DETAILED ACTION
Status of Claims
Claims 1-15 submitted on 05/22/2025 are pending and have been examined.
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 .
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119(a)-(d). The certified copy has been filed in parent application No. IN202311079234, filed on 05/22/2024.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. The claims recite an abstract idea. This judicial exception is not integrated into a practical application. The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Step 1
Claims 1-7 are directed to a process, claims 8-14 are directed to a machine, and claim 15 is directed to an article of manufacture (see MPEP 2106.03).
Step 2A, Prong 1
Claim 1, taken as representative, recites at least the following limitations that recite an abstract idea:
a method, comprising:
receiving, an input from an external system or a user;
determining, a set of requirements based on the input;
identifying, one or more candidate based on the set of requirements;
deploying, the one or more candidate s for analysis of each candidate;
evaluating, performance of each candidate based on feedback obtained from monitoring of analysis of the one or more candidate;
identifying, an optimal from the one or more candidate based on the evaluated performance of each candidate;
obtaining, at least one recommendation from the optimal; and
providing, at least one recommendation to the external system or the user in response to the input.
The above limitation, under its broadest reasonable interpretation, falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that it recites a commercial interaction. Claims 8 and 15 recites similar limitations as claim 1.
Thus, under Prong 1 of Step 2A, claims 1, 8, and 15 recite an abstract idea.
Step 2A, Prong 2
Claim 1 includes the following additional elements that are bolded:
a method for integrating Artificial Intelligent (Al) models with a plurality of Al agents within a secure cloud-based enclave, comprising:
receiving, by a primary Al agent of the plurality of Al agents, an input from an external system or a user;
determining, by the primary Al agent, a set of requirements based on the input;
identifying, by the primary Al agent, one or more candidate Al agents from the plurality of Al agents based on the set of requirements;
deploying, by the primary Al agent, the one or more candidate Al agents for analysis of each candidate Al agent;
evaluating, by the primary Al agent, performance of each candidate Al agent based on feedback obtained from monitoring of analysis of the one or more candidate Al agents;
identifying, by the primary Al agent, an optimal Al agent from the one or more candidate Al agents based on the evaluated performance of each candidate Al agent;
obtaining, by the primary Al agent, at least one recommendation from the optimal Al agent; and
providing, by the primary Al agent, at least one recommendation to the external system or the user in response to the input.
Claims 8 and 15 include the same additional elements as claim 1. In addition, claim 8 includes additional elements such as one or more processors associated with a primary AI agent of a plurality of AI agents; and a memory storing programmed instructions executable by the one or more processors, wherein the one or more processors execute the programmed instructions to. In addition, claim 15 includes additional elements such as a non-transitory machine-readable medium including data.
The additional elements recited in claims 1, 8, and 15 merely invoke such elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment (see MPEP 2106.05(f) and MPEP 2106.05(h). These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see Figs. 1-2 and ¶0022).
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the additional elements do not integrate the judicial exception into a practical application and, thus, claims 1, 8, and 15 are directed to an abstract idea.
Step 2B
As noted above, while the recitation of the additional elements in independent claims 1, 8, and 15 are acknowledged, claims 1, 8, and 15 merely invoke such additional elements as a tool to perform the abstract idea and generally link the use of the abstract idea to a particular technological environment of AI agents (see MPEP 2106.05(f) and MPEP 2106.05(h)).
Even when considered as an ordered combination, the additional elements of claim 1, 8, and 15 do not add anything that is not already present when they are considered individually. Therefore, under Step 2B, there are no meaningful limitations in claims 1, 8, and 15 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (see MPEP 2106.05).
As such, independent claims 1, 8, and 15 are ineligible.
Dependent claims 3-5, 7, 10-12, and 14 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because they do not add “significantly more” to the abstract idea. More specifically, dependent claims 3-5, 7, 10-12, and 14 merely further define the abstract limitations of claims 1, 8, and 15 or provide further embellishments of the limitations recited in independent claims 1, 8, and 15. Claims 3-5, 7, 10-12, and 14 do not introduce any further additional elements. Thus, dependent claims 3-5, 7, 10-12, and 14 are ineligible.
