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 .
This office action is in response to the applicant’s filing on 01/06/2025. Claims 1-18 are pending. Claims 1, 17, and 18 are independent.
Priority
Acknowledgment is made of applicant’s claim for foreign priority to IN202411081537 under 35 U.S.C. 119 (a)-(d). However, it’s noted that the priority document retrieved (IN 202411081537) does not provide support for the independent claims, and therefore neither the independent nor dependent claims are considered to receive the priority date for the remainder of this office action. The provisional specification of the document appears to generally discuss an AI platform and governance of AI agents, but not the embodiments recited in the independent claims. Specifically, the following limitations are not supported, “… by determining whether or not the Al agent is compliant with the associated respective governance policy, and when determining that the Al agent is not compliant with the associated respective governance policy, automatically executing a corrective action, and labeling the Al agent as untrusted.”. Therefore, neither the independent nor dependent claims are considered to receive the priority claim and date, and the claims will be interpreted that way for the remainder of this office action.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/06/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-2 and 6-18 are rejected under 35 U.S.C. 103 as being unpatentable over Fedoruk et al. (US PGPub No. 2025/0378386; hereinafter “Fedoruk”) in view of MYSORE et al. (US PGPub No. 2025/0322244; hereinafter “MYSORE”).
As per claim 1: Fedoruk discloses a method comprising using at least one hardware processor to (executed by a processor associated with a computing device [Fedoruk ¶ 0019, ¶ 0087-0088]):
associate each of a plurality of artificial intelligence (Al) agents with a respective governance policy (systems and methods for building AI agents with management policies [Fedoruk ¶ 0007]; Manifest Files (also called "configuration files" or "manifest files") are structured documents that formally define an AI Agent's composition and behavior. Typically written in XML, JSON, or YAML formats, these files specify the included Agent Objects, execution order and workflow sequencing, conditional rules governing operation, authentication details and permission scopes, and component parameter configurations. Manifest Files serve as both documentation and operational blueprints, enabling the Execution Engine to instantiate and run the AI Agent with consistent behavior across environments while facilitating version control of agent configurations [Fedoruk ¶ 0042]; Rule enforcers are governance components that ensure all platform operations comply with configured policies and constraints. They serve two primary functions: (1) enforcing configurations and settings across the platform, including agent behavior, model parameters, and security policies; and (2) monitoring for specific triggering conditions and applying predefined actions when those conditions are detected. Rule enforcers are critical for implementing guardrails, content moderation, cost controls, compliance requirements, and other governance measures that ensure the platform operates within established boundaries [Fedoruk ¶ 0054]; creating an AI agent that includes management policies [Fedoruk ¶ 0055]; the server or another device can be triggered to automatically generate a manifest file at stage 131 [Fedoruk ¶ 0060]), prior to activation of the Al agent (the server or another device can be triggered to automatically generate a manifest file at stage 131 [Fedoruk ¶ 0060]; At stage 141, the server can cause the manifest file to be validated against dependency rules for the agent objects [Fedoruk ¶ 0061]; At stage 151, the designed AI agent can then be executed. For example, the AI agent can be saved and activated, causing the AI agent to be assigned to an endpoint [Fedoruk ¶ 0065]) within an integration platform as a service (iPaaS) platform (An AI platform can execute on one or more servers. The AI platform can manage multiple AI agents, AI models, datasets, tools, and prompt packages. The AI platform can orchestrate AI agent execution [Fedoruk ¶ 0008]; The AI app service 113 can act as an AI platform that provides customers with a way to easily create, deploy, and manage AI agents 116. Customers can create AI agents 116 that uniquely suit their needs [Fedoruk ¶ 0078]; the AI platform application 115 is configured to facilitate the design, instantiation, modification, testing, and/or execution of AI agents 116 (referred to herein collectively as AI agents 116 and individually as an AI agent 116) that use one or more AI models 117 (referred to herein collectively as AI models and individually as an AI model 117), as discussed in greater detail below in conjunction with FIGS. 2-4 [Fedoruk ¶ 0090]);
