DETAILED ACTION
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 non-statutory subject matter, specifically an abstract idea without significantly more.
Claims (1-20) are directed to the abstract idea of Mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations); Mental processes – concepts performed in the human mind (including an observation, evaluation, judgement, opinion); Certain methods of organizing human activity (managing personal behavior), Grouping “managing personal behavior or relationships or interactions between people”.
This judicial exception is not integrated into a practical application. The claims recite additional limitations such as computing device, one or more processors, memory storing instructions, machine learning models, parallel execution of two models, detection based on movement/pose and object detection, output of indications. However, these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of Abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide practical application for an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims recite additional limitations which are computing device, one or more processors, memory storing instructions, machine learning models, parallel execution of two models, detection based on movement/pose and object detection, output of indications. However, these limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of Abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide significantly more to an abstract idea (MPEP 2106.05(f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Therefore, since there are no limitations in the claims (1-20) that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as a combination and as an ordered combination adds nothing that is not already present when looking at the elements taken individually, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
Claim(s) 1, 5-7, 9, 13-15, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over He et al ("Anomaly detection with test time augmentation and consistency evaluation." arXiv preprint arXiv:2206.02345, June 6 2022, pages 1-26, retrieved from the Internet on 2/13/2026) in view of Rowe et al (WO2022/235414A1).
Regarding claim 1, He teaches a method that is computer implemented for classification process evaluation (section 3, TTA-AD), comprising:
receiving, at a processing circuit, classification data generated by a classification process for augmented versions of a test sensed information unit (section 2, Using data augmentations in anomaly detection has been applied by existing algorithms in both training and evaluating phases…Our TTA-AD uses test data augmentation as well but with two main differences. First, we utilize the relations between augmentations of a single sample; Input in Algorithm 1 in section 3.1);
evaluating the classification data across the augmented versions, by analyzing a distribution of selected classification values of the classification data (section 3.2, We define the consistency score of a given data x as the inner product of the model output of x and T(x));
determining, based on the evaluation, a compatibility of the classification process with respect to the received classification data (section 3.2, Based on the observation (Figure 1), the model f tends to make more consistent outputs for in-distribution data than out-distribution data. So S(x) should be close to 0 for in-distribution data and S(x) should be approximately 1 for out-distribution data. Such score assigning process is the basis of our theoretical analysis in Section 5.2).
He fails to teach issuing a compatibility indication in accordance with the determined compatibility, the compatibility indication indicative of the compatibility of the classification process to classify an element captured by the test sensed information unit.
However Rowe teaches determining a compatibility of the classification process with respect to the received classification data (para. [0021], predicted confidence score; [0024], A confidence score is generated for a particular classification, indicating a level of confidence that the particular classification for the target data point is correct); and
issuing a compatibility indication in accordance with the determined compatibility, the compatibility indication indicative of the compatibility of the classification process to classify an element captured by the test sensed information unit (para. [0022]-[0023]).
Therefore taking the combined teachings of He and Rowe as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Rowe into the method of He. The motivation to combine Rowe and He would be to provide accurate and reliable confidence estimates for any machine learning model (para. [0025] of Rowe).
Regarding claim 5, the modified method of He teaches a method further comprising applying the classification process to provide the classification data (section 1 of He; Input in Algorithm 1 in section 3.1 of He).
Regarding claim 6, the modified method of He teaches a method comprising determining that the classification process is capable of classifying the test sensed information unit when the classification values are statistically significant (para. [0075]-[0076] of Rowe, the confidence score meets a particular threshold. Furthermore, any value is interpreted to be statistically significant).
Regarding claim 7, the modified method of He teaches a method comprising determining that the classification process is capable of classifying the test sensed information unit when at least a defined percent of the classification values are the same data (section 3.2 of He, We define the consistency score of a given data x as the inner product of the model output of x and T(x). Any percent greater than zero is interpreted to be the defined percent).
Regarding claim 9, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 13, the claim recites similar subject matter as claim 5 and is rejected for the same reasons as stated above.
Regarding claim 14, the claim recites similar subject matter as claim 6 and is rejected for the same reasons as stated above.
Regarding claim 15, the claim recites similar subject matter as claim 7 and is rejected for the same reasons as stated above.
