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 .
Notice to Applicant
The following is a Non-Final Office action. In response to Examiner’s Final Rejection of 11/10/2025, Applicant, on 2/09/2026, amended claims 1, 11 and 19. Claims 1-20 are pending in this application and have been rejected below.
Response to Arguments
Applicant’s arguments filed February 9, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed February 9, 2026.
On Pg. 8-10 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states A) Claims Should Not Be Evaluated at an Overly High Level of Generality B) The Desjardins Claims Recite Improvements to Machine Learning Technology and C.) Categorical Exclusion of AI Innovations Would Be Inappropriate. In response, Examiner finds Applicants arguments in relation to this matter are not persuasive. Specifically, in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting”, and the claims reflect the improvement identified in the specification. The improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation. Examiner finds no similar improvements to take into consideration here. Examiner maintains the claims are directed to an abstract idea of churn prediction in which computer components are used as a tool to perform the analysis. The machine learning is used as tool to perform the churn analysis. Applicant has not presented an argument that alters this analysis. For at least these reasons the claims remain rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
On Pg. 10-12 of the Remarks, regarding 35 U.S.C. § 101 rejections, Applicant states A.) The Claims Are Directed to Improvements in Machine Learning Technology and B.) The Examiner's § 101 Analysis Is Inconsistent with Desjardins. In response, Examiner respectfully disagrees. The aforementioned procedures are not
improvements to a problem in the software arts, a technology or technological field. The self-supervised churn prediction modelling is a judicial exception (i.e. abstract idea). The claimed invention is executed by generic computer elements performing generic
computer functions (see par. 0013-0014). Enfish recited claims that asserted improvements to the configuration of computer memory in accordance with a self-referential table with sufficient support in the specification that the claims were directed to a specific implementation of a solution to a problem in the software arts. Which shows the claimed invention made improvements in computer related
technology. In contrast, the present claims recite computer elements to perform the generic functions.
Examiner asserts, regardless of the complexity of the data analysis and/or processing, without recitation of improvements to the functioning of the technology, technological field and/or computer-related technology (i.e. software), the steps outlined in the claimed invention to identify and target profitable customers amount to no more than mere instructions to implement the idea on a general purpose computer. Applicant has not identified anything in the claimed invention that shows or even submits the technology is being improved or there was a problem in the technology that the claimed invention solves. (Please review 101 analysis below).
On Pg. 13-15 of the Remarks, regarding 35 U.S.C. § 112a rejection, the rejection has been withdrawn.
On Pg. 16-17 of the Remarks, regarding 35 U.S.C. § 103 rejections have been withdrawn.
Claim Objections
Claim 14 is objected to because of the following informalities: grammatical error. Claim limitation “retraining the second model based on retaining of the first model.”. Appropriate correction is required.
Claims 9 -10 state the limitation "the model". Claim 1 already recites "a first machine learning model" and “a second machine learning model”. Please specify the preferred machine learning model. Appropriate action is required.
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.
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 1-20 are directed to self-supervised churn predictions.
