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 .
Claims 1-20 are presented for examination.
Claim Objections
Claims 1, 6-8, 11 and 16-18 are objected to because of the following informalities:
Claim 1 [line 3]: “memory connected to the agent core” should be “a memory connected to the agent core
Claim 1 [line 9]: “break the request down into a plurality of sub-parts that are each individually simpler than the request” is grammatically correct but somewhat redundant, because “each” is already an individual. This should be rephrased as “break the request down into a plurality of sub-parts, each simpler than the request.”
Claim 6 [line 3]: “where a given answer is provide based on an associated questions” has multiple grammar errors. “where” should be “wherein”; “is provide” should be “is provided”; and “an associated questions” should be “an associated question”
Claim 7 [line 2]: “the answer matching the associated description” should be “the answer that matches the associated description”
Claim 8 [line 2]: “one or more of a database connection” should be “one or more of database connections”
Claim 11 [line 3]: “connected to memory” should be “connected to a memory”
Claim 11 [line 6]: “break the request down into a plurality of sub-parts that are each individually simpler than the request” is grammatically correct but somewhat redundant, because “each” is already an individual. This should be rephrased as “break the request down into a plurality of sub-parts, each simpler than the request.”
Claim 16 [line 3]: “where a given answer is provide based on an associated questions” has multiple grammar errors. “where” should be “wherein”; “is provide” should be “is provided”; and “an associated questions” should be “an associated question”
Claim 17 [line 2]: “the answer matching the associated description” should be “the answer that matches the associated description”
Claim 18 [line 2]: “one or more of a database connection” should be “one or more of database connections”
Appropriate corrections are required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims
Step 1
Claim 1 is drawn to an AI agent system comprising an agent core, a memory, tools and a planner, and claim 11 is drawn to a method for performing the agent system in claim 1. Therefore, each of these claim groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Claims 1 and 11 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1 and 11 recite a method of utilize the planner to break the request down into a plurality of sub-parts that are each individually simpler than the request that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with or without the physical aid such as pen and paper, to analyze or to decompose the information into sub-parts. Therefore, the step of utilizing the planner to break the request down into a plurality of sub-parts is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1 and 11 recite a method of generate an answer to the request using the plurality of sub-parts with the memory and the one or more tools that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with or without the physical aid such as pen and paper, to evaluate a piece of information to reach a result answer. Therefore, the step of generating an answer to the request using the plurality of sub-parts is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Claims 1 and 11 recite further Artificial Intelligence (Al) agent system comprising: an agent core; memory connected to the agent core; one or more tools connected to the agent core; and a planner connected to the agent core that fails to integrate the abstract idea into a practical application. The step of including a list of computer parts is a form of insignificant input and output solution activities, where AI agent system includes an agent core, a memory, one or more tools and a planner is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Claims 1 and 11 recite further receive a request from a user that fails to integrate the abstract idea into a practical application. The step of receiving a request from user is a form of insignificant input and output solution activities, where receiving a request from a user is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Step 2B
The additional elements in step 2A-Prong 2 those are a form of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decision has determined that these additional elements of AI agent system includes an agent core, a memory, one or more tools and a planner; and receiving a request from a user to be well-understood, routine, and conventional when claimed in a merely generic manner (MPEP 2106.05(d)(II)).
As such, claims 1 and 11 are not patent eligible.
Dependent claims
Claims 2-10 and 12-20 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to claims 1 and 11, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mental process that are practically capable of being performed in the human mind with the assistance of pen and paper. Therefore, claims 2-10 and 12-20 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101.