and for each of the plurality of Al agents, after activation of the Al agent, monitor the Al agent (The administrator can identify at least one execution metric to monitor as part of the simulated execution [Fedoruk ¶ 0067]; The simulated execution can involve executing the agent objects in an order specified by the manifest file [Fedoruk ¶ 0068]; Conditional compliance requirements can be analyzed by the agent executor (or other management service) prior to executing particular agent objects or workflow segments of the AI agent [Fedoruk ¶ 0069]), in real time as the Al agent is executing (The agent executor can execute each stage of the agent until the output is reached [Fedoruk ¶ 0070]; The simulated execution can execute an agent that corresponds to the validated manifest file. The agent can be active and available at an endpoint, or inactive and not currently available at an endpoint [Fedoruk ¶ 0066]; Conditional compliance requirements can be analyzed by the agent executor (or other management service) prior to executing particular agent objects or workflow segments of the AI agent. Failover execution paths, such as local-only execution, can be available for when a user device or profile becomes non-compliant [Fedoruk ¶ 0069, Examiner’s Note: analysis and monitoring occurring as the agent is executing]; The AI platform can also perform real-time monitoring and run alerting systems to detect anomalous or suspicious AI usage patterns. An administrative agent can regularly review logs and audit trails to ensure compliance with established policies [Fedoruk ¶ 0115, Examiner’s Note: real-time monitoring]),
by determining whether or not the Al agent is compliant with the associated respective governance policy (Conditional compliance requirements can be analyzed by the agent executor (or other management service) prior to executing particular agent objects or workflow segments of the AI agent. Failover execution paths, such as local-only execution, can be available for when a user device or profile becomes non-compliant [Fedoruk ¶ 0069]; The AI agent 143 can ensure that the computing device 140 complies with management policies, and vary access to objects at the AI platform based on the level of compliance. For example, a compliant computing device 140 can download or access an AI app 183 and/or objects of an AI agent 181. But the AI platform can prevent a noncompliant computing device from executing the AI agent 181 or specific objects within the agent, such as specific AI models 182, tools, datasets, or prompt packages [Fedoruk ¶ 0082]; The AI platform can also perform real-time monitoring and run alerting systems to detect anomalous or suspicious AI usage patterns [Fedoruk ¶ 0115]), and
when determining that the Al agent is not compliant with the associated respective governance policy (Rule enforcers are governance components that ensure all platform operations comply with configured policies and constraints. They serve two primary functions: (1) enforcing configurations and settings across the platform, including agent behavior, model parameters, and security policies; and (2) monitoring for specific triggering conditions and applying predefined actions when those conditions are detected. Rule enforcers are critical for implementing guardrails, content moderation, cost controls, compliance requirements, and other governance measures that ensure the platform operates within established boundaries [Fedoruk ¶ 0054]; Failover execution paths, such as local-only execution, can be available for when a user device or profile becomes non-compliant [Fedoruk ¶ 0069]), automatically executing a corrective action (But the AI platform can prevent a noncompliant computing device from executing the AI agent 181 or specific objects within the agent, such as specific AI models 182, tools, datasets, or prompt packages [Fedoruk ¶ 0082, Examiner’s Note: prevent it from executing]; compliance management service can execute at the platform and can communicate with the AI agent 143 to ensure that a computing device 140 remains compliant with compliance rules as a requisite to AI agent operation [Fedoruk ¶ 0084]; Compliance rules can encompass configurable criteria that must be met for a client device to be considered "in compliance" with the AI agent management service. These rules can be determined based on various factors such as the geographical location of the client device, its activation and management enrollment status, authentication data (including data obtained by a device management system), time, date, and network properties, among others. User profiles associated with specific users can also influence the compliance rules [Fedoruk ¶ 0085-0086]), and [labeling the Al agent as untrusted].
