DETAILED ACTION
Claims 1-20 are presented for examination.
This office action is in response to submission of application on 02-FEBRUARY-2023.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 rejected under 35 U.S.C. 101 because the claimed invention is direction to an abstract idea without significantly more.
MPEP 2106.04(a)(2)(Ill) “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, Judgments, and opinions.
Further, the MPEP recites “The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide run) to perform the claim limitation.
MPEP 2106.04(a)(2)(I) “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
Claim 1 recites:
Step 2A, Prong 1 will now be evaluated for this claim:
A judicial exception is recited in this claim as it recites a mental process:
decentralized optimal control for a large-scale multi-agent system
As this refers to the selection of actions for each agent based on specific criteria, it may be considered evaluation as the human mind is able to consider a particular environment when making choice on which action to take.
A judicial exception is recited in this claim as it recites a mathematical concept:
and if the initialized error of the actor NN is greater than or equal to the initialized error threshold of the actor NN, if the initialized error of the critic NN is greater than or equal to the initialized error threshold of the critic NN, and if the initialized error of the mass NN is greater than or equal to the initialized error threshold of the mass NN
The comparison of two values is an inequality and is therefore a mathematical calculation.
Step 2A, Prong 2 will now be evaluated for this claim:
Furthermore, the additional elements:
the large-scale multi-agent system including multiple agents each including three neural networks (NNs) including an actor NN, a critic NN, and a mass NN
This limitation describes only a generic neural network.
calculating NN weights of the actor NN, the critic NN, and the mass NN, respectively; and updating the actor NN, the critic NN, and the mass NN using corresponding calculated NN weights, respectively; and calculating NN errors of the actor NN, the critic NN, and the mass NN, respectively; and updating the actor NN, the critic NN, and the mass NN using corresponding calculated NN errors, respectively
Calculating and updating neural network weights is a generic computer function as it is a step of training a neural network.
are interpreted as a general purpose computer under MPEP 2106.05(f)
Furthermore, MPEP 2106.05(g) Insignificant Extra-Solution Activity has found mere data gathering and post-solution activity to be insignificant extra-solution activity.
The following steps are mere data gathering:
initializing errors to obtain an initialized error of the actor NN, an initialized error of the critic NN, and an initialized error of the mass NN; initializing error thresholds to obtain an initialized error threshold of the actor NN, an initialized error threshold of the critic NN, and an initialized error threshold of the mass NN
The initialization of errors and error thresholds is a means of inputting data in the neural network.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrate the exception into a practical application. Therefore, no meaningful limits are imposed practicing the abstract idea.
Therefore, the claim is related to an abstract idea.
Step 2B will now be discussed with regards to this claim:
The claim does not provide an inventive concept. There is no additional Insignificant Extra- Solution Activity, as identified in Step 2A Prong Two, that provides an inventive concept.
Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)) does not overcome a rejection.
Generally linking the use of the judicial exception to computer environments, e.g., a claim describing how the abstract idea of creating a contractual relationship that guarantees performance of a transaction be performed using a computer that receives and sends information over a network, as discussed in buySAFE Inc. v. Google, Inc., 765 F.3d 1350, 1354, 112 USPQ2d 1093, 1095-96 (Fed. Cir. 2014). (MPEP § 2106.05(h)) does not overcome a rejection.
The additional elements have been considered both individually and as an ordered combination as to whether they whether they warrant significantly more consideration.
The claim is ineligible.
Regarding claim 2, which depends upon claim 1:
The following would be a mathematical calculation:
if the initialized error of the actor NN is less than the initialized error threshold of the actor NN, obtaining previous calculated NN weights of the actor NN; or if the initialized error of the critic NN is less than the initialized error threshold of the critic NN, obtaining previous calculated NN weights of the critic NN; or if the initialized error of the mass NN is less than the initialized error threshold of the mass NN, obtaining previous calculated NN weights of the mass NN
The comparison of two values is an inequality and is therefore a mathematical calculation.
This claim fully incorporates the limitations of its parent claim and thus the deficiencies of those limitations. No limitation present in the claim is sufficient to overcome the rejection of its parent claim.
The claim is ineligible.
Regarding claim 3, which depends upon claim 2:
The following would be an evaluation:
using the previous calculated NN weights of the actor NN to calculate a control
This limitation refers to the selection of an action, wherein selecting an action based on specific criteria is accomplishable in the human mind.
The following would be post-solution activity:
executing the calculated control
Taking the action determined by the control is separate from the rest of the claim.