Step 1
Claims 2-10 are drawn to an AI agent system comprising an agent core, a memory, tools and a planner, and claims 12-20 are drawn to a method for performing the agent system in claims 2-10. Therefore, each of these claim groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Dependent claims 9 and 19 recite further the mental process by generate a plurality of related questions based on the request; and determine a plurality of algorithms, data sources, and user interface aspects, based on the plurality of related questions, and provide the plurality of algorithms, the data sources, and the user interface aspects to the agent core for orchestrating the answer that is based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Dependent claim 2 and 12 recite further the insignificant extra solution activities by wherein the agent core is a first Large Language Model (LLM) and the planner is a second LLM, different from the first LLM. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 3 and 13 recite further the insignificant extra solution activities by wherein the memory includes a history memory and a context memory, with the history memory storing a record of previous inputs, outputs, and outcomes of actions taken by the Al agent, and the context memory includes relevant information about a current state. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 4 and 14 recite further the insignificant extra solution activities by wherein the one or more tools are configured to perform specific functions based on a defined domain of the Al agent. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 5 and 15 recite further the insignificant extra solution activities by wherein the one or more tools include Retrieval- Augmented Generation (RAG). This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 6 and 16 recite further the insignificant extra solution activities by wherein the RAG includes a plurality of questions and corresponding answers and a plurality of descriptions and corresponding algorithms, where a given answer is provide based on an associated questions and a given algorithm is performed based on an associated description. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 7 and 17 recite further the insignificant extra solution activities by wherein the agent core is further configured to implement a given algorithm based on the answer matching the associated description. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 8 and 18 recite further the insignificant extra solution activities by wherein the one or more tools include one or more of a database connection, Natural Language Processing libraries, visualization tools, simulation environments, and monitoring frameworks. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 10 and 20 recite further the insignificant extra solution activities by wherein the Al agent system operates as an assistant to one or more cloud services. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
As such, dependent claims 2-10 and 12-20 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sheikh et al (US 20230368284 A1) hereafter Sheikh, and further in view of Vaughn et al (US 20240111498 A1) hereafter Vaughn.
With respect to claim 1, Sheikh teaches an Artificial Intelligence (Al) agent system (autonomous agents have been a promising technology in recent years, as they possess the ability to perceive their environment, make decisions, and take actions autonomously [par. 0002-0007]) comprising:
an agent core (registry component is a component that comprises a database of autonomous agents (AAs) and their components. This component acts as a central database storing agent capabilities, skills, protocols, and connections [par. 0088-0092]);
memory connected to the agent core (the system uses the software framework utilizes the full potential of AI models such as ChatGPT by integrating the Large Language Models (LLMs) with external data sources and making the LLMs more powerful by providing context to the service request and memory for storing previous interactions. At least one memory device of the decentralized computing network includes a domain-independent protocol specification language that is accessible to the plurality of AAs [par. 0043, 0097]); and
a planner connected to the agent core (a service request is received from the AI model based on the LLM to generate natural language responses or carry out tasks. The term “objective” refers to a desired outcome or goal that the client-agent device aims to achieve based on the service request received [par. 0066, 0067, 0088-0092]).
wherein the agent core is configured to:
receive a request from a user (a service request can be received from the digital representation of the user that refers to a computer-generated representation of the user. For example, a user’s digital representation engaging in a virtual meeting and making a request for a representation to be shared. The service request may include metadata that is additional information accompanying the request and is utilized by the LLM to provide relevant inferences or responses [par. 0061-0066]);
utilize the planner to break the request down into a plurality of sub-parts that are each individually simpler than the request (in task refinement the machine learning model agent (ML-Model AA) such as the LLM may be used to break down a given task into its subtasks or may provide alternatives or variants of doing the given task [par. 0066]);
generate an answer to the request using the plurality of sub-parts with the memory and (a service request is received from the AI model based on the LLM to generate natural language responses or carry out tasks. The term “objective” refers to a desired outcome or goal that the client-agent device aims to achieve based on the service request received. A task is broken down into subtasks to generate the best outcome for the service request [par. 0066, 0067]).
However, Sheikh does not disclose one or more tools connected to the agent core.
In the same field of endeavor, Vaughn teaches one or more tools connected to the agent core (Retrieval-Augmented Generation (RAG) tools is used to improve code generation with the help of context about the application. RAG is a framework used for integrating the dense retrieval of information with sequence-to-sequence models, such as Large Language Models (LLMs). The retriever component is responsible for obtaining relevant context or evidence from a corpus that can be helpful in generating a response [par. 0033, 0034]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of obtaining a prompt of a user for generating code for implementing an additional component comprising a textual description of a desired functionality as suggested by Vaughn into the concept of implementing autonomous agents to receive a service request from a user as suggested by Sheikh because both of these systems addressing the process of using LLMs in generating responses/outcomes based on the inputs of users. Doing so would be desirable because the concept of Sheikh would be more efficient by using the RAG framework to improve code generation and to integrate the dense retrieval of information with sequence-to-sequence models such as LLMs for natural language processing tasks, such as question answering, text completion, or conversational agents (Vaughn, [par. 0033, 0034]).
