DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 5/26/2023 for application number 18/202,450.
Claims 1-20 are pending.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 (representative of independent claims 9 and 17) recites:
1. A system for probability density estimation on an implicitly-defined manifold, comprising: one or more processors; one or more non-transitory computer-readable media, containing instructions executable by the one or more processors for: identifying a set of training data including a plurality of training data samples in a high-dimensional space; training parameters of a manifold-defining function that learns a manifold of the set of training data in the high-dimensional space as a zero set output by the manifold-defining function, the manifold-defining function trained based on the plurality of training data samples; and training parameters of an energy function based on the plurality of training data samples, the energy function outputting an energy density for points in the high-dimensional space, in which training of the energy function is constrained to the manifold.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically mathematical calculations and equations. Applicant’s specification states that training parameters of the manifold-defining function entails minimizing a loss function shown at paragraph 34 (as published), and training parameters of the energy function entails evaluating the contrastive divergence function at paragraph 46 (as published). Therefore, the underlined portions explicitly require mathematical calculations and evaluating equations.
(2A, prong 2) This judicial exception is not integrated into a practical application. The claims recite the additional elements of (a) generic computer components like a processor and memory, (b) identifying training data samples in a high-dimensional space. Element (a) is a mere instruction to apply the exception, because it merely adds generic computer components after the fact to the mathematical calculations. Element (b) is insignificant extra-solution activity because it is mere data gathering for the mathematical calculations. Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not integrate the abstract idea into a practical application because the additional elements only add mere instructions to apply the exception and insignificant extra-solution activity to the mathematical calculations.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Element (a) is a mere instruction to apply the exception, because it merely adds generic computer components after the fact to the mathematical calculations. Element (b) is well-understood, routine, and conventional activity analogous to storing and retrieving information in memory, see MPEP 2106.05(d) citing Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Even when all of the additional elements are considered in ordered combination with the recited abstract idea, the claim as a whole does not amount to significantly more than the abstract idea itself because the additional elements only add mere instructions to apply the exception and insignificant extra-solution activity that is well-understood, routine, and conventional to the mathematical calculations. When the claim is evaluated as a whole, the claim merely states that a computer loads training data for the mathematical calculations and executes the mathematical calculations.
The Examiner further notes that he has considered whether the claim improves the functioning of a computer or other technology or technical field. Here, Applicant’s specification states that the invention relates to, “density modeling with implicit manifold modeling and energy-based densities,” Prior art approaches can, “struggle to effectively model both the density and shape of the manifold in high-dimensional space,” and the disclosed invention aims to, “model a much broader class of topologies more effectively,” by training a manifold-defining function to learn the manifold and then training an energy function to learn a probability density on the manifold (spec. para. 0002-09 as published). The disclosed invention does not appear to be an improvement to the functioning of a computer (like more efficient machine learning or other computer functionality), but instead appears to be an improvement to the mathematics itself. The improved mathematical modeling could have a variety of practical applications, but none of these are claimed. Therefore, the claims are not an improvement to the functioning of a computer or other technical field.
With respect to dependent claims 2-8, 10-16, and 18-20 contain further mathematical calculation limitations.
Claims 2, 10, and 18 recite what the energy function calculates.
Claims 3, 11, and 19 recite the manifold-defining function and energy function are neural networks. Mathematically calculating a neural network is a mathematical calculation.
Claims 4, 12, and 20 describe the loss function for the manifold defining function works to output zero for training data points, non-zero for points not in the training data, and smoothness at the training data points. These are properties of the mathematical formula that is calculated.
Claims 5 and 13 recite properties of the manifold for the energy function, which further defines the mathematical calculations.
Claims 6-8 and 14-16 recite the energy function is trained based on a contrastive divergence loss function that includes points from the training data and a set of points sampled from the manifold with a constrained Hamiltonian Monte Carlo sampling algorithm. These are further mathematical calculations.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 10, and 18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The term “substantially” in claims, 2, 10, and 18 is a relative term which renders the claim indefinite. The term “substantially” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. It would be matter of opinion what would constitute “substantially” describing a probability density of a point.