This claim fully incorporates the limitations of its parent claim and thus the deficiencies of those limitations. No limitation present in the claim is sufficient to overcome the rejection of its parent claim.
The claim is ineligible.
Regarding claim 4, which depends upon claim 1:
The following would be data gathering:
initializing a state and a density of the agent, wherein the state of the agent includes a position and a velocity;
This limitation describes gathering information regarding the state of the agent.
The following would be mathematical calculation:
calculating an error of the agent using the state of the agent and a predefined trajectory
Calculation of an error could be calculating the difference between expected and actual trajectories.
This claim fully incorporates the limitations of its parent claim and thus the deficiencies of those limitations. No limitation present in the claim is sufficient to overcome the rejection of its parent claim.
The claim is ineligible.
Regarding claim 5, which depends upon claim 4:
The following would be mathematical calculation:
performing a barrier-function based system transformation on the error and the density of the agent to obtain to a transformed error state and a transformed density state, respectively
This limitation describes the application of a specific mathematical calculation, a barrier function, to calculate the transformed error state and density state.
This claim fully incorporates the limitations of its parent claim and thus the deficiencies of those limitations. No limitation present in the claim is sufficient to overcome the rejection of its parent claim.
The claim is ineligible.
Regarding claim 6, which depends upon claim 5:
The following would be a generic computer:
the transformed error state and the transformed density state are configured to calculate corresponding NN weights and errors
The calculation of neural network weights and errors from the state would be an inherent part of the training of a neural network.
This claim fully incorporates the limitations of its parent claim and thus the deficiencies of those limitations. No limitation present in the claim is sufficient to overcome the rejection of its parent claim.
The claim is ineligible.
Regarding claim 7, which depends upon claim 1:
The following would be data gathering:
randomly initializing the NN weights of the actor NN, the critic NN, and the mass NN
Random initialization is a form of inputting the data into the neural network.
This claim fully incorporates the limitations of its parent claim and thus the deficiencies of those limitations. No limitation present in the claim is sufficient to overcome the rejection of its parent claim.
The claim is ineligible.
Regarding claim 8, which depends upon claim 1:
The following would be mathematical calculation:
the critic NN is configured to estimate a cost function; and the mass NN is configured to estimate a probability density function
The estimation of these functions in their calculation.
This claim fully incorporates the limitations of its parent claim and thus the deficiencies of those limitations. No limitation present in the claim is sufficient to overcome the rejection of its parent claim.
The claim is ineligible.
Regarding claim 9, which depends upon claim 1:
The following limitation merely connects the abstract idea to a particular field of use, which is insufficient to overcome the rejection (MPEP 2106.05(h)).
the agent includes an unmanned aerial vehicle
This claim fully incorporates the limitations of its parent claim and thus the deficiencies of those limitations. No limitation present in the claim is sufficient to overcome the rejection of its parent claim.
The claim is ineligible.
Claims 10-16 recite a device that parallels the method of claims 1-7 respectively. Therefore, the analysis discussed above with respect to claims 1-7 also applies to claims 10-16 respectively. Accordingly, claims 10-16 are rejected based on substantially the same rationale as set forth above with respect to claims 1-7 respectively.
Claims 17-20 recite a non-transitory computer readable storage medium that parallels the method of claims 1-4 respectively. Therefore, the analysis discussed above with respect to claims 1-4 also applies to claims 17-20 respectively. Accordingly, claims 17-20 are rejected based on substantially the same rationale as set forth above with respect to claims 1-4 respectively.
Claim Rejections - 35 USC § 103
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 1-3, 7-8, 10-12, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kroener et al. (Pub. No. US 12511547 B2, filed November 2nd 2022, hereinafter Kroener) in view of Yeo et al. (Pub. No. US 20200097813 A1, filed September 26th 2018, hereinafter Yeo).