Furthermore, it is noted that certain dependent claims recite additional elements supplemental to those recited in independent claims 1, 8, and 15: Specialized AI models, data processing units (claims 2 and 9) and secure cloud-base enclave securely retains the user data within the database (claims 6 and 13). However, these elements do not integrate the abstract idea into a practical application because they merely amount to using a computer to apply the abstract idea to a particular technological environment or field of use and thus do not act to integrate the abstract idea into a practical application of the abstract idea. Additionally, the additional elements do not amount to significantly more because they merely amount to using a computer to apply the abstract idea and amount to no more than a general link of the use of the abstract idea to a particular technological environment.
Thus, dependent claims 2, 6, 9, and 13 are ineligible.
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. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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) 1-5, 7-12, 14, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 2025/0190449 A1) in view of Sewak et al. (US 2019/0355041 A1).
Regarding Claim 1, Zhang et al., hereinafter, Zhang, discloses a method for integrating Artificial Intelligent (Al) models with a plurality of Al agents within a secure cloud-based enclave, comprising (Fig. 1; ¶¶0024-0026[The system 100 may be configured to generate a response to a user prompt by leveraging generative artificial intelligence (AI) agents to perform analytics of at least partially numerical structured data… For example, in some implementations, computing resources and functionality described in connection with the computing device 102 may be provided in a distributed system using multiple servers or other computing devices, or in a cloud-based system using computing resources and functionality provided by a cloud-based environment that is accessible over a network, such as the one of the one or more networks 140. To illustrate, one or more operations described herein with reference to the computing device 102 may be performed by one or more servers or a cloud-based system that communicates with one or more client or user devices, such as the user device 150]):
receiving, by a primary Al agent of the plurality of Al agents, an input from an external system or a user (¶0040[After optionally performing the data disambiguation operations, the prompt disambiguation operations, and/or the prompt optimization operations described above, the computing device 102 may provide the prompt 170 (e.g., the modified prompt) as input to the agent orchestrator 124 to select one or more of the generative AI agents 126 to perform analytics tasks corresponding to the information included in the prompt 170.] in view of ¶0031[During operation of the system 100, the computing device 102 may receive a prompt 170 from the user device 150.]; Examiner notes that the “agent orchestrator” of Zhang is comparable to the “primary AI agent”);
determining, by the primary Al agent, a set of requirements based on the input (¶0040[The agent orchestrator 124 may be configured to select one or more of the generative AI agents 126 for execution based on characteristics of the prompt 170, the structured data 172 retrieved based on the prompt 170 (e.g., characteristics of the structured data set 154), other information, or a combination thereof.]; Examiner notes that “characteristics of the prompt” of Zhang are comparable to a “set of requirements”);
identifying, by the primary Al agent, one or more candidate Al agents from the plurality of Al agents based on the set of requirements (Fig. 1; ¶0040[The agent orchestrator 124 may be configured to select one or more of the generative AI agents 126 for execution based on characteristics of the prompt 170, the structured data 172 retrieved based on the prompt 170 (e.g., characteristics of the structured data set 154), other information, or a combination thereof.]);
deploying, by the primary Al agent, the one or more candidate Al agents for analysis of each candidate Al agent (¶0041[For example, the agent orchestrator 124 may identify or extract certain features (e.g., the orchestrator features 118) from the prompt 170, the structured data set 154 (or descriptions thereof), and descriptions and historic performance of the generative AI agents 126, and based on the orchestrator features 118, the agent orchestrator 124 may select a single agent, or multiple agents, of the generative AI agents 126 that are likely to have the best performance in performing analytics tasks based on the prompt 170.]);
evaluating, by the primary Al agent, performance of each candidate Al agent based on feedback obtained from monitoring of analysis of the one or more candidate Al agents (¶0041[The agent orchestrator 124 may weight the orchestrator features 118 and calculate an agent score for each of the generative AI agents 126, and the agent orchestrator 124 may generate a recommendation that indicates one or more of the highest scoring generative AI agents.] in view of ¶0045[The system 100 may also be configured to perform continuous learning such that the user feedback 180 may be used to adjust parameters of the prompt processing and agent selection processes performed by the computing device 102 to increase accuracy and user-satisfaction with the generative AI-assisted analytics performed by the system 100.]);
identifying, by the primary Al agent, an optimal Al agent from the one or more candidate Al agents based on the evaluated performance of each candidate Al agent (Fig. 8; ¶0005[The ensemble model may rank the responses of the selected generative AI agents based on various criteria and determine to output either the response from the highest ranked agent or a combination of responses from multiple agents as the response to the user prompt.]);
obtaining, by the primary Al agent, from the optimal Al agent (Fig. 8; ¶0081[Additionally or alternatively, the agent orchestrator 812 may select a particular number of highest scoring generative AI agents, or a highest scoring generative AI agent and any other generative AI agents within a threshold score range of the highest scoring agent. Alternatively, instead of selecting a single agent or multiple agents to perform tasks in parallel, the agent orchestrator 812 may select multiple agents to perform tasks in sequence, such as a first agent to generate a numerical result and a second agent to generate a visual output of the numerical result, as a non-limiting example]); and
providing, by the primary Al agent, to the external system or the user in response to the input (Fig. 8; ¶0081[Additionally or alternatively, the agent orchestrator 812 may select a particular number of highest scoring generative AI agents, or a highest scoring generative AI agent and any other generative AI agents within a threshold score range of the highest scoring agent. Alternatively, instead of selecting a single agent or multiple agents to perform tasks in parallel, the agent orchestrator 812 may select multiple agents to perform tasks in sequence, such as a first agent to generate a numerical result and a second agent to generate a visual output of the numerical result, as a non-limiting example]).
