DETAILED ACTION
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 and 19-28 are rejected under 35 U.S.C. 101.
Claims 1, 19 and 20 are directed to an abstract idea because it recites a high-level workflow for handling natural-language requests: (i) receive natural language content, (ii) semantically interpret it to identify a matching plugin, (iii) compare the identified plugin against a current-session plugin to decide what “task” to execute, (iv) send constructed content to a large language model to obtain plugin input parameters, and (v) call the plugin and obtain a result. This is essentially organizing and routing information and invoking a tool based on that decision, activities that can be performed as mental steps (or with pen-and-paper rules) and that are routinely characterized as abstract mental processes or information management.
The additional elements do not integrate the abstract idea into a practical application in a way that is particular/meaningfully technological. The claim does not recite a specific improvement to computer operation. Instead, it uses result oriented functional language (“performing semantic understanding,” “detecting whether…hits,” “determining,” “acquiring…content,” “sending…to obtain an input parameter,” “calling the plugin”) that merely states desired outcomes of generic computing/AI components.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims are (i) mere instructions to implement the idea on a computer, and/or (ii) recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. There is further no improvement to the computing device.
Dependent claims 2-9 and 21-28 are further recite an abstract idea performable by a human and do not amount to significantly more than the abstract idea as they do not provide steps other than what is known to be an abstract idea.
Claim 2: abstract decision logic implemented with generic AI/computing components and lacking a specific technological improvement.
Claim 3: machine style task routing and prioritization (an abstract idea) without concrete technical implementation details that provide significantly more.
Claim 4: remains abstract session management/branching logic for selecting tasks, with no particular technical mechanism beyond generic comparisons and task creation.
Claim 5: organizing work items is an abstract administrative function performed on a computer.
Claim 6: sorting and sequential execution are generic workflow scheduling, and the claim remains functional and high-level without a specific improvement in computer performance or architecture.
Claim 7: does not add a specific technical solution that amounts to significantly more than the abstract idea.
Claim 8: an abstract information-processing concept implemented using generic components.
Claim 9: administrative control of a task workflow (human-like override/control logic) and does not recite a concrete technical improvement that would supply an inventive concept.
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.
Claims 1, 8, 19-20 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (“HugginGPT:…”; 37th Conference on Neural Information Processing Systems 2023) in view of Pemberton et al. (US 11,640,823).
Claims 1 and 19-20,
Shen teaches a method for invoking a plugin of a large language model ([Abstract] LLM controller system), comprising:
acquiring natural language content ([Abstract] we use ChatGPT to conduct task planning when receiving a user request);
performing semantic understanding on the natural language content and detecting whether the natural language content hits a plugin to obtain a first plugin pointed to by a plugin hit result ([Abstract] [3 HuggingGPT] [Introduction] Task Planning: Using ChatGPT to analyze the requests of users to understand their intentions and selecting models according to descriptions);
acquiring language understanding content of the to-be-executed session understanding task and sending the language understanding content to the large language model to obtain an input parameter of the third plugin ([Introduction] [3.1 Task Planning] the LLM produces structured task representations with arguments (args [Wingdings font/0xE0] (parameters): Task Planning Stage: the LLM outputs tasks in a structured format with an “args” field and this is used downstream for execution; ); and
calling the third plugin according to the input parameter of the third plugin to obtain a calling result of the to-be-executed session understanding task ([3.3 Task Execution] Task Execution: invoke and execute each selected model and return the results to ChatGPT).
The difference between the prior art and the claimed invention is that Shen does not explicitly teach comparing the first plugin with a second plugin corresponding to a current session understanding task to determine a to-be-executed session understanding task and a third plugin corresponding to the to-be-executed session understanding task.