Claim 1 and Claim 11 recite a method for self-supervised churn predictions, and Claim 19 recites a system for self-supervised churn predictions, which include identifying features relevant to customer profitability for a most-recent interval of past time from a financial institution (FI); assigning each customer of a plurality of customers to a profitability cluster of a plurality of profitability clusters in each sub interval of time over the most-recent interval of past time based on the features comprising a K-means clustering model trained on the features extracted from historical customer and transactional data to cluster customers into categories based on varying degrees of profitability; predicting, for each customer, a predicted profitability cluster in a future interval of time based on the features flagging or identifying certain customers associated with a given profitability cluster that is a higher prioritized profitability cluster in the most-recent interval of time than a corresponding predicted profitability cluster in the future interval of time, wherein churn is defined as when a current active and profitable customer will become less profitable to the FI based on predicted movement from the higher prioritized cluster to a lower prioritized profitability cluster; and automatically triggering a remedial action comprising generating a targeted retention campaign for the certain customers and adjusting account parameters to prevent decline in predicted profitability, wherein the FI system automatically delivers at least one promotion, an offer, or a reward to each flagged customer to prevent the predicted movement to the lower prioritized profitability cluster (Claim 1); obtaining current features for the customers in a most-recent interval of past time; a K-means clustering model used to assign profitability clusters to customers in each sub interval of time over a historical period of time based on features relevant to each customer's profitability contribution to a financial institution (FI), wherein the first model clusters customers into categories for a given interval of time based on features extracted and calculated from historical data; training a second model on the features to predict profitability clusters for customers in a future interval of time based on assigned clusters made by the first model for the historical period of time notifying a FI system for each certain customer associated with a first profitability cluster in the most-recent interval of past time that is a higher prioritized profitability cluster than a corresponding certain customer's current predicted profitability cluster in the next interval of time, wherein churn is defined as when a currently active and profitable customer will become less profitable to the FI; and automatically implementing, by the FI system, a technical intervention comprising modifying customer account settings and initiating personalized retention protocols to counteract predicted churn, wherein the technical intervention comprises the FI system automatically processing at least one of a promotion, an offer, or a reward and delivering the processed promotion, offer, or reward to the certain customer through an application programming interface that causes automated delivery. (Claim 11) . Clustering customers to profitability clusters over a most-recent interval of past time based on features relevant to customer profitability contribution to a financial institution (FI) comprising a K-means clustering model that clusters customers into categories for a given interval of time based on features extracted and calculated from historical data; predicting profitability clusters for the customers in a next interval of time; notifying a FI system of certain customers assigned to a first profitability cluster in the most-recent interval of past time that is of a higher prioritized profitability cluster than a second profitability cluster assigned in the next interval of time, wherein the certain customers are identified as likely to churn within the next interval of time based on predicted movement from a higher prioritized cluster to a lower prioritized cluster, wherein churn is defined as when a currently active and profitable customer will become less profitable to the FI; and automatically executing a computer-implemented remedial process comprising dynamically reconfiguring customer service parameters and deploying targeted financial product recommendations to mitigate predicted churn, wherein the remedial process comprises automatically processing and delivering at least one of a promotion, an offer, or a reward to the certain customers through automated system interactions that prevent the predicted movement to the lower prioritized cluster (Claim 19).
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of Mental Processes- evaluation; Mathematical Concepts- mathematical relationships, and “Methods of Organizing Human Activity” – marketing/sales activities. The recitation of “system”, “server”, “processor”, and “computer-readable storage medium”, provide nothing in the claim elements to preclude the step from being Mental Processes- evaluation, Mathematical Concepts- mathematical relationships and “Methods of Organizing Human Activity”- marketing/sales activities. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims primarily recite the additional element of using computer components to perform each step. The “system”, “server”, “processor”, and “computer-readable storage medium” is recited at a high-level of generality, such that it amounts no more than mere instructions to apply the exception using a computer component. See MPEP 2106.05(f). Furthermore, the claim 1, claim 11 and claim 19 recite using one or more machine learning analysis techniques. The specification discloses the machine learning analysis at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning analysis does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, machine learning is solely used a tool to perform the instructions of the abstract idea. The claim language articulates the inherent nature of machine learning processing and how data is trained/ retrained (i.e. the second model is supervised during training using labeled customers from the first model but operates as a self-supervised made during production without requiring manual intervention unless the first model is retrained”) Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims also fail to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55. In particular, there is a lack of improvement to a computer or technical field in churn analysis.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “system”, “server”, “processor”, and “computer-readable storage medium” is insufficient to amount to significantly more. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. With regards to receiving data and step 2B, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information) and Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function(s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent Claims 2-10, and 12-18 and 20 recite iterating to the identifying at a preconfigured interval of time and identifying updated features for an updated most-recent interval of past time; sending a message to the FI system, wherein the message includes customer identifiers for the certain customers and identifies the future interval of time; sending customer identifiers for the certain customers and an identification of the future interval of time to a dashboard interface associated with the FI system; generating a report for the future interval of time, and sending the report to the FI system, wherein the report includes customer identifiers for the certain customers and a probability for each customer identifier indicating a likelihood the corresponding customer is going to churn within the future interval of time; predicting a mitigation action for each certain customer, wherein each mitigation action associated with avoiding the corresponding predicted profitability cluster in the future interval of time; and sending each customer identifier, corresponding mitigation action, and an identification of the future interval of time to the F1 system; calculating first features for each customer and each sub interval of time within the most-recent past interval of time from transaction and customer data of the Fl, wherein the first features include, per sub interval of time, average time between transactions, total number of the transactions, and sum of customer checking spend, savings, retirement, certificate accounts, and outstanding loan balances; extracting second features as demographic data for each customer and each sub interval of time within the most-recent past interval of time from the transaction and customer data; wherein assigning further includes providing the first features and the second features to the second machine-learning model that is a self- supervised machine-learning (model) and receiving assigned profitability clusters for each customer within each sub interval of time as output from the model; wherein predicting further includes receiving the corresponding predicted profitability cluster for each customer for the future interval of time as output from the model; retraining the first model with additional features or with additional available profitability clusters; retraining the second model based on retaining of the first model; training the first model further includes calculating first features for each customer and for each sub interval of time over the historical period of time from historical transaction and customer data of the FI; wherein calculating further includes extracting second features for each customer and for each sub interval of time over the historical period of time from historical transaction and customer data as customer demographic data; wherein notifying further includes obtaining an action identifier received for each certain customer from a third model based on probabilities associated with a corresponding current predicted profitability cluster and providing a corresponding action identifier for each certain customer to the FI system to process; wherein notifying further includes sending customer identifiers for certain customers and an identification for the next interval of time to a dashboard interface associated with the FI system; notifying the FI system of additional customers assisted to a third profitability cluster in the most-recent interval of past time that is of a lower prioritized profitability cluster than a fourth profitability cluster assigned in the next interval of time, wherein the additional customers are identified as potential valuable customers to the FI within the next interval of time; and further narrowing the abstract idea. These recited limitations in the dependent claims do not amount to significantly more than the above-identified judicial exceptions in Claims 1, 11 and 19. Regarding Claims 3-6, and 17-18 , 20 and the additional elements of “FI system” and “interface”, it is M2106.05(d)- Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Regarding claim 9-10, 13-15, 17 and the additional element of “machine learning”, “model” and “training/ retraining” - the specification discloses the machine learning at a high-level of generality, providing examples of different techniques that may be applied. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, currently, the machine learning is solely used a tool to perform the instructions of the abstract idea.
Reasons Claims are Patentably Distinguishable from the Prior Art
Examiner analyzed Claims 1-20 in view of the prior art on record and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below.
In regards to Claim 1 (similarly Claim 11 and Claim 20), the prior art does not teach or fairly suggest:
“… assigning each customer of a plurality of customers to a profitability cluster of a plurality of profitability clusters in each sub interval of time over the most-recent interval of past time based on the features by processing a first machine-learning model comprising a K-means clustering model trained on the features extracted from historical customer and transactional data to cluster customers into categories based on varying degrees of profitability; predicting, for each customer, a predicted profitability cluster in a future interval of time based on the features by processing a second machine-learning model trained on the features to predict particular clusters produced by the first machine-learning model, wherein the second machine-learning model learns how to predict a customer's future cluster during its training and wherein the second machine-learning model is a supervised model during training using labeled clusters provided by the first machine-learning model but operates as a self-supervised model during production without requiring manual intervention or retraining unless the first machine-learning model is retrained; flagging or identifying certain customers associated with a given profitability cluster that is a higher prioritized profitability cluster in the most-recent interval of time than a corresponding predicted profitability cluster in the future interval of time, wherein churn is defined as when a current active and profitable customer will become less profitable to the FI based on predicted movement from the higher prioritized cluster to a lower prioritized profitability cluster;…”.
