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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/07/2026 has been entered.
Response to Amendment
The Amendment filed 04/07/2026 has been entered. Claims 1-20 remain pending in this application.
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 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 of this title, 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 1, 4, 8, 9, 11, 14, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Marwah et al. (US 20200380117 A1 hereinafter Marwah) in view of Verma et al. (US 20230281518 A1 hereinafter Verma) and Givental et al. (US 20210264025 A1 hereinafter Givental2)
As to independent claim 1, Marwah teaches a method comprising:
first inferring, by each anomaly detector of at least three anomaly detectors, a respective anomaly inference for each tuple of a plurality of tuples; [three anomaly detectors (Fig. 1 108-1, 108-2, 108-p) for event data ¶27 " multiple anomaly detectors 108-1 to 108-p, where m≥2. An anomaly detector 108-i (i=1 to p) is able to produce a respective anomaly score"], [event data includes 3 features (time, userID, and ResourceID) (tuple) ¶22]
performing without supervision labels: [unsupervised, unlabeled data ¶29-30]
a) performing for each candidate anomaly detector of the at least three anomaly detectors:
measuring, respectively for each particular anomaly detector of the at least three anomaly detectors that is not the candidate anomaly detector, a respective similarity score for the candidate anomaly detector that indicates how similar are said anomaly inferences of the candidate anomaly detector to said anomaly inferences of the particular anomaly detector, and [determines if anomaly detectors are dependent/independent (similar inferences) with scores ¶51, ¶34-36 "anomaly detector score aggregation engine 112 includes a dependency determination logic 114 that is able to discover a dependency structure 116 among the anomaly detectors 108-1 to 108-p"]
wherein the method is performed by one or more computers. [computing environment ¶17]
Marwah does not specifically teach combining said similarity scores of the candidate anomaly detector into a combined similarity score for the candidate anomaly detector;
However, Verma teaches combining said similarity scores of the candidate anomaly detector into a combined similarity score for the candidate anomaly detector; [agreement score (combined similarity) of how the models compare with averages for an agreement matrix ¶17-18 "An agreement score, which measures how the model provided by each agent compares with those of other agent, can be computed for each agent locally, and averaged or combined over all the agents to get the agreement matrix."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection disclosed by Marwah by incorporating the combining said similarity scores of the candidate anomaly detector into a combined similarity score for the candidate anomaly detector disclosed by Verma because both techniques address the same field of machine learning and by incorporating Verma into Marwah ensures models come to a consensus to reach an agreement [Verma ¶17]
Marwah and Verma do not specifically teach selecting a single best anomaly detector of the at least three anomaly detectors that has a highest combined similarity score;
However, Givental2 teaches selecting a single best anomaly detector of the at least three anomaly detectors that has a highest combined similarity score; [combines scores for ranking and selects the best suited model ¶32-34 "scores are combined to generate a relative ranking for each of the trained ML models,"… " select a corresponding ML model that is best suited for classifying the incoming log"…"best suited ML model for processing the input log and generating the most accurate classification, with the highest achievable confidence, and lowest risk achievable by the trained ML models"]
second inferring, by the single best anomaly detector that has a highest combined similarity score, an anomaly inference for a tuple that is not in the plurality of tuples; [best suited model used to classify (infer) ¶32-34 "best suited ML model for processing the input log and generating the most accurate classification, with the highest achievable confidence, and lowest risk achievable by the trained ML models"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah and Verma by incorporating the selecting a single best anomaly detector of the at least three anomaly detectors that has a highest combined similarity score and second inferring, by the single best anomaly detector that has a highest combined similarity score, an anomaly inference for a tuple that is not in the plurality of tuples; disclosed by Givental2 because all techniques address the same field of machine learning and by incorporating Givental2 into Marwah and Verma enhances anomaly detection for more accurate identification and reducing false positives [Givental2 ¶18]
As to dependent claim 4, Marwah, Verma and Givental2 teach the method of claim 1 above that is incorporated, Marwah, Verma and Givental2 further teach wherein an anomaly inference of the single best anomaly detector is a binary detection class. [Givental2 binary output classification ¶26]
As to dependent claim 8, Marwah, Verma and Givental2 teach the method of claim 1 above that is incorporated, Marwah, Verma and Givental2 further teach teaches wherein said combining said similarity scores of the single best anomaly detector comprises calculating at least one selected from the group consisting of an average and a median. [Verma averaged ¶18, ¶26] [Marwah¶18 "A naïve aggregation (e.g., computing the mean, median, mode, etc.) of the anomaly scores from the large number of anomaly detectors can produce inaccurate results, such as false positives or false negatives. "]
As to dependent claim 9, Marwah, Verma and Givental2 teach the method of claim 1 above that is incorporated, Marwah, Verma and Givental2 further teach wherein the single best anomaly detector does not comprise an artificial neural network (ANN). [Givental forest or SVM model Col. 4 ln. 5-18]
As to independent claim 11, Marwah teaches one or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause: [computing environment ¶17 memory, processors ¶48],
