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 1/18/2024. Acknowledgment is made with respect to a claim of priority to Provisional Application 63/508,488 filed on 6/15/2023.
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:
generating a plurality of initializations: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating initializations, 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 generate initializations or initial guess predictions for a desired task.
performing a plurality of instances of an iterative technique based on the plurality of initializations to generate a plurality of results: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of performing instances of an iterative technique to generate results, which is performed by mathematical calculations as evidenced by paragraphs [0067, 0069, and 0071] and equations 3 and 6 of the originally filed specification.
generating a control signal based on one or more results included in the plurality of results: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of generating a control signal or prediction, which is performed by mathematical calculations as evidenced by paragraphs [0067, 0069, 0070, and 0071] and equations 3-6 of the originally filed specification.
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 “using a trained machine learning model” and “transmitting the control signal to the system to cause the system to perform one or more operations”.
The additional element of “using a trained machine learning model” 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 trained machine learning model is used to broadly generate a plurality of initializations. 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 “transmitting the control signal to the system to cause the system to perform one or more operations” is 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 “using a trained machine learning model” 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 trained machine learning model is used to broadly generate a plurality of initializations. 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 “transmitting the control signal to the system to cause the system to perform one or more operations” is 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, inter alia:
computing a plurality of costs associated with the plurality of results: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of computing costs associated with results, which is performed by mathematical computation as evidenced by paragraph [0067] and equations 8-10 of the originally filed specification.
selecting a first result from the plurality of results based on the plurality of costs, wherein the control signal is generated based on the first result: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of selecting results based on costs, 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 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
processing a first initialization and state data using the trained machine learning model.: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of processing states using a model, which is performed by mathematical computation as evidenced by paragraph [0067] of the originally filed specification.
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 4
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 plurality of instances of the iterative technique are performed in parallel via at least one parallel processing unit” 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 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
performing one or more instances of another iterative technique to generate a plurality of other results: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of performing instances of an iterative technique, which is performed by mathematical computation as evidenced by paragraph [0067] of the originally filed specification.
Step 2A Prong 2, Step 2B: The additional element of “performing one or more operations to train the trained machine learning model based on training data that includes the plurality of other results” 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 trained machine learning model is broadly trained with particular training data. 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 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
performing one or more reinforcement learning operations to train the trained machine learning model based on one or more rewards associated with one or more results generated while training the trained machine learning model: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of performing reinforcement learning, which is performed by mathematical computation as evidenced by paragraph [0070] of the originally filed specification.
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 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
performing one or more supervised learning operations and one or more reinforcement learning operations to train the trained machine learning model: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of performing supervised learning, which is performed by mathematical computation as evidenced by paragraphs [0067-0070] of the originally filed specification.
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 8
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 iterative technique comprises at least one of a model predictive control (MPC) technique, a linear quadratic regulator (LQR) technique, a gradient descent technique, a quasi-Newton method technique, an interior point technique, or a Sequential Quadratic Programming technique” 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 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 trained machine learning model comprises a Gaussian mixture model or a conditional variational autoencoder” 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 10
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 system comprises an autonomous vehicle, a robot, or a power plant” 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 11-17
Claims 11-17 recite one or more non-transitory computer-readable storage media (step 1: a manufacture) using a processor to perform the steps of claims 1-7, 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 1-7, respectively.
Claim 18
Step 1: A manufacture, 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 trained machine learning model comprises a neural network” 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 19
Step 1: A manufacture, 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 control signal comprises at least one of a steering signal, an acceleration signal, or a signal to one or more joint controllers of a robot” 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 20
Claims 20 recites a system (step 1: a machine) using one or more processors and memories to perform the steps of claim 1, 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 is thus rejected for the same reasons set forth in the rejection of claim 1.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 8-14, 19, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lembono et al. (Lembono et al., “Memory of Motion for Warm-starting Trajectory Optimization”, May 14, 2020, arXiv:1907.01474v4, pp. 1-8, hereinafter “Lembono”).