Regarding claim 1:
Claim 1 recites:
A method for decentralized optimal control for a large-scale multi-agent system, the large-scale multi-agent system including multiple agents each including three neural networks (NNs) including an actor NN, a critic NN, and a mass NN, the method comprising: initializing errors to obtain an initialized error of the actor NN, an initialized error of the critic NN, and an initialized error of the mass NN; initializing error thresholds to obtain an initialized error threshold of the actor NN, an initialized error threshold of the critic NN, and an initialized error threshold of the mass NN; and if the initialized error of the actor NN is greater than or equal to the initialized error threshold of the actor NN, if the initialized error of the critic NN is greater than or equal to the initialized error threshold of the critic NN, and if the initialized error of the mass NN is greater than or equal to the initialized error threshold of the mass NN:calculating NN weights of the actor NN, the critic NN, and the mass NN, respectively; and updating the actor NN, the critic NN, and the mass NN using corresponding calculated NN weights, respectively; and calculating NN errors of the actor NN, the critic NN, and the mass NN, respectively; and updating the actor NN, the critic NN, and the mass NN using corresponding calculated NN errors, respectively
Kroener discloses A method for decentralized optimal control for a large-scale multi-agent system, the large-scale multi-agent system including multiple agents each including three neural networks (NNs) including an actor NN, a critic NN, [and a mass NN]
Kroener teaches multiple agents (Column 6, lines 5-15), which may be a large-scale multi-agent system that comprise an actor-critic model, wherein the actor-critic model is comprised of two neural networks to perform the respective tasks (Column 9, lines 20-25).
Kroener does not teach a mass neural network and hence does not teach three neural networks, which is taught further below by Yeo.
Kroener discloses initializing errors to obtain an initialized error of the actor NN, an initialized error of the critic NN, and an initialized error of the mass NN; initializing error thresholds to obtain an initialized error threshold of the actor NN, an initialized error threshold of the critic NN, and an initialized error threshold of the mass NN; and if the initialized error of the actor NN is greater than or equal to the initialized error threshold of the actor NN, if the initialized error of the critic NN is greater than or equal to the initialized error threshold of the critic NN, and if the initialized error of the mass NN is greater than or equal to the initialized error threshold of the mass NN
Kroener teaches the use of backpropagation in order to train its models, wherein backpropagation is continued until such time that a predetermined performance level is reached (Column 5, lines 20-30). The predetermined performance level indicates the initialization of the error for each neural network as the performance metric is shown to be the difference between the expected and actual outcome. Therefore, the predetermined performance level for each model would also be the initialized error threshold, wherein the training only continues if the error of the neural network is greater or equal to the error threshold for the neural network.
Kroener does not teach a mass neural network, which is taught further below by Yeo.
Kroener discloses calculating NN weights of the actor NN, the critic NN, and the mass NN, respectively; and updating the actor NN, the critic NN, and the mass NN using corresponding calculated NN weights, respectively; and calculating NN errors of the actor NN, the critic NN, and the mass NN, respectively; and updating the actor NN, the critic NN, and the mass NN using corresponding calculated NN errors, respectively
Kroener teaches the use of backpropagation in order to train its models (Column 5, lines 20-30), which demonstrates the calculation of neural network weights and updating the respective neural networks using corresponding calculated weights as this is a step of backpropagation. Likewise, as discussed above, the predetermined performance level (Column 5, lines 20-30) requires the calculation of the errors of the neural network, wherein updating the neural network relies on the corresponding errors in order to determine if backpropagation continues.
Kroener does not teach a mass neural network, which is taught further below by Yeo.
Yeo in the same field of endeavor of machine learning discloses a mass neural network:
Yeo teaches the use of a mass neural network as it teaches a neural network which is configured to estimate a probability density function (Yeo, Paragraph 58), which is shown in the present application’s specification to be the defining feature of a mass neural network (Present application, Paragraph 50).
Furthermore, Yeo’s mass neural network may be combined with the neural networks of Kroener in order to provide more accurate forecasting of key variables (Yeo, Paragraph 3). Therefore, processes performed by the neural networks of Kroener may likewise be performed by the mass neural network of Yeo.
Yeo and the present application are analogous art because they are in the same field of endeavor.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology that utilized the teachings of Kroener and the teaching of Yeo. This would have provided the advantage of providing more accurate forecasting of key variables (Yeo, Paragraph 3).
Regarding claim 2, which depends upon claim 1:
Claim 2 recites:
The method according to claim 1, further including: if the initialized error of the actor NN is less than the initialized error threshold of the actor NN, obtaining previous calculated NN weights of the actor NN; or if the initialized error of the critic NN is less than the initialized error threshold of the critic NN, obtaining previous calculated NN weights of the critic NN; or if the initialized error of the mass NN is less than the initialized error threshold of the mass NN, obtaining previous calculated NN weights of the mass NN
Kroener in view of Yeo discloses the method of claim 1 upon which claim 2 depends. Furthermore, Kroener discloses the limitations of claim 2:
Kroener teaches the use of backpropagation in order to train its models, wherein backpropagation is continued until such time that a predetermined performance level is reached, at which point it outputs the trained model parameters (Column 5, lines 20-30) including the previously calculated weights, therefore obtaining them. This occurs when the error is less than the predetermined performance level i.e. the error threshold.