first inferring, by each anomaly detector of at least three anomaly detectors, a respective anomaly inference for each tuple of a plurality of tuples; [three anomaly detectors (Fig. 1 108-1, 108-2, 108-p) for event data ¶27 " multiple anomaly detectors 108-1 to 108-p, where m≥2. An anomaly detector 108-i (i=1 to p) is able to produce a respective anomaly score"], [event data includes 3 features (time, userID, and ResourceID) (tuple) ¶22]
performing without supervision labels: [unsupervised, unlabeled data ¶29-30]
a) performing for each candidate anomaly detector of the at least three anomaly detectors:
measuring, respectively for each particular anomaly detector of the at least three anomaly detectors that is not the candidate anomaly detector, a respective similarity score for the candidate anomaly detector that indicates how similar are said anomaly inferences of the candidate anomaly detector to said anomaly inferences of the particular anomaly detector, and [determines if anomaly detectors are dependent/independent (similar inferences) with scores ¶51, ¶34-36 "anomaly detector score aggregation engine 112 includes a dependency determination logic 114 that is able to discover a dependency structure 116 among the anomaly detectors 108-1 to 108-p"]
Marwah does not specifically teach combining said similarity scores of the candidate anomaly detector into a combined similarity score for the candidate anomaly detector;
However, Verma teaches combining said similarity scores of the candidate anomaly detector into a combined similarity score for the candidate anomaly detector; [agreement score (combined similarity) of how the models compare with averages for an agreement matrix ¶17-18 "An agreement score, which measures how the model provided by each agent compares with those of other agent, can be computed for each agent locally, and averaged or combined over all the agents to get the agreement matrix."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection disclosed by Marwah by incorporating the combining said similarity scores of the candidate anomaly detector into a combined similarity score for the candidate anomaly detector disclosed by Verma because both techniques address the same field of machine learning and by incorporating Verma into Marwah ensures models come to a consensus to reach an agreement [Verma ¶17]
Marwah and Verma do not specifically teach selecting a single best anomaly detector of the at least three anomaly detectors that has a highest combined similarity score;
However, Givental2 teaches selecting a single best anomaly detector of the at least three anomaly detectors that has a highest combined similarity score; [combines scores for ranking and selects the best suited model ¶32-34 "scores are combined to generate a relative ranking for each of the trained ML models,"… " select a corresponding ML model that is best suited for classifying the incoming log"…"best suited ML model for processing the input log and generating the most accurate classification, with the highest achievable confidence, and lowest risk achievable by the trained ML models"]
second inferring, by the single best anomaly detector that has a highest combined similarity score, an anomaly inference for a tuple that is not in the plurality of tuples; [best suited model used to classify (infer) ¶32-34 "best suited ML model for processing the input log and generating the most accurate classification, with the highest achievable confidence, and lowest risk achievable by the trained ML models"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah and Verma by incorporating the selecting a single best anomaly detector of the at least three anomaly detectors that has a highest combined similarity score and second inferring, by the single best anomaly detector that has a highest combined similarity score, an anomaly inference for a tuple that is not in the plurality of tuples; disclosed by Givental2 because all techniques address the same field of machine learning and by incorporating Givental2 into Marwah and Verma enhances anomaly detection for more accurate identification and reducing false positives [Givental2 ¶18]
As to dependent claim 14, Marwah, Verma and Givental2 teach the method of claim 11 above that is incorporated, Marwah, Verma and Givental2 further teach wherein an anomaly inference of the single best anomaly detector is a binary detection class. [Givental2 binary output classification ¶26]
As to dependent claim 18, Marwah, Verma and Givental2 teach the method of claim 11 above that is incorporated, Marwah, Verma and Givental2 further teach teaches wherein said combining said similarity scores of the single best anomaly detector comprises calculating at least one selected from the group consisting of an average and a median. [Verma averaged ¶18, ¶26] [Marwah¶18 "A naïve aggregation (e.g., computing the mean, median, mode, etc.) of the anomaly scores from the large number of anomaly detectors can produce inaccurate results, such as false positives or false negatives. "]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the wherein said combining said fitness scores of the single best anomaly detector comprises calculating at least one selected from the group consisting of an average and a median disclosed by Marwah because all techniques address the same field of machine learning and by incorporating Marwah into Marwah, Verma and Givental2 help reduce issues and maintain performance against threats [Marwah ¶12]
As to dependent claim 19, Marwah, Verma and Givental2 teach the method of claim 11 above that is incorporated, Marwah, Verma and Givental2 further teach wherein the single best anomaly detector does not comprise an artificial neural network (ANN). [Givental forest or SVM model Col. 4 ln. 5-18]
Claims 2-3, 6, 10, 12-13, 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Marwah in view of Verma and Givental2, as applied in the rejection of claim 1 and 11 above, and further in view of Ranganathan et al. (US 11965399 B2 hereinafter Ranganathan)
As to dependent claim 2, Marwah, Verma and Givental2 teach the method of claim 1 above that is incorporated,
Marwah, Verma and Givental2 do not specifically teach repeating a particular step for each contamination factor of a plurality of predefined contamination factors, wherein the particular step is at least one selected from the group consisting of said first inferring and said measuring.
