CTNF 18/397,770 CTNF 100903 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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(s) without significantly more. Claim 1 recites: A computer-implemented method comprising: accessing a hierarchy of agents for an application environment provided by an application, wherein the agents of the hierarchy interact in the application environment; assigning respective agent behavior models to individual agents based at least on respective levels of the individual agents in the hierarchy; configuring the respective agent behavior models based at least on one or more configuration parameters; coordinating communication among the respective agent behavior models during execution of the application; and controlling the application based at least on the respective agent behavior models. Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes. Claim 1 is a process. Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes: (an) abstract idea(s). The ‘assigning’ limitation in #2 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “assigning” in the context of this claim encompasses a person analyzing, evaluating, or assigning agent behavior models to agents based on the hierarchy, including comparison or judgement. Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application? No. The ‘accessing’ limitation in #1 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity . The limitation “accessing” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g). The ‘configuring’ limitation in #3 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “configuring” in the context of this claim encompasses merely configuring agent behavioral models based on configuration parameters. See MPEP 2106.05(f). The ‘coordinating’ limitation in #4 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “coordinating” in the context of this claim encompasses merely coordinating communication among agent behavior models. See MPEP 2106.05(f). The ‘controlling’ limitation in #5 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “controlling” in the context of this claim encompasses merely controlling the application based on agent behavior models. See MPEP 2106.05(f). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(g)&(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, Claim 1 is directed to (an) abstract idea(s) without significantly more. Claim 2 recites: wherein the assigning the respective agent behavior models comprises: determining resource utilization characteristics of the agent behavior models; and selecting the respective agent behavior models for the agents based at least on the resource utilization characteristics and the respective levels of the agents in the hierarchy. Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes. Claim 2 is a process. Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes: (an) abstract idea(s). The ‘determining’ limitation in #6 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “determining” in the context of this claim encompasses a person analyzing, evaluating, or determining resource utilization characteristics of agent behavior models, including comparison or judgement. The ‘selecting’ limitation in #7 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “selecting” in the context of this claim encompasses a person analyzing, evaluating, or selecting agent behavior models to agents based on the hierarchy and resource utilization characteristics, including comparison or judgement. Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 3 merely further describes the agent behavior models of Claim 2. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception. Claim 4 merely further describes the agent behavior models of Claim 2. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception. Therefore, Claims 2-4 are directed to (an) abstract idea(s) without significantly more. Claim 5 recites: wherein the coordinating communication includes: receiving two or more telemetry communications from two or more subordinate agents of a particular agent; prompting a particular generative language model assigned to the particular agent to generate a summary of the two or more telemetry communications; and sending the summary as a single communication to another generative language model assigned to another agent that is superior to the particular agent in the hierarchy. Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes. Claim 5 is a process. Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes: (an) abstract idea(s). The ‘prompting’ limitation in #9 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “prompting” in the context of this claim encompasses a person analyzing, evaluating, or prompting an agent to generate a summary of the two or more telemetry communications, including comparison or judgement. Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application? No. The ‘receiving’ limitation in #8 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity . The limitation “receiving” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g). The ‘sending’ limitation in #10 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “sending” in the context of this claim encompasses merely sending the summary to another generative language model. See MPEP 2106.05(f). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(g)&(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 6 merely further describes the telemetry communications of Claim 5. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception. Claim 7 merely further describes the telemetry communications of Claim 5. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception. Claim 8 merely further describes the telemetry communications of Claim 5. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception. Therefore, Claims 5-8 are directed to (an) abstract idea(s) without significantly more. Claim 9 recites: wherein the coordinating communication includes: receiving an instruction message output by a particular generative language model assigned to a particular agent; parsing the instruction message to identify a first instruction to a first subordinate agent of the particular agent and a second instruction to a second subordinate agent of the particular agent; and distributing the first instruction to a first agent behavior model of the first subordinate agent and the second instruction to a second agent behavior model of the second subordinate agent. Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes. Claim 9 is a process. Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes: (an) abstract idea(s). The ‘parsing’ limitation in #12 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “parsing” in the context of this claim encompasses a person analyzing, evaluating, or parsing the instruction message to identify a first and second instruction, including comparison or judgement. Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application? No. The ‘receiving’ limitation in #11 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity . The limitation “receiving” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g). The ‘distributing’ limitation in #13 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “distributing” in the context of this claim encompasses merely distributing the first and second instruction. See MPEP 2106.05(f). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(g)&(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, Claim 9 is directed to (an) abstract idea(s) without significantly more. Claim 10 recites: wherein the coordinating communication includes: prompting a particular generative language model of a particular agent with identifiers of one or more application programming interfaces of the application; receiving a message output by the particular generative language model; parsing the message to identify a particular application programming interface requested by the particular generative language model; and invoking the particular application programming interface on the application. Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes. Claim 10 is a process. Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes: (an) abstract idea(s). The ‘prompting’ limitation in #14 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “prompting” in the context of this claim encompasses a person analyzing, evaluating, or prompting a generative model with identifiers of an API, including comparison or judgement. The ‘parsing’ limitation in #16 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “parsing” in the context of this claim encompasses a person analyzing, evaluating, or parsing the message to identify a particular API, including comparison or judgement. Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application? No. The ‘receiving’ limitation in #15 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity . The limitation “receiving” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g). The ‘invoking’ limitation in #17 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “invoking” in the context of this claim encompasses merely invoking the particular API. See MPEP 2106.05(f). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(g)&(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 11 merely further describes the message of Claim 10. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception. Therefore, Claims 10-11 are directed to (an) abstract idea(s) without significantly more. Claim 12 recites: receiving feedback from users and providing the feedback to a particular agent behavior model of a particular agent with a request that the particular agent adjust the application environment based on the feedback. Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes. Claim 12 is a process. Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application? No. The ‘receiving’ limitation in #18 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity . The limitation “receiving” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g). The ‘providing’ limitation in #19 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “providing” in the context of this claim encompasses merely providing the feedback to the particular agent. See MPEP 2106.05(f). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(g)&(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 13 merely further describes the feedback of Claim 12. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception. Claim 14 merely further describes the feedback of Claim 12. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception. Therefore, Claims 12-14 are directed to (an) abstract idea(s) without significantly more. Claim 15 recites: A system comprising: a hardware processing unit; and a storage resource storing computer-readable instructions which, when executed by the hardware processing unit, cause the system to: coordinate communications among respective agent behavior models of agents of a hierarchy, wherein the agents of the hierarchy interact in an application environment provided by an application and the respective agent behavior models are assigned to the agents based at least on levels of individual agents in the hierarchy and resource utilization characteristics of the agent behavior models; and control the application based at least on the respective agent behavior models. Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes. Claim 15 is a machine. Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes: (an) abstract idea(s). The ‘assigning’ limitation in #21 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “assigning” in the context of this claim encompasses a person analyzing, evaluating, or assigning agent behavior models to agents based on the hierarchy and resource utilization characteristics, including comparison or judgement. Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application? No. The ‘coordinating’ limitation in #20 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “coordinating” in the context of this claim encompasses merely coordinating communication among agent behavior models. See MPEP 2106.05(f). The ‘controlling’ limitation in #22 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “controlling” in the context of this claim encompasses merely controlling the application based on agent behavior models. See MPEP 2106.05(f). Additionally, one or more of the claims recite the following additional elements: a hardware processing unit a storage resource storing computer-readable instructions These additional elements are recited at a high level of generality (i.e., as generic computer components) such that they amount to no more than components comprising mere instructions to apply the exception . Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract ideas(s). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 16 merely further describes the agent behavior models of Claim 15. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception. Claim 19 merely further describes the agent behavior models of Claim 15. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception. Therefore, Claims 15-16 and 19 are directed to (an) abstract idea(s) without significantly more. Claim 17 recites: at runtime, detect a change within the application environment; and responsive to detecting the change to within the application environment, promote a particular agent from a particular reinforcement learning or hard-coded model to a particular generative model. Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes. Claim 17 is a machine. Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes: (an) abstract idea(s). The ‘detecting’ limitation in #23 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “detecting” in the context of this claim encompasses a person analyzing, evaluating, or detecting a change within the application environment, including comparison or judgement. Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application? No. The ‘promoting’ limitation in #24 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “promoting” in the context of this claim encompasses merely promoting a particular agent to a particular generative model. See MPEP 2106.05(f). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 18 merely further describes the change of Claim 17. The claim does not include additional elements that integrate into practical application or are sufficient to amount to significantly more than the judicial exception. Therefore, Claims 17-18 are directed to (an) abstract idea(s) without significantly more. Claim 20 recites: A computer-readable storage medium storing computer-readable instructions which, when executed by a processing unit, cause the processing unit to perform acts comprising: accessing a hierarchy of agents for an application environment provided by an application, wherein the agents of the hierarchy interact in the application environment and respective agent behavior models are assigned to individual agents based at least on respective levels of the individual agents in the hierarchy and resource utilization characteristics of the respective agent behavior models; configuring the respective agent behavior models based at least on one or more configuration parameters; coordinating communication among the respective agent behavior models during execution of the application; and controlling the application based at least on the respective agent behavior models. Step 1: Is the claim to a process, machine, manufacture, or composition of matter? No. Claim 20 is directing to a computer-readable storage medium. Although the specification does define the computer-readable storage media excluding signals, it is unclear exactly whether the computer-readable storage medium is both transitory medium and non-transitory medium, wherein transitory medium is not eligible statutory subject matter. Step 2A, Prong I: Does the claim recite an abstract idea, law of nature, or natural phenomenon? Yes: (an) abstract idea(s). The ‘assigning’ limitation in #26 above, as claimed and under broadest reasonable interpretation (BRI), is a mental process that covers performance of the limitation in the mind. The limitation “assigning” in the context of this claim encompasses a person analyzing, evaluating, or assigning agent behavior models to agents based on the hierarchy and resource utilization characteristics, including comparison or judgement. Step 2A, Prong II: Does the claim recite additional elements that integrate the judicial exception into a practical application? No. The ‘accessing’ limitation in #25 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element that is insignificant extra-solution activity . The limitation “accessing” in the context of this claim encompasses mere data gathering. See MPEP 2106.05(g). The ‘configuring’ limitation in #27 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “configuring” in the context of this claim encompasses merely configuring agent behavioral models based on configuration parameters. See MPEP 2106.05(f). The ‘coordinating’ limitation in #28 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “coordinating” in the context of this claim encompasses merely coordinating communication among agent behavior models. See MPEP 2106.05(f). The ‘controlling’ limitation in #29 above, as claimed and under broadest reasonable interpretation (BRI), is an additional element as “apply it” that is mere instructions to apply an exception . The limitation “controlling” in the context of this claim encompasses merely controlling the application based on agent behavior models. See MPEP 2106.05(f). Additionally, one or more of the claims recite the following additional elements: processing unit computer-readable storage medium storing computer-readable instructions These additional elements are recited at a high level of generality (i.e., as generic computer components) such that they amount to no more than components comprising mere instructions to apply the exception . Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract ideas(s). Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No. As discussed above with respect to integration of the abstract idea(s) into a practical application, the aforementioned additional elements amount to no more than components for obtaining or gathering data and comprising mere instructions to apply the exception which is evidently seen in MPEP 2106.05(g)&(f). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Therefore, Claim 20 is directed to (an) abstract idea(s) without significantly more. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim (s) 1 and 12-14 are rejected under 35 U.S.C. 102(a)(2) as being unpatentable by Perry et al. (U.S. Patent No. US 20220309364 A1), hereinafter “Perry.” With regards to claim 1, Perry teaches: A computer-implemented method comprising: accessing a hierarchy of agents for an application environment provided by an application, wherein the agents of the hierarchy interact in the application environment (Paragraphs 56-57, “In one implementation, an AI agent is programmed as a mastermind within the environment of a multi-agent game. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and a hierarchy that AI agents are programmed to obey. The environment is programmed with different complexity levels of enemy AI intelligence… In some implementations a more complex AI engine mastermind is employed. For example, in one implementation, a more complex AI engine controls several simpler AI engines to support the player or compete against the player. In this implementation, a command structure is utilized as well as different levels of AI engine complexity. The different levels of complexity give rise to understanding the different performance characteristics of the different complexities.” The AI agents belonging to and programmed to obey a hierarchy where a mastermind can control several simpler AI engines in a multi-agent game environment correlates to accessing a hierarchy of agents for an application environment provided by an application, wherein the agents of the hierarchy interact in the application environment) ; assigning respective agent behavior models to individual agents based at least on respective levels of the individual agents in the hierarchy (Paragraphs 17, 40-41, and 52, “In one implementation, a game begins with multiple agents having varying complexity levels of intelligence. Over time, one or more of the AI agents becomes a mastermind based on RL-training using the actions taken during the game by the player and the other AI agents… In one implementation, the RL training loop is executed in a cloud environment. During the RL training loop, parameters such as delays, angles, and other settings are adjusted while the cloud is refining the neural network so as to improve the AI's chances on future attempts. When the training of the neural network is complete, the newly trained neural network is downloaded and swapped in at run-time. In various implementations, a video game application implements multi-agent control with a single RL-trained network, with each agent an independent AI engine. The agents are trained through live game play… Each AI agent has unique goals, sensor, and actions available to the AI agent.” Multiple agents starting the game with varying complexity levels of intelligence correlates to individual agents in the hierarchy. Each AI agent having unique actions available and trained through live game play, which would involve each AI agent utilizing its varying levels of intelligence, where the newly trained neural network is downloaded and swapped in at run-time, correlates to assigning respective agent behavior models to individual agents based at least on respective levels of the individual agents in the hierarchy) ; configuring the respective agent behavior models based at least on one or more configuration parameters (Paragraph 29, “The NPCs can implement a variety of schemes of different complexities depending on the particular video game application. For example, in one implementation, each NPC is assigned a personality, and the actions of the NPC are generated to match the assigned personality. Also, in another implementation, each NPC is assigned a mood, and each neural network 200 generates actions which correspond to the mood of the respective NPC.” Each neural network generating actions which correspond to the assigned mood of the respective NPC correlates to configuring the respective agent behavior models based at least on one or more configuration parameters) ; coordinating communication among the respective agent behavior models during execution of the application (Paragraphs 42, 50-51 and 54, “In some games, there are multi-agent systems with AI engines that communicate with each other… Each AI agent predicts which of the above categories a piece of information falls into when receiving the information from another AI agent… The behavior of an AI agent follows from the categorizing of the information received from another AI agent… In one implementation, the AI engines are programmed for cooperative group behavior in multi-agent games. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and communication. In one implementation, there are multiple AI agents that are enemies that collaborate to eliminate the player. The AI agents adapt to the player and work together by pooling their resources and taking advantage of opportunities created by each other. A training environment for the AI agents can include training in seclusion or training to collaborate. There can be inter-network stimulus to create a communication path between AI agents.” Each AI agent communicating with each other and categorizing the type of information received from another AI agent while collectively adapting to the player to take advantage of opportunities created by each other would occur during the gameplay and therefore correlates to coordinating communication among the respective agent behavior models during execution of the application) ; and controlling the application based at least on the respective agent behavior models (Paragraph 54, “In one implementation, the AI engines are programmed for cooperative group behavior in multi-agent games. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and communication. In one implementation, there are multiple AI agents that are enemies that collaborate to eliminate the player. The AI agents adapt to the player and work together by pooling their resources and taking advantage of opportunities created by each other. A training environment for the AI agents can include training in seclusion or training to collaborate. There can be inter-network stimulus to create a communication path between AI agents.” The AI agents adapting to the player, working together, and taking advantage of opportunities in a multi-agent game correlates to controlling the application based at least on the respective agent behavior models) . With regards to claim 12, Perry teaches the method of Claim 1 above. Perry further teaches: receiving feedback from users and providing the feedback to a particular agent behavior model of a particular agent with a request that the particular agent adjust the application environment based on the feedback (Paragraphs 35 and 37, “For example, an NPC will wait in front of the door and the collision mesh will prevent the player from leaving the room or building. To combat these shortcomings of today's NPCs, player feedback is enabled during development to punish bad behavior with an in-game reporting tool. An AI agent training environment is employed with feedback to train an AI agent to perform better when functioning as an NPC follower... An NPC is rewarded for normal, human-like behavior and punished for erratic, annoying behavior. For example, in one implementation, the NPC should face forward when in motion and face the player when idle. Also, the NPC should not produce erratic behavior such as spinning in circles, moving in a non-standard way, and so on. During training, any erratic behavior, not facing forward while in motion, not facing the player when idle, or other negative behavior will result in the NPC being docked points. The training sessions are used to reinforce desired behavior and to eliminate erratic or other undesired behavior by the NPC.” The player feedback in-game reporting tool enabling players to report an AI agent for undesired behavior, which docks points from the reported NPC to train the NPC to eliminate the undesired behavior, correlates to receiving feedback from users and providing the feedback to a particular agent behavior model of a particular agent) . With regards to claim 13, Perry teaches the method of Claim 12 above. Perry further teaches: the feedback comprising explicit or implicit feedback relating to user satisfaction with the application (Paragraph 35, “In one implementation, system 400 attempts to have non-player characters (NPCs) stay close to a player and not get in the player's way. In a typical game in the prior art, follower NPCs have many limitations. For example, there is a walking speed problem where NPCs do not walk at the same speed as the player, causing the player to be frustrated and having to adjust their walking speed. Also, NPCs have a pathfinding problem where they get stuck in the terrain, such as trees, holes, doors, and so on. Still further, a common problem for NPCs is blocking the door after entering a building. For example, an NPC will wait in front of the door and the collision mesh will prevent the player from leaving the room or building. To combat these shortcomings of today's NPCs, player feedback is enabled during development to punish bad behavior with an in-game reporting tool. An AI agent training environment is employed with feedback to train an AI agent to perform better when functioning as an NPC follower.” The player encountering common frustrating issues such as an NPC walking speed problem to a point where they decide to use the in-game reporting tool in an attempt to fix the problem correlates to the feedback comprising implicit feedback relating to user satisfaction with the application) . With regards to claim 14, Perry teaches the method of Claim 12 above. Perry further teaches: the feedback relating to a current state of the application environment (Paragraph 35, “In one implementation, system 400 attempts to have non-player characters (NPCs) stay close to a player and not get in the player's way. In a typical game in the prior art, follower NPCs have many limitations. For example, there is a walking speed problem where NPCs do not walk at the same speed as the player, causing the player to be frustrated and having to adjust their walking speed. Also, NPCs have a pathfinding problem where they get stuck in the terrain, such as trees, holes, doors, and so on. Still further, a common problem for NPCs is blocking the door after entering a building. For example, an NPC will wait in front of the door and the collision mesh will prevent the player from leaving the room or building. To combat these shortcomings of today's NPCs, player feedback is enabled during development to punish bad behavior with an in-game reporting tool. An AI agent training environment is employed with feedback to train an AI agent to perform better when functioning as an NPC follower.” The player utilizing the in-game reporting tool to report an AI agent for common issues such as the NPC getting stuck in the terrain correlates to the feedback relating to a current state of the application environment) . Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 2-8, 15-16 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Perry in view of Jain et al. (U.S. Patent No. US 20240007414 A1), hereinafter “Jain.” With regards to claim 2, Perry teaches the method of Claim 1 above. Perry does not explicitly teach: wherein the assigning the respective agent behavior models comprises: determining resource utilization characteristics of the agent behavior models; and selecting the respective agent behavior models for the agents based at least on the resource utilization characteristics and the respective levels of the agents in the hierarchy. However, Jain teaches: wherein the assigning the respective agent behavior models comprises: determining resource utilization characteristics of the agent behavior models (Paragraphs 227-228, “In some examples, the hyper parameter tuning circuitry ID5_C306 applies a reinforcement learning model for the assigned agent to process, in which a cost function is evaluated in view of one or more parameters corresponding to the SLA information… The example reconfiguration managing circuitry ID5_C308 retrieves current utilization information for the computing resources associated with the above-identified workload invocation. In some examples, utilization information is obtained with the aid of Intel® Resource Director Technology (RDT). Resource information may include, but is not limited to resource availability, current resource utilization (e.g., in view of multiple tenant utilization), and current resource cost (e.g., a dollar-per-cycle cost).” The reinforcement learning model being applied to the assigned agent to calculate a cost function based on SLA information, such as through resource utilization information and resource cost, correlates to determining resource utilization characteristics of the agent behavior models) ; and selecting the respective agent behavior models for the agents based at least on the resource utilization characteristics (Paragraphs 227-229, “In some examples, the hyper parameter tuning circuitry ID5_C306 applies a reinforcement learning model for the assigned agent to process, in which a cost function is evaluated in view of one or more parameters corresponding to the SLA information… The example reconfiguration managing circuitry ID5_C308 retrieves current utilization information for the computing resources associated with the above-identified workload invocation. In some examples, utilization information is obtained with the aid of Intel® Resource Director Technology (RDT). Resource information may include, but is not limited to resource availability, current resource utilization (e.g., in view of multiple tenant utilization), and current resource cost (e.g., a dollar-per-cycle cost). The example SLA managing circuitry ID5_C304 determines whether the currently identified computing resources will satisfy the current SLA parameters and, if so, no further model adjustments are needed.” The reinforcement learning model being applied to the assigned agent and the SLA managing circuitry determining that the currently identified computing resources satisfy the current SLA parameters with the agent and reinforcement learning model configuration correlates to selecting the respective agent behavior models for the agents based at least on the resource utilization characteristics) . Jain does not explicitly teach that the respective agent behavior models [are selected] based at least on the respective levels of the agents in the hierarchy. However, selecting respective agent behavior models based at least on the respective levels of the agents in the hierarchy is a popular method of agent behavior model selection as evidenced by Perry above (Paragraphs 17, 40-41, and 52, “In one implementation, a game begins with multiple agents having varying complexity levels of intelligence. Over time, one or more of the AI agents becomes a mastermind based on RL-training using the actions taken during the game by the player and the other AI agents… In one implementation, the RL training loop is executed in a cloud environment. During the RL training loop, parameters such as delays, angles, and other settings are adjusted while the cloud is refining the neural network so as to improve the AI's chances on future attempts. When the training of the neural network is complete, the newly trained neural network is downloaded and swapped in at run-time. In various implementations, a video game application implements multi-agent control with a single RL-trained network, with each agent an independent AI engine. The agents are trained through live game play… Each AI agent has unique goals, sensor, and actions available to the AI agent.” Multiple agents starting the game with varying complexity levels of intelligence correlates to individual agents in the hierarchy. Each AI agent having unique actions available and trained through live game play, which would involve each AI agent utilizing its varying levels of intelligence, where the newly trained neural network is downloaded and swapped in at run-time, correlates to assigning respective agent behavior models to individual agents based at least on respective levels of the individual agents in the hierarchy) . Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Perry with wherein the assigning the respective agent behavior models comprises: determining resource utilization characteristics of the agent behavior models; and selecting the respective agent behavior models for the agents based at least on the resource utilization characteristics as taught by Jain because cost functions can be used to evaluate parameters corresponding to SLA information. Additionally, optimized graphs can be calculated for computing resources to allow dynamic decision making during real time runtime phases of workloads. The currently assigned model and computing resources assigned to the agent can be evaluated to determine if the SLA parameters are satisfied, in which no further model adjustments are needed (Jain: paragraphs 227 and 229). With regards to claim 3, Perry in view of Jain teaches the method of Claim 2 above. Perry further teaches: wherein the respective agent behavior models include generative language models (Paragraph 22, “Neural networks have demonstrated excellent performance at tasks such as hand-written digit classification and face detection. Other applications for neural networks include speech recognition, language modeling, sentiment analysis, text prediction, and others. In one implementation, processor 105N is a data parallel processor programmed to execute one or more neural network application to implement movement schemes for one or more non-player characters (NPCs) as part of a video-game application.” The neural network applications being applied to one or more NPCs which include language modeling correlates to the respective agent behavior models including generative language models) . With regards to claim 4, Perry in view of Jain teaches the method of Claim 3 above. Perry further teaches: wherein the respective agent behavior models include at least one of reinforcement learning models or hard-coded models (Paragraphs 23 and 27, “In another implementation, reinforcement learning is used to generate the movement scheme for the NPC. Any number of different trained neural networks can control any number of NPCs… The training of a neural network can be performed using reinforcement learning (RL), supervised learning, or imitation learning in various implementations.” The neural network being trained using reinforcement learning to generate the movement scheme for the NPC correlates to the respective agent behavior models including at least one of reinforcement learning models or hard-coded models) . With regards to Claim 16, the method of Claims 3 and 4 perform the same steps as the machine of Claim 16, and Claim 16 is therefore rejected using the same rationale set forth above in the rejections of Claims 3 and 4. With regards to claim 5, Perry in view of Jain teaches the method of Claim 3 above. Perry further teaches: wherein the coordinating communication includes: receiving two or more telemetry communications from two or more subordinate agents of a particular agent (Paragraphs 42, 56, “In some games, there are multi-agent systems with AI engines that communicate with each other… In one implementation, an AI agent is programmed as a mastermind within the environment of a multi-agent game. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and a hierarchy that AI agents are programmed to obey. The environment is programmed with different complexity levels of enemy AI intelligence. In one implementation, multiple AI agents cooperate and/or attack the player during the game.” The multi-agent game including AI engines with different complexity levels that communicate with each other to cooperate or attack the player can involve at least two agents communicating with a third agent and therefore correlates to receiving two or more telemetry communications from two or more subordinate agents of a particular agent) ; prompting a particular generative language model assigned to the particular agent to generate a summary of the two or more telemetry communications (Paragraphs 42 and 49-51, “In one implementation, a video game application supports the use of rumors during gameplay to enhance the user experience. The concept of a rumor is a piece of information with a fair bit of uncertainty attached to it. In some games, there are multi-agent systems with AI engines that communicate with each other. For multi-agent systems, there is an inherent distrust of the information. When a piece of information is received, there are inherently multiple states to the information… In addition to the multiple states of information, rumors have reliability associated to the source of the information. The reliability will increase over time as a source is proved trustworthy. Rumors could be inconsequential or incredibly important. Ascertaining the importance of information helps to increase the performance of the agent. Accordingly, some portion of the AI engine will be dedicated to determining the importance and trustworthiness of information received from other AI engines. Each AI agent predicts which of the above categories a piece of information falls into when receiving the information from another AI agent... At a later point in time, the AI agent can reassess the previously received information to determine if the information should be recategorized into a new category based on subsequently obtained information.” The AI agent predicting the category of the information received from other agents and later reassessing previously received information based on subsequently obtained information correlates to prompting a particular generative language model assigned to the particular agent to generate a summary of the two or more telemetry communications) ; and sending the summary as a single communication to another generative language model assigned to another agent that is superior to the particular agent in the hierarchy (Paragraphs 42, 49, 51, and 57, “In one implementation, a video game application supports the use of rumors during gameplay to enhance the user experience. The concept of a rumor is a piece of information with a fair bit of uncertainty attached to it. In some games, there are multi-agent systems with AI engines that communicate with each other… Accordingly, some portion of the AI engine will be dedicated to determining the importance and trustworthiness of information received from other AI engines… The behavior of an AI agent follows from the categorizing of the information received from another AI agent. At a later point in time, the AI agent can reassess the previously received information to determine if the information should be recategorized into a new category based on subsequently obtained information… In some implementations a more complex AI engine mastermind is employed. For example, in one implementation, a more complex AI engine controls several simpler AI engines to support the player or compete against the player. In this implementation, a command structure is utilized as well as different levels of AI engine complexity. The different levels of complexity give rise to understanding the different performance characteristics of the different complexities.” A particular agent in a multi-agent environment can receive information from multiple other AI engines, such as when the AI agent reassesses previously received information based on subsequently obtained information. In the event that an agent reassesses the previously received information to align with subsequently obtained information, the agent may communicate the information to a different agent which correlates to sending the summary as a single communication to another generative language model assigned to another agent. The behavior of the AI agent following from the categorization of information received from another agent can include communication with a different AI agent, such as one with a more complex neural network or higher in the command structure, and therefore correlates to sending the summary as a single communication to another generative language model assigned to another agent that is superior to the particular agent in the hierarchy) . With regards to claim 6, Perry in view of Jain teaches the method of Claim 5 above. Perry further teaches: the two or more telemetry communications relating to observations of the application environment by the two or more subordinate agents (Paragraphs 39, 42, 56, “In one implementation, a room and facility is created where there is a fully controlled AI environment with traps. The goal of the AI engine is to prevent the player from reaching the player's goal via usage of traps. In one implementation, multiple independent AI constructs/engines also live in the environment and function cooperatively to stop the player… In some games, there are multi-agent systems with AI engines that communicate with each other… In one implementation, an AI agent is programmed as a mastermind within the environment of a multi-agent game. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and a hierarchy that AI agents are programmed to obey. The environment is programmed with different complexity levels of enemy AI intelligence. In one implementation, multiple AI agents cooperate and/or attack the player during the game.” The multi-agent game including AI engines that communicate with each other can involve at least two agents communicating with a third agent which correlates to two or more telemetry communications. The AI engines cooperating to prevent the player from reaching the goal using traps in the environment would involve communication about the usage of traps and therefore correlates to the two or more telemetry communications relating to observations of the application environment by the two or more subordinate agents) . With regards to claim 7, Perry in view of Jain teaches the method of Claim 5 above. Perry further teaches: the two or more telemetry communications relating to locations of the two or more subordinate agents in the application environment (Paragraphs 39, 42, 54, and 56, “In one implementation, a room and facility is created where there is a fully controlled AI environment with traps. The goal of the AI engine is to prevent the player from reaching the player's goal via usage of traps. In one implementation, multiple independent AI constructs/engines also live in the environment and function cooperatively to stop the player… In some games, there are multi-agent systems with AI engines that communicate with each other…The AI agents adapt to the player and work together by pooling their resources and taking advantage of opportunities created by each other… In one implementation, an AI agent is programmed as a mastermind within the environment of a multi-agent game. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and a hierarchy that AI agents are programmed to obey. The environment is programmed with different complexity levels of enemy AI intelligence. In one implementation, multiple AI agents cooperate and/or attack the player during the game.” The multi-agent game including AI engines that communicate with each other can involve at least two agents with lower complexity levels of intelligence communicating with a third agent which correlates to two or more telemetry communications. The AI engines cooperating to prevent the player from reaching the goal using traps in the environment and taking advantage of opportunities created by each other would involve communication about the usage of traps and the AI engine’s location in order to take advantage of the opportunity created from the trap usage and therefore correlates to the two or more telemetry communications relating to observations of the application environment by the two or more subordinate agents) . With regards to claim 8, Perry in view of Jain teaches the method of Claim 5 above. Perry further teaches: the two or more telemetry communications relating to status updates for the two or more subordinate agents (Paragraphs 54 and 56, “In one implementation, the AI engines are programmed for cooperative group behavior in multi-agent games. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and communication. In one implementation, there are multiple AI agents that are enemies that collaborate to eliminate the player. The AI agents adapt to the player and work together by pooling their resources and taking advantage of opportunities created by each other… Each AI agent can be a producer of some products and a consumer of other products… In one implementation, an AI agent is programmed as a mastermind within the environment of a multi-agent game. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and a hierarchy that AI agents are programmed to obey. The environment is programmed with different complexity levels of enemy AI intelligence. In one implementation, multiple AI agents cooperate and/or attack the player during the game.” Multiple agents with different complexity levels cooperating to attack the player would include two or more telemetry communications. The AI agents working together by pooling their resources, where agents can produce certain products and consume other products, would involve communication on the resources each agent is pooling, producing, or consuming, and therefore correlates to the two or more telemetry communications relating to status updates for the two or more subordinate agents) . With regards to claim 15, Perry teaches: A system comprising: a hardware processing unit; and a storage resource storing computer-readable instructions (Paragraph 19, “Referring now to FIG. 1, a block diagram of one implementation of a computing system 100 is shown. In one implementation, computing system 100 includes at least processors 105A-N, input/output (I/O) interfaces 120, bus 125, memory controller(s) 130, network interface 135, memory device(s) 140, display controller 150, and display 155.” The computing system including processors and memory devices correlates to a hardware processing unit and a storage resource storing computer-readable instructions) which, when executed by the hardware processing unit, cause the system to: coordinate communications among respective agent behavior models of agents of a hierarchy (Paragraphs 42, 50-51, 54 and 56-57, “In some games, there are multi-agent systems with AI engines that communicate with each other… Each AI agent predicts which of the above categories a piece of information falls into when receiving the information from another AI agent… The behavior of an AI agent follows from the categorizing of the information received from another AI agent… In one implementation, the AI engines are programmed for cooperative group behavior in multi-agent games. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and communication. In one implementation, there are multiple AI agents that are enemies that collaborate to eliminate the player. The AI agents adapt to the player and work together by pooling their resources and taking advantage of opportunities created by each other. A training environment for the AI agents can include training in seclusion or training to collaborate. There can be inter-network stimulus to create a communication path between AI agents… In one implementation, an AI agent is programmed as a mastermind within the environment of a multi-agent game. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and a hierarchy that AI agents are programmed to obey. The environment is programmed with different complexity levels of enemy AI intelligence… In some implementations a more complex AI engine mastermind is employed. For example, in one implementation, a more complex AI engine controls several simpler AI engines to support the player or compete against the player. In this implementation, a command structure is utilized as well as different levels of AI engine complexity. The different levels of complexity give rise to understanding the different performance characteristics of the different complexities.” The AI agents belonging to and programmed to obey a hierarchy where a mastermind can control several simpler AI engines in a multi-agent game environment correlates to a hierarchy of agents. Each AI agent communicating with each other and categorizing the type of information received from another AI agent while collectively adapting to the player to take advantage of opportunities created by each other would occur during the gameplay and therefore correlates to coordinating communication among the respective agent behavior models during execution of the application) , wherein the agents of the hierarchy interact in an application environment provided by an application (Paragraphs 56-57, “In one implementation, an AI agent is programmed as a mastermind within the environment of a multi-agent game. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and a hierarchy that AI agents are programmed to obey. The environment is programmed with different complexity levels of enemy AI intelligence… In some implementations a more complex AI engine mastermind is employed. For example, in one implementation, a more complex AI engine controls several simpler AI engines to support the player or compete against the player. In this implementation, a command structure is utilized as well as different levels of AI engine complexity. The different levels of complexity give rise to understanding the different performance characteristics of the different complexities.” The AI agents belonging to and programmed to obey a hierarchy where a mastermind can control several simpler AI engines in a multi-agent game environment correlates to the agents of the hierarchy interacting in an application environment provided by an application) and the respective agent behavior models are assigned to the agents based at least on levels of individual agents in the hierarchy (Paragraphs 17, 40-41, and 52, “In one implementation, a game begins with multiple agents having varying complexity levels of intelligence. Over time, one or more of the AI agents becomes a mastermind based on RL-training using the actions taken during the game by the player and the other AI agents… In one implementation, the RL training loop is executed in a cloud environment. During the RL training loop, parameters such as delays, angles, and other settings are adjusted while the cloud is refining the neural network so as to improve the AI's chances on future attempts. When the training of the neural network is complete, the newly trained neural network is downloaded and swapped in at run-time. In various implementations, a video game application implements multi-agent control with a single RL-trained network, with each agent an independent AI engine. The agents are trained through live game play… Each AI agent has unique goals, sensor, and actions available to the AI agent.” Multiple agents starting the game with varying complexity levels of intelligence correlates to individual agents in the hierarchy. Each AI agent having unique actions available and trained through live game play, which would involve each AI agent utilizing its varying levels of intelligence, where the newly trained neural network is downloaded and swapped in at run-time, correlates to assigning respective agent behavior models to individual agents based at least on respective levels of the individual agents in the hierarchy) ; and control the application based at least on the respective agent behavior models (Paragraph 54, “In one implementation, the AI engines are programmed for cooperative group behavior in multi-agent games. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and communication. In one implementation, there are multiple AI agents that are enemies that collaborate to eliminate the player. The AI agents adapt to the player and work together by pooling their resources and taking advantage of opportunities created by each other. A training environment for the AI agents can include training in seclusion or training to collaborate. There can be inter-network stimulus to create a communication path between AI agents.” The AI agents adapting to the player, working together, and taking advantage of opportunities in a multi-agent game correlates to controlling the application based at least on the respective agent behavior models) . Perry does not explicitly teach that the respective agent behavior models are assigned to the agents based at least on resource utilization characteristics of the agent behavior models. However, assigning [the respective agent behavior models] based at least on resource utilization characteristics of the agent behavior models is a popular method of assigning respective agent behavior models as evidenced by Jain above (Paragraphs 227-229, “In some examples, the hyper parameter tuning circuitry ID5_C306 applies a reinforcement learning model for the assigned agent to process, in which a cost function is evaluated in view of one or more parameters corresponding to the SLA information… The example reconfiguration managing circuitry ID5_C308 retrieves current utilization information for the computing resources associated with the above-identified workload invocation. In some examples, utilization information is obtained with the aid of Intel® Resource Director Technology (RDT). Resource information may include, but is not limited to resource availability, current resource utilization (e.g., in view of multiple tenant utilization), and current resource cost (e.g., a dollar-per-cycle cost). The example SLA managing circuitry ID5_C304 determines whether the currently identified computing resources will satisfy the current SLA parameters and, if so, no further model adjustments are needed.” The reinforcement learning model being applied to the assigned agent and the SLA managing circuitry determining that the currently identified computing resources satisfy the current SLA parameters with the agent and reinforcement learning model configuration correlates to assigning the respective agent behavior models for the agents based at least on the resource utilization characteristics) . Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Perry with the respective agent behavior models are assigned to the agents based at least on resource utilization characteristics of the agent behavior models as taught by Jain because cost functions can be used to evaluate parameters corresponding to SLA information. Additionally, optimized graphs can be calculated for computing resources to allow dynamic decision making during real time runtime phases of workloads. The currently assigned model and computing resources assigned to the agent can be evaluated to determine if the SLA parameters are satisfied, in which no further model adjustments are needed (Jain: paragraphs 227 and 229). With regards to claim 19, Perry in view of Jain teaches the method of Claim 15 above. Jain further teaches: the respective agent behavior models being executed on at least two different computing devices (Paragraphs 86 and 227, “Once training is complete, the model is deployed for use as an executable construct that processes an input and provides an output based on the network of nodes and connections defined in the model. The model is stored at one or more locations of the Edge network, including servers, platforms and/or IoT devices. The model may then be executed by the Edge devices… In some examples, the hyper parameter tuning circuitry ID5_C306 applies a reinforcement learning model for the assigned agent to process…” The model being executed by one of the multiple edge devices, where the model can be a reinforced learning model assigned to an agent, correlates to the respective agent behavior models being executed on at least two different computing devices) . Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Perry with the respective agent behavior models being executed on at least two different computing devices as taught by Jain because models can be stored at one or more locations on an edge network, such as on servers, platforms, or IoT devices. These models are trained and deployed for use as an executable construct based on the connections defined in the model (Jain: paragraph 86). With regards to claim 20, Perry teaches: A computer-readable storage medium storing computer-readable instructions which, when executed by a processing unit, cause the processing unit to perform acts comprising: accessing a hierarchy of agents for an application environment provided by an application, wherein the agents of the hierarchy interact in the application environment (Paragraphs 56-57, “In one implementation, an AI agent is programmed as a mastermind within the environment of a multi-agent game. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and a hierarchy that AI agents are programmed to obey. The environment is programmed with different complexity levels of enemy AI intelligence… In some implementations a more complex AI engine mastermind is employed. For example, in one implementation, a more complex AI engine controls several simpler AI engines to support the player or compete against the player. In this implementation, a command structure is utilized as well as different levels of AI engine complexity. The different levels of complexity give rise to understanding the different performance characteristics of the different complexities.” The AI agents belonging to and programmed to obey a hierarchy where a mastermind can control several simpler AI engines in a multi-agent game environment correlates to accessing a hierarchy of agents for an application environment provided by an application, wherein the agents of the hierarchy interact in the application environment) and respective agent behavior models are assigned to individual agents based at least on respective levels of the individual agents in the hierarchy (Paragraphs 17, 40-41, and 52, “In one implementation, a game begins with multiple agents having varying complexity levels of intelligence. Over time, one or more of the AI agents becomes a mastermind based on RL-training using the actions taken during the game by the player and the other AI agents… In one implementation, the RL training loop is executed in a cloud environment. During the RL training loop, parameters such as delays, angles, and other settings are adjusted while the cloud is refining the neural network so as to improve the AI's chances on future attempts. When the training of the neural network is complete, the newly trained neural network is downloaded and swapped in at run-time. In various implementations, a video game application implements multi-agent control with a single RL-trained network, with each agent an independent AI engine. The agents are trained through live game play… Each AI agent has unique goals, sensor, and actions available to the AI agent.” Multiple agents starting the game with varying complexity levels of intelligence correlates to individual agents in the hierarchy. Each AI agent having unique actions available and trained through live game play, which would involve each AI agent utilizing its varying levels of intelligence, where the newly trained neural network is downloaded and swapped in at run-time, correlates to assigning respective agent behavior models to individual agents based at least on respective levels of the individual agents in the hierarchy) ; configuring the respective agent behavior models based at least on one or more configuration parameters (Paragraph 29, “The NPCs can implement a variety of schemes of different complexities depending on the particular video game application. For example, in one implementation, each NPC is assigned a personality, and the actions of the NPC are generated to match the assigned personality. Also, in another implementation, each NPC is assigned a mood, and each neural network 200 generates actions which correspond to the mood of the respective NPC.” Each neural network generating actions which correspond to the assigned mood of the respective NPC correlates to configuring the respective agent behavior models based at least on one or more configuration parameters) ; coordinating communication among the respective agent behavior models during execution of the application (Paragraphs 42, 50-51 and 54, “In some games, there are multi-agent systems with AI engines that communicate with each other… Each AI agent predicts which of the above categories a piece of information falls into when receiving the information from another AI agent… The behavior of an AI agent follows from the categorizing of the information received from another AI agent… In one implementation, the AI engines are programmed for cooperative group behavior in multi-agent games. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and communication. In one implementation, there are multiple AI agents that are enemies that collaborate to eliminate the player. The AI agents adapt to the player and work together by pooling their resources and taking advantage of opportunities created by each other. A training environment for the AI agents can include training in seclusion or training to collaborate. There can be inter-network stimulus to create a communication path between AI agents.” Each AI agent communicating with each other and categorizing the type of information received from another AI agent while collectively adapting to the player to take advantage of opportunities created by each other would occur during the gameplay and therefore correlates to coordinating communication among the respective agent behavior models during execution of the application) ; and controlling the application based at least on the respective agent behavior models (Paragraph 54, “In one implementation, the AI engines are programmed for cooperative group behavior in multi-agent games. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and communication. In one implementation, there are multiple AI agents that are enemies that collaborate to eliminate the player. The AI agents adapt to the player and work together by pooling their resources and taking advantage of opportunities created by each other. A training environment for the AI agents can include training in seclusion or training to collaborate. There can be inter-network stimulus to create a communication path between AI agents.” The AI agents adapting to the player, working together, and taking advantage of opportunities in a multi-agent game correlates to controlling the application based at least on the respective agent behavior models) . Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Perry with the respective agent behavior models are assigned to the agents based at least on resource utilization characteristics of the agent behavior models as taught by Jain because cost functions can be used to evaluate parameters corresponding to SLA information. Additionally, optimized graphs can be calculated for computing resources to allow dynamic decision making during real time runtime phases of workloads. The currently assigned model and computing resources assigned to the agent can be evaluated to determine if the SLA parameters are satisfied, in which no further model adjustments are needed (Jain: paragraphs 227 and 229) . 07-21-aia AIA Claim (s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Perry in view of Jain and Hwang et al. (U.S. Patent No. US 20250165296 A1), hereinafter “Hwang.” With regards to claim 9, Perry in view of Jain teaches the method of Claim 3 above. Perry further teaches: wherein the coordinating communication includes: receiving an instruction message output by a particular generative language model assigned to a particular agent (Paragraphs 17 and 58, “In one implementation, the mastermind AI agent hires other agents to assist in the task the mastermind AI agent is carrying out. This allows a more complex mastermind AI agent to control several simpler AI agents in order to compete with the player… Also, in one implementation, one AI engine is programmed to manipulate other AI engines. The other agents are affected in varying degrees based on their individual characteristics. Generally speaking, these implementations use AI agents that think independently and are able to receive orders.” The mastermind AI agent hiring other agents to assist in its task by sending orders correlates to receiving an instruction message output by a particular generative language model assigned to a particular agent) ; and distributing the first instruction to a first agent behavior model of the first subordinate agent and the second instruction to a second agent behavior model of the second subordinate agent (Paragraphs 17 and 58, “In one implementation, the mastermind AI agent hires other agents to assist in the task the mastermind AI agent is carrying out. This allows a more complex mastermind AI agent to control several simpler AI agents in order to compete with the player… Also, in one implementation, one AI engine is programmed to manipulate other AI engines. The other agents are affected in varying degrees based on their individual characteristics. Generally speaking, these implementations use AI agents that think independently and are able to receive orders.” The mastermind AI agent hiring other simpler AI agents to assist in its task by sending orders would include multiple simpler AI agents and therefore correlates to distributing the first instruction to a first agent behavior model of the first subordinate agent and the second instruction to a second agent behavior model of the second subordinate agent) . Perry does not explicitly teach: parsing the instruction message to identify a first instruction to a first subordinate agent of the particular agent and a second instruction to a second subordinate agent of the particular agent However, Hwang teaches: parsing the instruction message to identify a first instruction to a first agent and a second instruction to a second agent (Paragraphs 148 and 225, “The instruction analysis engine 1120 is a component for identifying intent and a required service by analyzing converted text, and may include a natural language processor that analyzes a linguistic structure of text, an intent classifier that classifies a user's intent and/or an entity extractor that extracts important information (entity) from an instruction, as sub modules... In step 1820, the computer device 200 may determine a task by analyzing an instruction received from a user and select an AI agent for processing the determined task. For example, the computer device 200 may select the AI agent for processing the determined task by considering a least one of a function or service executable by the AI agent, the past use pattern of the user for the AI agent, preference of the user… According to an embodiment, the computer device 200 may determine a plurality of tasks for processing the instruction and select a plurality of AI agents for processing the plurality of tasks. Even in this case, each of the AI agents may be selected according to the aforementioned criteria.” The computer device analyzing an instruction received from a user and determining a plurality of tasks for processing the instruction by a plurality of AI agents correlates to parsing the instruction message to identify a first instruction to a first agent and a second instruction to a second agent). Hwang does not explicitly teach that the first and second agents are a first subordinate agent of the particular agent and a second subordinate agent of the particular agent respectively. However, a first subordinate agent of the particular agent and a second subordinate agent of the particular agent are a popular hierarchical configuration of agents as evidenced by Perry above (Paragraphs 56-57, “In one implementation, an AI agent is programmed as a mastermind within the environment of a multi-agent