered, at the client device, that characterizes that there will be a time delay before a comprehensive response is provided because doing so would inform the user about the answer to be generated in response to the user question.
Claim 14. The method of claim 13, wherein the notification further characterizes an anticipated duration of the time delay. (Kim, fig. 31A, wherein “Hang tight as I search the knowledge base for relevant content and help find the answer. This can sometimes take a few seconds” suggests providing an anticipated duration of the time delay)
Claim 15. The method of claim 14, further comprising: determining the anticipated duration of the time delay as a function of at least one of the corresponding tools. (Kim, fig. 31A, wherein “Hang tight as I search the knowledge base for relevant content and help find the answer. This can sometimes take a few seconds” suggests providing a time delay with respect to using search tool for searching a knowledge base)
Claim 18. The method of claim 1, wherein receiving the input query comprises: receiving an initial input query that is generated based on initial user interface input at the client device; causing a specification prompt to be provided, at the client device, requesting further specification of the initial input query; receiving a refinement of the initial input query that is based on further user interface input provided responsive to the specification prompt; and generating the input query based on the refinement and the initial input query. (Kim, ¶¶ 369-371, a specification prompt is provided to the user: “FIG. 31A depicts an example graphical user interface 3100 a that includes an initial user-generated request message 3102 including a natural language user input provided to a chat interface of a messaging platform…the interface 3100 b includes a response 3120 that includes a computed completeness score 3233 based on the user input of the initial request message 3120…If the user modifies the request manually or selecting one or more of the proposed questions, the sequence may be repeated until a satisfactory question completeness score 3122 is achieved. In some embodiments, the system generates the series of suggested questions or proposed queries 3120 regardless of the completeness score such that the user can review additional questions even when the initial request may be predicted to be sufficiently complete to obtain a successful resolution”)
Claim 19. The method of claim 18, further comprising: determining, based on processing the initial input query, to provide the specification prompt; wherein causing the specification prompt to be provided is in response to determining, based on processing the initial input query, to provide the specification prompt. (Kim, ¶¶ 369-371, based on processing an initial user query for completeness, a specification prompt is provided to the user: “FIG. 31A depicts an example graphical user interface 3100 a that includes an initial user-generated request message 3102 including a natural language user input provided to a chat interface of a messaging platform…the interface 3100 b includes a response 3120 that includes a computed completeness score 3233 based on the user input of the initial request message 3120…If the user modifies the request manually or selecting one or more of the proposed questions, the sequence may be repeated until a satisfactory question completeness score 3122 is achieved. In some embodiments, the system generates the series of suggested questions or proposed queries 3120 regardless of the completeness score such that the user can review additional questions even when the initial request may be predicted to be sufficiently complete to obtain a successful resolution”)
Conclusion
The prior arts made of record in PTO-326 and not relied upon are considered pertinent to applicant's disclosure.
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/MOHSEN ALMANI/Primary Examiner, Art Unit 2159