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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
The pending claims in the present application are claims 1-20, as presented in the “AMENDMENT & RESPONSE UNDER 37 C.F.R. § 1.111” of 20 April 2026 (hereinafter referred to as the “Amendment/Response”).
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. The paragraphs below provide rationales for the rejection. The rationales are based on the multi-step subject matter eligibility test outlined in MPEP 2106.
Step 1 of the eligibility analysis involves determining whether a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 USC 101. (See MPEP 2106.03(I).) That is, Step 1 asks whether a claim is to a process, machine, manufacture, or composition of matter. (See MPEP 2106.03(II).) Referring to the pending claims, the “method” of claims 1-10 constitutes a process under 35 USC 101, the “method” of claims 11-20 also constitutes a process under the statute, and the “system” of claims 19 and 20 constitutes a machine under the statute. Accordingly, claims 1-20 meet the criteria of Step 1 of the eligibility analysis. The claims, however, fail to meet the criteria of subsequent steps of the eligibility analysis, as explained in the paragraphs below.
The next step of the eligibility analysis, Step 2A, involves determining whether a claim is directed to a judicial exception. (See MPEP 2106.04(II).) This step asks whether a claim is directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea. (See id.) Step 2A is a two-prong inquiry. (See MPEP 2106.04(II)(A).) Prong One and Prong Two are addressed below.
In the context of Step 2A of the eligibility analysis, Prong One asks whether a claim recites an abstract idea, law of nature, or natural phenomenon. (See MPEP 2106.04(II)(A)(1).) Claim 1 recites the following abstract idea limitations:
“A method, comprising: ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... obtaining customer data and enterprise data for a financial institution (FI) over a first interval of time; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... jointly predict debit transactions and a debit churn likelihood per customer as combined outputs, and ... jointly predict credit transactions and a credit churn likelihood per customer as combined outputs, wherein the labeled parameter data includes customer identifier, transaction date, transaction time, transaction location, transaction device, insufficient fund violations, interchange fees, costs of a given transaction, profit of a given transaction, loan terms, and loan interest, and ... frequency and recency of transactions for each customer during the first interval of time to identify factors relevant to predicting the transactions or transaction rate in a given future period or interval of time; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... forecasting predicted future transactions and churn likelihood over a second interval of time for each customer of the FI based on the customer data and the enterprise data; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... predicting a customer lifetime value (CLV) for each customer over the second interval of time based on a savings account balance rating value assigned by the FI for each customer, wherein savings account balance rating value include low value, medium value, or high value; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... using outputted predicted CLVs as input ... along with the predicted future transactions and the churn likelihood to determine and calculate a final adjusted CLV per customer over the second interval of time; and ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... integrating the predicted future transactions, the churn likelihood, and the final adjusted CLV for each customer ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
Similarly, claim 11 recites the following abstract idea limitations:
“A method, comprising: ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... jointly predict credit transactions and a first churn likelihood per customer as combined outputs ..., and ... jointly predict debit transactions and a second churn likelihood per customer as combined outputs ..., wherein the labeled parameter data includes customer identifier, transaction date, transaction time, transaction location, transaction device, insufficient fund violations, interchange fees, costs of a given transaction, profit of a given transaction, loan terms, and loan interest, and ... frequency and recency of transactions for each customer to identify factors relevant to predicting the transactions or transaction rate in a given future period or interval of time; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generate predicted credit transactions and a first churn likelihood per customer of a financial institution (FI) over a given interval of time; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... generate predicted debit transactions and a second churn likelihood per customer of the FI over the given interval of time; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... predicting a customer lifetime value (CLV) per customer of the FI over the given interval of time based on the predicted credit transactions, the first churn likelihood, the predicted debit transactions, and the second churn likelihood and the CLV based on a savings account balance rating value assigned by the FI for each customer, wherein the savings account balance rating value includes low value, medium value, or high value; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... using outputted predicted CLVs as input ... along with the predicted credit transactions, the first churn likelihood, the predicted debit transactions, and the second churn likelihood to determine and calculate a final adjusted CLV per customer over the given interval of time; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... generating records per customer, each record includes a corresponding customer's predicted credit transactions, first churn likelihood, predicted debit transactions, second churn likelihood, and final adjusted CLV over the given interval of time; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... delivering the records ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
Similarly, claim 19 recites the following abstract idea limitations:
“... perform operations, comprising: ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... obtaining customer data and enterprise data from ... a FI; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... jointly predict debit transactions and a second churn likelihood per customer as combined outputs, and ... jointly predict credit transactions and a first churn likelihood per customer as combined outputs, wherein the labeled parameter data includes customer identifier, transaction date, transaction time, transaction location, transaction device, insufficient fund violations, interchange fees, costs of a given transaction, profit of a given transaction, loan terms, and loan interest, and wherein the training includes frequency and recency of transactions for each customer to identify factors relevant to predicting the transactions or transaction rate in a given future period or interval of time; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... forecasting predicted credit transactions and a first churn likelihood of each customer over a given interval of time based on the customer data and the enterprise data; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... forecasting predicted debit transactions and a second churn likelihood of each customer over the given interval of time based on the customer data and the enterprise data; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... predicting a customer lifetime value (CLV) of each customer over the given interval of time based on the customer data, the enterprise data, the predicted credit transactions, the first churn likelihood, the predicted debit transactions, and the second churn likelihood based on a savings account balance rating value assigned by the FI for each customer, wherein the savings account balance rating value includes low value, medium value, or high value; ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... using outputted predicted CLVs as input ... along with the predicted credit transactions, the first churn likelihood, the predicted debit transactions, and the second churn likelihood to determine and calculate a final adjusted CLV per customer over the given interval of time; ...” - See below regarding MPEP 2106.04(a), mathematical concepts, certain methods of organizing human activity, and mental processes
