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 12/16/2022 and the Remarks and Amendments filed on 4/3/2026. Acknowledgment is made with respect to a claim of priority to Provisional Application 63/305,481 filed on 2/1/2022.
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 and 12 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 2 and 12 recite the limitation “generating a data sample consistent with the learned manifold in the high-dimensional space by applying the decoder portion of the autoencoder model to a value sampled from the density model” (emphasis added). It is not clear how a decoder portion of the autoencoder model can be applied to both the “value sampled by the density model” in claims 2 and 12 and the “probability density of the low-dimensional space” in amended claims 1 and 11. It only makes sense for one of the sampled value or the probability density information to propagate through the autoencoder and to have the decoder portion applied against this information. Please explain what information the decoder portion of the autoencoder model is applied to. For examination purposes, the limitation will be interpreted to mean generating a data sample consistent with the learned manifold in the high-dimensional space by applying the decoder portion of the autoencoder model to samples of a state distribution of a low-dimensional representation. Appropriate correction is required.
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 system; therefore, it is directed to the statutory category of a machine.
Step 2A Prong 1: The claim recites, inter alia:
determine respective positions of the training data in the low-dimensional space: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining positions of training data in a low-dimensional space, 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, a human can, given a high dimensional data set that includes a set of objects described by many attributes, mentally decide which two attributes are the most important, imagine a simple 2-d coordinate plane, and mentally place each object on that plane based on the two selected attributes.
determining a probability density of the high-dimensional space by applying the decoder portion of the autoencoder model to the probability density of the low-dimensional space: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of determining a probability density of a high-dimensional space by applying a decoder or decoding function to the probability density of the low-dimensional space, which is performed through a series of mathematical computations as evidenced by paragraphs [0045-0046], and [0051-0053] and equations 1-6 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 “a processor that executes instructions; and a non-transitory computer-readable medium having instructions executable by the processor for:”, “training an autoencoder model based on a set of training data in a high-dimensional space, the autoencoder model having an encoder portion for encoding data in a high-dimensional space to a low-dimensional space and a decoder portion for decoding data from the low-dimensional space to a learned manifold of the high-dimensional space”, “applying the encoder portion to the training data to”, “training a density model to learn a probability density of the low-dimensional space based on the respective positions of the training data in the low-dimensional space”, and “based on … the decoder portion of the model”.
The additional elements of “a processor that executes instructions; and a non-transitory computer-readable medium having instructions executable by the processor for:” amount to generic computer components used as a tool to perform an existing process. The additional elements of “training an autoencoder model based on a set of training data in a high-dimensional space, the autoencoder model having an encoder portion for encoding data in a high-dimensional space to a low-dimensional space and a decoder portion for decoding data from the low-dimensional space to a learned manifold of the high-dimensional space”, “applying the encoder portion to the training data to”, “training a density model to learn a probability density of the low-dimensional space based on the respective positions of the training data in the low-dimensional space”, and “based on … the decoder portion of the model” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic autoencoder is broadly trained based on a set of training data, how the encoder portion of the generic autoencoder is broadly applied to training data t determine a position, how the generic density model is broadly trained to learn a probability density, or how the decoder portion of the generic autoencoder is broadly used to determine a probability density. 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)).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea.
Step 2B: Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea.
The additional elements of “a processor that executes instructions; and a non-transitory computer-readable medium having instructions executable by the processor for:” amount to generic computer components used as a tool to perform an existing process. The additional elements of “training an autoencoder model based on a set of training data in a high-dimensional space, the autoencoder model having an encoder portion for encoding data in a high-dimensional space to a low-dimensional space and a decoder portion for decoding data from the low-dimensional space to a learned manifold of the high-dimensional space”, “applying the encoder portion to the training data to”, “training a density model to learn a probability density of the low-dimensional space based on the respective positions of the training data in the low-dimensional space”, and “based on … the decoder portion of the model” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the generic autoencoder is broadly trained based on a set of training data, how the encoder portion of the generic autoencoder is broadly applied to training data t determine a position, how the generic density model is broadly trained to learn a probability density, or how the decoder portion of the generic autoencoder is broadly used to determine a probability density. 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 and thus the claim is subject-matter ineligible.