With respect to claim 2, the combination of Sheikh and Vaughn teaches wherein the agent core is a first Large Language Model (LLM) (Sheikh, registry component is a component that comprises a database of autonomous agents (AAs) and their components. This component acts as a central database storing agent capabilities, skills, protocols, and connections. The order of tasks is associated with the objective of service request validated by communication with the LLMs [par. 0087-0092]) and the planner is a second LLM, different from the first LLM (Sheikh, a service request is received from the AI model based on the LLM to generate natural language responses or carry out tasks. The term “objective” refers to a desired outcome or goal that the client-agent device aims to achieve based on the service request received [par. 0066, 0067).
With respect to claim 3, the combination of Sheikh and Vaughn teaches wherein the memory includes a history memory and a context memory, with the history memory storing a record of previous inputs, outputs, and outcomes of actions taken by the Al agent, and the context memory includes relevant information about a current state (Sheikh, the system uses the software framework utilizes the full potential of AI models such as ChatGPT by integrating the Large Language Models (LLMs) with external data sources and making the LLMs more powerful by providing context to the service request and memory for storing previous interactions. At least one memory device of the decentralized computing network includes a domain-independent protocol specification language that is accessible to the plurality of AAs. Relevant references or responses may be provided by the LLMs based on the information from the memory [par. 0043, 0061, 0097]).
With respect to claim 4, the combination of Sheikh and Vaughn teaches wherein the one or more tools are configured to perform specific functions based on a defined domain of the Al agent (Vaughn, RAG is a framework used for integrating the dense retrieval of information with LLMs for natural language processing tasks, such as question answering, text completion, or conversational agents. RAG can automatically fetch relevant information from a large dataset. The relevant information is then passed to the generator such as the LLMs to produce coherent and contextually relevant answers or text [par. 0033, 0034]).
With respect to claim 5, the combination of Sheikh and Vaughn teaches wherein the one or more tools include Retrieval- Augmented Generation (RAG) (Vaughn, Retrieval-Augmented Generation (RAG) tools is used to improve code generation with the help of context about the application. RAG is a framework used for integrating the dense retrieval of information with sequence-to-sequence models, such as Large Language Models (LLMs). The retriever component is responsible for obtaining relevant context or evidence from a corpus that can be helpful in generating a response [par. 0033, 0034]).
With respect to claim 6, the combination of Sheikh and Vaughn teaches wherein the RAG includes a plurality of questions and corresponding answers and a plurality of descriptions and corresponding algorithms, where a given answer is provide based on an associated questions and a given algorithm is performed based on an associated description (Vaughn, RAG is a framework used for integrating the dense retrieval of information with LLMs for natural language processing tasks, such as question answering, text completion, or conversational agents. RAG can automatically fetch relevant information from a large dataset. The relevant information is then passed to the generator such as the LLMs to produce coherent and contextually relevant answers or text. The formal description of the functionality and usage of the component of the existing architecture enables the definition of sufficient context to be used as input to the LLM model [par. 0033-0036]).
With respect to claim 7, the combination of Sheikh and Vaughn teaches wherein the agent core is further configured to implement a given algorithm based on the answer matching the associated description (Sheikh, the software application employs at least one data processing algorithm to match the metadata associated with previous service requests. AAs have the ability to perceive their environment, analyze information, and take actions based on predefined rules, algorithms, or learning capabilities. The AAs configured to employ AI algorithms and ML for the execution of the one or more tasks. Examples of the algorithms include a text-based search or a lookup function [par. 0015, 0034, 0054, 0071]).
With respect to claim 8, the combination of Sheikh and Vaughn teaches wherein the one or more tools include one or more of a database connection, Natural Language Processing libraries, visualization tools, simulation environments, and monitoring frameworks (Vaughn, RAG is a framework used for integrating the dense retrieval of information with LLMs for natural language processing tasks, such as question answering, text completion, or conversational agents. The prompt of user that is provided to the LLM as input comprises a textual description of a desired functionality of an additional component, wherein the textual description may comprise a natural language description of the functionality. A structured document structure (MAD) configured to facilitates the structuring and standardizing of information presented to the LLM via RAG [par. 0033-0036, 0041]).
With respect to claim 9, the combination of Sheikh and Vaughn teaches wherein the planner is configured to:
generate a plurality of related questions based on the request (Sheikh, the software framework enables or fulfill the service request by organizing a series of tasks and to identify a next task. the LLMs uses natural language processing techniques to analyze and to interpret the request and to extract relevant details from the service request [par. 0043, 0075, 0076]); and
determine a plurality of algorithms, data sources, and user interface aspects, based on the plurality of related questions, and provide the plurality of algorithms, the data sources, and the user interface aspects to the agent core for orchestrating the answer (Sheikh, the software application employs at least one data processing algorithm to match the metadata associated with previous service requests. AAs have the ability to perceive their environment, analyze information, and take actions based on predefined rules, algorithms, or learning capabilities. The AAs configured to employ AI algorithms and ML for the execution of the one or more tasks. Examples of the algorithms include a text-based search or a lookup function. A service request is received from the AI model based on the LLM to generate natural language responses or carry out tasks. The term “objective” refers to a desired outcome or goal that the client-agent device aims to achieve based on the service request received [par. 0015, 0034, 0054, 0066, 0067, 0071]).
