DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 6/7/2023 and the Remarks and Amendments filed on 5/4/2026. Acknowledgment is made with respect to a claim of priority to Provisional Application 63/349,856 filed on 6/7/2022.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
a reward calculation that optimizes the recommended behaviors to maximize the solving of the predefined problem by the team: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of calculating a reward that optimizes recommended behaviors, which is performed through mathematical computation.
Step 2A Prong 2: The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “receiving multimodal input data within a simulator configured to simulate solving a predefined problem by a team comprising a plurality of agents”, “generating one or more generative neural network models based on the multimodal input data and based on a predetermined threshold of success of the solving the predefined problem”, and “training, by the one or more generative neural network models, one or more multi-agent controllers, wherein each of the one or more multi-agent controllers comprise recommended behaviors for each of the plurality of agents to solve the predefined problem in a manner that is consistent with the multimodal input data”.
The additional elements of “generating one or more generative neural network models based on the multimodal input data and based on a predetermined threshold of success of the solving the predefined problem” and “training, by the one or more generative neural network models, one or more multi-agent controllers” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic generative model is broadly generated based on input data and a threshold of success or how the generic multi-agent controllers are broadly trained by a generative neural network model. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional element “receiving multimodal input data within a simulator configured to simulate solving a predefined problem by a team comprising a plurality of agents” is an insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)).
The additional element “wherein each of the one or more multi-agent controllers comprise recommended behaviors for each of the plurality of agents to solve the predefined problem in a manner that is consistent with the multimodal input data” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea.
Step 2B: Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea.
The additional elements of “generating one or more generative neural network models based on the multimodal input data and based on a predetermined threshold of success of the solving the predefined problem” and “training, by the one or more generative neural network models, one or more multi-agent controllers” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic generative model is broadly generated based on input data and a threshold of success or how the generic multi-agent controllers are broadly trained by a generative neural network model. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional element “receiving multimodal input data within a simulator configured to simulate solving a predefined problem by a team comprising a plurality of agents” is an insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”).
The additional element “wherein each of the one or more multi-agent controllers comprise recommended behaviors for each of the plurality of agents to solve the predefined problem in a manner that is consistent with the multimodal input data” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the one or more generative neural network models comprise one or more Deep Neural Networks (DNNs) having a generator configured to generate the one or more multi-agent controllers” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the generator comprises at least one of: a stateless generator, a reactive generator and an inductive generator” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
generate one or more multi-agent controllers that is reactive to dynamic changes in an environment in which the predefined problem is solved: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating one or more multi-agent controllers or a policy decision that is reactive to a change in environment, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception. As such, the claim is ineligible.
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the one or more multi-agent controllers comprise one or more behavior trees” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein each of the one or more behavior trees represents, in a natural language, at least: one or more goals of the team, one or more behaviors of one or more of the plurality of agents and one or more relationships between the one or more goals of the team and the one or more behaviors of the one or more of the plurality of agents” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the generator comprises a Behavior Tree Generative Adversarial Network (BT-GAN)” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
converting, … natural language sentences in the multimodal input data into one or more Intermediate Representations (IRs) of one or more constraints and/or one or more procedures: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of converting sentences into intermediate representations, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional element of “by a semantic parser” amounts to a generic computer component used as a tool to perform an existing process. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 9
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the one or more behavior trees comprise one or more nodes configured to learn scenario-specific controllers” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claims 10-18
Claims 10-18 recite a system (step 1: a machine) using processing circuitry, memory, an input device, and an output device to perform the steps of claims 1-9, respectively, which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claims 1-9, respectively.
Claims 19-20
Claims 19-20 recite a non-transitory computer-readable medium (step 1: a manufacture) using processing circuitry to perform the steps of claims 1-2, respectively, which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claims 1-2, respectively.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 10-13, 19, and 20 are rejected under 35 U.S.C. § 103 as being obvious over Krupnik et al. (Krupnik et al., “Multi-Agent Reinforcement Learning with Multi-Step Generative Models”, Nov. 1, 2019, arXiv:1901.10251v3, pp. 1-15, hereinafter “Krupnik”) in view of Gan et al. (Gan et al., “ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation”, Dec. 28, 2021, arXiv:2007.04954v2, pp. 1-23, hereinafter “Gan”) and Song et al. (Song et al., “Multi-Agent Generative Adversarial Imitation Learning”, Jul. 26, 2018, arXiv:1807.09936v1, pp. 1-23, hereinafter “Song”).