Fedoruk discloses the claimed subject matter as discussed above but does not explicitly disclose labeling the Al agent as untrusted. However, MYSORE teaches labeling the Al agent as untrusted (For example, in response to the query “I need to transfer money to my friend,” the agent 1204 can propose the action of communicating with an API of the banking application 1210b to prompt the user 1202 for a transaction amount, verifying the recipient's account details, checking the user's account balance to ensure sufficient funds, and so forth. However, if the agent 1204 proposes actions that fail to pass the validation layer 1206, such as a proposed action transferring an amount that exceeds the user's daily transfer limit, the validation layer 1206 can identify this action as non-compliant with the predefined guidelines and prevent its execution. The applications 1210 refer to the various software systems that the agent 1204 interacts with to perform the proposed actions. The applications 1210 can include cloud-based services, on-premises software, or a combination of both [MYSORE ¶ 0139, Examiner’s Note: identifying the action and agent as performing non-compliant behavior is interpreted as labeling it as untrusted]; The recommendations 1114 can be automatically implemented as corrective actions 1116 by the system. The automated approach streamlines the process of addressing identified issues and ensures swift remediation of non-compliant content within AI models, enhancing overall compliance and reliability [MYSORE ¶ 0133]). Fedoruk and MYSORE are analogous art because they are from the same field of endeavor of AI agents. Therefore, based on Fedoruk in view of MYSORE, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of MYSORE to the system of Fedoruk in order to perform the identification of the action by the agent which is non-compliant which enhances overall compliance and reliability. Hence, it would have been obvious to combine the references above to obtain the invention as specified in the instant claim.
As per claim 2: Fedoruk in view of MYSORE teach all the limitations of claim 1. Furthermore, Fedoruk and MYSORE disclose further comprising using the at least one hardware processor to, for each of the plurality of Al agents (systems and methods for building AI agents with management policies [Fedoruk ¶ 0007]; Rule enforcers are governance components that ensure all platform operations comply with configured policies and constraints. They serve two primary functions: (1) enforcing configurations and settings across the platform, including agent behavior, model parameters, and security policies; and (2) monitoring for specific triggering conditions and applying predefined actions when those conditions are detected. Rule enforcers are critical for implementing guardrails, content moderation, cost controls, compliance requirements, and other governance measures that ensure the platform operates within established boundaries [Fedoruk ¶ 0054]; creating an AI agent that includes management policies [Fedoruk ¶ 0055]; the server or another device can be triggered to automatically generate a manifest file at stage 131 [Fedoruk ¶ 0060]), monitor the Al agent, in real time as the Al agent is executing (The administrator can identify at least one execution metric to monitor as part of the simulated execution [Fedoruk ¶ 0067]; The simulated execution can involve executing the agent objects in an order specified by the manifest file [Fedoruk ¶ 0068]; The agent executor can execute each stage of the agent until the output is reached [Fedoruk ¶ 0070]; The simulated execution can execute an agent that corresponds to the validated manifest file. The agent can be active and available at an endpoint, or inactive and not currently available at an endpoint [Fedoruk ¶ 0066]; Conditional compliance requirements can be analyzed by the agent executor (or other management service) prior to executing particular agent objects or workflow segments of the AI agent. Failover execution paths, such as local-only execution, can be available for when a user device or profile becomes non-compliant [Fedoruk ¶ 0069, Examiner’s Note: analysis and monitoring occurring as the agent is executing]; The AI platform can also perform real-time monitoring and run alerting systems to detect anomalous or suspicious AI usage patterns. An administrative agent can regularly review logs and audit trails to ensure compliance with established policies [Fedoruk ¶ 0115, Examiner’s Note: real-time monitoring]), by further: determining whether or not the Al agent is behaving anomalously (The AI platform can also perform real-time monitoring and run alerting systems to detect anomalous or suspicious AI usage patterns [Fedoruk ¶ 0115]); and when determining that the Al agent is behaving anomalously, automatically executing the corrective action (The AI platform can also perform real-time monitoring and run alerting systems to detect anomalous or suspicious AI usage patterns [Fedoruk ¶ 0115, Examiner’s Note: alerting may also be a corrective action, see specification para. 0068]; The AI agent 143 can ensure that the computing device 140 complies with management policies, and vary access to objects at the AI platform based on the level of compliance. For example, a compliant computing device 140 can download or access an AI app 183 and/or objects of an AI agent 181. But the AI platform can prevent a noncompliant computing device from executing the AI agent 181 or specific objects within the agent, such as specific AI models 182, tools, datasets, or prompt packages [Fedoruk ¶ 0082]), and labeling the Al agent as untrusted (However, if the agent 1204 proposes actions that fail to pass the validation layer 1206, such as a proposed action transferring an amount that exceeds the user's daily transfer limit, the validation layer 1206 can identify this action as non-compliant with the predefined guidelines and prevent its execution. The applications 1210 refer to the various software systems that the agent 1204 interacts with to perform the proposed actions. The applications 1210 can include cloud-based services, on-premises software, or a combination of both [MYSORE ¶ 0139, Examiner’s Note: identifying the action and agent as performing non-compliant behavior is interpreted as labeling it as untrusted]).