“... generating records per customer, each record includes a corresponding customer's predicted credit transactions, first churn likelihood, predicted debit transaction, second churn likelihood, and final adjusted CLV for the given interval of time; and ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
“... integrating the records ...” - See below regarding MPEP 2106.04(a), certain methods of organizing human activity, and mental processes
The above-listed limitations of claims 1, 11, and 19, when applying their broadest reasonable interpretations in light of their context in the claim as a whole, fall under enumerated groupings of abstract ideas outlined in MPEP 2106.04(a). For example, limitations of the claims can be characterized as: fundamental economic principles or practices, including forecasting financial transactions and predicting customer lifetime value; commercial interactions, including business relations between customers and financial institutions; and managing relationships or interactions between people, and in particular, between financial institutions and their customers, which fall under the certain methods of organizing human activity grouping of abstract ideas (see MPEP 2106.04(a)). Limitations of the claims also can be characterized as: concepts performed in the human mind, including, using claim 1 as an example, observation (e.g., the recited “obtaining” step), and evaluation, judgment, and/or opinion (e.g., the recited “predict,” “forecasting,” “predicting,” “using,” and “integrating” steps), which fall under the mental processes grouping of abstract ideas (see MPEP 2106.04(a)). Similar limitations of claims 11 and 19 also can be characterized as concepts performed in the human mind for similar reasons. Accordingly, for at least these reasons, claims 1, 11, and 19 fail to meet the criteria of Step 2A, Prong One of the eligibility analysis.
In the context of Step 2A of the eligibility analysis, Prong Two asks if the claim recites additional elements that integrate the judicial exception into a practical application. (See MPEP 2106.04(II)(A)(2).) Claim 1 recites the following additional element limitations:
The claimed “jointly predict” follows “training a debit machine-learning model on labeled parameter data” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “jointly predict” follows “training a credit machine-learning model on labeled parameter data” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
“... wherein the training includes” the claimed “frequency and recency” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “input” is “to a CLV manager” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “integrating” is “into an interface or a system of the FI” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
Similarly, claim 11 recites the following additional elements limitations:
The claimed “jointly predict” follows “training a first machine learning model (MLM) on labeled parameter data” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “jointly predict” follows “training a second machine learning model (MLM) on labeled parameter data” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
“... wherein the training includes” the claimed “frequency and recency” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generate” is by “training the first MLM” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “generate” is by “training the second MLM” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “input” is “to a CLV manager” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “delivering” is “to a system or an interface of the FI” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
Similarly, claim 19 recites the following additional elements limitations:
“A system, comprising: at least one server comprising at least one processor and a non-transitory computer-readable storage medium; the non-transitory computer-readable storage medium comprising executable instructions; and the executable instructions when executed by at least one processor cause the at least one processor to” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “obtaining” is from “a financial institution (FI) server” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “jointly predict” is by “training a debit machine-learning model on labeled parameter data” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “jointly predict” is by “training a debit machine-learning model on labeled parameter data” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “input” is “to a CLV manager” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The claimed “integrating” is “into a system or an interface of the FI using an application programming interface” - See below regarding MPEP 2106.05(a)-(c) and (f)-(h)
The above-listed additional element limitations of claims 1, 11, and 19 when applying their broadest reasonable interpretations in light of their context in the claims as a whole, are analogous to: mere automation of manual processes, instructions to display two sets of information on a computer display in a non-interfering manner, without any limitations specifying how to achieve the desired result, and arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, which courts have indicated may not be sufficient to show an improvement in computer-functionality (see MPEP 2106.05(a)(I)); a commonplace business method being applied on a general purpose computer, gathering and analyzing information using conventional techniques and displaying the result, and selecting a particular generic function for computer hardware to perform from within a range of fundamental or commonplace functions performed by the hardware, which courts have indicated may not be sufficient to show an improvement to technology (see MPEP 2106.05(a)(II)); a general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, and merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, which do not qualify as a particular machine or use thereof (see MPEP 2106.05(b)(I)); a machine that is merely an object on which the method operates, which does not integrate the exception into a practical application (see MPEP 2106.05(b)(II)); use of a machine that contributes only nominally or insignificantly to the execution of the claimed method, which does not integrate a judicial exception (see MPEP 2106.05(b)(III)); transformation of an intangible concept such as a contractual obligation or mental judgment, which is not likely to provide significantly more (see MPEP 2106.05(c)); recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, which courts have found to be mere instructions to apply an exception, because they recite no more than an idea of a solution or outcome (see MPEP 2106.05(f)); use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea, a commonplace business method or mathematical algorithm being applied on a general purpose computer, and requiring the use of software to tailor information and provide it to the user on a generic computer, which courts have found to be mere instructions to apply an exception, because they do no more than merely invoke computers or machinery as a tool to perform an existing process (see MPEP 2106.05(f)); mere data gathering in the form of obtaining information about transactions using the Internet to verify transactions and consulting and updating an activity log, and selecting a particular data source or type of data to be manipulated in the form of selecting information, based on types of information and availability of information in an environment, for collection, analysis, and display, which courts have found to be insignificant extra-solution activity (see MPEP 2106.05(g)); and specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, which courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception (see MPEP 2106.05(h)). For at least these reasons, claims 1, 11, and 19 fail to meet the criteria of Step 2A, Prong Two of the eligibility analysis.