Claim 2
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
generating a data sample consistent with the learned manifold in the high-dimensional space by applying the decoder portion of the autoencoder model to a value sampled from the density model: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of generating a data sample by applying a decoder or decoding function a sampled value, which is performed through a series of mathematical computations as evidenced by paragraphs [0045-0046], and [0051-0053] and equations 1-6 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 3
Step 1: A machine, 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 density model is trained with a maximum-likelihood training objective” 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 machine, 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 autoencoder model is trained with a reconstruction error training objective” 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 machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
determining whether a second data set having one or more data points in the high-dimensional space are out-of-distribution with respect to the training data set based on the probability density on the low-dimensional space: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining whether a second data set contains anomalous data based on a probability density, 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 6
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
determine respective second positions of the second data set in the low-dimensional space: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining positions of data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
determining a second probability density of the second data set in the low-dimensional space based on the respective second positions: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining a probability density based on positions of data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
determining whether the second data set is out-of-distribution based on a comparison of the probability density learned for the training data and the second probability density: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of determining whether data is anomalous, 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 elements of “applying the encoder portion to a second data set to” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the encoder portion of the generic autoencoder is broadly applied to training data to determine a position. 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 7
Step 1: A machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
identifying a reconstruction error for the training data by applying the encoder portion and the decoder portion of the autoencoder to data points in the training data set: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of identifying a reconstruction error, which is performed through mathematical computation as evidenced by paragraph [0046] and equation 5 of the originally filed specification.
determining a second reconstruction error for a second data set by applying the encoder portion and then the decoder portion to data points in the second data set: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of identifying or determining a reconstruction error, which is performed through mathematical computation as evidenced by paragraph [0046] and equation 5 of the originally filed specification.
determining a similarity score of the second data set to the training data based on the reconstruction error for the training data compared to the second reconstruction error: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of determining a similarity score, which is performed through mathematical computation as evidenced by paragraph [0054] 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 machine, as above.
Step 2A Prong 1: The claim recites, inter alia:
wherein the probability density of the high-dimensional space is determined by a change-of-variable formula from the low-dimensional space to the high-dimensional space: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mathematical concept of determining a probability density based on a change of variable formula, which is performed through mathematical computation.
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 9
Step 1: A machine, 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 autoencoder model is bijective between the high-dimensional space and low-dimensional space only on the learned manifold of the high-dimensional space” 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 machine, 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 high-dimensional space is an image” 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 11-20
Claims 11-20 recite a method (step 1: a process) to perform the steps of claims 1-10, respectively, without any additional elements that integrate the abstract ideas 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 1-10, respectively.
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 1-7 and 11-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lv et al., (Lv. Et al., “Layer-constrained variational autoencoding kernel density estimation model for anomaly detection”, May 21, 2020, Knowledge-Based Systems, Volume 196, pp. 1-11, hereinafter “Lv”).