As per claim 6: Fedoruk in view of MYSORE teach all the limitations of claim 1. Furthermore, Fedoruk discloses further comprising using the at least one hardware processor to, for each type of the plurality of Al agents, generate a standardized connector framework for the generation of connector steps to be used by integration processes on the iPaaS platform to communicate with any one of the plurality of Al agents of that type (Models refer to the underlying machine learning models that power AI capabilities within a platform. These include large language models (LLMs) like GPT-4, Claude, or open-source alternatives like Llama; image generation models like DALL-E or Stable Diffusion; speech recognition models; and specialized models for specific tasks. In an AI platform context, these models are typically accessed via API calls, with the platform managing aspects like model selection, versioning, parameter configuration, and the orchestration of multiple models for complex workflows [Fedoruk ¶ 0044, ¶ 0043]; Integrator modules serve as a connection gateway between the AI platform and external services. It standardizes the way the platform interacts with various data sources and tools through APIs. The integrator handles authentication, data formatting, protocol differences, rate limiting, and maintains a consistent interface regardless of the underlying service being accessed. This abstraction layer allows users to focus on building workflows rather than dealing with the technical details of individual integrations [Fedoruk ¶ 0045]; The AI app service 113 can act as an AI platform that provides customers with a way to easily create, deploy, and manage AI agents 116. Customers can create AI agents 116 that uniquely suit their needs [Fedoruk ¶ 0078, ¶ 0079]; Alternate AI agents 116, 147, 181 can be provided based on the level of compliance of the computing device 140 [Fedoruk ¶ 0082]).
As per claim 7: Fedoruk in view of MYSORE teach all the limitations of claim 1. Furthermore, Fedoruk discloses wherein at least one of the respective governance policies comprises at least one data privacy protocol (Network and connectivity compliance includes: status and configuration ofWi-Fi, cellular, Bluetooth, NFC, USB, VPN, tethering, and hotspot connections; authentication and encryption methods; roaming and usage metrics; peripheral connectivity; SD-WAN, NAC, and WIPS integrations; protocol security, DNS configurations, and captive portal detection; voice and data communication technologies [Fedoruk ¶ 0135]).
As per claim 8: Fedoruk in view of MYSORE teach all the limitations of claim 1. Furthermore, Fedoruk discloses wherein at least one of the respective governance policies comprises at least one compliance standard (Rule enforcers are governance components that ensure all platform operations comply with configured policies and constraints. They serve two primary functions: (1) enforcing configurations and settings across the platform, including agent behavior, model parameters, and security policies; and (2) monitoring for specific triggering conditions and applying predefined actions when those conditions are detected. Rule enforcers are critical for implementing guardrails, content moderation, cost controls, compliance requirements, and other governance measures that ensure the platform operates within established boundaries [Fedoruk ¶ 0054]; User and identity compliance includes: User authentication, roles, and account lifecycle; access controls, MFA, SSO, and token-based mechanisms; user onboarding/offboarding, usage policies, and remote access; privacy, training, and compliance with regulatory and corporate standards; ownership models (BYOD, COBO, COPE) [Fedoruk ¶ 0139]).
