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 .
DETAILED ACTION
This action is responsive to patent application as filed on 7/12/2024 which claims priority to U.S. Provisional Pat. App. No: 63/513,440 filed 07/13/2023.
This action is made Non-Final.
Claims 1 – 20 are pending in the case. Claims 1, 10, and 16 are independent claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/6/2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed on 7/12/2024 have been accepted by the Examiner.
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, 2, 8-11, 16 and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dong et al (“Self-collaboration Code Generation via ChatGPT”, arXiv:2304.07590v2, 5/24/2023, 25 pages, from IDS filed 1/6/2025).
Claim 1:
Dong discloses A computer-implemented method comprising: receiving, via an interface, a natural language query about a request for a user device to perform a computer task (Figs 7 and 8 disclose a dialogue with a user and an LLM-based code generator); generating a prompt including the natural language query and a list of functions; transmitting the prompt to a language model (abstract, page 2 paragraph 2, page 3 section 2.1, paragraph 2, figs 7-9: code generation employing LLMs, exemplified by ChatGPT...LLMs use language as the foundation for input and output and align with human needs through instructions or prompts, offering the potential for inter-model interaction and collaboration...it is prevalent to control LLM generation using instructions or prompts”); receiving a response from the language model, the response including machine-readable instructions executable by the user device to perform the computer task, the machine-readable instructions using at least one function from the list of functions (abstract and figs 7-8: code generation employing LLMs, exemplified by the LLM); and executing the machine-readable instructions to perform the computer task (page 2 paragraph 1: team members can review and test the generated code).
Claim 2:
Dong discloses the prompt is a first prompt and the response is a first response, wherein executing the machine-readable instructions includes: performing a step of the computer task by executing a portion of the machine-readable instructions; in response to execution of the portion of the machine-readable instructions, detecting an error message; transmitting, to the language model, a second prompt requesting re-generation of code for the portion of the machine-readable instructions, the second prompt including the error message; receiving, from the language model, a second response that includes replacement code for the step; and executing the replacement code to perform the step (page 2 paragraph 4: generated code is executed and issues (equivalent to the claimed errors) are provided as feedback to the code generator).
Claim 8:
Dong discloses generating, by a parser, executable instructions from the machine-readable instructions, wherein the executable instructions are used to execute the computer task (Figs 7-8).
Claim 9:
Dong discloses wherein the machine-readable instructions include source code (Figs 7-8).
Claim 10:
Dong discloses A non-transitory computer-readable medium storing instructions that cause at least one processor to execute operations, the operations comprising: receiving, via an interface, a natural language query about a request for a user device to perform a computer task (Figs 7 and 8 disclose a dialogue with a user and an LLM-based code generator); generating a first prompt including the natural language query and a list of functions; transmitting the first prompt to a language model (abstract, page 2 paragraph 2, page 3 section 2.1, paragraph 2, figs 7-9: code generation employing LLMs, exemplified by ChatGPT...LLMs use language as the foundation for input and output and align with human needs through instructions or prompts, offering the potential for inter-model interaction and collaboration...it is prevalent to control LLM generation using instructions or prompts”); receiving a response from the language model, the response including machine-readable instructions executable by the user device to perform the computer task, the machine-readable instructions using a first function and a second function from the list of functions (abstract and figs 7-8: code generation employing LLMs, exemplified by the LLM); and executing the machine-readable instructions to perform the computer task, including: executing, in a first step, a first source code portion causing execution of the first function to obtain an execution result; and executing, in a second step, a second source code portion causing transmission of a second prompt that requests the language model to execute the second function on the execution result from the first step (page 2 paragraph 1: team members can review and test the generated code; Fig 7: source code is shown wherein the result of a function is used in another function).
Claim 11:
Dong discloses in response to execution of the first source code portion, detecting an error message; transmitting, to the language model, a third prompt requesting re-generation of code for the first source code portion, the third prompt including the error message; receiving, from the language model, replacement code for the first step; and executing the replacement code to perform the first step (page 2 paragraph 4: generated code is executed and issues (equivalent to the claimed errors) are provided as feedback to the code generator).