Although Zhang discloses obtaining an optimal AI agent, Zhang does not explicitly disclose obtaining a at least one recommendation and providing at least one recommendation.
However, Sewak et al., hereinafter, Sewak, teaches generating and providing a product recommendation (Fig. 4; ¶0069[In some embodiments, cognitive fashion product recommendation process 10 may recommend one or more fashion products on the website with a fashion-ability score within a pre-defined threshold of the fashion-ability score representative of the one or more fashion products associated with the user.]).
The method of Sewak is applicable to the method of Zhang as they share characteristics and capabilities, namely, they are both targeted to responding to a user inquiry. 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 AI agent as disclosed by Zhang to include providing a product recommendation as taught by Sewak. One of ordinary skill in the art would have been motivated to expand the method of Zhang in order to recommend relevant fashion products to a user (¶0041).
Regarding Claim 2, Zhang in view of Sewak teaches the method according to claim 1, Zhang discloses wherein the one or more candidate Al agents comprise at least one of specialized AI models, data processing units, coordinators, recommenders, an external marketplace, and integrated systems (¶0085[As shown in FIG. 9, the agent orchestrator 900 includes a task allocation and specialization module 902, an agent selection node 904, a decision node 906, and an agent interaction module 908. The task allocation and specialization module 902 is configured to assign specific tasks or domains to the appropriate generative AI agents based on the user prompt and features 910, such as by deciding (e.g., selecting) which generative AI agent(s) to invoke to handle the user prompt and features 910. The agent selection node 904 is configured to select the most suitable generative AI agent(s) for the assigned task (e.g., the tasks assigned by the task allocation and specialization module 902)]).
Regarding Claim 3, Zhang in view of Sewak teaches the method according to claim 2, Zhang discloses wherein the coordinators indicate specialized agents for dynamically manage and consolidate information and actions from multiple Al agents of the plurality of Al agents based on the input (¶0052[The agent orchestrator 218 may select one or multiple generative AI agents based on the nature of the prompt 202 and the related use case, thus providing context-specific agent recommendations. Because the generative AI agents are selected based on their individual characteristics and functionality, the agent orchestrator 218 also supports a generic architecture to plug in any new generative AI agent with minimal overhead. By routing prompts and inputs to the most appropriate generative AI agent based on the context and expertise of each generative AI agent, the agent orchestrator 218 may ensure that the generative AI agents work in harmony, prevent conflicts or redundancies between generative AI agents, and monitor the performance and health of individual generative AI agents, including making adjustments or dynamic changing agent selection when necessary, to ensure high availability and reliability.]), and
the recommenders indicate specialized agents for generating suggestions or directions based on the input (¶0085[As shown in FIG. 9, the agent orchestrator 900 includes a task allocation and specialization module 902, an agent selection node 904, a decision node 906, and an agent interaction module 908. The task allocation and specialization module 902 is configured to assign specific tasks or domains to the appropriate generative AI agents based on the user prompt and features 910, such as by deciding (e.g., selecting) which generative AI agent(s) to invoke to handle the user prompt and features 910. The agent selection node 904 is configured to select the most suitable generative AI agent(s) for the assigned task (e.g., the tasks assigned by the task allocation and specialization module 902)]).
