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
Introduction
This office action is in response to Applicant’s submission filed on 12/31//2024. As such, claims 1-20 have been examined.
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 1-5, and 13-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method that, under the broadest reasonable interpretation, claims limitations that cover performance of the limitations in the human mind with the assistance of physical aids (e.g., pen and paper), but for the recitation of generic or well-known or conventional computer components. That is, other than reciting “one or more processors and task language generator” nothing in these claim limitations precludes the steps from practically being performed in the mind. As a whole, claim 1 pertains to sending out a task to a number of people, obtaining results and providing the result that is in align with majority, which is a mental process that a human can do. Individually, each of the limitations also pertains to a mental process and/or insignificant extra solution activity, for example:
receiving, by one or more processors, a natural language request from a user; (e.g., a human looking in a computer or receiving a print out of question or request from a user/person.)
converting, using a task language generator, the natural language request into a task language input; (e.g., the human breaking down the request into a structure or format.)
transmitting the task language input to a plurality of agents, wherein each agent in the plurality of agents independently determines a response to the task language input; (e.g., the human asking other humans to each independently help with responding to the request.)
determining a satisfactory response by generating a quorum among the plurality of agents using a consensus algorithm based on the independently determined responses; (e.g., the human evaluating and determine that the results that is provided by the majority of the helpers would be the best response.)
converting the satisfactory response to a natural language response; (e.g., the human writing the response that majority agrees with in a piece of paper.)
and returning the natural language response to the user. (e.g., the human passing the paper with the result back to the user.)
The judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of receiving, determining, or outputting information) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of using generic computer components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 1 is not patent eligible.
The examiner further notes that the use of claimed generic computer components (“one or more processors and task language generator”) to obtain, extract, and/or generate data invokes such generic computer components “merely as a tool to perform an existing process”. MPEP 2106.05(f). MPEP 2106.05(f) further explains:
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Claim 1 recites generic computer components (“one or more processors and task language generator”), with respect to performing tasks. MPEP 2106.05(d) and (f) further provides examples of court decisions where the courts found generic computing components to be mere instructions to apply a judicial exception, and further explains “increased speed” (e.g., using a computer to increase the speed of an otherwise mental process) does not provide an inventive concept. For example:
A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) (emphasis added).
Performing repetitive calculations. Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")
Claim 10 recites a system claim that corresponds to the method of claim 1 and is therefore rejected under the same grounds as claim 1 above. While claim 10 further recites “a memory, and one processor coupled to the memory”, these are merely generic computer components recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Therefore, none of these limitations (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception, because in either case the additional limitations merely utilize generic computer components that amounts to no more than mere instructions to apply the exception using generic computer function. Claim 10 is not patent eligible.
Claim 19 recites a computer-readable medium claim that corresponds to the method of claim 1 and is therefore rejected under the same grounds as claim 1 above. While claim 19 further recites “computer-readable medium”, these are merely generic computer components recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Therefore, none of these limitations (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception, because in either case the additional limitations merely utilize generic computer components that amounts to no more than mere instructions to apply the exception using generic computer function. Claim 19 is not patent eligible.
Claims 2-7 depend from independent claims 1, do not remedy any of the deficiencies of claim 1, and therefore are rejected on the same grounds as claim 1 from above.
Claim 2 further comprising: wherein the natural language request is a transaction request and the natural language response is provided in response to the transaction request. (e.g., just further determine the request is to perform or complete an action, like purchasing an item or product, which the human can determine from reading the request.)
Claim 3 further recite: wherein each agent in the plurality of agents further comprises a language model and a task language library, each task language library being identical across the plurality of agents and each language model being identical across the plurality of agents. (e.g., defining the each agent has access to a language model which is a generic computer component, and that each agent has access to same tools (generic computer components), like a task language library and same language model. This is like a scenario where there is multiple human agent/assistant, each equipped with same language model and task language library and performing same task, like students given a cheat-sheet and/or same text book on an open book exam.)
Claim 4 further comprising: wherein the task language input is represented as a binary vector. (e.g., the human can break down input text into 1s and 0s with assistance of pen and paper.)
Claim 5 further recites: the transmitting further comprising: designating a primary node in the plurality of agents; transmitting the task language input to the primary node; and causing the primary node to transmit the task language input to other agents in the plurality of agents. (e.g., the human delegating someone as a primary contact lead/agent, provide the task to the primary contact person/agent, then the primary contact lead/agent will pass the task and messages onto other people/subagents.) [The delegation of task can also be related to another category of abstract idea, which is human gathering activities.]