The next step of the eligibility analysis, Step 2B, asks whether a claim recites additional elements that amount to significantly more than the judicial exception. (See MPEP 2106.05(II).) The step involves identifying whether there are any additional elements in the claim beyond the judicial exceptions, and evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept. (See id.) The ineligibility rationales applied at Step 2A, Prong Two, also apply to Step 2B. (See id.) For all of the reasons covered in the analysis performed at Step 2A, Prong Two, claims 1, 11, and 19 fail to meet the criteria of Step 2B. Further, claims 1, 11, and 19 also fail to meet the criteria of Step 2B because at least some of the additional elements are analogous to: receiving or transmitting data over a network, e.g., using the Internet to gather data, performing repetitive calculations, electronic recordkeeping, and storing and retrieving information in memory, which courts have recognized as well-understood, routine, conventional activity, and as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). As a result, claims 1, 11, and 19 are rejected under 35 USC 101 as ineligible for patenting.
Regarding claims 2-10, 12-18, and 20 the claims depend from claims 1, 11, and 19, and expand upon limitations introduced by claims 1, 11, and 19. The dependent claims are rejected at least for the same reasons as claims 1, 11, and 19. For example, the dependent claims recite abstract idea elements similar to the abstract idea elements of claims 1, 11, and 19 that fall under the same abstract idea groupings as the abstract idea elements of claims 1, 11, and 19 (e.g., the “iterating to the obtaining at a preconfigured period of time to update each of the predicted future transactions, churn likelihood, and CLV for each customer of the FI” of claim 2, the “forecasting predicted debit transactions ... and predicted credit transactions ... for each customer separately” of claim 3, the “forecasting further includes obtaining a first churn likelihood associated with the predicted debit transactions ... and obtaining a second churn likelihood associated with the predicted credit transactions” of claim 4, the “forecasting further includes obtaining the predicted debit transactions with the first churn likelihood” of claim 5, the “forecasting further includes obtaining the predicted credit transactions with the second churn likelihood” of claim 6, the “predicting further includes obtaining the CLV ... by providing as input the predicted debit transactions ..., the predicted credit transactions ..., the first churn likelihood, and the second churn likelihood to obtain the CLV” of claim 7, the “predicting further includes processing a statistical and heuristic algorithm using the predicted debit transactions ..., the predicted credit transactions ..., the first churn likelihood, and the second churn likelihood to obtain the CLV” of claim 8 (which also is a mathematical concept), the “integrating further includes providing the predicted future transactions, the churn likelihood, and the CLV for each customer” of claim 9, the “providing further includes providing the predicted future transactions, the churn likelihood, and the CLV for each customer” of claim 10, the “updating each of the predicted credit transactions, the first churn likelihood, the predicted credit transactions, the second churn likelihood, and the CLV at predefined intervals of time based on actual observed transactions of each customer” of claim 12, the “wherein predicting further includes ... to generate each CLV for each customer using as input corresponding predicted credit transactions, a corresponding first churn likelihood, a corresponding predicted debit transactions, and a corresponding second churn likelihood” of claim 13, the “predicting further includes processing a statistical and heuristic algorithm on each of the predicted credit transactions, the first churn likelihood, the predicted debit transactions, and the second churn likelihood to obtain a corresponding CLV for a particular customer” of claim 14, the “generating further includes adding a total predicted profit for the given interval of time for each record based on corresponding predicted credit transactions” of claim 15 (which also is a mathematical concept), the “generating further includes adding a total predicted cost for the given interval of time for each record based on corresponding predicted debit transactions” of claim 16 (which also is a mathematical concept), the “delivering further includes providing the records” of claim 17, the “providing further includes providing the records” of claim 18, and the “wherein the operations associated with predicting the CLV further includes predicting the CLV of each customer based on a savings account balance associated with a corresponding customer” of claim 20). The dependent claims recite further additional elements that are similar to the additional elements of claims 1, 11, and 19 that fail to warrant eligibility for the same reasons as the additional elements of claims 1, 11, and 19 (e.g., the “from the debit machine learning model ... from the credit machine learning model” of claim 3, the “from the debit machine learning model ... from the credit machine learning model” of claim 4, the “from the debit machine learning model” of claim 5, the “from the credit machine learning model” of claim 6, the “from a trained CLV machine learning model ... from the debit machine learning model ... from the credit machine learning model” of claim 7, the “from the debit machine learning model ... from the credit machine learning model” of claim 8, the “via an application programming interface (API) to the interface or the system” of claim 9, the “to a dashboard interface of the system via the API” of claim 10, the “training a third MLM” of claim 13, the “to the system or the interface via an application programming interface” of claim 17, the “to a dashboard interface associated with a system of the FI” of claim 18, and the “system” of claim 20). Accordingly, claims 2-10, 12-18, and 20 also are rejected as ineligible under 35 USC 101.
