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. 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:
outputting, … one or more multi-agent controllers, wherein each of the one or more multi-agent controllers comprises 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: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of outputting or generating one or more multi-agent controllers or a policy decision that include a recommended behavior, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. For example, one can practically and mentally learn a policy or a set of state action rules to reach a certain goal.
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 problem solving in the simulator”, and “by the one or more generative neural network models”.
The additional element of “by the one or more generative neural network models” amounts to a generic computer component or model used as a tool to perform an existing process. The additional element of “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” 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 generative model is broadly generated based on input data and a threshold of success. 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)).
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 element of “by the one or more generative neural network models” amounts to a generic computer component or model used as a tool to perform an existing process. The additional element of “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” 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 generative model is broadly generated based on input data and a threshold of success. 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”).
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 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 of the behavior tree 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”).
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 problem solving 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)
outputting, 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 comprises 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 (Page 6, §5.1; “We train a multi-step conditional model based on the TSM architecture [12] on data collected from a heuristic policy. We use our model to control the agent using an MPC-style approach [18]. Before executing each segment, we perform trajectory optimization (3) for the next 5 segments, and then execute the actions of the first segment of the optimal trajectory. For the other agent, we select the worst case (competitive) or best case (cooperative) segment predicted by our model (see Fig. 2c). We compare our model to a single-step MLP dynamics model, using the same control method. Full details of our model architecture and training parameters, including the policy used for data collection, can be found in the appendix”, which discloses outputting or generating a multi-agent controller or policy or trajectory optimization that includes recommended behavior or a trajectory for the agents to solve the problem or traverse the segments; and Figures 2 and 3; and §6; “We extended the idea of multi-step generative models to handle competitive and cooperative 2-agent problems, showing its promise for complex, high-fidelity tasks relevant to real-world robotics environments”; and §4; “optimizing a single segment”; and Figure 1)
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).
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 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 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, 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 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, 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 of the behavior tree configured to learn scenario-specific controllers (§III; and Figures 1 and 2).
The motivation to combine Krupnik, Gan, 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 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, 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 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 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, 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 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).
Conclusion
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/BRENT JOHNSTON HOOVER/Primary Examiner, Art Unit 2127