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 § 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. Claim (s) 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yoon, publication number: US 2022/0391724 in view of Pisner , publication number: US 2024/0161017 . As per claim 1, Yoon teaches a system, comprising: a non-transitory memory ; a processor communicatively coupled to the non-transitory memory, wherein the processor is configured to read a set of instructions to: receive a plurality of source-specific anchor values ( different sources, [0050], training data, [0047], feature values for training samples [0064] ) ; generate a plurality of model features ( categorical values, [0064] ) ; implement a plurality of trained classification models each associated with at least one of the plurality of source-specific anchor values and each configured to receive a subset of the plurality of model features (OCCs A-K, [0034] [0052][0057]) , wherein each of the plurality of trained classification models is configured to classify the associated at least one of the plurality of source-specific anchor values as one of anomalous or non-anomalous ( determining anomalous and non-anomalous data, [0060-0061] ) ; implement a trained weighted classification model to generate an optimal anchor value, wherein the optimal anchor value includes a weighted aggregation of each of the plurality of source-specific anchor values identified as non-anomalous ( Intersection engine 122 comparing each model output to a specific threshold based on a percentile and generating a prediction, [0061] ) ; and generate an optimal reference value based on the optimal anchor value ( Decision boundaries, [ 0048][ 0054], accuracy threshold [0054] ) . Yoon does not teach the trained classification model being source specific. In an analogous art, Pisner teaches the trained classification model being source specific (domain specific pre-trained models in ensemble learning, weighted ensemble, [0020-0023]) . Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to modify Yoon to include domain specific models as described in Pisner’s ensemble learning system for the advantage of having a diverse and robust representation of source knowledge. As per claim s 2 and 20 , the combination teaches wherein the plurality of model features comprise at least one of a markup-based transformation feature, a density-based transformation feature, a historical-based statistical feature, or a combination thereof (Yoon: Kernel density estimation, [0037] ) . As per claim 3, the combination teaches wherein the markup-based transformation feature comprises one or more ratio-based features transformed to include a similar distribution (Yoon: anomaly ratio, [0080-0081]) . As per claim 4, the combination teaches wherein the density-based transformation feature includes a kernel density estimation ( Yoon: Kernel density estimation, [0037] ) . As per claim 5, the combination teaches wherein the historical-based statistical features are generated by an unsupervised learning and rule-based process (Yoon: unsupervised training, [0054]) . As per claim 6, the combination teaches wherein at least one of the plurality of trained source-specific classification models is generated by an iterative training process based on a weakly-labeled training dataset (Yoon: iterative training, [ 0012][ 0034], pseudo labels, [0061], Pisner: domain specific pre-trained models in ensemble learning, weighted ensemble, [0020-0023] ) . As per claim 7, the combination teaches wherein the plurality of model features includes context-based statistical features, and wherein the trained weighted classification model is configured to receive the context-based statistical features as an input (Yoon: numerical and categorical values, [0064]) . As per claim 8, the combination teaches wherein the optimal reference value is generated by applying a multiplier to the optimal anchor value (Yoon: decision boundary, [ 0048][ 0054], percentile threshold, [0079]) . As per claim 9, the combination teaches wherein the processor is further configured to: compare the optimal reference value to a received feature value; label the received feature value as anomalous or non-anomalous based on the comparison; and in response to labeling the feature value as anomalous, generate a notification (sending anomaly information, [0056]) . As per claim 19, Yoon teaches a non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause at least one device to perform operations comprising: receiving a feature value (training data, [0047]) ; receiving a plurality of source-specific anchor values ( different sources, [0050], training data, [0047], feature values for training samples [0064] ) ; generating a plurality of model features ( categorical values, [0064] ) ; implementing a plurality of trained classification models each associated with at least one of the plurality of source-specific anchor values and each configured to receive a subset of the plurality of model features ( OCCs A-K, [0034][0052][0057] ) , wherein each of the plurality of trained classification models is configured to classify the associated at least one of the plurality of source-specific anchor values as one of anomalous or non-anomalous ( determining anomalous and non-anomalous data, [0060-0061] ) ; implementing a trained weighted classification model to generate an optimal anchor value, wherein the optimal anchor value includes a weighted aggregation of each of the plurality of source-specific anchor values identified as non-anomalous ( Intersection engine 122 comparing each model output to a specific threshold based on a percentile and generating a prediction, [0061] ) ; generating an optimal reference value based on the optimal anchor value ( Decision boundaries, [ 0048][ 0054], accuracy threshold [0054] ) ; comparing the optimal reference value to a received feature value; labeling the received feature value as anomalous or non-anomalous based on the comparison ( model classifier 150 determining class, [0054] ) ; and in response to labeling the feature value as anomalous, generating a notification ( sending anomaly notification, [0056] ) . Yoon does not teach the trained classification model being source specific. In an analogous art, Pisner teaches the trained classification model being source specific (domain specific pre-trained models in ensemble learning, weighted ensemble, [0020-0023]). Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to modify Yoon to include domain specific models as described in Pisner’s ensemble learning system for the advantage of having a diverse and robust representation of source knowledge. Claims 10-18 are rejected based on claims 1-9. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT OLUGBENGA O IDOWU whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-1450 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday 8am - 5pm . 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Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /OLUGBENGA O IDOWU/ Primary Examiner, Art Unit 2494