Examiner Remarks
This Office Action does not assert any prior art rejections against claims 1-20. The claims distinguish over the closest prior art of record. In a prior obviousness rejection (see Non-Final Office Action of 28 January 2026), a combination of cited references, including: U.S. Pat. App. Pub. No. 2019/0066130 A1 to Shen et al. (hereinafter referred to as “Shen”); U.S. Pat. App. Pub. No. 2021/0350464 A1 to Martinez et al. (hereinafter referred to as “Martinez”); Li, Chun-Qing, Weverbergh Marcel, and Ting Gao. "Does A Long-Term and Active Customer of A Current Savings Account Have A High Lifetime Value?." Contemporary Management Research 8.2 (2012) (hereinafter referred to as “Li”); and U.S. Pat. App. Pub. No. 2022/0405775 A1 to Siebel et al. (hereinafter referred to as “Siebel”), were cited, and are representative of the closest prior art of record.
As explained in the prior obviousness rejection, the cited references disclose elements that read on one or more limitations of the claims. For example, Shen discloses, “Turning to FIG. 4, a diagram of one embodiment of a densely connected neural network 400 is shown. The architecture for this densely connected neural network is broadly applicable, and need not be used only to calculate CV. In various embodiments, other quantities can be calculated for the model—indeed, this architecture can be used for any appropriate modeling task” (para. [0047]), “Neural network 400 can be optimized on an output task, such as CV, using historical data in various embodiments. For example, neural network 400 (which is densely connected) can be trained using past transaction data for users and/or other data (e.g. cost data, fraud loss data, revenues data, etc.)” (para. [0052]), “Once neural network 400 is trained, it can then be used to predict CV (or another quantity) for various input data” and “Input data 405 for neural network 400, for example, can include not just transaction history, but also various profile data about a user. Someone who has only recently joined PayPal™, for example, may still provide many pieces of information about themselves, such as mailing address, country of residence, linked funding sources (debit or credit card, checking account, etc.), an email address, and device information (e.g. what model of computer or smartphone the user has, whether they have connected to PayPal.com from different cities, network information such as IP addresses used to login, additional hardware device information like screen size and other fixed and/or changeable aspects of the device, etc.)” (para. [0054]), “Turning to FIG. 5, a diagram is shown of one embodiment of a unified model 500 for predicting customer value (CV) and component pieces of customer value such as cost, loss, revenue derived from a user sending money (Rev_S), and revenue derived from a user receiving money (Rev_R). This unified model allows simultaneous calculation from the same model for not only an overall objective (CV) but also calculations for related sub-variables (cost, loss, Rev_S, Rev_R) that are related components of the overall objective” (para. [0063]), “Once the first series of neural network modules (e.g. 510, 515, 520, 525) has finished its calculations, output from that series is then distributed to a plurality of variable sub-task neural network modules. In this example, the sub-task neural network modules comprise a first sub-task module including dense layers 530 and 531, a second sub-task module including dense layers 535 and 536, a third sub-task module including dense layers 540 and 541, and a fourth sub-task module including dense layers 545 and 546. These modules are each respectively designed to generate outputs for the sub-variables loss, cost, Rev_S, and Rev_R, as indicated by tasks 532, 537, 542, and 547” (para. [0063]). Shen also discloses “Account ID,” “Country,” “IP Address,” “Fee Costs,” (FIG. 2). Neither the unified model, neural network modules, or sub-task neural network modules, involves “training a debit machine learning model on labeled parameter data to jointly predict debit transactions and a debit churn likelihood per customer as combined outputs, and training a credit machine learning model on labeled parameter data to jointly predict credit transactions and a credit churn likelihood per customer as combined outputs, wherein the labeled parameter data includes customer identifier, transaction date, transaction time, transaction location, transaction device, insufficient fund violations, interchange fees, costs of a given transaction, profit of a given transaction, loan terms, and loan interest, and wherein the training includes frequency and recency of transactions for each customer during the first interval of time to identify factors relevant to predicting the transactions or transaction rate in a given future period or interval of time” as recited by claim 1. Further, none of the sub-variables, in Shen, either alone or in combination, read on the recited “savings account balance rating value assigned by the FI for each customer, wherein savings account balance rating value include low value, medium value, or high value” limitation of claim 1.
