DETAILED ACTION
The following is a Final Office action. In response to Non-Final communications received 3/11/2026, Applicant, on 6/11/2026, amended Claims 1, 8, and 15. Claims 1-20 are pending in this action, have been considered in full, and are rejected below.
Response to Arguments
Arguments regarding 35 USC §101 Alice – Applicant states the claims do not recite a judicial exception as they are neither a “Mental Process” or a “Certain Method of Organizing Human Activity”, reciting the amended limitations and stating that the claims are eligible because the claims cannot be performed in the human mind and that they utilize a machine learning model. Examiner disagrees as there are two clearly stated identified abstract ideas, that of a “Mental Process” and a “Certain Method of Organizing Human Activity”, which are detailed in the rejection below. The determining of an anomaly score from a first input of a featured data set for a machine learning model of a deployment environment, for instance, can be performed in the human mind, as a human can determine a score to be used in a machine learning model, utilization of current technologies, and this is clearly abstract, which is analyzing data for sending a notification to a user about the model, Organizing Human Activity. Further, the claims are not directed to an improvement in any additional element, combination, a technology, or technological field, but rather recites claims directed at an abstraction, that of merely analyzing data using a computer and artificial intelligence. The use of a computing device, processor, memory, cache store, etc. does not make the claim eligible, and this is utilization of current technologies such as a computer to perform the abstract limitations of the Claims. The claims as a whole do not improve any claimed addition element, such as by generally linking the claim to a processor, memory, etc., and the whole of the rest, including the amended limitations, are part of the abstraction, as per the rejection below, as they recite merely receiving, analyzing, and transmitting steps which are observations, evaluations, and judgments and also can be designated as a Certain Method of Organizing Human Activity. These are not practically integrated, as the claim limitations merely utilize current technologies, such as a computer, processor, and memory with a machine learning model, to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It”. There is no improvement to any technology or any technological process, and any inventive concept would be contained wholly within the abstraction.
Applicant asserts that the claims recite are integrated into a practical application by reciting parts of the MPEP and specific parts of the Specification, such as stating this can reduce the amount of computing resources used for retraining machine learning models and improve functionality and the efficiency of a computing system, stating that this is a technical improvement. Examiner disagrees as the claims are not directed to an improvement in the machine learning model, but rather utilizes the machine learning model and retrained model to perform the abstract process. There is no improvement to any additional element, combination, a technology, or technological field, or the machine learning model, as the model is not improved as it is run at a specific time, but there is nothing as to why this would improve the model, but rather the claims are directed at utilization of current technologies such as machine learning models to perform the abstract limitations of the Claims. Any purported improvement is part of the abstraction, as per the rejection below, as they recite merely receiving, analyzing, and transmitting steps which are observations, evaluations, and judgments and also can be designated as a Certain Method of Organizing Human Activity. Again, there is no improvement to any technology or any technological process, and any inventive concept would be contained wholly within the abstraction.
Applicant asserts the claims recite significantly more by reciting the amended limitations of the claims, stating that these claims are not well-understood, routine, and conventional activity, and that they improve the functioning of a computer and improve the artificial intelligence, and thus are eligible under 101. Examiner disagrees as this is a mere allegation of eligibility under 101 and Applicant has not stated what additional elements would be improved or combination thereof. Further, the claims as a whole, alone or in combination, do not improve any claimed addition element, such as by generally linking the claim to processor, memory, machine learning model, etc., and the whole of the rest, including the amended limitations, are part of the abstraction, as per the rejection below, as they recite merely receiving, analyzing, and transmitting steps which are observations, evaluations, and judgments and also can be designated as a Certain Method of Organizing Human Activity. These are not practically integrated, as the claim limitations merely utilize current technologies, such as computer with a processor and memory, to perform the abstract limitations of the claims, similar to that of Alice, essentially “Applying It”. There is no improvement to any technology or any technological process, nor the machine learning model, and any inventive concept would be contained wholly within the abstraction.
Therefore, the arguments are non-persuasive, the Claims are ineligible as there is no inventive concept, and the rejection of the Claims and their dependents are maintained under 35 USC 101.
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; a Fundamental Economic Process, i.e. analyzing data, 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; a Fundamental Economic Process, i.e. analyzing data, a Certain Method of Organizing Human Activity), update a store with 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 (Collecting and Analyzing the Information, an Observation and Evaluation, a Mental Process; a Fundamental Economic Process, i.e. analyzing data, a Certain Method of Organizing Human Activity), and automatically generate a retrained machine learning model file based at least in part on the machine learning model and training data, wherein generating the retrained machine learning model file occurs at a time based at least in part on the model retraining frequency time period (Analyzing the Information, an Evaluation, a Mental Process; a Fundamental Economic Process, i.e. analyzing data, 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, cache store, 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 a Fundamental Economic Process, 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 a Fundamental economic process, 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, cache store, 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. 2023/003,3716), and Gusat (U.S. Publication No. 2023/025,9794). Dua, an automated methods and system for troubleshooting problems in a distributed computing system, teaches calculation 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 compute a frequency of a property change in the problem time scope, compute 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 includes 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.
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MACHINE-GENERATED TRAFFIC DETECTION (BEACONING)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH M WAESCO whose telephone number is (571)272-9913. The examiner can normally be reached on 8 AM - 5 PM M-F.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 9/5/2026
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