Regarding claim 1, Krupnik discloses [a] method comprising: (Abstract; “we investigate whether this approach can be extended to 2-agent competitive and cooperative settings. The fundamental challenge is how to learn models that capture interactions between agents, yet are disentangled to allow for optimization of each agent behavior separately. We propose such models based on a disentangled variational auto-encoder, and demonstrate our approach on a simulated 2-robot manipulation task, where one robot can either help or distract the other”)
receiving [[multimodal]] input data within a simulator configured to simulate solving a predefined problem by a team comprising a plurality of agents; (Page 2, ¶1-2; “Our main observation is that when optimizing over more than one agent, the generative model needs to admit two properties: it has to capture the interaction between the agents in the trajectory; and the latent space needs to be disentangled, such that the behavior of each agent can be optimized separately (as each agent may have a different objective). … We demonstrate our approach in simulation on a continuous predator-prey domain, and on a 2 robot manipulation task”, which discloses receiving input data from a 2 robot/agent simulation within a simulator configured to simulate solving a problem or task by a team of agents and Page 5, §5; “To test our models, we use simulators and data collected from two domains: Multi-Agent Particle Environments [5]– a standard, versatile and configurable benchmark for multi-agent RL, and a more complex, 2-robot simulation environment”; and §5.1; the section discloses the predefined problem such as chasing an object around an obstacle)
generating one or more generative neural network models based on the [[multimodal]] input data and based on a predetermined threshold of success of the solving the predefined problem in the simulator; and (Page 2, §2; “To the best of our knowledge, our attempt to use deep generative models and the prediction of agent behavior to optimize over trajectories for a given task in a multi-agent environment is, as of yet, a novel contribution”; and Page 3, §4; “For a two player game, we would like to learn a generative model for the distribution P(X+,U+,Y +,W+) with two latent components, Zx and Zy, which correspond to the sequences of actions in a segment for agent x, U+, and agent y, W+, respectively”; and Page 4, §4.1; “In such cases, learning two independent models for the agents is compatible with the competitive optimization”; and see generally §4.2 for a further discussion of generating the generative models based on input data and a threshold of success of solving a problem or optimizing the policies)
Krupnik fails to explicitly disclose but Gan discloses multimodal input data (Abstract; “We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments”; and Figure 1; and Page 2, ¶3; “ThreeDWorld (TDW) is a general-purpose virtual world simulation platform that supports multi modal physical interactions between objects and agents. TDW was designed to accommodate a range of key domains in AI, including perception, interaction, and navigation, with the goal of enabling training in each of these domains within a single simulator.”).
Krupnik and Gan are analogous art because both are concerned with agent simulation and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in agent simulation and machine learning to combine the multimodal input data of Gan with the simulation method of Krupnik to yield to the predictable result of receiving multimodal input data within a simulator configured to simulate solving a predefined problem by a team comprising a plurality of agents and generating one or more generative neural network models based on the multimodal input data and based on a predetermined threshold of success of problem solving in the simulator. The motivation for doing so would be to provide for interactive multi-modal physical simulation (Gan; Abstract).