While Martinez discloses, “generating the set of performance drivers using machine learning on a plurality of data from a plurality of customers” (para. [0022]),“the remaining lifetime of the customer is based on a remaining lifetime of a cohort. In an embodiment, the cohort is the first cohort. In an embodiment, the remaining lifetime of the cohort is based on an attrition driver for the cohort. In an embodiment, the attrition driver is at least one of a risk score, a usage rate, a default rate, and a delinquency rate. In an embodiment, the attrition driver is a customer exit rate based on historical customer data for the cohort. In an embodiment, the remaining lifetime is indicative of a point in time wherein 50% of or less of the customers originally in the cohort are no longer expected to remain in one of the plurality of cohorts” (para. [0030]), and “a computer-implemented method for determining a customer lifetime value (CLV) for a customer is disclosed, the method including segmenting a plurality of customers into a plurality of cohorts based on a performance driver indicative of future customer performance, wherein a first cohort includes the customer; generating a plurality of cohort forecasts corresponding to the plurality of cohorts, each cohort forecast based on the performance driver of each customer belonging to a corresponding cohort, wherein the plurality of cohort forecasts are generated for a remaining lifetime of the customer; and, calculating the CLV metric based on the plurality of cohort forecasts and a set of transition probabilities indicative of a likelihood that the customer remains in the first cohort, or transitions to a different cohort” (para. [0034]), the machine learning features of Martinez fail to remedy the machine learning deficiencies of Shen, and the forms of customer data of Martinez fail to remedy the customer data deficiencies of Shen.
While Li discloses, “The current savings account CLV model has been introduced before. We used the average monthly balance of current savings account multiplied by the net interest rate at that time to arrive at the NIR; balance change times multiplied by the service cost allocation ratio to arrive at IE; the difference between NIR and IE is the customer profitability. In addition, the CLV is the customer’s profitability of all lifetime discounted values,” “In the calculation of CLV, we assume that customers’ transaction behavior in 2009.12 and after would be the same as in 2009.11; that is, the average monthly account balance and the balance change times remain unchanged. Then we calculate the customer’s present value of its profits in the infinite period.” and “a method to calculate CLV” (p. 142), and “CLV calculations are divided into three phases, the first phase of the training period, 1-12; 13-16 for the second stage of the forecast period, the third stage, 17-N for the infinite life-cycle of the CLV” (p. 151). Siebel discloses, “a user interface 1700 represents an executive dashboard interface that can be used to summarize information” (para. [0362]), and “The user interface 1700 also includes a forecast categories section 1706, which identifies various overall forecasts” (para. [0363]), Li lacks disclosure of machine learning features, and thus, would not remedy the machine learning deficiencies of Shen (and/or Martinez). Further, while Li discloses consideration of account balance information, Li lacks disclosure of associated rating values that would remedy the rating values deficiencies of Shen (and/or Martinez).
Nor would Siebel address the above-referenced deficiencies of Shen, Martinez, and Li, and was not cited for such a purpose. As such, claim 1 distinguishes over the closest prior art of record. Claims 11 and 19, which recite limitations similar to those of claim 1, also distinguish over the closest prior art of record for similar reasons. Claims 2-10, 12-18, and 20 depend from one of claims 1, 11, and 19, and thus, distinguish over the closest prior art of record due at least to their dependency.
Additional searching also turned up references like WIPO Int’l Pub. No. 2022/154842 A1 to Mimassi (hereinafter referred to as Mimassi). Mimassi discloses, “retrieve a subset of the customer history data from the customer database that corresponds to the specific customer; retrieve a subset of the business enterprise sales data from the business enterprise database; compute a customer average transaction amount using the customer receipt data from the subset of the customer history data; compute a business average transaction amount using the business enterprise sales data; and determine, using a machine-learned predictive algorithm, a pre-authorization amount using the customer average transaction amount, the business average transaction amount, and the customer preferences from the subset of the customer history data as inputs into the machine-learned predictive algorithm” (para. [006]). While Mimassi discloses using machine learning in conjunction with transactions and associated amounts, Mimassi lacks disclosure of machine learning features and savings account balance rating features that would address the aforementioned deficiencies of the other closest prior of record (see paragraphs above). As such, the claims also distinguish over Mimassi.
Response to Arguments
On pp. 8-11 of the Amendment/Response, the applicant requests reconsideration and withdrawal of the claim rejection under 35 USC 101. More specifically, the applicant contends that the claims are directed to a specific improvement to the technology of machine learning model training and customer lifetime value prediction for financial institutions. (See Amendment/Response, p. 8). The applicant argues that the Desjardins and Kelley decisions support the applicant’s contention. (See id. at p. 8.)
