DETAILED ACTION
Claims 1-20 are pending in the Instant Application.
Claims 1-20 are rejected (Non-Final Rejection).
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 .
Priority
The Instant Application, filed 01/19/2024, claims priority from provisional application 63/442,353, filed 01/31/2023. Thus, the earliest effective filing date of the invention is 01/31/2023 for what is recited therein.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/11/2024 was considered by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4-8, 11-15 and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by Yoon et al. (“Yoon”), ”SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch,” 2022.
As per claim 1, Spade discloses a system, comprising: a non-transitory memory ([Abstract] wherein computer technology is described requiring memory); a processor communicatively coupled to the non-transitory memory ([Abstract] wherein computer technology is described requiring memory and a processor), wherein the processor is configured to read a set of instructions to:
obtain, from the non-transitory memory, a training dataset including data representative of a plurality of interactions ([Page 4, 3. Problem Formulation] wherein a training dataset includes both labeled and unlabeled training data);
generate a plurality of anomaly detection models, wherein at least one of the plurality of anomaly detection models is generated by an unsupervised training process ([Page 5, 4.3 Pseudo-labeling via consensus] wherein multiple OCCs are a plurality of anomaly detection models and are generated by unsupervised one-class classifier training using consensus);
generate a unified anomaly score by combining outputs of a subset of the plurality of anomaly detection models ([Page 6, 4.3 Pseudo-labeling via consensus] wherein outputs are combined, and a score is determined by determining if all the OCCs are in agreement);
generate an augmented training dataset by labeling at least one of the interactions in the plurality of interactions based on the unified anomaly score ([Page 6, 4.3 Pseudo-labeling via consensus] wherein a set of positive and negative labeled data is generated and are considered augmented training datasets to the labeled dataset); and generate an anomaly classification model by applying a supervised training process including the augmented training dataset ([Page 5, 4.1 Desiderata-4.2 Building Blocks] wherein a binary classifier is trained with both the supervised, labeled dataset, and augmented training dataset).
As per claim 4, Yoon teaches the system of claim 1, wherein the outputs of the subset of the plurality of anomaly detection models are combined based on a skewness of each of the outputs ([Page 6, Fig. 3] wherein the subset is generated (an ensemble in the prior art) are generated/combined based on an intersection of different skewed, positive and negative, examples).
As per claim 5, Yoon discloses the system of claim 1, wherein the at least one of the interactions in the plurality of interactions includes an original label, and wherein the at least one of the interactions is relabeled in the augmented training dataset to have a label other than the original label ([Page 5, 4.1 Desiderata] some of the training data is labeled, while another interaction is relabeled by the OCCs with a label other than the same label as the original label.)
As per claim 6, Yoon discloses the system of claim 5, wherein the at least one of the interactions has an evaluation metric above a cutoff threshold ([Page 6, 4.3 Pseudo-labeling via consensus] wherein an evaluation must be above a threshold, which is full consensus). .
As per claim 7, Yoon discloses the system of claim 5, wherein the original label comprises a label in a first category, and wherein the label other than the original label comprises a label in a second category ([Page 6] wherein the original labels can be both positive and negative labeled data, and wherein the label other than the original label can also be positive and negative labeled data, so if the original data can be positive then the relabeled augmented data can be negative). .
As per claim 8, claim 8 is the method performed by the system of claim 1 and is rejected for the same rationale and reasoning.
As per claim 11, claim 11 is the method performed by the system of claim 4 and is rejected for the same rationale and reasoning.
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As per claim 12, claim 12 is the method performed by the system of claim 5 and is rejected for the same rationale and reasoning.
As per claim 13, claim 13 is the method performed by the system of claim 6 and is rejected for the same rationale and reasoning.
As per claim 14, claim 14 is the method performed by the system of claim 7 and is rejected for the same rationale and reasoning.
As per claim 15, claim 15 is the computer program product that provides the system of claim 1 and is rejected for the same rationale and reasoning.
As per claim 18, claim 18 is the computer program product that provides the system of claim 4 and is rejected for the same rationale and reasoning.
As per claim 19, claim 19 is the computer program product that provides the system of claim 5 and is rejected for the same rationale and reasoning.
As per claim 20. The non-transitory computer readable medium of claim 15, wherein the at least one of the interactions has an evaluation metric above a cutoff threshold ([Page 6, 4.3 Pseudo-labeling via consensus] wherein an evaluation must be above a threshold, which is full consensus), wherein the original label comprises a label in a first category, and wherein the label other than the original label comprises a label in a second category ([Page 6] wherein the original labels can be both positive and negative labeled data, and wherein the label other than the original label can also be positive and negative labeled data, so if the original data can be positive then the relabeled augmented data can be negative).
Claim Rejections - 35 USC § 103
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 2, 3, 9, 10, 16 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Yoon in view of Zuluaga et al. (“Zuluaga”), United States Patent Application Publication No. 2019/0392351.
As per claim 2, Yoon discloses the system of claim 1, but does not disclose wherein the subset of plurality of anomaly detection models comprises a set of top ranked individual anomaly detection models selected from the plurality of anomaly detection models. However, Zuluaga teaches wherein the subset of plurality of anomaly detection models comprises a set of top ranked individual anomaly detection models selected from the plurality of anomaly detection models ([0010] wherein a subset of one of the plurality of models is determined based on a ranking).
Both Yoon and Zuluaga describe training anomaly detection models. One could use the ranking of models in Zuluaga with the unsupervised model labeling in Toon to teach the claimed invention. It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the method of using multiple anomaly detection models in Yoon with the ranking and evaluating of those models to determine the set of models in Zuluaga in order to only use the best available models to obtain better results.
As per claim 3, note the rejection of claim 2 where Yoon and Zuluaga are combined. The combination teaches the system of claim 2. Zuluaga further teaches wherein the processor is configured to read the set of instructions to: generate an evaluation metric for each of the plurality of anomaly detection models by applying a uniform evaluation process ([0043]-[0044] wherein a uniform evaluation process is descried); and rank the plurality of anomaly detection models based on the evaluation metric, wherein the set of top ranked individual anomaly detection models includes the plurality of anomaly detection models having a highest rank based on the evaluation metric ([0010] wherein the plurality of models are ranked and a set of 1 top model is selected.).
As per claim 9, claim 9 is the method performed by the system of claim 2 and is rejected for the same rationale and reasoning.
As per claim 10, claim 10 is the method performed by the system of claim 3 and is rejected for the same rationale and reasoning.
As per claim 16, claim 16 is the computer program product that provides the system of claim 2 and is rejected for the same rationale and reasoning.
As per claim 17, claim 17 is the method performed by the system of claim 3 and is rejected for the same rationale and reasoning.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KANNAN SHANMUGASUNDARAM whose telephone number is (571)270-7763. The examiner can normally be reached M-F 9:00 AM -6:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached at (571) 272-4085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KANNAN SHANMUGASUNDARAM/Primary Examiner, Art Unit 2168
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