Regarding claim 1, Lv discloses [a] system for density estimation of a data set in a high-dimensional space comprising: a processor that executes instructions; and a non-transitory computer-readable medium having instructions executable by the processor for: (Abstract; “we propose a novel high dimensional anomaly detection method called LAKE. The key idea of LAKE is to unify the representation learning capacity of layer-constrained variational autoencoder with the density estimation power of kernel density estimation (KDE). Then a probability density distribution of the high dimensional data can be learned, which is able to effectively separate the anomalies out”; and Page 5, §4; the experiments section is inherently performed using a processor and a computer-readable medium)
training an autoencoder model based on a set of training data in a high-dimensional space, the autoencoder model having an encoder portion for encoding data in a high-dimensional space to a low-dimensional space and a decoder portion for decoding data from the low-dimensional space to a learned manifold of the high-dimensional space; (Page 4, Algorithm 1; the autoencoder training process is detailed in lines 2-11 of the algorithm. Note that the encoder portion is denoted by theta and the decoder is denoted by phi; and Page 4, §3.4; the section discloses the autoencoder training process which makes use of an encoder and decoder portion; and Page 3, §3.2; “For a given input data x, the variational autoencoder calculates its low-dimensional representation z as follows: z = q(x, θ), (1) xˆ = p(z, φ), (2) where qθ (z|x) denotes the encoder, pφ(x|z) denotes the decoder, θ and φ are the network parameters of the encoder and decoder, and xˆ is the reconstruction of original data”; and Equation 5; and Figure 2; the figure discloses the decoder path)
applying the encoder portion to the training data to determine respective positions of the training data in the low-dimensional space; (Page 4, Algorithm 1; the algorithm at lines 4-6 disclose applying the encoder portion (fe, fr, or fre) to the training data (xi) to determine respective positions of the training data (wi or zi, which are the low dimensional positions)
training a density model to learn a probability density of the low-dimensional space based on the respective positions of the training data in the low-dimensional space; and (Page 4, §3.3; “In probability density estimation model, we use the learned low-representation to model a probability density distribution of input data. We denote the low-dimensional representations obtained by Eq. (8) for the input data by c1, c2, . . . , cn.”; and Page 4, Algorithm 1; lines 13-19 of the algorithm discloses the training of the density model to learning the probability density of the low-dimensional space based on the positions of the training data; and §3.4)
determining a probability density of the high-dimensional space by applying the decoder portion of the autoencoder model to the probability density of the low-dimensional space (Page 4, Figure 2; the figure discloses, under a broadest reasonable interpretation of the claim language, determining a probability density of the high-dimensional space by applying the decoder portion (the decoder portion is the right side of the box in red dashed lines) of the autoencoder mode to the probability density of the low-dimensional space (the mu and sigma values in the figure), and the probability density is output from “c” in the figure; and §3.2; and Page 8, §5; “The KDE model takes the low-dimensional representation and reconstruction error features as feeds, and learns a probability density distribution of training samples”).
Regarding claim 11, it is a method 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 Lv further discloses generating a data sample consistent with the learned manifold in the high-dimensional space by applying the decoder portion of the autoencoder model to a value sampled from the density model (Page 4, Figure 2; the figure discloses, under a broadest reasonable interpretation of the claim language, generating a data sample “z” that is consistent with the learned manifold in the high-dimensional space by applying the decoder portion (the decoder portion is the right side of the box in red dashed lines) of the autoencoder model to a value sampled from the density model; and Page 4, §3.2; “The general model uses z from Eq. (7) as the low-dimensional representation, but we use w from Eq. (5) as the low-dimensional representation. Since z is randomly sampled from the distribution obtained by Eq. (6), which is effective to enhance the generalization ability of LVAE model, but is not conducive to our next density estimation”).
Regarding claims 3 and 13, the rejection of claims 1 and 11 are incorporated and Lv further discloses wherein the density model is trained with a maximum-likelihood training objective (Page 4, Algorithm 1; the algorithm discloses the maximum likelihood training objective in lines 13-19; and §3.3).
Regarding claims 4 and 14, the rejection of claims 1 and 11 are incorporated and Lv further discloses wherein the autoencoder model is trained with a reconstruction error training objective (Page 4, Algorithm 1; the algorithm discloses the reconstruction error training objective in lines 9-10; and §3.2 and §3.4; reconstruction error and KL divergence).
Regarding claims 5 and 15, the rejection of claims 1 and 11 are incorporated and Lv further discloses wherein the instructions are further executable for determining whether a second data set having one or more data points in the high-dimensional space are out-of-distribution with respect to the training data set based on the probability density on the low-dimensional space (§3.5; the section discloses that the encodes produces latent embeddings for test data, the kernel density estimator is used to compute a probability for test points, and low-density points are flagged as anomalies).