Regarding Claim 4, Zhang in view of Sewak teaches the method according to claim 1, Zhang discloses wherein the feedback comprises at least one of user interaction with recommendations, conversion rates, and stated preferences (¶0041[The agent orchestrator 124 may weight the orchestrator features 118 and calculate an agent score for each of the generative AI agents 126, and the agent orchestrator 124 may generate a recommendation that indicates one or more of the highest scoring generative AI agents.] in view of ¶0045[The system 100 may also be configured to perform continuous learning such that the user feedback 180 may be used to adjust parameters of the prompt processing and agent selection processes performed by the computing device 102 to increase accuracy and user-satisfaction with the generative AI-assisted analytics performed by the system 100.]).
Regarding Claim 5, Zhang in view of Sewak teaches the method according to claim 1, Zhang discloses where the identification of the optimal Al agent is dynamic (Fig. 2; ¶0054[To illustrate, generative AI agents' performance can change over time, resulting in performance degradation if the same set of agents are always selected. Exploration by the agent orchestrator 218 allows adaptation to changes, ensuring that the ensemble model 220 is not reliant on outdated information.]).
Regarding Claim 7, Zhang in view of Sewak teaches the method according to claim 1, Zhang discloses further comprising translating, by the primary Al agent, information between the one or more candidate Al agents without loss of semantic meaning of the information (Fig. 1; ¶¶0036-0037[As another example, a user prompt can have semantically different meaning than intended… To enable more accurate processing of the prompt 170, the computing device 102 may perform prompt disambiguation operations based on the prompt 170. The prompt disambiguation operations may include performing spell check, performing grammar check, and other automated revisions. The prompt disambiguation operations may also be performed to determine an intent of an input prompt, to perform entity mapping to determine whether one or more entities that correspond to the intent are present (or missing) from the input prompt, and modifying the input prompt based on one or more of the mapped entities to more clearly define the prompt. If one or more entities are missing, the input prompt may be modified to compensate for the missing entity or additional information may be requested from the user.]).
Regarding Claim 8, Zhang discloses a system for integrating Artificial Intelligent (Al) models with a plurality of Al agents within a secure cloud-based enclave, comprising: one or more processors associated with a primary AI agent of a plurality of AI agents (Fig. 1; ¶¶0024-0026[The system 100 may be configured to generate a response to a user prompt by leveraging generative artificial intelligence (AI) agents to perform analytics of at least partially numerical structured data… For example, in some implementations, computing resources and functionality described in connection with the computing device 102 may be provided in a distributed system using multiple servers or other computing devices, or in a cloud-based system using computing resources and functionality provided by a cloud-based environment that is accessible over a network, such as the one of the one or more networks 140. To illustrate, one or more operations described herein with reference to the computing device 102 may be performed by one or more servers or a cloud-based system that communicates with one or more client or user devices, such as the user device 150] in view of Claim 17[A non-transitory computer-readable storage device comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations for generative artificial intelligence-assisted analytics of structured data sets]); and
a memory storing programmed instructions executable by the one or more processors, wherein the one or more processors execute the programmed instructions to (Claim 17[A non-transitory computer-readable storage device comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations for generative artificial intelligence-assisted analytics of structured data sets]):
receive an input from an external system or a user (¶0040[After optionally performing the data disambiguation operations, the prompt disambiguation operations, and/or the prompt optimization operations described above, the computing device 102 may provide the prompt 170 (e.g., the modified prompt) as input to the agent orchestrator 124 to select one or more of the generative AI agents 126 to perform analytics tasks corresponding to the information included in the prompt 170.] in view of ¶0031[During operation of the system 100, the computing device 102 may receive a prompt 170 from the user device 150.]; Examiner notes that the “agent orchestrator” of Zhang is comparable to the “primary AI agent”);
determine a set of requirements based on the input (¶0040[The agent orchestrator 124 may be configured to select one or more of the generative AI agents 126 for execution based on characteristics of the prompt 170, the structured data 172 retrieved based on the prompt 170 (e.g., characteristics of the structured data set 154), other information, or a combination thereof.]; Examiner notes that “characteristics of the prompt” of Zhang are comparable to a “set of requirements”);