Examiner finds that Siebel et al., US Publication No. 20220405775A1 teaches obtaining one or more data models including an industry-specific data model from the curated CRM data and orchestrating a plurality of machine learning models for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective (see Abstract). In particular, Seibel discloses revenue forecasting can be used to accurately forecast revenue, balances, and assets under management (AUM) with machine learning to identify risks and opportunities, explain drivers, and coach users how to address them. Customer churn prediction can be used to build complete and unified views of customers and leverage customer sentiments, such as through natural language processing and machine learning algorithms, to detect mismatches between client offerings and client needs, rate client sensitivities and identify other churn risk drivers, and identify effective intervention strategies to retain and grow wallet share with each profitable client. (see par. 0425; 0451).
Elser et al., US Patent No. 10636097B1 teaches systems and methods are provided that allow for generating and applying an improved predictive data model that aggregates two or more models performed sequentially, for the purposes of improving the prediction of overall profitability of individuals or households in a population. The models may be generated by the processing of customer profitability data and third-party population data together. One of the two aggregated models may be an inherently probabilistic, binary model tasked with determining whether an individual is a high-loss individual and using that result to improve the predictive capability of the system. (see Abstract).
Maga et al. (U.S. PG Publication 20140278779) teaches A system and method for managing churn among the customers of a business is provided. The system and method provide for an analysis of the causes of customer churn and identifies customers who are most likely to churn in the future. Identifying likely churners allows appropriate steps to be taken to prevent customers who are likely to churn from actually churning. The system included a dedicated data mart, a population architecture, a data manipulation module, a data mining tool and an end user access module for accessing results and preparing preconfigured reports. The method includes adopting an appropriate definition of churn, analyzing historical customer to identify significant trends and variables, preparing data for data mining, training a prediction model, verifying the results, deploying the model, defining retention targets, and identifying the most responsive targets. (see Abstract).
Although Siebel, Elser and Maga teach the churn prediction elements of the claim, none of the cited prior art, singularly or in combination, teach or fairly suggest, the combination of, the model elements.
Additionally, Examiner finds Feng et al. (U.S. PG Publication 20230136809) teaches method and a system for predicting and using customer lifetime value (CLV). The method include: providing a classifier trained using customer feature data during a first period of time as input and whether there is spending during a second period of time as classifier label; providing a regressor trained using the customer feature data during the first period of time as input and amount of spending during a second period of time as regressor label; performing the classifier using customer feature data during a third period of time to obtain customers having positive predicted classifier labels; and performing the regressor using the customer feature data during the third period of time for the customers having positive predicted classifier labels, to obtain CLVs of the customers. (see Abstract).
Shah (U.S. Patent 11507967) teaches the K-Means algorithm is applied to the variables to clusters of the customer base. The behavior of each cluster is analyzed to find the group of customers who give more profits to the company. Similarly, clustering is performed using two other algorithms namely, Fuzzy C—Means clustering and the proposed method with chosen initial centroids in the existing K—Means algorithm. Now that clusters of customers are found, it is necessary to understand the differences between these groups of customers. A thorough analysis is performed on the clusters to aid in finding the targeted customers and bestows them with appropriate promotions and offers. Also, a novel Repetitive Median based K-Means algorithm is proposed with an intention to reduce the number of iterations than the traditional clustering algorithms. The outcome of the proposed work is a meaningful customer segmentation which will be useful for marketing people. (see para. [0137]). However Feng and Shah, individually and in combination, fail to teach the clustering steps. Therefore, for at least these reasons, Claim 1 (similarly Claim 11 and Claim 19) is eligible over the prior art.
The dependent claims 2-10, 12-18 and 20 are eligible under 35 U.S.C. 102 and 35 U.S.C. 103 because they depend on claim 1 (claim 11 and claim 19) that is determined to be eligible.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Chesiree Walton, whose telephone number is (571) 272-5219. The examiner can normally be reached from Monday to Friday between 8 AM and 5 PM. If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Patricia Munson, can be reached at (571) 270-5396. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”).
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Applicants are invited to contact the Office to schedule an in-person interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/CHESIREE A WALTON/ Examiner, Art Unit 3624
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