Pemberton teaches comparing the first plugin with a second plugin corresponding to a current session understanding task to determine a to-be-executed session understanding task and a third plugin corresponding to the to-be-executed session understanding task ([col. 2 line 54 to col. 3 line 50] a comparison/evaluation stage before routing to a particular skill; “prior to routing a request to a particular application (e.g. a skill) for processing, a ‘Can fulfill intent request’ (CFIR) may be sent to a number of candidate applications (e.g. … a skill) that could be selected for processing the current request; skill query service sends CFIR to candidate skills and CFIR includes “intent data and/or slot data” enabling candidate comparison and selection of which application/skill will process the current request).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Shen with teachings of Pemberton by modifying the HugginGPT: solving AI tasks with ChatGPT and its Friends in Hugging Face as taught by Shen to include comparing the first plugin with a second plugin corresponding to a current session understanding task to determine a to-be-executed session understanding task and a third plugin corresponding to the to-be-executed session understanding task as taught by Pemberton for the benefit of improving human-computer interactions and to control various systems (Pemberton [col. 1 lines 21-22]).
Claims 8 and 27,
Shen further teaches the method according to claim 1, wherein acquiring the language understanding content of the to-be-executed session understanding task comprises: in response to the to-be-executed session understanding task being a new session understanding task, determining the language understanding content of the to-be-executed session understanding task according to the natural language content ([3 HuggingGPT] [Specification-based Instruction] [Table 9] that task arguments (args; language understanding content used downstream) are resolved from either the user’s request or the generated resources of the dependent tasks); or
in response to the to-be-executed session understanding task being different from the new session understanding task, acquiring a context of the to-be-executed session understanding task ([3 HuggingGPT] [Demonstration-based Parsing] “To assist with task planning, the chat history is available as {{ Chat Logs }}, where you can trace the user-mentioned resources and incorporate them into the task planning.”); and determining the language understanding content of the to-be-executed session understanding task according to the context of the to-be-executed session understanding task and the natural language content ([3 HuggingGPT] [Demonstration-based Parsing] “trace the user-mentioned resources and incorporate them into the task planning”).
Claims 9 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (“HugginGPT:…”; 37th Conference on Neural Information Processing Systems 2023) in view of Pemberton et al. (US 11,640,823) and further in view of Vibbert et al. (2016/0042735).
Claims 9 and 28,
Shen and Pemberton teach all the limitations in claim 1. The difference between the prior art and the claimed invention is that Shen nor Pemberton explicitly teach in response to the natural language content being an intervention command, adjusting the current session understanding task according to the intervention command.
Vibbert teaches in response to the natural language content being an intervention command ([0007-0009] a second natural language input received during execution of a first dialog task (an intervening user input that changes what the assistant is doing); a second natural language user input received during execution of the first dialog comprises a request to perform a second dialog task), adjusting the current session understanding task according to the intervention command ([0007-0009] modifying the running task in response to the intervening input by suspending the current task and shifting to the new task; the computing device may determine that the second dialog task is to be executed before exaction of the first dialog task is completed and the computing device may suspend execution the first dialog task and identify which dialog task is to be executed).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the teachings of Shen with teachings of Vibbert by modifying the HugginGPT: solving AI tasks with ChatGPT and its Friends in Hugging Face as taught by Shen to include in response to the natural language content being an intervention command, adjusting the current session understanding task according to the intervention command as taught by Vibbert for the benefit of initiating execution of the second task before execution of the first task is complete (Vibbert [Abstract]).
Allowable Subject Matter
Claims 2-7 and 21-26 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 AND overcome the 101 Abstract Idea rejection set forth.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Schick et al. (“ToolFormer: Language Models can teach themselves to use Tools”) – Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with basic functionality, such as arithmetic or factual lookup, where much simpler and smaller specialized models excel. In this paper, we show that LMs can teach themselves to use external tools via simple APIs and achieve the best of both worlds. We introduce Toolformer, a model trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction. This is done in a self-supervised way, requiring nothing more than a handful of demonstrations for each API. We incorporate a range of tools, including a calculator, a Q&A system, a search engine, a translation system, and a calendar. Toolformer achieves substantially improved zero-shot performance across a variety of downstream tasks, often competitive with much larger models, without sacrificing its core language modeling abilities ([Abstract]).
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SHREYANS A. PATEL
Primary Examiner
Art Unit 2653
/SHREYANS A PATEL/ Examiner, Art Unit 2659