DETAILED ACTION
This is in reference to communication received 15 January 2026. Cancellation of claims 2, 10, 14 and 17 is acknowledged. Claims 1, 3 – 9, 11 – 13, 15 – 16 and 18 – 20 are pending for examination. 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.
Claims 1, 3 – 9, 11 – 13, 15 – 16 and 18 – 20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent claim 13, representative of claims 1 and 20, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 1 recites invention directed to obtaining data indicative of one or more attributes of a customer of a financial institution (e.g., historical transaction data of a financial-institution); analyzing the historical data to make a determination whether a customer of the financial-institution will be lost, and based upon the determination that the customer may switch to another financial institution, perform a remedial action to reduce the likelihood of losing the customer.
These limitations describe activities of a Account-Management-Team or Retention-Specialist or a person. Analyzing transactions of customers/clients to determine customer activity trend, identifying transactions which are not normal, making a determination that the customer may be considering to leave the organization, and perform a remedial action to reduce the likelihood of losing the customer, which, pursuant to MPEP 2106.04, is aptly categorized as a method of organizing human activity (i.e. customer-retention, customer-satisfaction). Therefore, the claims recite a judicial exception.
Next, the aforementioned claims recite additional functional elements that are associated with the judicial exception, including: generating and providing a feature set for use by an ensemble of machine-learning models trained to predict customer behavior of whether the customer of the financial institution will be lost to a competitor financial institution wherein a machine-learning-models are trained to determine whether a customer will be transferring their funds to another institution (e.g., customer may be closing their account, or keeping their account with minimum balance to keep the account open); and using the prediction generated by the ensemble of machine-learning models to determine whether to perform a remedial action to reduce a likelihood of losing the customer. Not only do these features fail to integrate the abstract idea into a practical application (see below), but it can also reasonably be seen as the conventional application of well-known machine learning concepts to build and train a model to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f).
Represented claims 1 and 20, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 1), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 20).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. 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 claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor, memory, and non-transitory machine-readable medium amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 3 – 9, 11 – 12, 15 – 16 and 18 – 19, these claims recite limitations that further define the same abstract idea and simply disclose additional limitations that further limit the abstract idea with details regarding which data elements of the collected data record will be considered to determine the likelihood of customer switching to another financial institution; defining that the offerings given by competing financial-institutions will be taken into consideration; what data elements will be considered for training ensemble of machine-learning models; defining type of remedial action that may be performed. Thus, the dependent claims merely provide additional non-structural (and predominantly non-functional) details that fail to meaningfully limit the claims or the abstract idea(s). Accordingly, the claim recites an abstract idea.
Therefore, claims 1, 3 – 9, 11 – 13, 15 – 16 and 18 – 20 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
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.
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Maddalena Moretti published article “Predicting customer churn using machine learning” hereinafter referred to as Moretti in view of YBI Foundation YouTube Video “Bank Customer Churn Model – Real World Example” hereinafter referred to as YBI-Foundation, Jason Brownlee published article “A Gentle Introduction to Ensemble Learning Algorithms” hereinafter referred to as Brownlee and Michael Beird published article “Three Tactics To Keep Customers from Switching” hereinafter referred to as Beird.
Regarding claim 13 and represented claim 1 and 20, Moretti teaches system and method for using machine learning for predicting customer churn (Moretti, Predicting customer churn using machine learning has proved effective in identifying potential churners and developing successful retention strategies for financial sector) [Moretti, page 1] comprising:
a circuitry (Moretti, using machine learning to predict customer churn) [Moretti, page 1];
one or more machine-readable storage media comprising a plurality of computer executable instructions stored thereon (Moretti, using machine learning to predict customer churn) [Moretti, page 1];
Moretti does not explicitly teach obtaining data indicative of one or more attributes of a customer of a financial institution. However, YBI-Foundation teaches how machine learning can be used by banks to identify customers who are going to leave the bank or left the bank. YBI-Foundation further teaches using customer data set of 10,000 rows with 13 columns [YBI-Foundation, page 2].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Moretti by adopting teachings of YBI-Foundation to generate a usable report for a financial-institution based on their customer data.