However, Ranganathan teaches repeating a particular step for each contamination factor of a plurality of predefined contamination factors, wherein the particular step is at least one selected from the group consisting of said first inferring and said measuring. [calculates several contamination values for anomalies Col. 4-5 ln. 61-6 "anomalies for each contamination value is recorded. The contamination value which has the highest variance or rate of change of the number points marked as anomalies is chosen as the ideal contamination value."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the repeating a particular step for each contamination factor of a plurality of predefined contamination factors, wherein the particular step is at least one selected from the group consisting of said first inferring and said measuring disclosed by Ranganathan because all techniques address the same field of machine learning and by incorporating Ranganathan into Marwah, Verma and Givental2 provides more ideal parameter values for models thereby improving anomaly detection [Ranganathan Col. 4 ln. 48-65]
As to dependent claim 3, Marwah, Verma and Givental2 teach the method of claim 1 above that is incorporated,
Marwah, Verma and Givental2 do not specifically teach wherein an unknown of the plurality of tuples is at least one selected from the group consisting of an actual contamination factor and correct labels. Givental does teach label confidence or correctness of labels [Givental correct class (label) confidence Col. 12 ln. 30-40]
However, Ranganathan teaches wherein an unknown of the plurality of tuples is at least one selected from the group consisting of an actual contamination factor and correct labels. [score compared to contamination Col. 4 ln. 42-50]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the repeating a particular step for each contamination factor of a plurality of predefined contamination factors, wherein the particular step is at least one selected from the group consisting of said first inferring and said measuring disclosed by Ranganathan because all techniques address the same field of machine learning and by incorporating Ranganathan into Marwah, Verma and Givental2 provides more ideal parameter values for models thereby improving anomaly detection [Ranganathan Col. 4 ln. 48-65]
As to dependent claim 6, Marwah, Verma and Givental2 teach the method of claim 1 above that is incorporated,
Marwah, Verma and Givental2 do not specifically teach wherein the plurality of anomaly detectors contains a first anomaly detector and a second anomaly detector that has a different value for a same hyperparameter as the first anomaly detector.
However, Ranganathan teaches wherein the plurality of anomaly detectors contains a first anomaly detector and a second anomaly detector that has a different value for a same hyperparameter as the first anomaly detector. [Varies parameters like minPts and DBscan Col. 5 ln .7-40 " The minPts is varied within a range example: 50 to 300 (the range may be an empirical percentage of total number of depths) at a step of 5 or 10. The number of points marked as anomalies for each minPts value is recorded."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the wherein the plurality of anomaly detectors contains a first anomaly detector and a second anomaly detector that has a different value for a same hyperparameter as the first anomaly detector disclosed by Ranganathan because all techniques address the same field of machine learning and by incorporating Ranganathan into Marwah, Verma and Givental2 provides more ideal parameter values for models thereby improving anomaly detection [Ranganathan Col. 4 ln. 48-65]
As to dependent claim 10, Marwah, Verma and Givental2 teach the method of claim 1 above that is incorporated,
Marwah, Verma and Givental2 do not specifically teach wherein contamination factor is not a hyperparameter of the single best anomaly detector.
However, Ranganathan teaches wherein contamination factor is not a hyperparameter of the single best anomaly detector. [Ranganathan algorithm/model/detector requires contamination Col. 4 ln. 48-60 "(28) The isolation forest algorithm requires a value for contamination at 203. There are three options to estimate contamination."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the wherein contamination factor is not a hyperparameter of the single best anomaly detector disclosed by Ranganathan because all techniques address the same field of machine learning and by incorporating Ranganathan into Marwah, Verma and Givental2 provides more ideal parameter values for models thereby improving anomaly detection [Ranganathan Col. 4 ln. 48-65]
As to dependent claim 12, Marwah, Verma and Givental2 teach the method of claim 11 above that is incorporated,
Marwah, Verma and Givental2 do not specifically teach repeating a particular step for each contamination factor of a plurality of predefined contamination factors, wherein the particular step is at least one selected from the group consisting of said first inferring and said measuring.