Kroener does not teach a mass neural network, which has been taught previously by Yeo, as in claim 1, and to which the same process may be applied.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology that utilized the teachings of Kroener and the teaching of Yeo. This would have provided the advantage of providing more accurate forecasting of key variables (Yeo, Paragraph 3).
Regarding claim 3, which depends upon claim 2:
Claim 3 recites:
The method according to claim 2, further including: using the previous calculated NN weights of the actor NN to calculate a control; and executing the calculated control.
Kroener in view of Yeo discloses the method of claim 2 upon which claim 3 depends. Furthermore, Kroener discloses the limitations of claim 3:
Kroener teaches that the agent selects an action, or calculates control and executes it, wherein the part of the agent that executes this is the actor neural network as previously describes, therefore using the previous calculated neural network weights. (Column 6, lines 10-25).
Regarding claim 7, which depends upon claim 1:
Claim 7 recites:
The method according to claim 1, wherein before initializing the errors, the method further includes: randomly initializing the NN weights of the actor NN, the critic NN, and the mass NN.
Kroener in view of Yeo discloses the method of claim 1 upon which claim 7 depends. Furthermore, Kroener discloses the limitations of claim 7:
Kroener teaches the weights of the neural networks throughout the training process may adapted in order to reflect the training on the training data , wherein this would demonstrate random initialization of the neural network weights for each neural network as they are not initially dependent upon the training. (Column 5, lines 40-45).
Kroener does not teach a mass neural network, which has been taught previously by Yeo, as in claim 1, and to which this process may also apply.
Regarding claim 8, which depends upon claim 1:
Claim 8 recites:
The method according to claim 1, wherein: the critic NN is configured to estimate a cost function; and the mass NN is configured to estimate a probability density function
Kroener in view of Yeo discloses the method of claim 1 upon which claim 8 depends. Furthermore, Kroener discloses the critic NN is configured to estimate a cost function:
Kroener teaches that the critic neural network is updated using backpropagation, which includes the estimation of a cost function (Column 5, lines 20-30).
Furthermore, Yeo discloses the mass NN is configured to estimate a probability density function:
Yeo teaches the use of a mass neural network as it teaches a neural network which is configured to estimate a probability density function (Yeo, Paragraph 58).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology that utilized the teachings of Kroener and the teaching of Yeo. This would have provided the advantage of providing more accurate forecasting of key variables (Yeo, Paragraph 3).
Claims 10-12 and 16 recite a device that parallels the method of claims 1-3 and 7 respectively. Therefore, the analysis discussed above with respect to claims 1-3 and 7 also applies to claims 10-12 and 16 respectively. Accordingly, claims 10-12 and 16 are rejected based on substantially the same rationale as set forth above with respect to claims 1-3 and 7 respectively.
Claims 17-19 recite a non-transitory computer readable storage medium that parallels the method of claims 1-3 respectively. Therefore, the analysis discussed above with respect to claims 1-3 also applies to claims 17-19 respectively. Accordingly, claims 17-19 are rejected based on substantially the same rationale as set forth above with respect to claims 1-3 respectively.
Claims 4, 9, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kroener in view of Yeo, further in view of Soukarie et al. (Pub. No. US 20220415189 A1, filed November 23rd 2020, hereinafter Soukarie).
Regarding claim 4, which depends upon claim 1:
Claim 4 recites:
The method according to claim 1, wherein before initializing the errors, the method further includes: initializing a state and a density of the agent, wherein the state of the agent includes a position and a velocity; and calculating an error of the agent using the state of the agent and a predefined trajectory.
Kroener in view of Yeo discloses the method of claim 1 upon which claim 4 depends. However, neither art fully discloses the limitation of claim 4. Instead, Soukarie discloses the limitations of claim 4:
Soukarie in the same field of endeavor of machine learning teaches tracking the position and velocity of an agent as part of its environment, which may be its state. Furthermore, this may include the position of other agents, which would indicate the density of the area (Paragraph 62). The aircraft agent additionally has points indicated that it is able to directly fly to, creating a predefined trajectory (Paragraph 62).
Soukarie and the present application are analogous art because they are in the same field of endeavor. Furthermore, Soukarie and Kroener may be combined in order to use the predefined trajectory and the position as factors in Kroener’s calculation of the error. This would provide the advantage of managing air conflicts (Soukarie, Paragraph 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology that utilized the teachings of Kroener in view of Yeo, and the teachings of Soukarie. This would have provided the advantage of managing air conflicts (Soukarie, Paragraph 3).