Claims 5 and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 5 and 13 recites the limitation "the manifold for the energy function.” There is insufficient antecedent basis for this limitation in the claim. It is unclear if the energy function has a second manifold, or if this limitation is referring to the learned manifold in the independent claims.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4, 9-12, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brehmer et al. (Flows for simultaneous manifold learning and density estimation, NPL [10] cited in IDS of 8/9/2023) in view of Gropp et al. (Implicit Geometric Regularization for Learning Shapes, NPL [34] cited in IDS of 8/9/2023).
In reference to claim 1, Brehmer teaches a system for probability density estimation on an implicitly-defined manifold, comprising: one or more processors; one or more non-transitory computer-readable media, containing instructions executable by the one or more processors (it would be obvious that the machine learning of Brehmer would be performed on a computer with processor and memory) for: identifying a set of training data including a plurality of training data samples in a high-dimensional space (training data in high-dimensional space – like images, pages 16-18 – is obtained, 3. Efficient training and evalulation, pages 6-8); training parameters of a manifold-defining function that learns a manifold of the set of training data in the high-dimensional space … by the manifold-defining function, the manifold-defining function trained based on the plurality of training data samples (shape of manifold is learned during manifold phase based on training data, Separate manifold and density training (M/D), pages 8-9); and training parameters of an energy function based on the plurality of training data samples, the energy function outputting an energy density for points in the high-dimensional space, in which training of the energy function is constrained to the manifold (density on manifold is trained, Separate manifold and density training (M/D), pages 8-9).
However, Brehmer does not explicitly teach learns a manifold of the set of training data in the high-dimensional space as a zero set output by the manifold-defining function.
Gropp teaches learns a manifold of the set of training data in the high-dimensional space as a zero set output by the manifold-defining function (see, e.g. 1. Introduction, pages 1-2: shape is learned as a zero level set).
It would have been obvious to one of ordinary skill in art, having the teachings of Brehmer and Gropp before the earliest effective filing date, to modify the manifold learning of Brehmer to include the zero set of Gropp.
One of ordinary skill in the art would have been motivated to modify the manifold learning of Brehmer to include the zero set of Gropp because it can help better find the surface of a shape, like a manifold (Gropp, 1. Introduction, pages 1-2).
In reference to claim 2, Brehmer teaches the system of claim 1, wherein the energy function evaluated at a point of the manifold substantially describes a probability density of the point (density on manifold at point, Separate manifold and density training (M/D), pages 8-9).
In reference to claim 3, Brehmer teaches the system of claim 1, wherein the manifold-defining function and energy function are neural networks (both may be neural networks, D. Particle physics, pages 14-15).
In reference to claim 4, Brehmer teaches the system of claim 1, wherein the manifold-defining function is trained with a loss function including terms that encourage a) an output value of zero for the training data points, b) an output value of non-zero for positions in the high-dimensional space that are not training data points, and c) the manifold-defining function to be smooth at the training data points (see equation [23] and algorithm 1, pages 8-9 and corresponding description).
In reference to claim 9, this claim is directed to a method associated with the system claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 10, this claim is directed to a method associated with the system claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 11, this claim is directed to a method associated with the system claimed in claim 3 and is therefore rejected under a similar rationale.
In reference to claim 12, this claim is directed to a method associated with the system claimed in claim 4 and is therefore rejected under a similar rationale.
In reference to claim 17, this claim is directed to a non-transitory computer-readable medium associated with the system claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 18, this claim is directed to a non-transitory computer-readable medium associated with the system claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 19, this claim is directed to a non-transitory computer-readable medium associated with the system claimed in claim 3 and is therefore rejected under a similar rationale.
In reference to claim 20, this claim is directed to a non-transitory computer-readable medium associated with the system claimed in claim 4 and is therefore rejected under a similar rationale.
Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brehmer et al. (Flows for simultaneous manifold learning and density estimation, NPL [10] cited in IDS of 8/9/2023) in view of Gropp et al. (Implicit Geometric Regularization for Learning Shapes, NPL [34] cited in IDS of 8/9/2023) as applied to claims 1 and 9 above, and in further view of Elhamifar et al. (Sparse Manifold Clustering and Embedding, NPL [W]).
In reference to claim 5, Brehmer and Gropp do not explicitly teach the system of claim 1, wherein the manifold for the energy function is the intersection or union of a first set of points associated with the zero set and a second set of points associated with another zero set of another manifold-defining function.