identify one or more candidate AI agents from the plurality of AI agents based on the set of requirements (Fig. 1; ¶0040[The agent orchestrator 124 may be configured to select one or more of the generative AI agents 126 for execution based on characteristics of the prompt 170, the structured data 172 retrieved based on the prompt 170 (e.g., characteristics of the structured data set 154), other information, or a combination thereof.]);
deploy the one or more candidate AI agents for analysis of each candidate AI agent (¶0041[For example, the agent orchestrator 124 may identify or extract certain features (e.g., the orchestrator features 118) from the prompt 170, the structured data set 154 (or descriptions thereof), and descriptions and historic performance of the generative AI agents 126, and based on the orchestrator features 118, the agent orchestrator 124 may select a single agent, or multiple agents, of the generative AI agents 126 that are likely to have the best performance in performing analytics tasks based on the prompt 170.]);
evaluate performance of each candidate AI agent based on feedback obtained from monitoring of analysis of the one or more candidate AI agents (¶0041[The agent orchestrator 124 may weight the orchestrator features 118 and calculate an agent score for each of the generative AI agents 126, and the agent orchestrator 124 may generate a recommendation that indicates one or more of the highest scoring generative AI agents.] in view of ¶0045[The system 100 may also be configured to perform continuous learning such that the user feedback 180 may be used to adjust parameters of the prompt processing and agent selection processes performed by the computing device 102 to increase accuracy and user-satisfaction with the generative AI-assisted analytics performed by the system 100.]);
identify an optimal AI agent from the one or more candidate AI agents based on the evaluated performance of each candidate AI agent (Fig. 8; ¶0005[The ensemble model may rank the responses of the selected generative AI agents based on various criteria and determine to output either the response from the highest ranked agent or a combination of responses from multiple agents as the response to the user prompt.]);
obtain from the optimal AI agent (Fig. 8; ¶0081[Additionally or alternatively, the agent orchestrator 812 may select a particular number of highest scoring generative AI agents, or a highest scoring generative AI agent and any other generative AI agents within a threshold score range of the highest scoring agent. Alternatively, instead of selecting a single agent or multiple agents to perform tasks in parallel, the agent orchestrator 812 may select multiple agents to perform tasks in sequence, such as a first agent to generate a numerical result and a second agent to generate a visual output of the numerical result, as a non-limiting example]); and
provide to the external system or the user in response to the input (Fig. 8; ¶0081[Additionally or alternatively, the agent orchestrator 812 may select a particular number of highest scoring generative AI agents, or a highest scoring generative AI agent and any other generative AI agents within a threshold score range of the highest scoring agent. Alternatively, instead of selecting a single agent or multiple agents to perform tasks in parallel, the agent orchestrator 812 may select multiple agents to perform tasks in sequence, such as a first agent to generate a numerical result and a second agent to generate a visual output of the numerical result, as a non-limiting example]).
Although Zhang discloses obtaining an optimal AI agent, Zhang does not explicitly disclose obtaining a at least one recommendation and providing at least one recommendation.
However, Sewak teaches generating and providing a product recommendation (Fig. 4; ¶0069[In some embodiments, cognitive fashion product recommendation process 10 may recommend one or more fashion products on the website with a fashion-ability score within a pre-defined threshold of the fashion-ability score representative of the one or more fashion products associated with the user.]).
The system of Sewak is applicable to the system of Zhang as they share characteristics and capabilities, namely, they are both targeted to responding to a user inquiry. 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 AI agent as disclosed by Zhang to include providing a product recommendation as taught by Sewak. One of ordinary skill in the art would have been motivated to expand the system of Zhang in order to recommend relevant fashion products to a user (¶0041).
Regarding Claim 9, Zhang in view of Sewak teaches the system according to claim 8, Zhang discloses wherein the one or more candidate AI agents comprise at least one of specialized AI models, data processing units, coordinators, recommenders, an external marketplace, and integrated systems (¶0085[As shown in FIG. 9, the agent orchestrator 900 includes a task allocation and specialization module 902, an agent selection node 904, a decision node 906, and an agent interaction module 908. The task allocation and specialization module 902 is configured to assign specific tasks or domains to the appropriate generative AI agents based on the user prompt and features 910, such as by deciding (e.g., selecting) which generative AI agent(s) to invoke to handle the user prompt and features 910. The agent selection node 904 is configured to select the most suitable generative AI agent(s) for the assigned task (e.g., the tasks assigned by the task allocation and specialization module 902)]).