Regarding claim 17, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Claim(s) 2, 3, 10, 11, 18 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over He et al ("Anomaly detection with test time augmentation and consistency evaluation." arXiv preprint arXiv:2206.02345, June 6 2022, pages 1-26, retrieved from the Internet on 2/13/2026) and Rowe et al (WO2022/235414A1) in view of Williams, Jr et al (US20150254555).
Regarding claim 2, the modified method of He fails to teach a method further comprising triggering an allocation of another classification process to classify the test sensed information contingent on the determined compatibility.
However Williams, Jr teaches selecting a classification process to classify a test sensed information (712 and 714 in fig. 7, para. [0123]-[0124]) contingent on a determined compatibility (710 in fig. 7, para. [0121], At decision block 710, in at least one of the various embodiments, if a classification confidence score associated a classification result generated by the FL model exceeds a confidence score for a classification result generated by the DLNN model, control may flow to block 712; otherwise, control may flow to block 714).
Therefore taking the combined teachings of He and Rowe with Williams, Jr as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Williams, Jr into the method of He and Rowe. The motivation to combine Rowe, Williams, Jr and He would be to allow for faster training of a classifier (para. [0116] of Williams, Jr).
Regarding claim 3, the modified method of He teaches a further comprising routing the augmented versions of the test sensed information unit to the other classification process (para. [0121] of Williams, Jr).
Regarding claim 10, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
Regarding claim 11, the claim recites similar subject matter as claim 3 and is rejected for the same reasons as stated above.
Regarding claim 18, the claim recites similar subject matter as claim 2 and is rejected for the same reasons as stated above.
Regarding claim 19, the claim recites similar subject matter as claim 3 and is rejected for the same reasons as stated above.
Claim(s) 4, 12, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over He et al ("Anomaly detection with test time augmentation and consistency evaluation." arXiv preprint arXiv:2206.02345, June 6 2022, pages 1-26, retrieved from the Internet on 2/13/2026) and Rowe et al (WO2022/235414A1) in view of Cubuk et al ("Autoaugment: Learning augmentation policies from data." arXiv preprint arXiv:1805.09501, 2018, pages 1-14, retrieved from the Internet on 2/20/2026).
Regarding claim 4, the modified method of He fails to teach a method wherein the augmented versions of the test sensed information unit are generated by using one or more machine learning processes.
However Cubuk teaches wherein the augmented versions of test information (abstract, In our implementation, we have designed a search space where a policy consists of many subpolicies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied; section 5, The difference of our method to theirs is that our method tries to optimize classification accuracy directly whereas their method just tries to make sure the augmented images are similar to the current training images) are generated by using one or more machine learning processes (abstract, Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet).
Therefore taking the combined teachings of He and Rowe with Cubuk as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Cubuk into the method of He and Rowe. The motivation to combine Rowe, Cubuk and He would be to improve the accuracy of modern image classifiers (abstract of Cubuk).
Regarding claim 12, the claim recites similar subject matter as claim 4 and is rejected for the same reasons as stated above.
Regarding claim 20, the claim recites similar subject matter as claim 4 and is rejected for the same reasons as stated above.
Claim(s) 8 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over He et al ("Anomaly detection with test time augmentation and consistency evaluation." arXiv preprint arXiv:2206.02345, June 6 2022, pages 1-26, retrieved from the Internet on 2/13/2026) and Rowe et al (WO2022/235414A1) in view of Zhang et al (US20250200634).
Regarding claim 8, the modified method of He fails to teach a method wherein the classification process is an embedding-based classification process.
However Zhang teaches using an embedding-based classification process (para. [0057]-[0058]).
Therefore taking the combined teachings of He and Rowe with Zhang as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the steps of Zhang into the method of He and Rowe. The motivation to combine Rowe, Zhang and He would be to provides a more accurate way of identifying and filtering irrelevant search results for different use cases (para. [0085] of Zhang).
Regarding claim 16, the claim recites similar subject matter as claim 8 and is rejected for the same reasons as stated above.
Related Art
Harrison (US20200249316) – see para. [0048], [0052]
Conclusion
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/LEON VIET Q NGUYEN/Primary Examiner, Art Unit 2663