The applicant appears to view the claimed invention as similar to the one in the Desjardins decision. In the Desjardins decision, the claims were found eligible as being directed to training a machine learning model in a specific way that reflected an improvement to how the machine learning model itself operates, which provides the required integration. (See id.) The applicant states that the concrete improvement to machine learning model operation, according to the Desjardins decision, involved adjusting parameter values to optimize performance on a second task while protecting performance on a first task. (See id. at pp. 8 and 9.)
Regarding the Kelley decision, the applicant asserts that reasoning from the Desjardins decision was applied, resulting in eligibility of claims, where the claims reflected a specific improvement to how a machine learning algorithm is trained. Specifically, that claims directed to training a machine learning algorithm using linear proxy constraints for non-linear constraints were patent eligible because the specific steps of modifying a linear proxy constraint based on its relationship to a non-linear constraint and replacing the non-linear constraint with the modified linear proxy constraint improved the training and operation of the machine learning algorithm. (See id. at p. 9.)
The applicant argues that the claims of the present application are analogous to the claims found eligible in the Desjardins and Kelley decisions. (See id.) According to the applicant, the claims of the present application recite a specific improvement to how the machine learning models themselves are trained, not merely the application of generic machine learning to a new field of use. (See id.) The applicant points to the claimed joint training architecture, in which a single model is trained to produce both a transaction prediction and a churn likelihood as combined outputs for its specific transaction type, which is, allegedly, a specific improvement to how the models are trained. (See id. at pp. 9 and 10.)
The applicant also contends that the examiner has failed to provide an adequate explanation of why the specific joint training architecture constitutes merely conventional or generic machine learning training. (See id. at pp. 10 and 11.) Also, the applicant argues that the examiner erred in characterizing the additional elements as generic computer components performing generic functions, as the claims of the present application reflect a specific improvement to how the machine learning model itself operates. (See id. at pp. 10 and 11.)
The examiner finds the arguments above unpersuasive. The claims in the Desjardin and Kelley decisions are eligible because they recite steps that improve machine learning training. The claims involve actual improving of the steps by which machine learning models are trained. The claims of the present application, on the other hand, only recite that training is performed. The claims lack any limitations describing how the training is performed. As such, the eligibility rationales from the Desjardins and Kelley decisions do not apply to the claims of the present application. The claims of the present application establish that a machine learning model is trained to make predictions, that another machine learning model is trained to make other predictions, neither of which is an improvement to machine learning training, or machine learning in general. The claim merely recites steps performed by virtually all machine learning models. The claims of the present application, therefore, remain ineligible at Step 2A, Prong Two and Step 2B of the multi-step eligibility analysis.
On pp. 12-16 of the Amendment/Response, the applicant requests reconsideration and withdrawal of the claim rejections under 35 USC 103. The requests have been granted for the reasons specific in the Examiner Remarks section above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Such prior art includes the following:
U.S. Pat. No. 11,416,779 B2 to Scavo discloses, “A system comprising: at least one hardware processor; and one or more software modules comprising computer-executable instructions configured to, when executed by the at least one hardware processor, receive a plurality of data associated with a plurality of customers from a plurality of different data sources, wherein each different data source provides a different type of data representing credit card transactions, debit card transactions, or electronic purchase transactions, standardize the plurality of data to generate a standardized plurality of historical financial transactions, tag the standardized plurality of historical financial transactions with one or more companies based on at least one of merchant names in descriptions of transactions of the standardized plurality of historical financial transactions or one or more brands common to the standardized plurality of historical financial transactions and the one or more companies, generate at least one panel of customers for each of the one or more companies based on the tagged, standardized plurality of historical financial transactions, wherein the at least one panel of customers for each of the one or more companies is representative of a customer population and comprises a stable number of transactions associated with that company over a time interval, train a neural network prediction model using back propagation and the tagged, standardized plurality of historical financial transactions comprising historical measurements of at least one company of the one or more companies and historical stock prices of the at least one company to predict a metric for each of the one or more companies, wherein the historical measurements comprise sales and revenue, and wherein the metric comprises at least one of an amount of revenue, a number of users, an amount of time spent, or a number of visits, apply the neural network prediction model to new data from the plurality of different data sources to iteratively train the neural network and predict the metric for at least one panel of customers the at least one panel of customers for each of the one or more companies, and provide a recommendation to a display of at least one user device based on the predicted metric, wherein the recommendation comprises the predicted metric and at least one of a buy, sell, or hold recommendation for a stock of each of the one or more companies” (claim 12).
U.S. Pat. App. Pub. No. 2015/0206252 A1 to Rephlo et al. discloses “At block 224, a financial institution may utilize machine learning to correct and/or adjust calculations of predicted credits or debits, such as financial transactions and/or income, associated with the account holder upon matching a predicted financial transaction and/or income with an actual financial transaction and/or income. In this manner, the financial institution may continuously improve calculations of predicted financial transactions and/or income associated with an internal account and account balance calculations” (para. [0057]).