Regarding Claim 10, Zhang in view of Sewak teaches the system according to claim 9, Zhang discloses wherein the coordinators indicate specialized agents for dynamically manage and consolidate information and actions from multiple AI agents of the plurality of AI agents based on the input (¶0052[The agent orchestrator 218 may select one or multiple generative AI agents based on the nature of the prompt 202 and the related use case, thus providing context-specific agent recommendations. Because the generative AI agents are selected based on their individual characteristics and functionality, the agent orchestrator 218 also supports a generic architecture to plug in any new generative AI agent with minimal overhead. By routing prompts and inputs to the most appropriate generative AI agent based on the context and expertise of each generative AI agent, the agent orchestrator 218 may ensure that the generative AI agents work in harmony, prevent conflicts or redundancies between generative AI agents, and monitor the performance and health of individual generative AI agents, including making adjustments or dynamic changing agent selection when necessary, to ensure high availability and reliability.]), and
the recommenders indicate specialized agents for generating suggestions or directions based on the input (¶0085[As shown in FIG. 9, the agent orchestrator 900 includes a task allocation and specialization module 902, an agent selection node 904, a decision node 906, and an agent interaction module 908. The task allocation and specialization module 902 is configured to assign specific tasks or domains to the appropriate generative AI agents based on the user prompt and features 910, such as by deciding (e.g., selecting) which generative AI agent(s) to invoke to handle the user prompt and features 910. The agent selection node 904 is configured to select the most suitable generative AI agent(s) for the assigned task (e.g., the tasks assigned by the task allocation and specialization module 902)]).
Regarding Claim 11, Zhang in view of Sewak teaches the system according to claim 8, Zhang discloses wherein the feedback comprises at least one of user interaction with recommendations, conversion rates, and stated preferences (¶0041[The agent orchestrator 124 may weight the orchestrator features 118 and calculate an agent score for each of the generative AI agents 126, and the agent orchestrator 124 may generate a recommendation that indicates one or more of the highest scoring generative AI agents.] in view of ¶0045[The system 100 may also be configured to perform continuous learning such that the user feedback 180 may be used to adjust parameters of the prompt processing and agent selection processes performed by the computing device 102 to increase accuracy and user-satisfaction with the generative AI-assisted analytics performed by the system 100.]).
Regarding Claim 12, Zhang in view of Sewak teaches the system according to claim 8, Zhang discloses where the identification of the optimal AI agent is dynamic (Fig. 2; ¶0054[To illustrate, generative AI agents' performance can change over time, resulting in performance degradation if the same set of agents are always selected. Exploration by the agent orchestrator 218 allows adaptation to changes, ensuring that the ensemble model 220 is not reliant on outdated information.]).
Regarding Claim 14, Zhang in view of Sewak teaches the system according to claim 8, Zhang discloses wherein the one or more processors are configured to translate information between the one or more candidate AI agents without loss of semantic meaning of the information (Fig. 1; ¶¶0036-0037[As another example, a user prompt can have semantically different meaning than intended… To enable more accurate processing of the prompt 170, the computing device 102 may perform prompt disambiguation operations based on the prompt 170. The prompt disambiguation operations may include performing spell check, performing grammar check, and other automated revisions. The prompt disambiguation operations may also be performed to determine an intent of an input prompt, to perform entity mapping to determine whether one or more entities that correspond to the intent are present (or missing) from the input prompt, and modifying the input prompt based on one or more of the mapped entities to more clearly define the prompt. If one or more entities are missing, the input prompt may be modified to compensate for the missing entity or additional information may be requested from the user.]).