game. This concept is an extension of a multi-agent ecosystem game but with an emphasis on independent group cooperation and a hierarchy that AI agents are programmed to obey. The environment is programmed with different complexity levels of enemy AI intelligence… In some implementations a more complex AI engine mastermind is employed. For example, in one implementation, a more complex AI engine controls several simpler AI engines to support the player or compete against the player. In this implementation, a command structure is utilized as well as different levels of AI engine complexity. The different levels of complexity give rise to understanding the different performance characteristics of the different complexities.” The AI agents belonging to and programmed to obey a hierarchy where a mastermind can control several simpler AI engines in a multi-agent game environment correlates to a first subordinate agent of the particular agent and a second subordinate agent of the particular agent) Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Perry with parsing the instruction message to identify a first instruction to a first agent and a second instruction to a second agent as taught by Hwang because analyzing an instruction and selecting an AI agent for processing the determined task can involve considering functions or services executable by the AI agent, the past use pattern of the user for the AI agent, preference of the user, whether to operate in conjunction with a payment system into which a task that requires payment has been incorporated, information on the location of the user, and advertising association possibility. A plurality of tasks may be determined for processing the instruction which are processed by a plurality of AI agents each selected according to the criteria (Hwang: paragraph 225) . 07-21-aia AIA Claim (s) 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Perry in view of Jain and Nokbak Nyembe et al. (U.S. Patent No. US 20190318238 A1), hereinafter “Nokbak Nyembe.” With regards to claim 10, Perry in view of Jain teaches the method of Claim 3 above. Perry further teaches: wherein the coordinating communication includes: prompting a particular generative language model of a particular agent with identifiers (Paragraphs 17 and 58, “In one implementation, the mastermind AI agent hires other agents to assist in the task the mastermind AI agent is carrying out. This allows a more complex mastermind AI agent to control several simpler AI agents in order to compete with the player… Also, in one implementation, one AI engine is programmed to manipulate other AI engines. The other agents are affected in varying degrees based on their individual characteristics. Generally speaking, these implementations use AI agents that think independently and are able to receive orders.” The mastermind AI agent hiring other agents to assist in its task by sending orders correlates to prompting a particular generative language model of a particular agent with identifiers) ; receiving a message output by the particular generative language model (Paragraphs 17 and 58, “In one implementation, the mastermind AI agent hires other agents to assist in the task the mastermind AI agent is carrying out. This allows a more complex mastermind AI agent to control several simpler AI agents in order to compete with the player… Also, in one implementation, one AI engine is programmed to manipulate other AI engines. The other agents are affected in varying degrees based on their individual characteristics. Generally speaking, these implementations use AI agents that think independently and are able to receive orders.” The other agents receiving orders to assist the mastermind AI agent in its task correlates to receiving a message output by the particular generative language model) ; Perry does not explicitly teach that the identifiers are of one or more application programming interfaces of the application. However, identifiers of one or more application programming interfaces of the application are a popular type of input for a message directed to an AI agent as evidenced by Nokbak Nyembe below (Paragraphs 39 and 44, “In some cases, the input 212 is an input received from a skill agent… The input 212 may be represented as numerical data, textual data, spoken data (e.g., representing a spoken utterance), video data, data defined by an application programming interface (API), or any combination thereof… The routing engine 228 transmits an instruction 218 to the identified skill agents to perform actions to fulfill the intent 214… The instruction 218 may be represented in any appropriate format (e.g., in accordance with a given API, such as a RESTful API).” The skill agent sending input represented as data defined by an API which is transmitted to identified skill agents to fulfill the instruction correlates to identifiers of one or more application programming interfaces of the application). Perry does not explicitly teach: parsing the message to identify a particular application programming interface requested by the particular generative language model; and invoking the particular application programming interface on the application. However, Nokbak Nyembe teaches: parsing the message to identify a particular application programming interface requested by the particular agent (Paragraphs 38-39, 41 and 44-45, “In some cases, skill agents may be artificial intelligence agents (e.g., agents trained to perform actions using machine learning techniques) … In some cases, the input 212 is an input received from a skill agent… The input 212 may be represented as numerical data, textual data, spoken data (e.g., representing a spoken utterance), video data, data defined by an application programming interface (API), or any combination thereof… The orchestrator agent 202 provides the input 212 to an intent mapping engine 224 which is configured to process the input 212 to determine an intent 214 expressed by the input 212… The orchestrator agent 202 may transmit the instruction 218 to the identified skill agents by any appropriate means (e.g., over the Internet by way of a wired or wireless connection). The instruction 218 may be represented in any appropriate format (e.g., in accordance with a given API, such as a RESTful API). In some implementations, in response to receiving the instructions 218 to perform actions to fulfill the intent 214, each of the identified skill agents determine actions 216 to be performed in the telecommunications system 210 to fulfill the intent 214.” The input being received from an AI skill agent and represented as data defined by an API correlates to a particular application programming interface requested by the particular generative language model. The identified AI skill agent being transmitted an instruction in an API format and determining actions to be performed to fulfill the intent correlates to parsing the message to identify a particular application programming interface requested by the particular agent) ; and invoking the particular application programming interface on the application (Paragraph 44, “The routing engine 228 transmits an instruction 218 to the identified skill agents to perform actions to fulfill the intent 214… The instruction 218 may be represented in any appropriate format (e.g., in accordance with a given API, such as a RESTful API).” The identified skill agents performing actions to fulfill the intent, where the instruction can be represented in accordance with a given API, correlates to invoking the particular application programming interface on the application) . Nokbak Nyembe does not explicitly teach that the agent is a generative language model. However, generative language models are a popular type of agent behavior model associated with an agent as evidenced by Perry above (Paragraph 22, “Neural networks have demonstrated excellent performance at tasks such as hand-written digit classification and face detection. Other applications for neural networks include speech recognition, language modeling, sentiment analysis, text prediction, and others. In one implementation, processor 105N is a data parallel processor programmed to execute one or more neural network application to implement movement schemes for one or more non-player characters (NPCs) as part of a video-game application.” The neural network applications being applied to one or more NPCs which include language modeling correlates to generative language models). Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Perry with identifiers of one or more application programming interfaces of the application and parsing the message to identify a particular application programming interface requested by the particular agent; and invoking the particular application programming interface on the application as taught by Nokbak Nyembe because orchestrator agents can receive inputs from other skill agents and identify one or more skill agents to perform the actions based on their capabilities. These inputs can be represented as numerical data, textual data, spoken data, video data, data defined by an application programming interface, or any combination thereof. The input can be processed to determine an intent, where a routing engine can identify one or more skill agents trained to perform actions and transmit instructions in accordance with a given API to fulfill the intent (Nokbak Nyembe: paragraph 38-39 and 41-44). With regards to claim 11, Perry in view of Jain and Nokbak Nyembe teaches the method of Claim 10 above. Nokbak Nyembe further teaches: the message including parameters for the particular application programming interface (Paragraphs 39 and 44, “In some cases, the input 212 is an input received from a skill agent… The input 212 may be represented as numerical data, textual data, spoken data (e.g., representing a spoken utterance), video data, data defined by an application programming interface (API), or any combination thereof… The routing engine 228 transmits an instruction 218 to the identified skill agents to perform actions to fulfill the intent 214… The instruction 218 may be represented in any appropriate format (e.g., in accordance with a given API, such as a RESTful API).” The skill agent sending input represented as data defined by an API which is transmitted to identified skill agents to fulfill the instruction that is represented in accordance with a given API correlates to the message including parameters for the particular application programming interface) . Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Perry with the message including parameters for the particular application programming interface as taught by Nokbak Nyembe because orchestrator agents can receive inputs from other skill agents and identify one or more skill agents to perform the actions based on their capabilities. These inputs can be represented as numerical data, textual data, spoken data, video data, data defined by an application programming interface, or any combination thereof. The input can be processed to determine an intent, where a routing engine can identify one or more skill agents trained to perform actions and transmit instructions in accordance with a given API to fulfill the intent (Nokbak Nyembe: paragraph 38-39 and 41-44) . 