DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 1/17/2024 for application number 18/451,620.
Claims 1-10 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.
Claim 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims recite:
Claim 1:
A multi-manifold embedding learning method, comprising steps of: using an ID (identity) training data train a multi-manifold embedding learning model, and freezing parameters of the multi-manifold embedding learning model to obtain a trained multi-manifold embedding learning model; and feeding a test data to the trained multi-manifold embedding learning model, so as to use a threshold to distinguish OOD (out-of-distribution) samples from ID samples.
Claim 7:
A multi-manifold embedding learning system, comprising: a storage device configured to store at least one instruction; and a processor coupled to the storage device, and the processor configured to access and execute the at least one instruction for: initializing a plurality of branches of a multi-manifold embedding learning model, so as to encode different manifolds; using an ID training data train the multi-manifold embedding learning model, and freezing parameters of the multi-manifold embedding learning model to obtain a trained multi-manifold embedding learning model; and feeding a test data to the trained multi-manifold embedding learning model, so as to use a threshold to distinguish OOD (out-of-distribution) samples from ID samples.
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically mathematical equations and calculations. Applicant’s specification shows the equations for initializing and training a multi-manifold embedding learning model at para. 0032-45 (as published), and equations and calculations distinguishing out-of-distribution data from in-distribution data at para. 0046 (as published). Therefore, the broadest reasonable interpretation of the underlined limitations includes mathematical equations and calculations.
(2A, prong 2) This judicial exception is not integrated into a practical application. Claim 7 recites the additional element of [a] generic computer hardware including a processor and storage. This additional element is a mere instruction to apply the exception because it merely adds generic computer hardware after-the-fact to the abstract idea. 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 to the mathematical equations and calculations.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional element [a] is a mere instruction to apply the exception, as explained above. 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 to the mathematical equations and calculations.
With respect to dependent claims 2-6 and 8-10, (2A, prong 1) these claims recite additional mathematical calculation steps (see para. 0032-45 of spec. as filed for the corresponding calculations).
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.
Claim(s) 1- is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by.
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) 1-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hong et al., Curved Geometric Networks for Visual Anomaly Recognition (NPL [U], see Notice of References Cited) in view of Germain et al. (US 2025/0077840 A1).
In reference to claim 1, Hong teaches a multi-manifold embedding learning method, comprising steps of: using an ID (identity) training data train a multi-manifold embedding learning model (multi-manifold model including both spherical and hyperbolic manifolds is trained using in-distribution data, IV. Approach, page 17925-26; C. Model Training, page 17926; fig. 3, page 17924), and freezing parameters of the multi-manifold embedding learning model to obtain a trained multi-manifold embedding learning model; and feeding a test data to the trained multi-manifold embedding learning model, so as to … distinguish OOD (out-of-distribution) samples from ID samples (after training, the model’s parameters are no longer updated, and the model scores test data to distinguish out-of-distribution data through an anomaly score, IV. Approach, page 17925-26; fig. 4, page 17926).
However, Hong does not teach use a threshold to distinguish OOD (out-of-distribution) samples.
Germain teaches use a threshold to distinguish OOD (out-of-distribution) samples (inputs are given an anomaly score, and a threshold is used to determine if the input is anomalous, para. 0051-52).
It would have been obvious to one of ordinary skill in art, having the teachings of Hong and Germain before the earliest effective filing date, to modify the anomaly score of Hong to include the threshold of Germain.
One of ordinary skill in the art would have been motivated to modify the anomaly score of Hong to include the threshold of Germain because it helps calculate an actionable anomaly score value (para. 0051-52).
In reference to claim 2, Hong teaches the multi-manifold embedding learning method of claim 1, further comprising: initializing a plurality of branches of the multi-manifold embedding learning model, so as to encode different manifolds (spherical and hyperbolic manifold branches, IV. Approach, page 17925-26, particularly paragraph under equation 11 and B. GiT Model, first paragraph).
In reference to claim 3, Hong teaches the multi-manifold embedding learning method of claim 2, wherein the branches comprise a hypersphere branch and a hyperbolic branch, and the different manifolds comprise a hypersphere manifold and a hyperbolic manifold (spherical and hyperbolic manifold branches, IV. Approach, page 17925-26, particularly paragraph under equation 11 and B. GiT Model, first paragraph).
In reference to claim 4, Hong teaches the multi-manifold embedding learning method of claim 2, wherein the step of using the ID training data train a multi-manifold embedding learning model, and freezing parameters of the multi-manifold embedding learning model to obtain a trained multi-manifold embedding learning model comprises: for each training iteration, extracting an embedding of each of the different manifolds, computing a loss correspondingly, and updating the multi-manifold embedding learning model based on the loss (C. Model Training, page 17926 and fig. 3, page 17924); after the multi-manifold embedding learning model is trained, freezing the parameters of the multi-manifold embedding learning model to obtain the trained multi-manifold embedding learning model … (after training, the model’s parameters are no longer updated, and the model scores test data to distinguish out-of-distribution data through an anomaly score, IV. Approach, page 17925-26; fig. 4, page 17926).
However, Hong does not teach feeding the ID training data to the trained multi-manifold embedding learning model to extract an ID reference embedding.
Germain teaches feeding the ID training data to the trained multi-manifold embedding learning model to extract an ID reference embedding (reference embedding from K-nearest inlier neighbors, para. 0051-52).
It would have been obvious to one of ordinary skill in art, having the teachings of Hong and Germain before the earliest effective filing date, to modify the anomaly score of Hong to include the threshold of Germain.