Claim 6 further recites: determining that the natural language request is a deterministic query. (e.g., the human determining if the request/question can be determined by facts.)
Claim 7 further recites: wherein the consensus algorithm is a practical byzantine fault tolerance consensus protocol. (e.g., the use of a generic computer component.)
Claim 8 further recites: wherein the task language generator comprises a predicate representation scheme. (e.g., the human can apply logic and knowledgeable representation framework to determine facts and relationship using mathematical logic.)
Claim 9 further recites: wherein the plurality of agents communicate via a message bus. (e.g., the human can communicate with other agents verbally or through writing.) [the use of a message bus is a generic computer component to communicate data]
The analysis of Claims 11-18 corresponds to claims 2-9, and therefore similar rationale of rejection is applied to these claims respectively.
The analysis of Claim 20 corresponds to claim 3, and therefore similar rationale of rejection is applied to the claim.
In sum, claims 2-9, 11-18, and 20 depend from claims 1, 10 and 19 respectively, and further recite mental processes as explained above. None of the additional limitations recited in claims 2-9, 11-18, and 20 amount to anything more than the same or a similar abstract idea as recited in claims 1, 10 and 19. Nor do any limitations in claims 2-9, 11-18, and 20: (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception because the additional limitations of using generic computer components amounts to no more than mere instructions to apply the exception using generic computer components. Claims 2-9, 11-18, and 20 are not patent eligible.
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.
Claims 1-2, 5, 9-11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Collins (US 20250200447), in view of Rose (US 20220335089).
1. A method, comprising: receiving, by one or more processors, a natural language request from a user; ([0085] requestor makes a request 206 via hub 200 to the decider model 202. For example, request 206 may comprise a single work request requiring natural language processing.)
transmitting the task language input to a plurality of agents, wherein each agent in the plurality of agents independently determines a response to the task language input; ([0089] The decider model 202 publishes the request 206 to the set of AI models 204 that make up a quorum that responds. One or more AI models 204 accept the request sent via the proposal message 212.)
determining a satisfactory response by generating a quorum among the plurality of agents using a consensus algorithm based on the independently determined responses; ([0025] It should be understood that the use of multiple AI models, each of which may be trained on foundational algorithms to perform specific tasks, may collaborate or operate in ensembles with other AI models in the consensus-based network to provide AI agents that provide more advanced systems that may utilize the AI models to perform tasks autonomously. [0089] The decider model 202 publishes the request 206 to the set of AI models 204 that make up a quorum that responds. One or more AI models 204 accept the request sent via the proposal message 212. Once outcomes or results 214 are received from the AI models 204, the decider model 202 aggregates in its aggregation module 116 these results and decides the final result to apply and pass back to the originating client application as the desired trusted outcome response 208.)[ decider's aggregation module 116 which aggregates the results and mathematically decides the final result to apply reads on consensus algorithm]
converting the satisfactory response to a natural language response; ([0089] Once outcomes or results 214 are received from the AI models 204, the decider model 202 aggregates in its aggregation module 116 these results and decides the final result to apply and pass back to the originating client application as the desired trusted outcome response 208. The requestor receives a trusted outcome 208 through the hub 200.) [it is implied that the output to the requester would be converted to natural language since the requester is a human user]
and returning the natural language response to the user. ([0089] Once outcomes or results 214 are received from the AI models 204, the decider model 202 aggregates in its aggregation module 116 these results and decides the final result to apply and pass back to the originating client application as the desired trusted outcome response 208. The requestor receives a trusted outcome 208 through the hub 200.)
Collin teaches natural language process (see para 0098) and NLU model to determine intent of the work request (see para 0109 – 0112), it would seem like it would process natural language task into machine readable execution and structure, but nevertheless Rose explicitly discloses this feature.
Rose in the related art discloses: converting, using a task language generator, the natural language request into a task language input; ([0028] The user device 104 is configured to interact with a user over the display 106 and the user input device 107 to enable the user to provide natural language queries or commands. The natural language input is processed by the natural language interpreter 103, which converts the natural language queries or commands into a set of machine-executable instructions.)
Collins and Rose are considered analogous art. 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 Collins to combine the teaching of Rose to incorporate the above mentioned features, because it breaks down user requests into structured, machine readable format that computer can process on (Rose, [0028]).