Regarding claim 1, Lembono discloses [a] computer-implemented method for controlling a system, the method comprising: (Abstract; “we build a memory of motion based on a database of robot paths to provide good initial guesses. The memory of motion relies on function approximators and dimensionality reduction techniques to learn the mapping between the tasks and the robot paths. … We demonstrate the proposed approach with motion planning examples on the dual-arm robot PR2 and the humanoid robot Atlas”, which discloses a computer-implemented method for controlling a system or robot; and §IV)
generating a plurality of initializations using a trained machine learning model; (Algorithm 3, Line 2; “compute the initial guess ˜yj = fj(x∗)”, which discloses, under a broadest reasonable interpretation of the claim language, generating a plurality of initializations or initial guesses from 1 to M via trained function approximators; §III.A(3); “Alternatively, we can also use the mean of each t-distributions as separate predictions, which gives us several possible solutions”, which discloses a single trained Bayesian Gaussian Mixture Regression (model) that generates multiple initial-guess predictions or plurality of initializations from one query)
performing a plurality of instances of an iterative technique based on the plurality of initializations to generate a plurality of results; (Algorithm 3, Lines 1-3; the algorithm discloses using TrajOpt, an iterative sequential convex optimization technique, that is run as multiple instances, where each instance is warm started or initialized from one of the plurality of initializations ˜yj and produces a plurality of results yj)
generating a control signal based on one or more results included in the plurality of results; and (Algorithm 3, Lines 1-3; the algorithm discloses a control signal or output result Y* (interpreted as the robot path) that is generated or selected based on one of the plurality of results yj; and §IV; the output path y is used as the robot’s motion command; and Figures 1 and 2)
transmitting the control signal to the system to cause the system to perform one or more operations (Abstract; “We demonstrate the proposed approach with motion planning examples on the dual-arm robot PR2 and the humanoid robot Atlas”; and Figure 1 Caption; “moving the PR2 base between two points while avoiding an obstacle dual arm motion of PR2 to pick items from a shelf to another, and (c) whole body motion of Atlas.”, the operation being the robot movement and the resulting path y* is provided to or executed by the PR2 and Atlas robots and the signal is transmitted to the robotic system to cause it to move and §IV).
Regarding claim 11, it is a non-transitory computer-readable media claim corresponding to the steps of claim 1 and is rejected for the same reasons as claim 1.
Regarding claim 20, it is a system claim corresponding to the steps of claim 1 and is rejected for the same reasons as claim 1.
Regarding claims 2 and 12, the rejection of claims 1 and 11 are incorporated and Lembono discloses computing a plurality of costs associated with the plurality of results; and selecting a first result from the plurality of results based on the plurality of costs, wherein the control signal is generated based on the first result (Algorithm 2, Lines 2-3 and 5; “compute the initial guess ˜yj = f(xj) compute the cost (˜yj) … j∗ ← argminj (˜yj)”; and §III.B; “Another method is to plan the paths to each goal and select the one having the smallest cost … The initial guess ˜y∗ i and the corresponding task x∗ i with the lowest cost is then taken as the chosen goal to be given to the trajectory optimizer”).
Regarding claims 3 and 13, the rejection of claims 1 and 11 are incorporated and Lembono discloses wherein the plurality of initializations is generated by processing a first initialization and state data using the trained machine learning model (§III.A; “To learn the mapping f : x → y, we firstly generate a set of tasks {x} and the corresponding robot paths {y}. This is done by sampling x from a uniform distribution covering the space of possible tasks and run the trajectory optimizer to obtain the robot paths y until we obtain N samples (x,y)”).
Regarding claims 4 and 14, the rejection of claims 1 and 11 are incorporated and Lembono discloses wherein the plurality of instances of the iterative technique are performed in parallel via at least one parallel processing unit (Algorithm 3, Line 1; “for all j = 1, 2, ..., M do in parallel”; and §III.C; “We propose to use an ensemble method where we run multiple trajectory optimizations in parallel”).
Regarding claim 8, the rejection of claim 1 is incorporated and Lembono further discloses wherein the iterative technique comprises at least one of a model predictive control (MPC) technique, a linear quadratic regulator (LQR) technique, a gradient descent technique, a quasi-Newton method technique, an interior point technique, or a Sequential Quadratic Programming technique (§1; “Trajectory optimization methods such as TrajOpt [1], CHOMP [2], or STOMP [3] solve the non-convex problem by iteratively optimizing around the current solution” which discloses a a Sequential Quadratic Programming technique through the use of trajopt).
