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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1, it requires a “large learning model (LLM);” however, the specification only discloses “large language models (LLMs).” Therefore, the recited “large learning model” lacks adequate descriptive support. For the purpose of examination, the recited “large learning model (LLM)” is interpreted as “large language
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7 and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “more detailed information” in claims 7 and 14 is a relative term which renders the claim indefinite. The term “more detailed information” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3, 8, 10, 15, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shen (Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. 2023. HuggingGPT: Solving AI tasks with ChatGPT and its friends in HuggingFace. arXiv preprint arXiv:2303.17580 (2023)) and further in view of Talebirad (Talebirad Y, Nadiri A. Multi-agent collaboration: harnessing the power of intelligent LLM agents. 2023. ArXiv:2306.03314).
Regarding claim 1, Shen teaches: A method comprising:
producing, by a first large learning model (LLM), a processing plan based on a first prompt, wherein the processing plan comprises a plurality of tasks corresponding to a plurality of services (Section 3.1, “1) An LLM (e.g., ChatGPT) first parses the user request, decomposes it into multiple tasks, and plans the task order and dependency based on its knowledge”);
sending, by a processing device, a plurality of messages corresponding to the plurality of tasks to a plurality of service agents (Section 3.2, “After parsing the list of tasks, HuggingGPT next needs to match the tasks and models, i.e., select the appropriate model for each task in the task list”); and
generating a query response based on the plurality of agent responses (Section 3.3, “Once a task is assigned to a specific model, the next step is to execute the task, i.e., to perform model inference. For speedup and computational stability, HuggingGPT runs these models on hybrid inference endpoints. By taking the task arguments as inputs, the models compute the inference results and then send them back to the large language model”).
Shen does not teach; however, Talebirad discloses: wherein the plurality of service agents correspond to the plurality of services and comprise a plurality of second LLMs that produce a plurality of agent responses (Section 2.1, “Each agent i ∈ V is represented as a tuple Ai = (Li,Ri,Si,Ci,Hi), where: Li refers to the language model instance utilized by the agent. This encompasses the model’s type (such as GPT-4 or GPT-3.5-turbo)”).
It would have been obvious to a person having ordinary skill in the art, at the effective filing date of the invention, to have applied the known technique of the plurality of service agents correspond to the plurality of services and comprise a plurality of second LLMs that produce a plurality of agent responses, as taught by Talebirad, in the same way to the plurality of service agents, as taught by Shen. Both inventions are in the field of multi-agent LLM systems for complex task solving, and combining them would have predictably resulted in “a collaborative environment where multiple intelligent agent components, each with distinctive attributes and roles, work together to handle complex tasks more efficiently and effectively,” as indicated by Talebirad (abstract).
Regarding claim 3, Talebirad teaches: The method of claim 1, further comprising: generating, by the plurality of second LLMs executing on the plurality of service agents, a plurality of API calls (Section 4.3.1, “In this system, API calls and their documentation are used to instruct the LLM about the specific tasks each API can handle. The model learns to map prompts to API calls by using a retrieval system to access the most up-to-date API documentation from the database”); executing, by the plurality of service agents, the plurality of API calls to their corresponding one of the plurality of services to produce a plurality of API responses (Section 3.1, “By connecting agents to plugins, agents gain access to tools, resources, or external services that enhance their capabilities. These connections allow agents to leverage the functionalities of the plugins”); receiving the plurality of API responses from the plurality of service agents (Section 4.1.1, “Messages sent through these connections may include task assignments, requests for information, or commands to execute certain operations”); and generating the query response based on the plurality of API responses (Section 1, “Our proposed abstraction allows users to engage with a “black box” by providing an initial prompt and receiving the final output without grappling with the underlying complexities of agent collaborations and interactions”).
Claim(s) 8, 10, 15, and 17 recite(s) commensurate subject matter as claim(s) 1 and 3. Therefore, it/they is/are rejected for the same reasons.
Claim(s) 2, 9, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Shen and Talebirad, as applied above, and further in view of Singh (US 2024/0095077).
Regarding claim 2, Shen and Talebirad do not teach; however, Singh discloses: the plurality of agent responses comprise a plurality of Application Programming Interface (API) calls (¶ 108, “the task(s) T1-T4 include action primitives “walk,” “find,” “grab,” and “putin,” respectively, which may each be an API call”), the method further comprising: organizing the plurality of API calls into an execution stack (¶ 108, “the plan 220 includes task(s) T1-T4 that the plan generator functionality 122 generated based at least in part on the prompt 200 of FIG. 2A”); and executing the plurality of API calls to the plurality of services in an order based on the execution stack (¶ 104, “The task(s) (e.g., a task 215) may include one or more Application Programming Interface (“API”) calls to action primitives (e.g., “grab(‘wineglass’)”)” and ¶ 105, “The comments (e.g., a comment 216) may provide natural language summaries for subsequent sequences of actions”).
It would have been obvious to a person having ordinary skill in the art, at the effective filing date of the invention, to have applied the known technique of the plurality of agent responses comprise a plurality of Application Programming Interface (API) calls, the method further comprising: organizing the plurality of API calls into an execution stack; and executing the plurality of API calls to the plurality of services in an order based on the execution stack, as taught by Singh, in the same way to the plurality of agent responses, as taught by Shen and Talebirad. Both inventions are in the field of LLM multi-agent orchestration systems, and combining them would have predictably resulted in a method that “generate(s) a plan to perform a task (identified in the prompt) that is to be performed by an agent (real world or virtual),” as indicated by Singh (¶ 2).
Claims 9 and 16 recite commensurate subject matter as claim 2. Therefore, they are rejected for the same reasons.
Allowable Subject Matter
Claims 4-7, 11-14, and 18-20 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.
The following is a statement of reasons for the indication of allowable subject matter: No reference or combination of references were uncovered that teach or at least suggest “responsive to sending the first message to the first service agent, receiving a first agent response from the first service agent, wherein the first agent response is included in the plurality of agent responses,” as recited in dependent claim 4 as a whole, and commensurately recited in dependent claims 11 and 18.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Oermann (US 2024/0265174) discloses “retrieve one or more entries of an LLM agent output from an LLM agent memory to include in the next planning step, plan an LLM agent response to an environment for the presented information, send one or more conditioned intra-agent communications to a plurality of additional LLM agents” (abstract), which relates to the disclosed multi-agent LLM system.
Malamut (US 9,424,112) discloses “a remote application programming interface (API) by defining an execution plan using an interface definition language and a dependency configuration file to generate a constrained directed graph of hierarchically dependent functions of the API” (abstract), which relates to the disclosed plurality of API calls.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACOB D DASCOMB whose telephone number is (571)272-9993. The examiner can normally be reached M-F 9:00-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital can be reached at (571) 272-4215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JACOB D DASCOMB/ Primary Examiner, Art Unit 2198