Moretti in view of YBI-Foundation teaches system and method further comprising:
obtaining, by a compute device and from one or more devices of a digital data processing system for processing financial transactions (YBI-Foundation, import the data set) [YBI-Foundation, page 2], data indicative of one or more attributes of a customer of a financial institution (YBI-Foundation, data set has 13 columns so it has customer id which is unique surname of the customer credit score geography gender age tenure the balance number of products purchased in the bank credit card is active member or not estimated salary) [YBI-Foundation, page 2];
generating, by the compute device and from the obtained data, a feature set for use by an ensemble of machine-learning models trained to predict customer behavior (YBI-Foundation, churn based upon geography so in this way you can create more group by functions to check the hypothesis or explore the data frame on your own now move to the modeling part so to create a model first we need to extract our feature or define our features or attributes or independent variable and to define our dependent variable or y or a label or a target so we drop surname and churn so surname has no predictive power it's just a name so we are dropping a surname and churn which is our label or y variable from the data frame using the function dot drop x is equal to 1 because the surname and churn are the label of column and x equal to 1) [YBI-Foundation, page 5];
Moretti in view of YBI-Foundation does not teach ensemble of machine-learning. However Brownlee teaches Ensemble Learning is a general meta approach to machine learning that seeks better predictive performance by computing predictions from multiple models [Brownlee, page 1].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Moretti in view of YBI-Foundation by adopting teachings of Brownlee and use Ensemble of machine-learning to generate better predictive results.
Moretti in view of YBI-Foundation and Brownlee teaches system and method further comprising:
providing, by the compute device, the feature set to the ensemble of machine- learning models (Brownlee, Bagging Ensemble Learning typically involves using a single machine learning algorithm, almost always an unpruned decision tree, and training each model on a different sample of the same training dataset. The predictions made by the ensemble members are then combined using simple statistics, such as voting or averaging.) [Brownlee, page 4] to produce a prediction of whether the customer of the financial institution will be lost to a competitor financial institution (Moretti, By exploiting the full potential of customer data, banks can better understand client behaviors and learn churn patterns from past records. This allows them to predict their customers’ future movements and respond accordingly) [Moretti, page 5], wherein obtaining the prediction comprises determining, by the compute device, one or more of a likelihood of an initial transfer of money from an account of the customer with the financial institution to an account of the customer with a competitor financial institution or a likelihood of a transfer of money satisfying a predefined threshold size from the account of the customer with the financial institution to the account of the customer with the competitor financial institution (YBI-Foundation, now in this step we would like to do some future engineering so here we would like to find out number of customers who have zero bank balance in their bank so we think that the customers who are keeping zero bank balance may have higher chance of churning or closing or going out of the bank so we use the function dot lock and we want to extract the rows where our balance okay balance is a integer in our data frame so balance is equal to zero and we only interested to extract the churn column so these are the customers who have zero bank balance in our data set so the customers 500 customers who have zero bank balance have left the bank and three thousand customers who have zero bank balance is still in the bank so substantial amount of customers have left who have zero bank balance so we will create a new column for our data frame named as zero bank balance so we are using the np dot pair function to create a new column zero balance and giving it a number) [YBI-Foundation, page 4].;
obtaining, by the compute device, the prediction from the ensemble of machine- learning models (Moretti, By exploiting the full potential of customer data, banks can better understand client behaviors and learn churn patterns from past records. This allows them to predict their customers’ future movements and respond accordingly) [Moretti, page 5], wherein providing the feature set to an ensemble of machine-learning models comprises providing the feature set to an ensemble of machine-learning models that includes:
a first model trained to determine a likelihood of an initial transfer of money from an account of the customer with the financial institution to an account of the customer with the competitor financial institution (Brownlee, Bagging Ensemble Learning typically involves using a single machine learning algorithm, almost always an unpruned decision tree, and training each model on a different sample of the same training dataset. The predictions made by the ensemble members are then combined using simple statistics, such as voting or averaging.) [Brownlee, page 4]; and
a second model trained to determine a likelihood of a transfer of money of a predefined threshold size from the account of the customer with the financial institution to the account of the customer with the competitor financial institution (Brownlee, Bagging Ensemble Learning typically involves using a single machine learning algorithm, almost always an unpruned decision tree, and training each model on a different sample of the same training dataset. The predictions made by the ensemble members are then combined using simple statistics, such as voting or averaging.) [Brownlee, page 4]
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Moretti in view of YBI-Foundation and Brownlee does not teach performing of a remedial action by a financial-institution. However, Beird teaches Banks need to understand that industry fees, absent of associated real or perceived value, have a direct impact on both retention and acquisition. However, there are three strategies that banks large and small can take to keep their customers from switching accounts [Beird, page 3].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Moretti in view of YBI-Foundation and Brownlee by adopting teachings of Beird to maintain customer base by retaining customers by providing customer with better services at reduced expenses to the customer.