One of ordinary skill in the art would have been motivated to modify the anomaly score of Hong to include the threshold of Germain because it helps calculate an actionable anomaly score value (para. 0051-52).
In reference to claim 5, Hong teaches the multi-manifold embedding learning method of claim 4, wherein the loss comprises losses of the different manifolds and a cross-entropy classification loss (C. Model Training, page 17926; also see corresponding manifold loss calculations under IV. Approach, page 17925-26).
In reference to claim 6, Germain teaches the multi-manifold embedding learning method of claim 4, wherein the step of feeding the test data to the trained multi-manifold embedding learning model, so as to use the threshold to distinguish the OOD samples from the ID samples comprises: feeding the test data to the trained multi-manifold embedding learning model to extract a latent embedding; computing a OOD score based on a distance between the latent embedding and the ID reference embedding; and comparing the OOD score to the threshold to perform a OOD detection, and the OOD detection distinguishes the OOD samples from the ID samples in the test data (embeddings for input and K-nearest inlier neighbors is calculated, an outlier score is calculated based on distance between the embeddings, and score is compared to threshold to see if input is an outlier, para. 0051-52).
In reference to claim 7, Hong teaches a multi-manifold embedding learning system, comprising: a storage device configured to store at least one instruction; and a processor coupled to the storage device, and the processor configured to access and execute the at least one instruction for (it would be obvious that Hong would be executed on a computer): initializing a plurality of branches of a multi-manifold embedding learning model, so as to encode different manifolds (multi-manifold model including both spherical and hyperbolic manifolds, IV. Approach, page 17925-26; C. Model Training, page 17926; fig. 3, page 17924, particularly paragraph under equation 11 and B. GiT Model, first paragraph); using an ID training data train the multi-manifold embedding learning model (model trained on in-distribution data, IV. Approach, page 17925-26; C. Model Training, page 17926; fig. 3, page 17924), and freezing parameters of the multi-manifold embedding learning model to obtain a trained multi-manifold embedding learning model; and feeding a test data to the trained multi-manifold embedding learning model, so as to … distinguish OOD (out-of-distribution) samples from ID samples (after training, the model’s parameters are no longer updated, and the model scores test data to distinguish out-of-distribution data through an anomaly score, IV. Approach, page 17925-26; fig. 4, page 17926).
However, Hong does not teach use a threshold to distinguish OOD (out-of-distribution) samples.
Germain teaches use a threshold to distinguish OOD (out-of-distribution) samples (inputs are given an anomaly score, and a threshold is used to determine if the input is anomalous, para. 0051-52).
It would have been obvious to one of ordinary skill in art, having the teachings of Hong and Germain before the earliest effective filing date, to modify the anomaly score of Hong to include the threshold of Germain.
One of ordinary skill in the art would have been motivated to modify the anomaly score of Hong to include the threshold of Germain because it helps calculate an actionable anomaly score value (para. 0051-52).
In reference to claim 8, Hong teaches the multi-manifold embedding learning system of claim 7, wherein the processor accesses and executes the at least one instruction for: for each training iteration, extracting an embedding of each of the different manifolds, computing a loss correspondingly, and updating the multi-manifold embedding learning model based on the loss (C. Model Training, page 17926 and fig. 3, page 17924); after the multi-manifold embedding learning model is trained, freezing the parameters of the multi-manifold embedding learning model to obtain the trained multi-manifold embedding learning model … (after training, the model’s parameters are no longer updated, and the model scores test data to distinguish out-of-distribution data through an anomaly score, IV. Approach, page 17925-26; fig. 4, page 17926).
However, Hong does not teach feeding the ID training data to the trained multi-manifold embedding learning model to extract an ID reference embedding.
Germain teaches feeding the ID training data to the trained multi-manifold embedding learning model to extract an ID reference embedding (reference embedding from K-nearest inlier neighbors, para. 0051-52).
It would have been obvious to one of ordinary skill in art, having the teachings of Hong and Germain before the earliest effective filing date, to modify the anomaly score of Hong to include the threshold of Germain.
One of ordinary skill in the art would have been motivated to modify the anomaly score of Hong to include the threshold of Germain because it helps calculate an actionable anomaly score value (para. 0051-52).
In reference to claim 9, Germain teaches the multi-manifold embedding learning system of claim 8, wherein the processor accesses and executes the at least one instruction for: feeding the test data to the trained multi-manifold embedding learning model to extract a latent embedding; computing a OOD score based on a distance between the latent embedding and the ID reference embedding; and comparing the OOD score to the threshold to perform a OOD detection, and the OOD detection distinguishes the OOD samples from the ID samples in the test data (embeddings for input and K-nearest inlier neighbors is calculated, an outlier score is calculated based on distance between the embeddings, and score is compared to threshold to see if input is an outlier, para. 0051-52).
In reference to claim 10, Hong teaches the multi-manifold embedding learning system of claim 8, wherein the branches comprise a hypersphere branch and a hyperbolic branch (spherical and hyperbolic manifold branches, IV. Approach, page 17925-26, particularly paragraph under equation 11 and B. GiT Model, first paragraph), the different manifolds comprise a hypersphere manifold and a hyperbolic manifold, and the loss comprises a hypersphere loss, a hyperbolic loss and a cross-entropy classification loss (C. Model Training, page 17926; also see corresponding manifold loss calculations under IV. Approach, page 17925-26).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Notice of References Cited: [V] which is not prior art to this application, but appears to be a publication by the inventors, and [W] and [X] which teach representation learning in hyperbolic and hyperspherical spaces.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
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