Regarding Claim 2, Collins/Rose disclose all the element of claim 1,
Rose further discloses: wherein the natural language request is a transaction request and the natural language response is provided in response to the transaction request. ([0044] FIG. 9 shows diagrams of the display 106 when the user wishes to create a new folder on the web-based storage system 110 and save particular files in the new folder. The user speaks the utterance 942 “make a folder and put all the documents in the Chicago trip last week in the folder. Call it last week's trip.” Alternatively, the user may type or otherwise provide the utterance 942 into the user device 104 via the user input device 107. As shown in FIG. 9, in response to receiving the utterance 942, the display 106 on the user device 104 shows a list of three documents (A, B, and C) that are stored in a folder named “last week's trip.”)
Where the rational for the combination would be similar to the one already provided.
Regarding Claim 5, Collins/Rose disclose all the element of claim 1,
Collins further discloses: the transmitting further comprising: designating a primary node in the plurality of agents; ([0064] The orchestration engine 102 provides smooth execution, synchronization, and collaboration among the various AI models 100, including computational models, or components and resources involved in the consensus-based network 110. The orchestration engine 102 handles the overall workflow and coordination of tasks within the consensus-based network 110. It manages the allocation and utilization of resources, schedules the execution of AI models 100, and facilitates communication and data flow between different components in the consensus-based network 110 through a high-performance algorithm.)
transmitting the task language input to the primary node; and causing the primary node to transmit the task language input to other agents in the plurality of agents. ([0064] The orchestration engine 102 provides smooth execution, synchronization, and collaboration among the various AI models 100, including computational models, or components and resources involved in the consensus-based network 110. The orchestration engine 102 handles the overall workflow and coordination of tasks within the consensus-based network 110. It manages the allocation and utilization of resources, schedules the execution of AI models 100, and facilitates communication and data flow between different components in the consensus-based network 110 through a high-performance algorithm.) Also see para 0057-0058, and 0063.
Regarding Claim 9, Collins/Rose disclose all the element of claim 1,
Collins further discloses: wherein the plurality of agents communicate via a message bus. ([0008] The network further comprises an orchestration engine communicatively coupled to and organizing operation of the plurality of decision-focused computational models. The orchestration engine is configured to receive requests from requestors and generate the input information related to the requests. The orchestration engine comprises at least one decider model. The at least one decider model comprises an invoke module and an aggregation module. The invoke module is configured to forward the input information to the plurality of decision-focused computational models and the aggregation module is configured to receive and aggregate the singular outcomes to produce the desired trusted outcome. In the network the at least one decider module is configured to communicate the desired trusted outcome response to the requestor from the orchestration engine.) [This engine serves the exact function of a message bus by routing and distributing requests across the system.]
Regarding Claim 10, Collins discloses: 10. A system, comprising a memory; and at least one processor coupled to the memory and configured to ([0077] interaction hub comprise one or more computers, which would imply the use of processor and memory)
As for the rest of the claim, they recite similar elements as claim 1, therefore the rationale applied in rejection of claim 1 is equally applicable.
Claims 11, 14, 18 recites limitations similar to the limitations of Claim 2, 5, and 9 respectively, and are rejected under similar rationale.
Regarding Claim 19, Collins discloses: 19. A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations, the operations comprising: ([0077] interaction hub comprise one or more computers, which would imply the use of processor and memory and CRM.)
As for the rest of the claim, they recite similar elements as claim 1, therefore the rationale applied in rejection of claim 1 is equally applicable.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Collins (US 20250200447), in view of Rose (US 20220335089), and further in view of Shah (US 20190115027).
Regarding Claim 4, Collins/Rose disclose all the element of claim 1,
Collins/Rose do not disclose task input language are represented in binary vector.
Shah in the related art discloses: wherein the task language input is represented as a binary vector. ([0044] In some implementations in which dialog manager 126 employs a neural network, inputs to the neural network may include, but are not limited to, a user action, a previous responsive action (i.e., the action performed by dialog manager in the previous turn), a current dialog state (e.g., a binary vector provided by dialog state tracker 124 that indicates which slots have been filled), and a responsive action database state, which in some implementations may be encoded as a binary vector comprising flags which indicate if the responsive action database contains zero, one, two, or three and more results matching the constraints specified up to the current turn of the dialog session.)
Collins/Rose/Shah are considered analogous art. 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 Collins/Rose to combine the teaching of Shah to incorporate the above mentioned features, because using binary vector to encode dialog states simplifies complex continuous information into standardized computer readable format which helps with math calculation/process and dimensionality reduction (Shah, [0044]).
Claim 13 recites limitations similar to the limitations of Claim 4, and are rejected under similar rationale.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Collins (US 20250200447), in view of Rose (US 20220335089), and further in view of Wald (US 9396235).