Moretti in view of YBI-Foundation, Brownlee and Beird teaches system and method further comprising:
performing, by the compute device and in response to a determination that the that the customer is predicted to be lost, a remedial action to reduce a likelihood of losing the customer (Beird, Deliver value through quality in-person service; Ensure customer needs align with the right products and services; provide alternatives for low branch and ATM density) [Beird, page 3 – 6].
Regarding claim 15 and represented claim 3, as combined and under the same rationale as above, Moretti in view of YBI-Foundation, Brownlee and Beird teaches system and method, wherein obtaining data comprises obtaining data indicative of transactions regarding inflows and outflows of money from an account of the customer (YBI-Foundation, as responded to above) [YBI-Foundation, page 4].
Regarding claim 16 and represented claim 7, as combined and under the same rationale as above, Moretti in view of YBI-Foundation, Brownlee and Beird teaches system and method, wherein obtaining data comprises obtaining data indicative of behavior of the customer comprising:
sensitivity to interest rate changes;
frequency or likelihood of account balance movements;
activities of the customer on one or more digital platforms; and/or
one or more complaints from the customer
(YBI-Foundation, as respond to above in response to claim 14) [Beird, page 5].
Regarding claim 4, as combined and under the same rationale as above, Moretti in view of YBI-Foundation, Brownlee and Beird teaches system and method, wherein to obtain data indicative of transactions comprises to obtain data indicative of one or more channels through which the transactions were initiated (YBI-Foundation, as respond to above in response to claim 14) [Beird, page 4, 5].
Regarding claim 5, as combined and under the same rationale as above, Moretti in view of YBI-Foundation, Brownlee and Beird teaches system and method, wherein to obtain data indicative of one or more channels comprises to obtain data indicative of transactions initiated from at least one: (i) branch office of the financial institution; and/or (ii) network-connected compute device (YBI-Foundation, as respond to above in response to claim 14) [YBI-Foundation, page 4].
Regarding claim 6, as combined and under the same rationale as above, Moretti in view of YBI-Foundation, Brownlee and Beird teaches system and method, wherein to obtain data indicative of transactions comprises to obtain data indicative of: (i) purchases for goods or services; and/or (ii) transfers of money between accounts of the customer (YBI-Foundation, as respond to above in response to claim 14) [YBI-Foundation, page 4].
Regarding claim 8, as combined and under the same rationale as above, Moretti in view of YBI-Foundation, Brownlee and Beird teaches system and method, wherein the circuitry is further configured to obtain data indicative of one or more attributes of the competitor financial institution comprising: (i) an interest rate paid by the competitor institution; and/or a balance between an online and a physical presence of the competitor financial institution (Beird, competitive pricing and promotions for offsetting the costs associated with foreign ATM usage offers many smaller bank customers access to their funds across the nation without paying a penalty for that convenience) [Beird, page 6].
Regarding claim 9, as combined and under the same rationale as above, Moretti in view of YBI-Foundation, Brownlee and Beird teaches system and method, wherein to generate a feature set comprises one or more of:
to map non-numerical data to numerical data;
to map numerical data from one range to a different range; and/or
to partition the data as a function of predefined time windows
(YBI-Foundation, we found there are three unique values france germany spain with their unique row numbers or count so we encode them so with the use of a function df dot replace and providing as a dictionary so geography is our column name and then we want to encode the france a key with value 2 germany a key with value 1 spain a key with value 0 and why this in place equal to true because we want to change the encoded value 2 for france one for germany and 0 for spain in our original data frame df) [YBI-Foundation, page 3].
Regarding claim 12, as combined and under the same rationale as above, Moretti in view of YBI-Foundation, Brownlee and Beird teaches system and method, wherein to perform the remedial action comprises to offer an increased interest rate to the customer (Beird, Banks need to understand that industry fees, absent of associated real or perceived value, have a direct impact on both retention and acquisition. However, there are three strategies that banks large and small can take to keep their customers from switching accounts) [Beird, page 3].