Krupnik fails to explicitly disclose but Song discloses training, by the one or more generative neural network models, one or more multi-agent controllers, (Page 8, §4.1 and Equation 11; “We update πθ through reinforcement learning, where we also use a baseline Vφ to reduce variance”, wherein πθ is interpreted as the one or more multi-agent controller that is trained using the minmax equation found in equation 11, and it is the generator of the generative adversarial imitation learning system; and Page 2, §1; “The generator controls the policies of all the agents in a distributed way, and the discriminator contains a classifier for each agent that is trained to distinguish that agent’s behavior from that of the corresponding expert. Upon training, the behaviors produced by the policies are indistinguishable from the training data through the discriminator”)
wherein each of the one or more multi-agent controllers comprise recommended behaviors for each of the plurality of agents to solve the predefined problem in a manner that is consistent with the multimodal input data, and (Page 8, §4.1; “For each agent i, we have a discriminator (denoted as Dωi ) mapping state action-pairs to scores optimized to discriminate expert demonstrations from behaviors produced by πi. Implicitly, Dωi plays the role of a reward function for the generator, which in turn attempts to train the agent to maximize its reward thus fooling the discriminator”, which discloses each trained policy πi constitutes the recommended behavior for agent I, generated to be consistent with the demonstration data or multimodal input data)
wherein the training comprises a reward calculation that optimizes the recommended behaviors to maximize the solving of the predefined problem by the team (Page 8, §4.1; “For each agent i, we have a discriminator (denoted as Dωi ) mapping state action-pairs to scores optimized to discriminate expert demonstrations from behaviors produced by πi. Implicitly, Dωi plays the role of a reward function for the generator, which in turn attempts to train the agent to maximize its reward thus fooling the discriminator”, wherein the reward function is the claimed reward calculation that optimizes behavior to maximize the solving of the problem; and §4.1; “The easiest case is to assume that the agents are fully cooperative, i.e. they share the same reward function. … One could argue this corresponds to the GAIL case, where the RL procedure operates on multiple agents (a joint policy)”; and §5.1; “The environments include: Cooperative Communication– two agents must cooperate to reach one of three colored landmarks. One agent (“speaker”) knows the goal but cannot move, so it must convey the message to the other agent (“listener”) that moves but does not observe the goal. Cooperative Navigation– three agents must cooperate through physical actions to reach three landmarks; ideally, each agent should cover a single landmark”; and §5.1.1; “We evaluate performance in cooperative tasks via the average expected reward obtained by all the agents in an episode”, where the average team reward teaches a reward calculation that optimizes recommended behaviors to maximize the solving of the problem by the team).
Krupnik, Gan, and Song are analogous art because all are concerned with agent simulation and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in agent simulation and machine learning to combine the training of the multi-agent controllers of Song with the simulation method of Krupnik and the multimodal input data of Gan to yield to the predictable result of training, by the one or more generative neural network models, one or more multi-agent controllers, wherein each of the one or more multi-agent controllers comprise recommended behaviors for each of the plurality of agents to solve the predefined problem in a manner that is consistent with the multimodal input data, and wherein the training comprises a reward calculation that optimizes the recommended behaviors to maximize the solving of the predefined problem by the team. The motivation for doing so would be to imitate complex behaviors in high-dimensional environments with multiple cooperative or competing agents (Song; Abstract).
Regarding claim 10, it is a system claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1.
Regarding claim 19, it is a non-transitory computer-readable medium claim corresponding to the steps of claim 1, and is rejected for the same reasons as claim 1.
Regarding claims 2, 11, and 20, the rejection of claims 1, 10, and 19 are incorporated and Krupnik further discloses wherein the one or more generative neural network models comprise one or more Deep Neural Networks (DNNs) having a generator configured to generate the one or more multi-agent controllers (Page 3, §3.1; “The distribution P(X+|X−,Z) is typically represented as a neural network that encodes the mean of a Gaussian, and the expectation in (2) is approximated with the deterministic prediction of the mean trajectory”).
Regarding claims 3 and 12, the rejection of claims 1, 2, 10, and 11 are incorporated and Krupnik further discloses wherein the generator comprises at least one of: a stateless generator, a reactive generator and an inductive generator (Page 4, §4.1; “To see this, note that since we are optimizing over Zx, and P(X+,U+) does not depend on Zy, we are effectively letting agent x ‘play first’ and choose its trajectory, while agent y can only react to the trajectory of x that has already been chosen”, which discloses a reactive generator).
Regarding claims 4 and 13, the rejection of claims 1-3 and 10-12 are incorporated and Krupnik further discloses wherein the stateless generator is configured to generate one or more multi-agent controllers that is reactive to dynamic changes in an environment in which the predfined problem is solved (Page 13, ¶4; “Actions taken by the agents are torques applied to their joints, and are limited to [−1,1]. This gives each agent a 2-dimensional, continuous action space”, the action space is interpreted as a change in an environment in which the agents act; and Page 2, ¶1; “We demonstrate our approach in simulation on a continuous predator-prey domain, and on a 2 robot manipulation task”).