With respect to claim 10, the combination of Sheikh and Vaughn teaches wherein the Al agent system operates as an assistant to one or more cloud services (Sheikh, a software application executing on a device of user that may be coupled to a cloud-based software application based on the LLMs. For example, the service request may be received from the Google Calendar [par. 0020, 0038, 0063, 0064]).
With respect to claim 11, it is a method claim that is corresponding to the AI agent system of claim 1. Therefore, it is rejected for the same as claimed in claim 1 above.
With respect to claim 12, it is a method claim that is corresponding to the AI agent system of claim 2. Therefore, it is rejected for the same as claimed in claim 2 above.
With respect to claim 13, it is a method claim that is corresponding to the AI agent system of claim 3. Therefore, it is rejected for the same as claimed in claim 3 above.
With respect to claim 14, it is a method claim that is corresponding to the AI agent system of claim 4. Therefore, it is rejected for the same as claimed in claim 4 above.
With respect to claim 15, it is a method claim that is corresponding to the AI agent system of claim 5. Therefore, it is rejected for the same as claimed in claim 5 above.
With respect to claim 16, it is a method claim that is corresponding to the AI agent system of claim 6. Therefore, it is rejected for the same as claimed in claim 6 above.
With respect to claim 17, it is a method claim that is corresponding to the AI agent system of claim 7. Therefore, it is rejected for the same as claimed in claim 7 above.
With respect to claim 18, it is a method claim that is corresponding to the AI agent system of claim 8. Therefore, it is rejected for the same as claimed in claim 8 above.
With respect to claim 19, it is a method claim that is corresponding to the AI agent system of claim 9. Therefore, it is rejected for the same as claimed in claim 9 above.
With respect to claim 20, it is a method claim that is corresponding to the AI agent system of claim 10. Therefore, it is rejected for the same as claimed in claim 10 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Turek et al (US 20250001597 A1) disclosed techniques for task error correction for robots, such as collaborative robots (cobots). A controller of a robot may include an error detector to detect an error in a performance of a human-robot collaborative task, and an error corrector to correct the detected error. The error corrector may include a correction planner and a facilitator. The correction planner may determine an error correction plan based on the detected error. The error correction plan may include corrective subtasks to control the cobot to correct the detected error.
Friedman et al (US 20220165007 A1) disclosed a computing machine accesses a directed graph representing one or more sequences of actions. The directed graph comprises nodes and edges between the nodes. Each node is either a beginning node, an intermediate node, or an end node. Each intermediate is downstream from at least one beginning node and upstream from at least one end node. Each beginning node in at least a subset of the beginning nodes has an explainability value vector. The computing machine computes, for each first node from among a plurality of first nodes that are intermediate nodes or end nodes, a provenance value representing dependency of an explainability value vector of the first node on the one or more nodes upstream from the first node.
Gupta et al (US 20200027006 A1) disclosed methods for temporal planning employ a temporal logic for representing and reasoning about temporal constraints over both logic and numeric (discrete and continuous) variables and continuous time. A temporal planning language represents a world model, and adapts the temporal planning language to support formulas expressing the temporal logic. Embodiments of the present disclosure also can receive a temporal planning problem and derive one or more solutions to the temporal planning problem using one or more of the formulas. Embodiments of the present disclosure further address re-planning such as, for example, when a new objective task is added to the set of objective tasks to be performed, when an existing objective task is cancelled, and/or when some event occurs unpredictably and invalidates the current plan.
Van Seijen et al (US 10977551 B2) disclosed a method for decomposing single-agent reinforcement learning problems into simpler problems addressed by multiple agents. Actions proposed by the multiple agents are then aggregated using an aggregator, which selects an action to take with respect to an environment. Aspects provided herein are also relevant to a hybrid reward model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Quoc Phung whose telephone number is (703) 756 1330. The examiner can normally be reached on Monday through Friday from 9am to 5pm PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached on 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Q.L.P./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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