Elhamifar teaches the system of claim 1, wherein the manifold for the energy function is the intersection or union of a first set of points associated with the zero set and a second set of points associated with another zero set of another manifold-defining function (see 1. Introduction through 2. Proposed Method, pages 1-6: cluster of points that overlap in two manifolds is learned).
It would have been obvious to one of ordinary skill in art, having the teachings of Brehmer, Gropp, and Elhamifar before the earliest effective filing date, to modify the energy function of Brehmer to include the overlap of Elhamifar.
One of ordinary skill in the art would have been motivated to modify the energy function of Brehmer to include the overlap of Elhamifar because for some real-world problems, relevant data likes in multiple manifolds (Elhamifar, 1.2 Manifold Clustering, page 2).
In reference to claim 13, this claim is directed to a method associated with the system claimed in claim 5 and is therefore rejected under a similar rationale.
Claim(s) 6-8 and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Brehmer et al. (Flows for simultaneous manifold learning and density estimation, NPL [10] cited in IDS of 8/9/2023) in view of Gropp et al. (Implicit Geometric Regularization for Learning Shapes, NPL [34] cited in IDS of 8/9/2023) as applied to claims 1 and 9 above, and in further view of Du (Improved Contrastive Divergence Training of Energy-Based Model, NPL [V]).
In reference to claim 6, Brehmer and Gropp do not explicitly teach the system of claim 1, wherein training parameters of the energy function comprises training the energy function based on a contrastive divergence loss function.
Du teaches the system of claim 1, wherein training parameters of the energy function comprises training the energy function based on a contrastive divergence loss function (2 An Improved Contrastive Divergence Framework for Energy-Based Models, page 2).
It would have been obvious to one of ordinary skill in art, having the teachings of Brehmer, Gropp, and Du before the earliest effective filing date, to modify the energy function of Brehmer to include the contrastive divergence loss of Du.
One of ordinary skill in the art would have been motivated to modify the energy function of Brehmer to include the contrastive divergence loss of Du because it can helps stabilize training (Du, 1 Introduction, pages 1-2).
In reference to claim 7, Brehmer teaches the system of claim 6, wherein the … loss function includes points from the plurality of training data samples and a set of sampled points from the energy density on the manifold (see equation [23] and algorithm 1, pages 8-9 and corresponding description).
However, Brehmer and Gropp do not explicitly teach a contrastive divergence loss function.
Du teaches a contrastive divergence loss function (2 An Improved Contrastive Divergence Framework for Energy-Based Models, page 2).
It would have been obvious to one of ordinary skill in art, having the teachings of Brehmer, Gropp, and Du before the earliest effective filing date, to modify the energy function of Brehmer to include the contrastive divergence loss of Du.
One of ordinary skill in the art would have been motivated to modify the energy function of Brehmer to include the contrastive divergence loss of Du because it can helps stabilize training (Du, 1 Introduction, pages 1-2).
In reference to claim 8, Brehmer and Gropp do not explicitly teach the system of claim 7, wherein training parameters of the energy function further comprises generating the set sampled points with a constrained Hamiltonian Monte Carlo sampling algorithm of the energy density on the manifold.
Du teaches the system of claim 7, wherein training parameters of the energy function further comprises generating the set sampled points with a constrained Hamiltonian Monte Carlo sampling algorithm of the energy density on the manifold (Langevin sampling used as Markov Chain Monte Carlo, 2.3 Data Augmentation Transitions, page 4; Langevin sampling is a particular case of Hamiltonian Monte Carlo).
It would have been obvious to one of ordinary skill in art, having the teachings of Brehmer, Gropp, and Du before the earliest effective filing date, to modify the energy function of Brehmer to include the contrastive divergence loss of Du.
One of ordinary skill in the art would have been motivated to modify the energy function of Brehmer to include the contrastive divergence loss of Du because it can helps stabilize training (Du, 1 Introduction, pages 1-2).
In reference to claim 14, this claim is directed to a method associated with the system claimed in claim 6 and is therefore rejected under a similar rationale.
In reference to claim 15, this claim is directed to a method associated with the system claimed in claim 7 and is therefore rejected under a similar rationale.
In reference to claim 16, this claim is directed to a method associated with the system claimed in claim 8 and is therefore rejected under a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. [U] Ross et al. which appears to be a publication by the inventors corresponding to the disclosed invention.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
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