However, Ranganathan teaches repeating a particular step for each contamination factor of a plurality of predefined contamination factors, wherein the particular step is at least one selected from the group consisting of said first inferring and said measuring. [calculates several contamination values for anomalies Col. 4-5 ln. 61-6 "anomalies for each contamination value is recorded. The contamination value which has the highest variance or rate of change of the number points marked as anomalies is chosen as the ideal contamination value."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the repeating a particular step for each contamination factor of a plurality of predefined contamination factors, wherein the particular step is at least one selected from the group consisting of said first inferring and said measuring disclosed by Ranganathan because all techniques address the same field of machine learning and by incorporating Ranganathan into Marwah, Verma and Givental2 provides more ideal parameter values for models thereby improving anomaly detection [Ranganathan Col. 4 ln. 48-65]
As to dependent claim 13, Marwah, Verma and Givental2 teach the method of claim 11 above that is incorporated,
Marwah, Verma and Givental2 do not specifically teach wherein an unknown of the plurality of tuples is at least one selected from the group consisting of an actual contamination factor and correct labels. Givental does teach label confidence or correctness of labels [Givental correct class (label) confidence Col. 12 ln. 30-40]
However, Ranganathan teaches wherein an unknown of the plurality of tuples is at least one selected from the group consisting of an actual contamination factor and correct labels. [score compared to contamination Col. 4 ln. 42-50]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the repeating a particular step for each contamination factor of a plurality of predefined contamination factors, wherein the particular step is at least one selected from the group consisting of said first inferring and said measuring disclosed by Ranganathan because all techniques address the same field of machine learning and by incorporating Ranganathan into Marwah, Verma and Givental2 provides more ideal parameter values for models thereby improving anomaly detection [Ranganathan Col. 4 ln. 48-65]
As to dependent claim 16, Marwah, Verma and Givental2 teach the method of claim 11 above that is incorporated,
Marwah, Verma and Givental2 do not specifically teach wherein the plurality of anomaly detectors contains a first anomaly detector and a second anomaly detector that has a different value for a same hyperparameter as the first anomaly detector.
However, Ranganathan teaches wherein the plurality of anomaly detectors contains a first anomaly detector and a second anomaly detector that has a different value for a same hyperparameter as the first anomaly detector. [Varies parameters like minPts and DBscan Col. 5 ln .7-40 " The minPts is varied within a range example: 50 to 300 (the range may be an empirical percentage of total number of depths) at a step of 5 or 10. The number of points marked as anomalies for each minPts value is recorded."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the wherein the plurality of anomaly detectors contains a first anomaly detector and a second anomaly detector that has a different value for a same hyperparameter as the first anomaly detector disclosed by Ranganathan because all techniques address the same field of machine learning and by incorporating Ranganathan into Marwah, Verma and Givental2 provides more ideal parameter values for models thereby improving anomaly detection [Ranganathan Col. 4 ln. 48-65]
As to dependent claim 20, Marwah, Verma and Givental2 teach the method of claim 11 above that is incorporated,
Marwah, Verma and Givental2 do not specifically teach wherein contamination factor is not a hyperparameter of the single best anomaly detector.
However, Ranganathan teaches wherein contamination factor is not a hyperparameter of the single best anomaly detector. [Ranganathan algorithm/model/detector requires contamination Col. 4 ln. 48-60 "(28) The isolation forest algorithm requires a value for contamination at 203. There are three options to estimate contamination."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the wherein contamination factor is not a hyperparameter of the single best anomaly detector disclosed by Ranganathan because all techniques address the same field of machine learning and by incorporating Ranganathan into Marwah, Verma and Givental2 provides more ideal parameter values for models thereby improving anomaly detection [Ranganathan Col. 4 ln. 48-65]
Claims 5, 7, 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Givental in view of Berls and Givental2, as applied in the rejection of claim 1 and 11 above, and further in view of Das et al. (US 20220172004 A1 hereinafter Das)
As to dependent claim 5, Marwah, Verma and Givental2 teach the method of claim 1 above that is incorporated,
Marwah, Verma and Givental2 do not specifically teach wherein said measuring said similarity scores for the single best anomaly detector comprises applying at least one selected from the group consisting of: F 1 scoring, precision at n (PAN), normalized discounted cumulative gain (NDCG), mutual information, cross entropy, logistic loss, log loss, and Kullback-Leibler (KL) divergence.