Regarding Claim 15, Zhang discloses a non-transitory machine-readable medium including data, which when used by a system for integrating Artificial Intelligent (AI) models with a plurality of AI agents within a secure cloud-based enclave, causes the system to perform instructions that cause the system to perform operations comprising (Fig. 1; ¶¶0024-0026[The system 100 may be configured to generate a response to a user prompt by leveraging generative artificial intelligence (AI) agents to perform analytics of at least partially numerical structured data… For example, in some implementations, computing resources and functionality described in connection with the computing device 102 may be provided in a distributed system using multiple servers or other computing devices, or in a cloud-based system using computing resources and functionality provided by a cloud-based environment that is accessible over a network, such as the one of the one or more networks 140. To illustrate, one or more operations described herein with reference to the computing device 102 may be performed by one or more servers or a cloud-based system that communicates with one or more client or user devices, such as the user device 150] in view of Claim 17[A non-transitory computer-readable storage device comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations for generative artificial intelligence-assisted analytics of structured data sets]):
receiving, by a primary AI agent of the plurality of AI agents, an input from an external system or a user (¶0040[After optionally performing the data disambiguation operations, the prompt disambiguation operations, and/or the prompt optimization operations described above, the computing device 102 may provide the prompt 170 (e.g., the modified prompt) as input to the agent orchestrator 124 to select one or more of the generative AI agents 126 to perform analytics tasks corresponding to the information included in the prompt 170.] in view of ¶0031[During operation of the system 100, the computing device 102 may receive a prompt 170 from the user device 150.]; Examiner notes that the “agent orchestrator” of Zhang is comparable to the “primary AI agent”);
determining, by the primary AI agent, a set of requirements based on the input (¶0040[The agent orchestrator 124 may be configured to select one or more of the generative AI agents 126 for execution based on characteristics of the prompt 170, the structured data 172 retrieved based on the prompt 170 (e.g., characteristics of the structured data set 154), other information, or a combination thereof.]; Examiner notes that “characteristics of the prompt” of Zhang are comparable to a “set of requirements”);
identifying, by the primary AI agent, one or more candidate AI agents from the plurality of AI agents based on the set of requirements (Fig. 1; ¶0040[The agent orchestrator 124 may be configured to select one or more of the generative AI agents 126 for execution based on characteristics of the prompt 170, the structured data 172 retrieved based on the prompt 170 (e.g., characteristics of the structured data set 154), other information, or a combination thereof.]);
deploying, by the primary AI agent, the one or more candidate AI agents for analysis of each candidate AI agent (¶0041[For example, the agent orchestrator 124 may identify or extract certain features (e.g., the orchestrator features 118) from the prompt 170, the structured data set 154 (or descriptions thereof), and descriptions and historic performance of the generative AI agents 126, and based on the orchestrator features 118, the agent orchestrator 124 may select a single agent, or multiple agents, of the generative AI agents 126 that are likely to have the best performance in performing analytics tasks based on the prompt 170.]);
evaluating, by the primary AI agent, performance of each candidate AI agent based on feedback obtained from monitoring of analysis of the one or more candidate AI agents (¶0041[The agent orchestrator 124 may weight the orchestrator features 118 and calculate an agent score for each of the generative AI agents 126, and the agent orchestrator 124 may generate a recommendation that indicates one or more of the highest scoring generative AI agents.] in view of ¶0045[The system 100 may also be configured to perform continuous learning such that the user feedback 180 may be used to adjust parameters of the prompt processing and agent selection processes performed by the computing device 102 to increase accuracy and user-satisfaction with the generative AI-assisted analytics performed by the system 100.]);
identifying, by the primary AI agent, an optimal AI agent from the one or more candidate AI agents based on the evaluated performance of each candidate AI agent (Fig. 8; ¶0005[The ensemble model may rank the responses of the selected generative AI agents based on various criteria and determine to output either the response from the highest ranked agent or a combination of responses from multiple agents as the response to the user prompt.]);
obtaining, by the primary AI agent, from the optimal AI agent (Fig. 8; ¶0081[Additionally or alternatively, the agent orchestrator 812 may select a particular number of highest scoring generative AI agents, or a highest scoring generative AI agent and any other generative AI agents within a threshold score range of the highest scoring agent. Alternatively, instead of selecting a single agent or multiple agents to perform tasks in parallel, the agent orchestrator 812 may select multiple agents to perform tasks in sequence, such as a first agent to generate a numerical result and a second agent to generate a visual output of the numerical result, as a non-limiting example]); and
providing, by the primary AI agent, to the external system or the user in response to the input (Fig. 8; ¶0081[Additionally or alternatively, the agent orchestrator 812 may select a particular number of highest scoring generative AI agents, or a highest scoring generative AI agent and any other generative AI agents within a threshold score range of the highest scoring agent. Alternatively, instead of selecting a single agent or multiple agents to perform tasks in parallel, the agent orchestrator 812 may select multiple agents to perform tasks in sequence, such as a first agent to generate a numerical result and a second agent to generate a visual output of the numerical result, as a non-limiting example]).