As per claim 9: Fedoruk in view of MYSORE teach all the limitations of claim 1. Furthermore, Fedoruk discloses further comprising using the at least one hardware processor to, for each of the plurality of Al agents, establish a baseline value for each of one or more performance metrics for the Al agent (Prompt libraries allow organizations to standardize interactions with AI models, implement best practices, and maintain consistent outputs. Advanced platforms often include prompt management systems with versioning, performance tracking, and the ability to parameterize prompts for different use cases [Fedoruk ¶ 0048]; Rules can set allowed similarity measures, filtering strategies, embedding dimensions, hybrid modeling techniques, and performance metrics. These types of management rules help ensure fairness and consistency in personalized recommendations, support explainable logic for content delivery, and constrain recommendation scope across domains [Fedoruk ¶ 0157]).
As per claim 10: Fedoruk in view of MYSORE teach all the limitations of claim 1. Furthermore, Fedoruk discloses wherein the corrective action comprises modifying the Al agent (But the AI platform can prevent a noncompliant computing device from executing the AI agent 181 or specific objects within the agent, such as specific AI models 182, tools, datasets, or prompt packages [Fedoruk ¶ 0082, Examiner’s Note: modifying it by stopping it from executing]).
As per claim 11: Fedoruk in view of MYSORE teach all the limitations of claim 10. Furthermore, Fedoruk discloses wherein modifying the Al agent comprises one or more of: terminating execution of the Al agent (But the AI platform can prevent a noncompliant computing device from executing the AI agent 181 or specific objects within the agent, such as specific AI models 182, tools, datasets, or prompt packages [Fedoruk ¶ 0082, Examiner’s Note: modifying it by stopping it from executing]); deactivating the Al agent; adjusting at least one parameter of the Al agent; detecting and correcting a bias in the Al agent; or rolling back the Al agent to a previous version.
As per claim 12: Fedoruk in view of MYSORE teach all the limitations of claim 1. Furthermore, Fedoruk discloses wherein the corrective action comprises adjusting an amount of each of one or more computational resources that is allocated to the Al agent (Hardware and performance compliance includes: monitoring of power, thermal, memory, CPU, GPU, and storage performance; sensor usage (motion, biometric, environmental); device component status (camera, mic, display, audio, input); tamper and shock detection; hardware condition and integrity assessment [Fedoruk ¶ 0136]; the agent executor 1752 can select an AI service and operation time. This allows for dynamically adjusting the execution of asynchronous AI agents to save money for the customer [Fedoruk ¶ 0335]).
As per claim 13: Fedoruk in view of MYSORE teach all the limitations of claim 1. Furthermore, Fedoruk discloses wherein the corrective action comprises modifying an access of the Al agent to one or more systems (the AI platform application 115 is configured to facilitate the design, instantiation, modification, testing, and/or execution of AI agents 116 (referred to herein collectively as AI agents 116 and individually as an AI agent 116) that use one or more AI models 117 (referred to herein collectively as AI models and individually as an AI model 117), as discussed in greater detail below in conjunction with FIGS. 2-4 [Fedoruk ¶ 0090]; The interface can offer sliders, input fields, and drop-down menus to adjust memory settings and optimize the performance of the agent, ensuring efficient handling of data and intermediate results without exceeding available system resources [Fedoruk ¶ 0362]; Compliance rules set predefined constraints that must be satisfied for the AI agent management service or other applications to allow access to enterprise data or other features of the client device. In certain cases, the AI agent management service interacts with a management application, migration application, or other client application running on the device to identify states that violate one or more compliance rules [Fedoruk ¶ 0086]).