U.S. Pat. App. Pub. No. 2019/0325327 A1 to Blanchette et al. discloses, “At step/operation 403, the prediction-based action engine 114 utilizes the engine predictions to initiate, execute, perform, and/or similar words used herein interchangeably one or more prediction-based actions. Examples of prediction-based actions include report generation, for example, report generation in response to user requests for particular reports. Examples of such reports include customer portfolio reports which may include a detailed reporting of customer-level data in a monthly calendar format; new customer vintage reports which may include information/data about customer statistics for new customers (e.g., customer attrition rates during the first n months of membership); customer lifetime value dynamic reports which may include information/data about how customer value is generated or lost across time periods; the opportunity sizing engine report which may include summary of entity opportunity predictions; deposit account reports which may include information/data about basic account-level information/data for deposit accounts, where customer accounts may be divided into segments based at least in part on customer-level attributes (e.g., deposit account balances and types by business units that manage particular customer entities); loan account reports which may include information/data about basic account-level information/data for deposit accounts, where customer accounts may be divided into segments based at least in part on customer-level attributes (e.g., deposit account balances and types by business units that manage particular customer entities); and portfolio monitoring reports which may include information/data generated by using statistical testing to detect movements in custom base across all customer segments” (para. [0091]).
U.S. Pat. App. Pub. No. 2023/0073719 A1 to Bellman et al. discloses, “operating a machine learning target factory may include generating a plurality of machine learning targets. The method may include storing each of the plurality of machine learning targets on a network-attached database. The method may include receiving, at the network-attached database, a request for at least one of the plurality of machine learning targets for a machine learning model. The method may include providing the at least one of the plurality of machine learning targets to a computing device housing the machine learning model” (Abstract).
U.S. Pat. App. Pub. No. 2023/0237495 A1 to Weinstein discloses, “the machine learning model(s) may receive account balance history information, transaction history, debits, credits, etc. and predict how much money will be safely available (e.g., with a comfortable cushion) to invest for a few days or a few weeks. The model(s) may also identify a time period for the investment. If the model(s) determines that $20,000 will be available for a two-week period, and then the user will need the money back to purchase supplies, the model(s) can recommend an investment window of 10 days for the purpose of the application is to gain valuable interest on the funds inside the bank account in the short-term. However, instead of recommending $20,000, the model may recommend $15,000 as an investment in order to leave a safety cushion just in case unexpected expenses occur. The total amount, the cushion, the time period, etc., may be output by the model(s) in response to receiving the account history information as input” (para. [0021]).
U.S. Pat. App. Pub. No. 2024/0273339 A1 to Zhao discloses, “configuring, training, and utilizing a machine learning model that includes different experts corresponding to different domains, such that the machine learning model may facilitate transfer of knowledge acquired from one domain to another domain and to use different mixtures of experts to perform tasks across the different domains. The machine learning model includes individual domain experts configured to process input values corresponding to features that are unique to the corresponding domains. The machine learning model also includes a common expert configured to process input values corresponding to features that are common to the different domains. By training the machine learning model using training data associated with a first domain, both a first domain expert and the common expert are trained. The knowledge acquired by the common expert can then be utilized when processing tasks associated with a second domain” (Abstract), and “When the machine learning model 300 perform tasks associated with the domain 254 (e.g., predicting a risk associated with a debit card transaction), input values associated with the debit card transaction and corresponding to features related to the domain 254 may be received via the input layers 304 and 304” (para. [0058]).
U.S. Pat. App. Pub. No. 2024/0289877 A1 to Ricchuiti et al. discloses, “a method for validating dynamic income. The method may include receiving, via a first user device, estimated income amount associated with a customer, and receiving or retrieving a plurality of transactions comprising associated text data. The method further includes dynamically determining, using a first machine learning model, a repeating source of deposits by identifying from among the plurality of transactions a portion of the text data that repeats and corresponds to one or more credits, dynamically generating, using a second machine learning model, an income amount and a confidence score based on the repeating source of deposits and the estimated income amount, dynamically generating a graphical user interface comprising the income amount and the confidence score, and dynamically transmitting the graphical user interface to a second user device for display” (Abstract).
U.S. Pat. App. Pub. No. 2024/0303551 A1 to Li et al. discloses, “computer-implemented apparatuses and processes that facilitate a real-time prediction of future events using trained artificial-intelligence processes and inferred ground-truth labelling in multiple data populations. For example, an apparatus may receive application data characterizing an exchange of data from a device, and based on an application of an artificial-intelligence process to an input dataset that includes at least a portion of the application data, the apparatus may generate, in real time, output data indicative of a likelihood of an occurrence of at least one targeted event associated with the data exchange during a future temporal interval. The artificial-intelligence process may trained using datasets associated with inferred ground-truth labels and multiple data populations, and the apparatus may transmit at least a portion of the output data to the device for presentation within a digital interface” (Abstract).