07-21-aia AIA Claim (s) 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Perry in view of Jain and Poirier et al. (U.S. Patent No. US 20240202600 A1), hereinafter “Poirier.” With regards to claim 17, Perry in view of Jain teaches the system of Claim 16 above. Perry further teaches: at runtime, detect a change within the application environment (Paragraph 58, “In one implementation, a mastermind does not exist at the beginning of the game. Rather, one of the AI engines learns from its own actions and also learns from the experiences of other AI engines to become a more capable AI engine. As the AI engine becomes more capable through reinforcement learning, the AI engine hires other AI engines gradually as the AI engine gets more powerful. Also, in one implementation, one AI engine is programmed to manipulate other AI engines. The other agents are affected in varying degrees based on their individual characteristics. Generally speaking, these implementations use AI agents that think independently and are able to receive orders.” The AI engine learning from its own actions to become a more capable AI engine and hiring other AI engines through issuing orders correlates to at runtime, detect a change within the application environment) ; and responsive to detecting the change to within the application environment, promote a particular agent with a particular reinforcement learning or hard-coded model (Paragraphs 57-58, “In some implementations a more complex AI engine mastermind is employed. For example, in one implementation, a more complex AI engine controls several simpler AI engines to support the player or compete against the player. In this implementation, a command structure is utilized as well as different levels of AI engine complexity… In one implementation, a mastermind does not exist at the beginning of the game. Rather, one of the AI engines learns from its own actions and also learns from the experiences of other AI engines to become a more capable AI engine. As the AI engine becomes more capable through reinforcement learning, the AI engine hires other AI engines gradually as the AI engine gets more powerful. Also, in one implementation, one AI engine is programmed to manipulate other AI engines. The other agents are affected in varying degrees based on their individual characteristics. Generally speaking, these implementations use AI agents that think independently and are able to receive orders.” The AI engine becoming more capable through reinforcement learning correlates to a particular agent with a particular reinforcement learning model. The AI engine hiring other AI simpler engines and eventually becoming a more complex AI engine mastermind correlates to responsive to detecting the change to within the application environment, promote a particular agent with a particular reinforcement learning or hard-coded model) Perry does not explicitly teach that the particular agent [is promoted] from a particular reinforcement learning or hard-coded model to a particular generative model. However, promoting particular agent[s] from a particular reinforcement learning or hard-coded model to a particular generative model is a popular method of promoting agent models in response to application environment changes as evidenced by Poirier (Paragraphs 44, 62, 73, 96, “Agents can include one or more multimodal models (e.g., large language models) to accomplish the prescribed tasks using a variety of different tools… For example, the model inference service system 304 can perform model load-balancing operations on models (e.g., generative artificial intelligence models of the enterprise artificial intelligence system 302), as well other functionality described herein (e.g., swapping, compression, and the like) … In some implementations, the model generation module 404 can use a variety of machine learning techniques or algorithms to generate models. Artificial intelligence and/or machine learning can include… reinforcement learning algorithms and/or models, and/or the like… The model swapping module 430 can function to change models (e.g., at or during run-time in addition to before or after run-time). For example, a model may be executing a particular system or unit, and the model swapping module 430 may swap that model for a model that has been trained on a specific dataset (e.g., a domain-specific data set) because that model has been receiving requests related to that specific dataset.” The agents including one or more multimodal models generated by a model generation module, such as a reinforced learning model, correlates to a particular agent with a particular reinforcement learning or hard-coded model. The model swapping module changing the model at run-time in response to a particular model receiving requests related to a specific dataset, where the model can be a generative artificial intelligence model, correlates to responsive to detecting the change to within the application environment, promote a particular agent from a particular reinforcement learning or hard-coded model to a particular generative model) . Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Perry with responsive to detecting the change to within the application environment, promote a particular agent from a particular reinforcement learning or hard-coded model to a particular generative model as taught by Poirier because load-balancing module can automatically trigger model load-balancing operations, such as automatically scaling model executions and associated software and hardware and changing models. Load balancing can be done to increase or decrease the number of executing models to meet a current or predicted demand to ensure requests are processed with low latency (Poirier: paragraph 94). With regards to claim 18, Perry in view of Jain and Poirier teaches the system of Claim 17 above. Perry further teaches: the change relating to movement of the particular agent toward a particular object in the application environment (Paragraphs 39 and 54 and 57-58, “In one implementation, a room and facility is created where there is a fully controlled AI environment with traps. The goal of the AI engine is to prevent the player from reaching the player's goal via usage of traps. In one implementation, multiple independent AI constructs/engines also live in the environment and function cooperatively to stop the player… In one implementation, there are multiple AI agents that are enemies that collaborate to eliminate the player. The AI agents adapt to the player and work together by pooling their resources and taking advantage of opportunities created by each other… In some implementations a more complex AI engine mastermind is employed. For example, in one implementation, a more complex AI engine controls several simpler AI engines to support the player or compete against the player. In this implementation, a command structure is utilized as well as different levels of AI engine complexity... As the AI engine becomes more capable through reinforcement learning, the AI engine hires other AI engines gradually as the AI engine gets more powerful.” The AI engines cooperating to stop the player from reaching a goal through the usage of traps can include an AI engine mastermind hiring several simpler AI engines with the intent to use a trap. The AI engines being hired to take advantage of the opportunity created from the trap would include changes to the movement of at least one agent towards the trap and therefore correlates to the change relating to movement of the particular agent toward a particular object in the application environment) . Prior Art Made of Record 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Gillian et al. (U.S. Patent No. US 20230061808 A1); teaching a method of determining for each interactive object, a respective portion of the machine learning model for execution by the interactive object during the activity. Interactive object configuration data can be generated indicative of the respective portion of the machine-learned model for execution by the interactive object during the portion of the activity. Execution of the machine learning model across the set of interactive objects in response to resource state changes can be dynamically redistributed . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SELINA HU whose telephone number is (571)272-5428. The examiner can normally be reached Monday-Friday 8:30-5:30. 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. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chat Do can be reached at (571) 272-3721. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. The publicPAIR and privatePAIR systems are no longer available. 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/apply/patent-center for more information about Patent Center and 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. SELINA HU Examiner Art Unit 2193 /Chat C Do/ Supervisory Patent Examiner, Art Unit 2193 Application/Control Number: 18/397,770 Page 2 Art Unit: 2193 Application/Control Number: 18/397,770 Page 3 Art Unit: 2193 Application/Control Number: 18/397,770 Page 4 Art Unit: 2193 Application/Control Number: 18/397,770 Page 5 Art Unit: 2193 Application/Control Number: 18/397,770 Page 6 Art Unit: 2193 Application/Control Number: 18/397,770 Page 7 Art Unit: 2193 Application/Control Number: 18/397,770 Page 8 Art Unit: 2193 Application/Control Number: 18/397,770 Page 9 Art Unit: 2193 Application/Control Number: 18/397,770 Page 10 Art Unit: 2193 Application/Control Number: 18/397,770 Page 11 Art Unit: 2193 Application/Control Number: 18/397,770 Page 12 Art Unit: 2193 Application/Control Number: 18/397,770 Page 13 Art Unit: 2193 Application/Control Number: 18/397,770 Page 14 Art Unit: 2193 Application/Control Number: 18/397,770 Page 15 Art Unit: 2193 Application/Control Number: 18/397,770 Page 16 Art Unit: 2193 Application/Control Number: 18/397,770 Page 17 Art Unit: 2193 Application/Control Number: 18/397,770 Page 18 Art Unit: 2193 Application/Control Number: 18/397,770 Page 19 Art Unit: 2193 Application/Control Number: 18/397,770 Page 20 Art Unit: 2193 Application/Control Number: 18/397,770 Page 21 Art Unit: 2193 Application/Control Number: 18/397,770 Page 22 Art Unit: 2193 Application/Control Number: 18/397,770 Page 23 Art Unit: 2193 Application/Control Number: 18/397,770 Page 24 Art Unit: 2193 Application/Control Number: 18/397,770 Page 25 Art Unit: 2193 Application/Control Number: 18/397,770 Page 26 Art Unit: 2193 Application/Control Number: 18/397,770 Page 27 Art Unit: 2193 Application/Control Number: 18/397,770 Page 28 Art Unit: 2193 Application/Control Number: 18/397,770 Page 29 Art Unit: 2193 Application/Control Number: 18/397,770 Page 30 Art Unit: 2193 Application/Control Number: 18/397,770 Page 31 Art Unit: 2193 Application/Control Number: 18/397,770 Page 32 Art Unit: 2193 Application/Control Number: 18/397,770 Page 33 Art Unit: 2193 Application/Control Number: 18/397,770 Page 34 Art Unit: 2193 Application/Control Number: 18/397,770 Page 35 Art Unit: 2193 Application/Control Number: 18/397,770 Page 36 Art Unit: 2193 Application/Control Number: 18/397,770 Page 37 Art Unit: 2193 Application/Control Number: 18/397,770 Page 38 Art Unit: 2193 Application/Control Number: 18/397,770 Page 39 Art Unit: 2193 Application/Control Number: 18/397,770 Page 40 Art Unit: 2193 Application/Control Number: 18/397,770 Page 41 Art Unit: 2193 Application/Control Number: 18/397,770 Page 42 Art Unit: 2193 Application/Control Number: 18/397,770 Page 43 Art Unit: 2193 Application/Control Number: 18/397,770 Page 44 Art Unit: 2193 Application/Control Number: 18/397,770 Page 45 Art Unit: 2193 Application/Control Number: 18/397,770 Page 46 Art Unit: 2193 Application/Control Number: 18/397,770 Page 47 Art Unit: 2193 Application/Control Number: 18/397,770 Page 48 Art Unit: 2193 Application/Control Number: 18/397,770 Page 49 Art Unit: 2193 Application/Control Number: 18/397,770 Page 50 Art Unit: 2193 Application/Control Number: 18/397,770 Page 51 Art Unit: 2193 Application/Control Number: 18/397,770 Page 52 Art Unit: 2193 Application/Control Number: 18/397,770 Page 53 Art Unit: 2193 Application/Control Number: 18/397,770 Page 54 Art Unit: 2193
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