As per claim 14: Fedoruk in view of MYSORE teach all the limitations of claim 1. Furthermore, Fedoruk discloses wherein modifying the access of the Al agent to one or more systems comprises one or more of: restricting access by the Al agent to an application programming interface; restricting access by the Al agent to at least one data source (Configuration profiles can also be enforced against computing devices 140 that are managed by the platform or a device management service. For example, a device management service integrated with the AI management service can require particular device setting configurations as prerequisite to accessing particular AI-enabled apps, AI agents, and AI agent objects. Configuration profile parameters define structured, enforceable settings that control the behavior of AI model objects (e.g., NLP, CV, time series, recommender systems). These parameters can be applied at different agent stages----during model creation, data ingestion, preprocessing, postprocessing, or runtime-and support centralized enforcement of model governance policies [Fedoruk ¶ 0145]; App restriction settings may include allowing or blocking specific apps, limiting app store access, restricting built-in app use, requiring VPNs per app, isolating app data, limiting app permissions, enforcing expiration and deletion, wrapping corporate apps, checking licenses, and blocking sideloading [Fedoruk ¶ 0146]); adjusting an authentication level required from the Al agent; revoking a credential of the Al agent; or downgrading at least one permission assigned to the Al agent.
As per claim 15: Fedoruk in view of MYSORE teach all the limitations of claim 1. Furthermore, Fedoruk discloses wherein the corrective action comprises adjusting a communication control associated with the Al agent (compliance management service can execute at the platform and can communicate with the AI agent 143 to ensure that a computing device 140 remains compliant with compliance rules as a requisite to AI agent operation [Fedoruk ¶ 0084]; App restriction settings may include allowing or blocking specific apps, limiting app store access, restricting built-in app use, requiring VPNs per app, isolating app data, limiting app permissions, enforcing expiration and deletion, wrapping corporate apps, checking licenses, and blocking sideloading [Fedoruk ¶ 0146]).
As per claim 16: Fedoruk in view of MYSORE teach all the limitations of claim 15. Furthermore, Fedoruk discloses wherein adjusting the communication control of the Al agent comprises one or more of: limiting a rate by which at least one application programming interface can be called (Integrator modules serve as a connection gateway between the AI platform and external services. It standardizes the way the platform interacts with various data sources and tools through APIs. The integrator handles authentication, data formatting, protocol differences, rate limiting, and maintains a consistent interface regardless of the underlying service being accessed. This abstraction layer allows users to focus on building workflows rather than dealing with the technical details of individual integrations [Fedoruk ¶ 0045, Fig. 4]); or adjusting a response timeout associated with the Al agent.
As per claim 17: Fedoruk discloses a system comprising: at least one hardware processor (executed by a processor associated with a computing device [Fedoruk ¶ 0019, ¶ 0087-0088]); and software that is configured to, when executed by the at least one hardware processor (The system memory 114 of the server device 110 stores content, such as software applications and data, for use by the processor(s) 112 and the GPU(s) and/or other processing units. The system memory 114 can be any type of memory capable of storing data and software applications, such as a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash ROM), or any suitable combination of the foregoing. In some embodiments, a storage (not shown) can supplement [Fedoruk ¶ 0088]). The limitations of claim 17 are substantially similar to claim 1 above, and therefore the claim is likewise rejected.
As per claim 18: Fedoruk discloses A non-transitory computer-readable medium having instructions stored therein, wherein the instructions, when executed by a processor, cause the processor to (a non-transitory, computer-readable medium having instructions that, when executed by a processor associated with a computing device [Fedoruk ¶ 0019, ¶ 0087-0088]; The system memory 114 of the server device 110 stores content, such as software applications and data, for use by the processor(s) 112 and the GPU(s) and/or other processing units. The system memory 114 can be any type of memory capable of storing data and software applications, such as a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash ROM), or any suitable combination of the foregoing. In some embodiments, a storage (not shown) can supplement [Fedoruk ¶ 0088]): The limitations of claim 18 are substantially similar to claim 1 above, and therefore the claim is likewise rejected.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Fedoruk in view of MYSORE and further in view Haley et al. (US PGPub No. 2026/0141406; hereinafter “Haley”).