Claim 16:
Dong discloses An apparatus comprising: at least one processor; and a non-transitory computer-readable medium storing executable instructions that when executed by the at least one processor cause the at least one processor to: receive, via an interface, a natural language query about a request for a user device to perform a computer task (Figs 7 and 8 disclose a dialogue with a user and an LLM-based code generator); generate a prompt including the natural language query and a list of functions; transmit the prompt to a language model (abstract, page 2 paragraph 2, page 3 section 2.1, paragraph 2, figs 7-9: code generation employing LLMs, exemplified by ChatGPT...LLMs use language as the foundation for input and output and align with human needs through instructions or prompts, offering the potential for inter-model interaction and collaboration...it is prevalent to control LLM generation using instructions or prompts”); receive a response from the language model, the response including machine-readable instructions executable by the user device to perform the computer task, the machine-readable instructions using at least one function from the list of functions (abstract and figs 7-8: code generation employing LLMs, exemplified by the LLM); and execute the machine-readable instructions to perform the computer task (page 2 paragraph 1: team members can review and test the generated code).
Claim 17:
Dong discloses the prompt is a first prompt and the response is a first response, wherein the executable instructions cause the at least one processor to: perform a step of the computer task by executing a portion of the machine-readable instructions; in response to execution of the portion of the machine-readable instructions, detect an error message; transmit, to the language model, a second prompt requesting re-generation of code for the portion of the machine-readable instructions, the second prompt including the error message; receive, from the language model, a second response that includes replacement code for the step; and execute the replacement code to perform the step (page 2 paragraph 4: generated code is executed and issues (equivalent to the claimed errors) are provided as feedback to the code generator).
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) 3, 12 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dong in view of Singh (USPUB 20240296315 A1 filed Mar. 3, 2023).
Substantially similar claims 3, 12 and 18:
Dong discloses every feature of claims 1, 10 and 16.
Dong, by itself, does not seem to completely teach in response to successful performance of the computer task, storing the computer task in a memory device; and initiating a display of a user interface element corresponding to the computer task on the interface, wherein the user interface element, when selected, is configured to cause the machine-readable instructions to be re-executed.
The Examiner maintains that these features were previously well-known as taught by Singh.
Singh teaches in response to successful performance of the computer task, storing the computer task in a memory device; and initiating a display of a user interface element corresponding to the computer task on the interface, wherein the user interface element, when selected, is configured to cause the machine-readable instructions to be re-executed (0097-100: the prompt identifiers are actuatable so that the user can select one (e.g., by clicking on the prompt identifier on a user interface display)... Prompt tuning and chaining system 310 then populates a prompt template in a prompt editor (such as a text entry box on a user interface display). Populating a prompt template for tuning or editing or chaining is indicated by block 551 in the flow diagram of FIG. 10C. The prompt template may thus be representative of an example prompt 553 and example prompt chains...) The developer or user may then interact (e.g., edit) with the prompt template as indicated by block 559. For instance, prompt tuning and chaining system 310 may detect that the user has edited the prompt template as indicated by block 561, saved it as indicated by block 563, dismissed it as indicated by block 565, or has interacted with the prompt in the prompt template in other ways.... The prompts can also be stored and reused and shared with others for tuning. Further, the prompts can be populated, automatically, into a prompt library which may be stored in a tenant or user data shard for reuse or in the development environment).
Dong and Singh are analogous art because they are from the same problem-solving area, managing LLM prompts.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Dong and Singh before him or her, to combine the teachings of Dong and Singh. The rationale for doing so would have been to allow for a user to revisit previous LLM prompts.
Therefore, it would have been obvious to combine Dong and Singh to obtain the invention as specified in the instant claim(s).
Claim(s) 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dong in view of Tan (USPUB 20240289632 A1 filed Aug. 29, 2024).
Claim 13:
Dong discloses every feature of claim 10.
Dong, by itself, does not seem to completely teach the first function includes obtaining session state information about a computing session of a user, the session state information identifying a list of session items.