Regarding claims 6 and 16, the rejection of claims 1 and 11 are incorporated and Lv further discloses applying the encoder portion to a second data set to determine respective second positions of the second data set in the low-dimensional space; determining a second probability density of the second data set in the low-dimensional space based on the respective second positions; and determining whether the second data set is out-of-distribution based on a comparison of the probability density learned for the training data and the second probability density (§3.5; the section discloses that the latent embeddings are computer, the KDE density is evaluated for a test set, and an anomaly decision is made based on a density comparison).
Regarding claims 7 and 17, the rejection of claims 1 and 11 are incorporated and Lv further discloses identifying a reconstruction error for the training data by applying the encoder portion and the decoder portion of the autoencoder to data points in the training data set; determining a second reconstruction error for a second data set by applying the encoder portion and then the decoder portion to data points in the second data set; and determining a similarity score of the second data set to the training data based on the reconstruction error for the training data compared to the second reconstruction error (§3.3; the section discloses computing a reconstruction error for training samples using the autoencoder, applying the autoencoder to test data, and combining a latent density and reconstruction error to score anomalies).
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 8 and 18 are rejected under 35 U.S.C. § 103 as being obvious over Lv in view of Huang et al. (Huang et al., “Neural Autoregressive Flows”, Apr. 3, 2018, arXiv:1804.00779v1, pp. 1-16, hereinafter “Huang”).
Regarding claims 8 and 18, the rejection of claims 1 and 11 are incorporated and Lv fails to explicitly disclose but Huang discloses wherein the probability density of the high-dimensional space is determined by a change-of-variable formula from the low-dimensional space to the high-dimensional space (Abstract; “We demonstrate that the proposed neural autoregressive flows (NAF) are universal approximators for continuous probability distributions, and their greater expressivity allows them to better capture multimodal target distributions”, wherein the NAF is interpreted as the change of variable formula; and Page 2, Column 1; “Empirically, we show that our method works better than the state-of-the-art affine autoregressive flows of Kingma et al. (2016) and Papamakarios et al. (2017), both as a sample generator which captures multimodal target densities with higher fidelity, and as a density model which more accurately evaluates the likelihood of data samples drawn from an unknown distribution”; and Figure 4; and §3 and §4).
Lv and Huang are analogous art because both are concerned with density estimation. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in density estimation and machine learning to combine the change-of-variable formula of Huang with the system of Lv to yield to the predictable result of wherein the probability density of the high-dimensional space is determined by a change-of-variable formula from the low-dimensional space to the high-dimensional space. The motivation for doing so would be to better capture multimodal target distributions (Huang; Abstract).
Claims 10 and 20 are rejected under 35 U.S.C. § 103 as being obvious over Lv in view of Niculescu-Mizil et al. (US 20190244337 A1, hereinafter “Nicu”).
Regarding claims 10 and 20, the rejection of claims 1 and 11 are incorporated and Lv fails to explicitly disclose but Nicu discloses wherein the high-dimensional space is an image (Abstract; The system further includes a CONDA-GMM, having a first and a second conditional deep autoencoder for respectively (i) taking each cropped image without a respective center block as input for measuring a discrepancy between a reconstructed and a target center block, and (ii) taking an entirety of cropped images with the target center block. The CONDA-GMM constructs density estimates based on reconstruction error features and low-dimensional embedding representations derived from image encodings”).
Lv and Nicu are analogous art because both are concerned with density estimation. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in density estimation and machine learning to combine the image data of Nice with the system of Lv to yield to the predictable result of wherein the high-dimensional space is an image. The motivation for doing so would be to determine an anomaly existence based on a prediction of a likelihood of the anomaly existing in a framework (Nicu; Abstract).
Response to Arguments
Applicant’s arguments and amendments, filed on 4/3/2026, with respect to the 35 USC § 101 rejection of the pending claims have been fully considered but are not persuasive.
With respect to Step 2A, Prong One, Applicant argues “the invention is not directed to an abstract idea because it inherently requires application of and evaluation of computer models. The claimed invention provides a way to improve on computer modeling of data that lies on a manifold in high-dimensional spaces”. Examiner respectfully disagrees.