Claims 5, 6, 9, 14, 15, and 18 are rejected under 35 U.S.C. § 103 as being obvious over Krupnik in view of Gan and Song and further in view of Wang et al. (Wang et al., “Object behavior simulation based on behavior tree and multi-agent model”, Feb. 8, 2018, 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 1-4, hereinafter “Wang”).
Regarding claims 5 and 14, the rejection of claims 1, 2, 10, and 11 are incorporated and Krupnik fails to explicitly disclose but Wang discloses wherein the one or more multi-agent controllers comprise one or more behavior trees (Abstract; “In this paper, behavior tree and multi-agent model in game artificial intelligence are applied to modeling the behavior characteristics of scene objects. Behavioral decision is made by the combination of behavior tree and scene object features”; and §III).
Krupnik, Gan, Song and Wang are analogous art because all are concerned with agent simulation and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in agent simulation and machine learning to combine the behavior tree of Wang with the simulation method of Krupnik and Gan and Song to yield to the predictable result of wherein the one or more multi-agent controllers comprise one or more behavior trees. The motivation for doing so would be to optimize deployment schemes for public safety events (Wang; Abstract).
Regarding claims 6 and 15, the rejection of claims 1, 2, 5, 10, 11, and 14 are incorporated and Krupnik fails to explicitly disclose but Wang discloses wherein each of the one or more behavior trees represents, in a natural language, at least: one or more goals of the team, one or more behaviors of one or more of the plurality of agents and one or more relationships between the one or more goals of the team and the one or more behaviors of the one or more of the plurality of agents (§III; and Figures 1 and 2).
The motivation to combine Krupnik, Gan, Song, and Wang is the same as discussed above with respect to claim 5.
Regarding claims 9 and 18, the rejection of claims 1, 2, 5, 10, 11, and 14 are incorporated and Krupnik fails to explicitly disclose but Wang discloses wherein the one or more behavior trees comprise one or more nodes configured to learn scenario-specific controllers (§III; and Figures 1 and 2).
The motivation to combine Krupnik, Gan, Song, and Wang is the same as discussed above with respect to claim 5.
Claims 7 and 16 are rejected under 35 U.S.C. § 103 as being obvious over Krupnik in view of Gan and Song and Wang and further in view of Liu et al. (Liu et al., “TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks”, Aug. 22, 2018, arXiv:1808.07582v1, pp. 1-11, hereinafter “Liu”).
Regarding claims 7 and 16, the rejection of claims 1, 2, 5, 10, 11, and 14 are incorporated and Krupnik fails to explicitly disclose but Liu discloses wherein the generator comprises a Behavior Tree Generative Adversarial Network (BT-GAN) (Abstract; we study the problem of syntax-aware sequence generation with GANs, in which a collection of real sequences and a set of pre-defined grammatical rules are given to both discriminator and generator. We propose a novel GAN framework, namely TreeGAN, to incorporate a given Context Free Grammar (CFG) into the sequence generation process. In TreeGAN, the generator employs a recurrent neural network (RNN) to construct a parse tree. Each generated parse tree can then be translated to a valid sequence of the given grammar”).
Krupnik, Gan, Song, Wang, and Liu are analogous art because all are concerned with machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning to combine the BTGAN of Liu with the simulation method of Krupnik and Gan and Wang and Song to yield to the predictable result of wherein the generator comprises a Behavior Tree Generative Adversarial Network (BT-GAN). The motivation for doing so would be to generate sequences for any context free grammar (Liu; Abstract).
Claims 8 and 17 are rejected under 35 U.S.C. § 103 as being obvious over Krupnik in view of Gan and Song and further in view of Liu.
Regarding claims 8 and 17, the rejection of claims 1 and 10 are incorporated and Krupnik fails to explicitly disclose but Liu discloses wherein generating the one or more generative neural network models further comprises converting, by a semantic parser, natural language sentences in the multimodal input data into one or more Intermediate Representations (IRs) of one or more constraints and/or one or more procedures (Abstract; and §III).