Regarding claim 9, which depends upon claim 1:
Claim 9 recites:
The method according to claim 1, wherein: the agent includes an unmanned aerial vehicle.
Kroener in view of Yeo discloses the method of claim 1 upon which claim 9 depends. However, neither art discloses the limitation of claim 9. Instead, Soukarie discloses the limitations of claim 9:
Soukarie teaches that its agents are autonomous aircraft (Paragraph 58), wherein autonomous would be analogous to unmanned.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology that utilized the teachings of Kroener in view of Yeo, and the teachings of Soukarie. This would have provided the advantage of managing air conflicts (Soukarie, Paragraph 3).
Claim 13 recites a device that parallels the method of claim 4. Therefore, the analysis discussed above with respect to claim 4 also applies to claim 13. Accordingly, claim 13 is rejected based on substantially the same rationale as set forth above with respect to claim 4.
Claim 20 recites a non-transitory computer readable storage medium that parallels the method of claim 4. Therefore, the analysis discussed above with respect to claim 4 also applies to claim 20. Accordingly, claim 20 is rejected based on substantially the same rationale as set forth above with respect to claim 4.
Claims 5-6 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kroener in view of Yeo, further in view of Soukarie, further in view of Yang et al. (“Safety-Aware Reinforcement Learning Framework with an Actor-Critic-Barrier Structure”, published August 29th 2019, hereinafter Yang).
Regarding claim 5, which depends upon claim 4:
Claim 5 recites:
The method according to claim 4, wherein before initializing the errors and after calculating the error of the agent, the method further includes: performing a barrier-function based system transformation on the error and the density of the agent to obtain to a transformed error state and a transformed density state, respectively.
Kroener in view of Yeo further in view of Soukarie discloses the method of claim 4 upon which claim 4 depends. However, none of the above art discloses the limitation of claim 5 in full. Instead, Yang discloses performing a barrier-function based system transformation on [the error and the density of] the agent to obtain to a transformed [error state and a transformed density] state, respectively:
Yang recites “In this section, we present a novel online algorithm with an actor-critic-barrier structure to learn the optimal control policy. First, the barrier function is employed to transform the origin system (1) to system (12).
Yang in the same field of endeavor of machine learning teaches the use of a barrier function in order to transform the system. Furthermore, the methodology of Yang could be combined with the previously taught error and density of Kroener in view of Yeo further in view of Soukarie for purposes of providing the advantage of Yang of improving stability and control performance (Yang, page 1, “In addition to stability and control performance, the constraints on state and/or control is critical for safety purposes”).
Yang and the present application are analogous art because they are in the same field of endeavor
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology that utilized the teachings of Kroener in view of Yeo further in view of Soukarie, and the teachings of Yang. This would have provided the advantage of improving stability and control performance (Yang, page 1, “In addition to stability and control performance, the constraints on state and/or control is critical for safety purposes”)
Regarding claim 6, which depends upon claim 5:
Claim 6 recites:
The method according to claim 5, wherein: the transformed error state and the transformed density state are configured to calculate corresponding NN weights and errors
Kroener in view of Yeo further view of Soukarie further in view of Yang discloses the method of claim 5 upon which claim 6 depends. Furthermore, Yang discloses the limitations of claim 6:
Yang recites “The ideal weight, W in (24), provides the best approximate to the optimal value function V ˚psq on the compact set Ω and is unknown. Therefore, the estimation of W is implemented by the critic network with the approximations of the value function and value gradient” (Page 4).
Yang further recites “For the optimal control policy u˚ psq, the Bellman equation (20) approximation error using the value function approximation (24) can be expressed” (Page 3)
Yang teaches in these sections the calculation of neural network weights and an error, as the estimate and approximation of the weight and error respectively are determined through mathematical equations, wherein both follow the transformation of the state as previous discussed in claim 5.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a methodology that utilized the teachings of Kroener in view of Yeo further in view of Soukarie, and the teachings of Yang. This would have provided the advantage of improving stability and control performance (Yang, page 1, “In addition to stability and control performance, the constraints on state and/or control is critical for safety purposes”)
Claims 14-15 recite a device that parallels the method of claims 5-6 respectively. Therefore, the analysis discussed above with respect to claims 5-6 also applies to claims 14-15 respectively. Accordingly, claims 14-15 are rejected based on substantially the same rationale as set forth above with respect to claims 5-6 respectively.
Conclusion
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/A.J.M./Examiner, Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142