Applicant has failed to provide any evidence or arguments as to why certain limitations in the independent claims identified as abstract ideas are not mental processes or mathematical concepts as claimed. The various improvements discussed in and cited from the originally filed specification do not support the position that certain limitations in the independent claims identified as abstract ideas in the rejection above are improperly characterized as abstract ideas in the form of mental processes or mathematical concepts
With respect to Step 2A, Prong Two and Step 2B, Applicant cites to various alleged technical improvements in the specification and further argues “Under at least prong 2, the claims as a whole are thus directed to improvements in computer modeling technology and how to train different types of models (an autoencoder and a density model) to successfully capture data on a manifold of a high-dimensional space that was not possible with prior approaches”. Examiner respectfully disagrees.
Applicant has failed to identify any specific additional elements in the claim language beyond the identified abstract ideas that reflect the alleged technical improvements discussed in the originally filed specification. It appears that the alleged improvements are reflected in the identified abstract ideas of the claims, and that there are no additional elements in the claims that integrate the abstract ideas into a practical application, reflect a technical improvement, or provide significantly more than the abstract ideas. Abstract ideas alone cannot reflect a technical improvement. See MPEP §2106.05(a).
Accordingly, Applicant’s arguments and amendments are not persuasive, and the 35 USC § 101 rejection of the pending claims is maintained.
Applicant’s arguments and amendments, filed on 4/3/2026, with respect to the 35 USC § 103 rejection of claims 9 and 19 have been fully considered and are persuasive. The 35 USC § 103 rejection of claims 9 and 19 is withdrawn. However, Applicant’s arguments and amendments, filed on 4/3/2026, with respect to the 35 USC § 102(a)(1) and 35 USC § 103 rejection of claims 1-8, 10-18, and 20 have been fully considered and are not persuasive.
With respect to the 35 USC § 102(a)(1) rejection of the independent claims, Applicant first argues “Lv does not disclose determining a probability density of the high-dimensional space ‘by applying the decoder portion of the autoencoder model to the probability density of the low-dimensional space.’" Examiner respectfully disagrees.
The limitation “determining a probability density of the high-dimensional space by applying the decoder portion of the autoencoder model to the probability density of the low-dimensional space” is disclosed by Figure 2 of Lv. Specifically, Figure 2 discloses, under a broadest reasonable interpretation of the claim language, determining a probability density of the high-dimensional space by applying the decoder portion (the decoder portion is the right side of the box in red dashed lines) of the autoencoder mode to the probability density of the low-dimensional space (the mu and sigma values in the figure), and the probability density is output from “c” in the figure. Applicant has failed to provide any arguments or evidence to support the assertion that Lv does not disclose this amended limitation of the independent claims.
Applicant next argues “rather than applying the decoder portion of the autoencoder to the probability density of the low-dimensional space, Lv discloses processing the low-dimensional representation to "reconstruct the original data" and obtain "the reconstruction error" for anomaly detection … Accordingly, Lv does not disclose "applying the decoder portion of the autoencoder model to the probability density of the low-dimensional space" as claimed. Nor would such an approach make sense in Lv's configuration Lv expressly discusses the benefit of using reconstruction error alongside low- dimensional features in its out-of-distribution detection”. Examiner respectfully disagrees.
As discussed above, Lv discloses the amended limitation “applying the decoder portion of the autoencoder model to the probability density of the low-dimensional space” in at least Figure 2 of Lv. The decoder is the right portion og the figure outlined in red dashed lines, and it is “applied to” the probability density (mu and sigma) of the low-dimensional space or low-dimensional representation. Applicant has failed to provide persuasive evidence or arguments as to why Figure 2 of Lv fails to disclose this amended limitation as claimed.
Accordingly, Applicant’s arguments and amendments are not persuasive, and the 35 USC § 102(a)(1) and 35 USC § 103 rejection of claims 1-8, 10-18, and 20 is maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brent Hoover whose telephone number is (303)297-4403. The examiner can normally be reached Monday - Friday 9-5 MST.
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/BRENT JOHNSTON HOOVER/ Primary Examiner, Art Unit 2127
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