Regarding Claim 6, Collins/Rose disclose all the element of claim 1,
Collins/Rose do not disclose checking for deterministic query.
Wald in the related art discloses: determining that the natural language request is a deterministic query. ([col. 2, lines 4-15] the actions of receiving a query; determining a particular fact associated with the query; based on the particular fact, obtaining a likelihood that the query is a fact-seeking query, where the likelihood is based at least in part on comparing a first query count associated with a set of query patterns with a second query count associated with a subset of natural language query patterns selected from the set of query patterns; based on determining that the likelihood satisfies a threshold, adding a factual answer to a set of search results for the query; and providing the set of search results in response to the query.)
Collins/Rose/Wald are considered analogous art. 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 Collins/Rose to combine the teaching of Wald to incorporate the above mentioned features, because figuring out that the question is fact based may reduce search time (Wald, [col. 2, lines 4-15]).
Claim 15 recites limitations similar to the limitations of Claim 6, and are rejected under similar rationale.
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Collins (US 20250200447), in view of Rose (US 20220335089), and further in view of Sanghvi (US 20210064763).
Regarding Claim 7, Collins/Rose disclose all the element of claim 1,
Collins/Rose do not disclose PBFT consensus protocol.
Sanghvi in the related art discloses: wherein the consensus algorithm is a practical byzantine fault tolerance consensus protocol. ([0019] To achieve consensus (e.g., agreement to the addition of a block to a blockchain), a consensus protocol is implemented within the consortium blockchain network. Example consensus protocols include, without limitation, practical Byzantine fault tolerance (PBFT))
Collins/Rose/Sanghvi are considered analogous art. 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 Collins/Rose to combine the teaching of Sanghvi to incorporate the above mentioned features, because PBFT is more energy efficient and faster than Proof of Work PoW and Proof of Stake (PoS) (Sanghvi, [0019]).
Claim 16 recites limitations similar to the limitations of Claim 7, and are rejected under similar rationale.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Collins (US 20250200447), in view of Rose (US 20220335089), and further in view of Cornali (US 8180758).
Regarding Claim 8, Collins/Rose disclose all the element of claim 1,
Collins/Rose do not disclose predicate representation scheme.
Cornali in the related art discloses: wherein the task language generator comprises a predicate representation scheme. ([col. 7, lines 26-43] In order to extract the relevant information and generate a predicate logic corpus of facts 204, a predicate logic inference engine 206 is used to convert normal SQL (or other such) queries into queries that can be used to generate predicate logic facts.)
Collins/Rose/Cornali are considered analogous art. 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 Collins/Rose to combine the teaching of Cornali to incorporate the above mentioned features, because predicate logic enable transforming static data into dynamic knowledge base, enabling user to explore facts and deduce new information without the need to write complex transactions (Cornali, [col. 7, lines 26-43]).
Claim 17 recites limitations similar to the limitations of Claim 8, and are rejected under similar rationale.
Potentially Allowable Subject Matter
Claims 3, 12 and 20 would be potentially allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and amended to overcome the pertinent rejections under section 35 U.S.C. 101.
Regarding Claim 3, the closest reference, Osi US 20250363327 – discloses “Utilizing multiple LLMs in parallel for the same task may reduce errors, as diverse model reasoning enhances accuracy through consensus and error-checking.” See para 0062. However, it does not teach or suggest all the LLM are identical, and it also does not teach or suggest each agent contains a identical task language library.
Claims 12 and 20, although in different statutory categories, but it nevertheless contain similar elements as claim 3, therefore similar rationale is being applied to these claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Tong (US 20240419950)- discloses system/method that use specialized LLM to generate domain specific language script for enterprise system integration. In para 0024, “validating the output comprises performing a consensus protocol by the plurality of ML agents based on pretrained knowledge of the subset of the plurality of ML agents and external data sources.” See Abstract, para 0024, 0030, 0035, 0062, 0071-0073 and figs 1A/1B for additional details.
Suzuoki, S., & Hatano, K. (2024). Reducing Hallucinations in Large Language Models: A Consensus Voting Approach Using Mixture of Experts. – discloses using a method of consensus voting using combination of different skilled LLMs to manage hallucination. See Abstract, section 3 and fig. 1 for additional details.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip H Lam whose telephone number is (571)272-1721. The examiner can normally be reached 9 AM-2 PM Pacific time.
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/PHILIP H LAM/Examiner, Art Unit 2656
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