Although Zhang discloses obtaining an optimal AI agent, Zhang does not explicitly disclose obtaining a at least one recommendation and providing at least one recommendation.
However, Sewak teaches generating and providing a product recommendation (Fig. 4; ¶0069[In some embodiments, cognitive fashion product recommendation process 10 may recommend one or more fashion products on the website with a fashion-ability score within a pre-defined threshold of the fashion-ability score representative of the one or more fashion products associated with the user.]).
The system of Sewak is applicable to the system of Zhang as they share characteristics and capabilities, namely, they are both targeted to responding to a user inquiry. 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 AI agent as disclosed by Zhang to include providing a product recommendation as taught by Sewak. One of ordinary skill in the art would have been motivated to expand the system of Zhang in order to recommend relevant fashion products to a user (¶0041).
Claim(s) 6 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Sewak in view of Beran et al. (US 2024/0062081 A1).
Regarding Claim 6, Zhang in view of Sewak teaches the method according to claim 1, Zhang discloses wherein the secure cloud-based enclave securely retains the user data within the database (Fig. 1; ¶0027[Although shown as being stored in memory 106, in some other implementations, the system 100 may include one or more databases integrated in or communicatively coupled to the computing device 102 (e.g., communicatively coupled to the one or more processors 104) that are configured to store any of the response 110, the embedding 112, the data set embeddings 114, the mapped entities 116, the orchestrator features 118, the knowledge graph 120, the cached prompts 122, the agent orchestrator 124, the generative AI agents 126, one or more parameters corresponding to the generative AI models 128, the visualization engine 129, or a combination thereof.]).
Although Zhang discloses retaining user data within a database, Zhang in view of Sewak does not explicitly teach storing without directly exposing to an external AI system.
However, Beran et al., hereinafter, Beran, teaches securing data and preventing exposure to external AI systems (Claim 6[restricting storage of the private AI model to the autonomous personal companion; and preventing external access of the private AI model.]).
The method of Beran is applicable to the method of Zhang in view of Sewak as they share characteristics and capabilities, namely, they are all targeted to AI systems. 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 AI agent as taught by Zhang in view of Sewak to include preventing exposure to external AI systems as taught by Beran. One of ordinary skill in the art would have been motivated to expand the method of Zhang in view of Sewak in order to build an AI model personalized to a user (¶0034).
Regarding Claim 13, Zhang in view of Sewak teaches the system according to claim 8, Zhang discloses wherein the secure cloud-based enclave securely retains the user data within the database (Fig. 1; ¶0027[Although shown as being stored in memory 106, in some other implementations, the system 100 may include one or more databases integrated in or communicatively coupled to the computing device 102 (e.g., communicatively coupled to the one or more processors 104) that are configured to store any of the response 110, the embedding 112, the data set embeddings 114, the mapped entities 116, the orchestrator features 118, the knowledge graph 120, the cached prompts 122, the agent orchestrator 124, the generative AI agents 126, one or more parameters corresponding to the generative AI models 128, the visualization engine 129, or a combination thereof.]).
Although Zhang discloses retaining user data within a database, Zhang in view of Sewak does not explicitly teach storing without directly exposing to an external AI system.
However, Beran teaches securing data and preventing exposure to external AI systems (Claim 6[restricting storage of the private AI model to the autonomous personal companion; and preventing external access of the private AI model.]).
The system of Beran is applicable to the system of Zhang in view of Sewak as they share characteristics and capabilities, namely, they are all targeted to AI systems. 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 AI agent as taught by Zhang in view of Sewak to include preventing exposure to external AI systems as taught by Beran. One of ordinary skill in the art would have been motivated to expand the system of Zhang in view of Sewak in order to build an AI model personalized to a user (¶0034).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Livingston (US 2017/0316319 A1) discloses enhancing exploratory data analysis using a recommender system.
“Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems” discloses a structural search model to characterize consumers' decision-making processes in response to varying recommendation sets.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHOORA LADONI whose email is Ahoora.Ladoni@uspto.gov and telephone number is (703) 756-5617. The examiner can normally be reached M-F 0900–1700 ET.
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.
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/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.
/AHOORA LADONI/Examiner, Art Unit 3689
/ANNA MAE MITROS/Examiner, Art Unit 3689
Prosecution Insights