However, Das teaches wherein said measuring said similarity scores for the single best anomaly detector comprises applying at least one selected from the group consisting of: F 1 scoring, precision at n (PAN), normalized discounted cumulative gain (NDCG), mutual information, cross entropy, logistic loss, log loss, and Kullback-Leibler (KL) divergence.
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the wherein said measuring said similarity scores for the single best anomaly detector comprises applying at least one selected from the group consisting of: F 1 scoring, precision at n (PAN), normalized discounted cumulative gain (NDCG), mutual information, cross entropy, logistic loss, log loss, and Kullback-Leibler (KL) divergence disclosed by Das because all techniques address the same field of machine learning and by incorporating van Das into Marwah, Verma and Givental2 improves the performance of models for better understanding of data [Das ¶21].
As to dependent claim 7, Marwah, Verma and Givental2 teach the method of claim 1 above that is incorporated,
Marwah, Verma and Givental2 do not specifically teach performed in polynomial time with respect to a count of at least one selected from the group consisting of the at least three anomaly detectors and the plurality of tuples.
However, Das teaches performed in polynomial time with respect to a count of at least one selected from the group consisting of the at least three anomaly detectors and the plurality of tuples. [pipeline with metrics ¶26, and polynomial ¶32 "-polynomial mean"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the performed in polynomial time with respect to a count of at least one selected from the group consisting of the at least three anomaly detectors and the plurality of tuples disclosed by Das because all techniques address the same field of machine learning and by incorporating van Das into Marwah, Verma and Givental2 improves the performance of models for better understanding of data [Das ¶21].
As to dependent claim 15, Marwah, Verma and Givental2 teach the method of claim 1 above that is incorporated,
Marwah, Verma and Givental2 do not specifically teach wherein said measuring said similarity scores for the single best anomaly detector comprises applying at least one selected from the group consisting of: F 1 scoring, precision at n (PAN), normalized discounted cumulative gain (NDCG), mutual information, cross entropy, logistic loss, log loss, and Kullback-Leibler (KL) divergence.
However, Das teaches wherein said measuring said similarity scores for the single best anomaly detector comprises applying at least one selected from the group consisting of: F 1 scoring, precision at n (PAN), normalized discounted cumulative gain (NDCG), mutual information, cross entropy, logistic loss, log loss, and Kullback-Leibler (KL) divergence.
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the wherein said measuring said similarity scores for the single best anomaly detector comprises applying at least one selected from the group consisting of: F 1 scoring, precision at n (PAN), normalized discounted cumulative gain (NDCG), mutual information, cross entropy, logistic loss, log loss, and Kullback-Leibler (KL) divergence disclosed by Das because all techniques address the same field of machine learning and by incorporating van Das into Marwah, Verma and Givental2 improves the performance of models for better understanding of data [Das ¶21].
As to dependent claim 17, Marwah, Verma and Givental2 teach the method of claim 11 above that is incorporated,
Marwah, Verma and Givental2 do not specifically teach performed in polynomial time with respect to a count of at least one selected from the group consisting of the at least three anomaly detectors and the plurality of tuples.
However, Das teaches performed in polynomial time with respect to a count of at least one selected from the group consisting of the at least three anomaly detectors and the plurality of tuples. [pipeline with metrics ¶26, and polynomial ¶32 "-polynomial mean"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the anomaly detection system disclosed by Marwah, Verma and Givental2 by incorporating the performed in polynomial time with respect to a count of at least one selected from the group consisting of the at least three anomaly detectors and the plurality of tuples disclosed by Das because all techniques address the same field of machine learning and by incorporating van Das into Marwah, Verma and Givental2 improves the performance of models for better understanding of data [Das ¶21].
Response to Arguments
Applicant's arguments filed 04/07/2026. In the remark, applicant argues that:
(1) Givental, Berls and Govemtal2 fail to teach “without supervision” and “highest… similarity score”. as recited by amended claim 1.
As to point (1), Applicant’s arguments with respect to claim 1 have been considered but are moot in view of a new ground of rejection as set forth above of Merwah in view of Verma and Givental2.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
AGARWAL et al. (US 20220138504 A1) teaches contamination factor with model fitness (see ¶16-18)
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beau Spratt whose telephone number is 571 272 9919. The examiner can normally be reached 8:30am to 5:00pm (PST).
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/BEAU D SPRATT/ Primary Examiner, Art Unit 2143
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