Krupnik, Gan, Song, and Liu are analogous art because all are concerned with machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning to combine the semantic parser of Liu with the simulation method of Krupnik and Gan and Song to yield to the predictable result of wherein generating the one or more generative neural network models further comprises converting, by a semantic parser, natural language sentences in the multimodal input data into one or more Intermediate Representations (IRs) of one or more constraints and/or one or more procedures. The motivation for doing so would be to generate sequences for any context free grammar (Liu; Abstract).
Response to Arguments
Applicant’s arguments and amendments, filed on 5/4/2026, with respect to the 35 USC § 101 rejection of the pending claims have been fully considered but are not persuasive.
With respect to Step 2A, Prong one, Applicant argues “that claim 1, as amended, is not directed to a mental process … it is a claim to ‘...training, by the one or more generative neural network models, one or more multi-agent controllers ... and wherein the training comprises a reward calculation that optimizes the recommended behaviors to maximize the solving of the predefined problem by the team...’”.
Examiner agrees with Applicant that amended claim 1 is not directed towards a mental process. However, the claims as presently amended are now directed towards the abstract idea of a reward calculation that optimizes the recommended behaviors to maximize the solving of the predefined problem by the team, which is, under a broadest reasonable interpretation of the claim language, a mathematical concept of calculating a reward that optimizes recommended behaviors, which is performed through mathematical computation. Applicant has failed to provide any evidence or arguments that this limitation of claim 1 is not directed towards an abstract idea in the form of a mathematical concept.
With respect to Step 2A, Prong Two, Applicant argues “even if it were assumed for the sake of argument (and Applicant does not concede this) that claim 1 recited a judicial exception or abstract idea, claim 1 as amended integrates it into a practical application … Claim 1 as amended recites a concrete technological arrangement which includes … This amended claim language, inter alia, overcomes the argument that the original claims did not recite "how" the solution was achieved and merely recited a desired result … claim 1, as amended clearly recites an improvement to technology, not merely the use of a computer as a tool”. Examiner respectfully disagrees.
First, the limitation “training, by the one or more generative neural network models, one or more multi-agent controllers” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic multi-agent controllers are broadly trained by a generative neural network model. Thus, this additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Applicant has failed to provide any argument or evidence to the contrary. Second, Applicant has failed to provide any evidence from the claim language, specification, or elsewhere that demonstrates that this limitation reflects a technical improvement or integrates the abstract idea of the claim into a practical application. Rather, Applicant concludes or asserts that a technical improvement or practical improvement is reflected by the “training” limitation without any evidence to support this assertion.
With respect to Step 2B, Applicant argues “the claims recite significantly more than any alleged abstract idea. … These aspects of claim 1 are clearly not a routine instruction to "apply" an abstract idea on a computer. Rather, they are a specific technical improvement to "...generative neural network [s] … the amended claims recite their particular interaction to improve "...generative neural network [s]...". Examiner respectfully disagrees.
As discussed above, Applicant has failed to provide any evidence from the claim language, specification, or elsewhere that demonstrates that the “training” limitation of claim 1 represents “significantly more” than the abstract idea of the claim. Rather, Applicant concludes or asserts that the “significantly more” aspect is reflected by the “training” limitation without any evidence to support this assertion.
Accordingly, Applicant’s arguments and amendments are not persuasive to overcome the rejection, and the 35 USC § 101 rejection of the pending claims is maintained.
Applicant’s arguments and amendments, filed on 5/4/2026, with respect to the 35 USC § 103 rejection of the pending claims have been fully considered but are moot because the arguments do not apply to the references used to reject the amended independent claims. Krupnik, Gan, and Song are now being used to render the independent claims obvious under 35 USC § 103.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brent Hoover whose telephone number is (303)297-4403. The examiner can normally be reached Monday - Friday 9-5 MST.
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, Abdullah Kawsar can be reached on 571-270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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.
/BRENT JOHNSTON HOOVER/Primary Examiner, Art Unit 2127
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