DETAILED ACTION Claims 1-20 are pending. Claims 1-20 are considered in this Office 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. Alice - Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 , 8, and 15 recite the limitations to determine an anomaly score from a first input of a featurized data set for a machine learning model of a deployment environment, the featurized data set being a time series of features and being generated from a data preparation process of raw data (Analyzing the Information, an Evaluation, a Mental Process; Managing Human Behavior, i.e. managing trips, a Certain Method of Organizing Human Activity) , determine a feature correlation score for the machine learning model based at least in part on a second input of a featurized historical data set for the machine learning model, the featurized historical data set representing a previous data set that has been processed by the machine learning model, the feature correlation score indicating a change in an outcome correlation between a first variable and a second variable (Analyzing the Information, an Evaluation, a Mental Process; Managing Human Behavior, i.e. managing trips, a Certain Method of Organizing Human Activity) , and determine a model retraining frequency time period for the machine learning model based at least in part on the feature correlation score and the anomaly score (Analyzing the Information, an Evaluation, a Mental Process; Managing Human Behavior, i.e. managing trips, a Certain Method of Organizing Human Activity) , which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of determining a model retraining frequency time , but for the recitation of generic computer components. That is, other than reciting a computing device, a processor, a memory, and medium, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of sending a notification to a user associated with a model , which is Managing Human Behavior, a Certain Method of Organizing Human Activity. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation, evaluation, and judgment. Further, as described above, the claims recite limitations for Managing Human Behavior, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The computing system, processor, memory, and medium are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states: “ [0026] The deployment environment data 124 can represent data associated with a deployment environment for a deployed machine learning model. The deployment environment data 124 can represent the parameters, criteria, and other suitable data to represent a particular use-case scenario for the deployment of a machine learning model. In some instances, the deployment environment data 124 can describe a production environment for the deployed machine learning model. For example, the machine learning model can be deployed in a server, a cloud computing service, a laptop, a mobile device, an edge device, and other suitable devices. The differences in the computing environment 103 and the particular use of the machine learning model can cause the machine learning model to be used in a manner that prioritizes certain criteria, such as real-time predictions, batch predictions, minimizing compute processing, and other suitable factors associated with the deployment environment. ” Which shows that the limitations can be deployed in any generic computing device to perform the abstract limitations, such as a laptop, phone, desktop, etc., and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For these reasons, there is no inventive concept. The claim is not patent eligible. Claims 2-7, 9-14, and 16-20 contain the identified abstract ideas, further narrowing them, with no new additional elements to be considered as part of a practical application or under prong 2 of the Alice analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above. After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298. Allowable Subject Matter Claims 1-20 have overcome the prior art and would be allowable if amended to overcome the 35 USC 101 rejection and any other rejections. The closest prior art of record are Dua (U.S. Publication No. 2022/002,7249 ), Das (U.S. Publication No. 2 023/003,3716 ) , and Gusat (U.S. Publication No. 202 3/025,9794 ). Dua , a n automated methods and system for troubleshooting problems in a distributed computing system , teaches c alculation of an anomaly score for each metric with a threshold violation in a run-time period , where the anomaly score indicates whether a run-time violation of a corresponding time-dependent, or time-independent, threshold rises to the level of an interesting pattern that is worthy of attention based on a historical anomaly score , to c ompute a frequency of a property change in the problem time scope , c ompute a similarity score between pre-time event-type distribution and the post-time event-type distribution , which provides a quantitative measure of a change to the object associated with the log messages and indicates how much the relative frequencies of the event types in the pre-time event-type distribution differ from the same event types of the post-time event-type distribution , but does not teach the model retraining frequency time period based on a correlation score and anomaly score . Das, an autonomous machine learning method for detecting and thwarting malicious database access, teaches an anomaly detection system which include s one or more machine learning algorithms that are automatically retrained, either continuously or according to a predefined schedule , a correlation between multiple aspects of the data, and teaches use of a similarity , but does not teach the model retraining frequency time period based on a correlation score and anomaly score . Gusat , a system and method for characterizing a computerized system based on clusters of key performance indicators, teaches a similarity metric which use clustered KPIs which can be determined based on normalized cross-correlation values, and retraining of a neural network, but it does not teach the model retraining frequency time period based on a correlation score and anomaly score . None of the current prior art teaches the model retraining frequency time period based on a correlation score and anomaly score , along with the other limitations of the claims , and these are the reasons which adequately reflect the Examiner's opinion as to why Claims 1-20 are allowable over the prior art of record, and are objected to as provided above. Conclusion The prior art made of record is considered pertinent to applicant's disclosure. US 20240377808 A1 Korablev ; Vladislav et al. SYSTEMS AND METHODS FOR AUTONOMOUS ANOMALY MANAGEMENT OF AN INDUSTRIAL SITE US 20230259794 A1 Gusat ; Mircea R. et al. CHARACTERIZING A COMPUTERIZED SYSTEM BASED ON CLUSTERS OF KEY PERFORMANCE INDICATORS US 20230033716 A1 DAS; Purandar Gururaj et al. AUTONOMOUS MACHINE LEARNING METHODS FOR DETECTING AND THWARTING MALICIOUS DATABASE ACCESS US 20220027249 A1 Dua; Sunny et al. AUTOMATED METHODS AND SYSTEMS FOR TROUBLESHOOTING PROBLEMS IN A DISTRIBUTED COMPUTING SYSTEM US 20250328505 A1 Stanley; Jeremy et al. Benchmarking Algorithms for Data Quality Monitoring US 20230259443 A1 Gusat ; Mircea R. et al. CHARACTERIZING A COMPUTERIZED SYSTEM WITH AN AUTOENCODER HAVING MULTIPLE INGESTION CHANNELS US 20220027257 A1 Harutyunyan; Ashot Nshan et al. Automated Methods and Systems for Managing Problem Instances of Applications in a Distributed Computing Facility US 20210406671 A1 Gasthaus ; Jan et al. SYSTEMS, APPARATUSES, AND METHODS FOR ANOMALY DETECTION Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT JOSEPH M WAESCO whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-9913 . The examiner can normally be reached on FILLIN "Work Schedule?" \* MERGEFORMAT 8 AM - 5 PM M-F . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BETH BOSWELL can be reached on FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-67 37 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-1348. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JOSEPH M WAESCO/ Primary Examiner, Art Unit 3625B 3/ 9 /202 6