As per claim 3: Fedoruk in view of MYSORE teach all the limitations of claim 1. Furthermore, Fedoruk and MYSORE disclose further comprising using the at least one hardware processor to, for each of the plurality of Al agents, [determine a value of a governance effectiveness index that quantifies an effectiveness of governance of the Al agent across a plurality of dimensions].
Fedoruk in view of MYSORE discloses the claimed subject matter as discussed above but does not explicitly disclose determine a value of a governance effectiveness index that quantifies an effectiveness of governance of the Al agent across a plurality of dimensions. However, Haley teaches determine a value of a governance effectiveness index that quantifies an effectiveness of governance of the Al agent across a plurality of dimensions (provide an AI governance system that integrates an agentic compliance evaluation framework with an agentic governance ontology framework to automate AI risk assessment and management. The concept of such an AI governance system is implemented through an operational AI guardrail system, as described herein according to various embodiments. [Haley ¶ 0026]; evaluate the compliance checklists for the AI Use Case Object based on semantic similarity search on a database of AI use cases and their associated compliance checklists [Haley ¶ 0027, ¶ 0047-0051]; Then, the DRCO casts the policy into compliance checklists, shaped by templates/prompts for each regulatory concern (Ethics, Legal, EA, Security, Data Governance, etc.) and a measured similarity to defined AI Use Cases. As shown, the generated compliance checklists may be appended to the instantiated AI Use Case Object to form a compiled Compliance Checklist Object [Haley ¶ 0045]; The DRCO agent 202a defines the AI Use Case Object by populating various use case attributes, which may be fields of the Use Case Object or even objects themselves. These attributes may include Owner, AI type, List of ComplianceChecklists, Required ComplianceChecklists Specification, Description, RiskFormula, Risk, RiskMitigationThreshold, RiskAutomationThreshold, State, AIUseCaseComplianceReport, CollaborationChannel, and Transcript. These attributes may be automatically generated by the DRCO agent 202a [Haley ¶ 0038]; As ComplianceChecklists are completed, the DRCO agent 202a computes risk scores for each checklist and aggregates them to determine the overall risk of the AI Use Case using a configurable RiskFormula [Haley ¶ 0057]). Fedoruk in view of MYSORE and Haley are analogous art because they are from the same field of endeavor of AI agents. Therefore, based on Fedoruk in view of MYSORE in view of Haley, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Haley to the system of Fedoruk in view of MYSORE in order to assess the AI agent across multiple metrics that improve the quality of the AI risk assessment. Hence, it would have been obvious to combine the references above to obtain the invention as specified in the instant claim.
As per claim 4: Fedoruk in view of MYSORE further in view Haley teaches all the limitations of claim 3. Furthermore, Haley discloses wherein the plurality of comprises risk management, reliability, ethical alignment, performance, and security and privacy (Then, the DRCO casts the policy into compliance checklists, shaped by templates/prompts for each regulatory concern (Ethics, Legal, EA, Security, Data Governance, etc.) and a measured similarity to defined AI Use Cases. As shown, the generated compliance checklists may be appended to the instantiated AI Use Case Object to form a compiled Compliance Checklist Object [Haley ¶ 0045]; The DRCO agent 202a defines the AI Use Case Object by populating various use case attributes, which may be fields of the Use Case Object or even objects themselves. These attributes may include Owner, AI type, List of ComplianceChecklists, Required ComplianceChecklists Specification, Description, RiskFormula, Risk, RiskMitigationThreshold, RiskAutomationThreshold, State, AIUseCaseComplianceReport, CollaborationChannel, and Transcript. These attributes may be automatically generated by the DRCO agent 202a [Haley ¶ 0038, Fig. 3]).
Allowable Subject Matter
Claim 5 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES P MOLES whose telephone number is (703)756-1043. The examiner can normally be reached M-F 8:00am-5:00pm.
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/JAMES P MOLES/Examiner, Art Unit 2494
/JUNG W KIM/Supervisory Patent Examiner, Art Unit 2494
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