The Examiner maintains that these features were previously well-known as taught by Tan.
Tan teaches the first function includes obtaining session state information about a computing session of a user, the session state information identifying a list of session items (0048 and 0079: The content presentation module 210 scores items based on a relatedness of the items to the search query. For example, the content presentation module 210 may apply natural language processing (NLP) techniques to the text in the search query to generate a search query representation (e.g., an embedding) that represents characteristics of the search query... The online system assigns scores to chatbot sessions based on the extent to which the outcomes of these sessions align with the objectives of the online system. For example, the online system may assign each possible workflow outcome a score and assign the corresponding score to the chatbot sessions based on which workflow outcome the user takes from the chatbot session. These scores may be manually generated by users of the online system or may be automatically generated using metrics computed based on user data associated with the user of the chat session, item data associated with items of the chat session).
Dong and Tan are analogous art because they are from the same problem-solving area, managing LLM prompts.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Dong and Tan before him or her, to combine the teachings of Dong and Tan. The rationale for doing so would have been to provide a user with desired information.
Therefore, it would have been obvious to combine Dong and Tan to obtain the invention as specified in the instant claim(s).
Claim 14:
Dong, by itself, does not seem to completely teach the second function includes identifying one or more session items from the list of session items that are semantically related to one or more terms included in the natural language query.
The Examiner maintains that these features were previously well-known as taught by Tan.
Tan teaches the second function includes identifying one or more session items from the list of session items that are semantically related to one or more terms included in the natural language query (0048 and 0079: The content presentation module 210 scores items based on a relatedness of the items to the search query. For example, the content presentation module 210 may apply natural language processing (NLP) techniques to the text in the search query to generate a search query representation (e.g., an embedding) that represents characteristics of the search query... The online system assigns scores to chatbot sessions based on the extent to which the outcomes of these sessions align with the objectives of the online system. For example, the online system may assign each possible workflow outcome a score and assign the corresponding score to the chatbot sessions based on which workflow outcome the user takes from the chatbot session. These scores may be manually generated by users of the online system or may be automatically generated using metrics computed based on user data associated with the user of the chat session, item data associated with items of the chat session).
Dong and Tan are analogous art because they are from the same problem-solving area, managing LLM prompts.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Dong and Tan before him or her, to combine the teachings of Dong and Tan. The rationale for doing so would have been to provide a user with desired information.
Therefore, it would have been obvious to combine Dong and Tan to obtain the invention as specified in the instant claim(s).
Claim 15:
Dong, by itself, does not seem to completely teach the second function includes generating a textual description about a session item from the list of session items.
The Examiner maintains that these features were previously well-known as taught by Tan.
Tan teaches the second function includes generating a textual description about a session item from the list of session items (0048 and 0079: The content presentation module 210 scores items based on a relatedness of the items to the search query. For example, the content presentation module 210 may apply natural language processing (NLP) techniques to the text in the search query to generate a search query representation (e.g., an embedding) that represents characteristics of the search query... The online system assigns scores to chatbot sessions based on the extent to which the outcomes of these sessions align with the objectives of the online system. For example, the online system may assign each possible workflow outcome a score and assign the corresponding score to the chatbot sessions based on which workflow outcome the user takes from the chatbot session. These scores may be manually generated by users of the online system or may be automatically generated using metrics computed based on user data associated with the user of the chat session, item data associated with items of the chat session).
Dong and Tan are analogous art because they are from the same problem-solving area, managing LLM prompts.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Dong and Tan before him or her, to combine the teachings of Dong and Tan. The rationale for doing so would have been to provide a user with desired information.
Therefore, it would have been obvious to combine Dong and Tan to obtain the invention as specified in the instant claim(s).
Allowable Subject Matter
Claims 4-7 and 19-20 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.
Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed in the attached PTOL-892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED-IBRAHIM ZUBERI whose telephone number is (571)270-7761. The examiner can normally be reached on M-Th 8-6 Fri: 7-12/OFF.
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/MOHAMMED H ZUBERI/ Primary Examiner, Art Unit 2178
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