Regarding claim 9, the rejection of claim 1 is incorporated and Lembono further discloses wherein the trained machine learning model comprises a Gaussian mixture model or a conditional variational autoencoder (§III.A.3; “Gaussian Mixture Regression (GMR) is an example of such local models approaches [22]. It can be seen as a probabilistic mixture of linear regressions”).
Regarding claim 10, the rejection of claim 1 is incorporated and Lembono further discloses wherein the system comprises an autonomous vehicle, a robot, or a power plan (Figure 1; the figure discloses a robot as part of the system; and Abstract).
Regarding claim 19, the rejection of claim 11 is incorporated and Lembono further discloses wherein the control signal comprises at least one of a steering signal, an acceleration signal, or a signal to one or more joint controllers of a robot (Figure 1; the figure discloses a robot as part of the system that uses a signal to control the robot; and Abstract).
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 5-7 and 15-18are rejected under 35 USC § 103 as being obvious over Lembono in view of Tang et al. (US 20210276187 A1, hereinafter “Tang”).
Regarding claims 5 and 15, the rejection of claims 1 and 11 are incorporated and Lembono fails to explicitly disclose but Tang discloses performing one or more instances of another iterative technique to generate a plurality of other results; and performing one or more operations to train the trained machine learning model based on training data that includes the plurality of other results ([0049]; “Step 905 includes generating a dataset of optimized trajectories using a non-convex optimizer. In some examples, several thousand, million, or more trajectories are generated in step 905 .. Step 910 includes training the neural network model to imitate the optimized trajectories of the training dataset produced in step 905. Once the neural network model is trained to imitate the optimized trajectories”, which discloses performing another iterative technique using a non-convex optimizer and then training the trained Ml model).
Lembono and Tang are analogous art because both are concerned with trajectory optimization and machine learning. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in trajectory optimization and machine learning to combine the other iterative technique of Tang and the method of Lembono to yield to the predictable result of performing one or more instances of another iterative technique to generate a plurality of other results; and performing one or more operations to train the trained machine learning model based on training data that includes the plurality of other results. The motivation for doing so would be to quickly predict optimized robotic arm trajectories in a variety of scenarios (Tang; Abstract).
Regarding claims 6 and 16, the rejection of claims 1 and 11 are incorporated and Lembono fails to explicitly disclose but Tang discloses performing one or more reinforcement learning operations to train the trained machine learning model based on one or more rewards associated with one or more results generated while training the trained machine learning model ([0008]; “training the one or more deep artificial neural nets using one or more of reinforcement learning and imitation learning”; and [0028]; “Many various types of training and machine learning methods presently exist and are commonly used including supervised learning, unsupervised learning, reinforcement learning, imitation learning, and many more. During training, the weights in a neural network are continually updated in response to errors, failures, or mistakes”).
The motivation to combine Lembono and Tang is the same as discussed above with respect to claim 5.
Regarding claims 7 and 17, the rejection of claims 1 and 11 are incorporated and Lembono fails to explicitly disclose but Tang discloses performing one or more supervised learning operations and one or more reinforcement learning operations to train the trained machine learning model ([0008]; “training the one or more deep artificial neural nets using one or more of reinforcement learning and imitation learning”; and [0028]; “Many various types of training and machine learning methods presently exist and are commonly used including supervised learning, unsupervised learning, reinforcement learning, imitation learning, and many more. During training, the weights in a neural network are continually updated in response to errors, failures, or mistakes”).
The motivation to combine Lembono and Tang is the same as discussed above with respect to claim 5.
Regarding claim 18, the rejection of claim 11 is incorporated and Lembono fails to explicitly disclose but Tang discloses wherein the trained machine learning model comprises a neural network ([0028]; “Many various types of training and machine learning methods presently exist and are commonly used including supervised learning, unsupervised learning, reinforcement learning, imitation learning, and many more. During training, the weights in a neural network are continually updated in response to errors, failures, or mistakes”).
The motivation to combine Lembono and Tang is the same as discussed above with respect to claim 5.
Conclusion
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/BRENT JOHNSTON HOOVER/Primary Examiner, Art Unit 2127
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