Regarding claim 18, as combined and under the same rationale as above, Moretti in view of YBI-Foundation, Brownlee and Beird teaches system and method, wherein providing the feature set to an ensemble of machine-learning models comprises providing the feature set to an ensemble of machine-learning models that comprise decision trees (Brownlee, Bagging Ensamble Learning typically involves using a single machine learning algorithm, almost always an unpruned decision tree, and training each model on a diff erent sample of the same training dataset. The predictions made by the ensemble members are then combined using simple statistics, such as voting or averaging.) [Brownlee, page 4].
Regarding claim 19, as combined and under the same rationale as above, Moretti in view of YBI-Foundation, Brownlee and Beird teaches system and method, wherein providing the feature set to an ensemble of machine-learning models comprises providing the feature set to an ensemble of machine-learning models that have been trained using gradient boosting (Brownlee, Since AdaBoost, many boosting methods have been developed and some, like stochastic gradient boosting, may be among the most effective techniques for classification and regression on tabular (structured) data) [Brownlee, page 9].
Regarding claim 11, as combined and under the same rationale as above, Moretti in view of YBI-Foundation, Brownlee and Beird teaches system and method, wherein to provide the feature set to an ensemble of machine-learning models comprises one or more of:
to provide the feature set to an ensemble of machine-learning models that comprise decision trees (Brownlee, Bagging Ensamble Learning typically involves using a single machine learning algorithm, almost always an unpruned decision tree, and training each model on a diff erent sample of the same training dataset. The predictions made by the ensemble members are then combined using simple statistics, such as voting or averaging.) [Brownlee, page 4];
(ii) to provide the feature set to an ensemble of machine-learning models that have been trained using gradient boosting; and/or to provide the feature set to an ensemble of machine-learning models that have been trained using extreme and/or light gradient boosting (Brownlee, Since AdaBoost, many boosting methods have been developed and some, like stochastic gradient boosting, may be among the most effective techniques for classification and regression on tabular (structured) data) [Brownlee, page 9].
Response to Arguments
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because the claims are not directed to an Abstract Idea, and, he claims are not directed a "method of organizing human activity" merely because it could be used in business or finance contexts, is acknowledged and considered.
However, upon further review of the claimed invention, it is deemed that the invention as currently claimed describe activities of an Account-Management-Team or Retention-Specialist or a person. Analyzing transactions of customers/clients to determine customer activity trend, identifying transactions which are not normal, making a determination that the customer may be considering to leave the organization, and perform a remedial action to reduce the likelihood of losing the customer, which, pursuant to MPEP 2106.04, is aptly categorized as a method of organizing human activity (i.e. customer-retention, customer-satisfaction). Therefore, the claims recite a judicial exception.
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because the claims are not directed to an Abstract Idea, and, he claims are not directed a "method of organizing human activity" merely because it could be used in business or finance contexts, is acknowledged and considered.
However, upon further review of the claimed invention, it is deemed that the invention as currently claimed describe activities of an Account-Management-Team or Retention-Specialist or a person. Analyzing transactions of customers/clients to determine customer activity trend, identifying transactions which are not normal, making a determination that the customer may be considering to leave the organization, and perform a remedial action to reduce the likelihood of losing the customer, which, pursuant to MPEP 2106.04, is aptly categorized as a method of organizing human activity (i.e. customer-retention, customer-satisfaction). Therefore, the claims recite a judicial exception.
Applicant's argument that pending claimed amended invention is eligible for patent under 35 USC 101 because claimed limitations impose meaningful boundaries that confine any alleged abstract idea to a specific, practical implementation that improves the functioning of the computer system and/or a related technological field, is acknowledged and considered.
However, upon further review, it is deemed that the claimed invention is not eligible for patent under 35 USC 101 because it can also reasonably be seen as the conventional application of well-known machine learning concepts to build and train a model to implement the abstract idea on a computer, and merely uses a computer as a tool to perform the abstract idea. See MPEP 2106.05(f). Therefore, the claims recite a judicial exception.
Applicant's argument that pending claimed amended invention is eligible for patent because combination of cited prior art does not teach amended claimed invention is acknowledged and considered.
However, applicant is arguing amended invention which have been responded to in Rejection under 35 USC 103 section.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jonathan Costet published article “The ultimate guide on churn and customer loyalty”.
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 Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Fri 06:30a - 04:00p.
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/NARESH VIG/Primary Examiner, Art Unit 3622
April 15, 2026
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