U.S. Pat. App. Pub. No. 2024/0412078 A1 to Ghelichi et al. discloses, “FI computing system 130 may perform operations that train adaptively a machine-learning or artificial-intelligence process to predict a likelihood of an occurrence of a target event involving a corresponding customer of the financial institution during a target temporal interval using training datasets associated with a first prior temporal interval (e.g., a “training” interval), and using validation datasets associated with a second, and distinct, prior temporal interval (e.g., an out-of-time “validation” interval). By way of example, the target event may include, but are not limited to, an a default or a delinquency involving an unsecured lending product, such as a credit-card account or an unsecured personal loan, or an occurrence of a default or delinquency event involving a secured lending product, such as a home mortgage, a home-equity line-of-credit, or an auto loan, and the target temporal interval may include, among other things, a future, one-month interval, a future, six-month interval, or a future, twelve-month interval. Further, a predictive output of the trained, machine-learning or artificial-intelligence process may, in some examples, inform a decision of the financial institution to pre-approve, or approve, an issuance of a corresponding one of the secured or unsecured lending products to a customer (e.g., in response to an application, etc.), or to modify a term or condition of a previously issued secured or unsecured lending product (e.g., a credit limit associated with a credit-card account, etc.). The disclosed embodiments are, however, not limited to these exemplary occurrences of targeted events, and in other instances, FI computing system 130 may perform operations that train adaptively a machine-learning or artificial-intelligence process to predict a likelihood of an occurrence of any additional, or alternate, event appropriate to the consolidated elements of customer-profile, account, transaction, or credit-bureau data maintained within consolidated data records 138, and appropriate to the machine-learning or artificial-intelligence process” (para. [0027]).
U.S. Pat. App. Pub. No. 2025/0139694 A1 to Jonathans et al. discloses, “Computing system 102 with combined account management platform 104, along with credit card transaction reassignment application 105, forecast application 103, and virtual account management application 101, have a number of technical advantages and practical advantages over prior systems. Combined account management platform 104 may provide account access across different bank and institutions in a single user interface. This saves time and makes it easier for a user to get a grasp on their finances which is especially an issue for small business owners. A single user interface is easier to upgrade, store and use at the user device. Credit card transaction reassignment application 105 makes it easy for a user to use a single credit card for both personal and business use. Rather than using two cards or requiring the user to keep personal records of which purchase should be assigned to personal or business finances for record keeping and accounting at the end of the month, credit card transaction reassignment application 105 enables users to easily reassign assign transactions between a business and personal account. Forecast application 103 enables users to have a better understanding of their finances and enables them to make financial decisions before a potential issue occurs. Virtual account management application 101 may enable users to associate underlying accounts with specific purposes and this helps users keep track of their finances and plan for life events such as taxes. Combined account management platform 104, along with credit card transaction reassignment application 105, forecast application 103, and virtual account management application 101 may use continual learning by machine learning mode models to improve recommendations for virtual accounts, forecasted shortfalls and offers as well as improve predictions for predicted account balances and generally improving the user experience” (para. [0071]).
CA Pat. App. Pub. No. 3225698 A1 to Chen et al. discloses, “automatic adjustment of limits based on machine learning forecasting. An entity, such as company or other entity, may purchase items utilizing a payment instrument or card provided to the company by a credit provider system or entity. In order to provide proper underwriting for credit extensions, such as balances and limits of extendable credit, the credit provider system may utilize a forecasting machine learning (ML) model trained to predict a future global balance of funds or a likelihood of repayment of the extended credit limit. This may be based on information retrievable balances from a banking system and a staleness of this data. When the data is stale and has not been updated, the forecasted balance may have a wider range, and thus risk factors may designate less risky and lower limits” (Abstract).
WIPO Int’l Pub. No. 2022/154842 A1 to Mimassi discloses “a system for predictive pre-authorization of transactions using biometrics is disclosed, comprising: a computing device comprising a memory, a processor, and a non-volatile data storage device; a customer information database on the non-volatile data storage device, the database comprising customer history data, the customer history data comprising customer receipt data, bank, debit, or credit card details, and customer preferences; a business enterprise database on the non-volatile data storage device, the database comprising business enterprise sales data; a biometrics verification module comprising a first plurality of programming instructions stored in the memory, and operating on the processor, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to: receive audio data, or video data, or both; match received audio data, or video data, or both with stored biometric data to confirm an identity of a specific customer; and send a confirmation of identity signal to an authorization generator; and an authorization generator module comprising a first plurality of programming instructions stored in the memory, and operating on the processor, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to: receive the confirmation of identity signal from the biometrics verification module; retrieve a subset of the customer history data from the customer database that corresponds to the specific customer; retrieve a subset of the business enterprise sales data from the business enterprise database; compute a customer average transaction amount using the customer receipt data from the subset of the customer history data; compute a business average transaction amount using the business enterprise sales data; and determine, using a machine-learned predictive algorithm, a pre-authorization amount using the customer average transaction amount, the business average transaction amount, and the customer preferences from the subset of the customer history data as inputs into the machine-learned predictive algorithm” (para. [006]).
WIPO Int’l Pub. No. 2022/159148 A1 to Cabrera et al. discloses “The account management system 102 executes the on-chain transaction based at least in part on committing an on-chain transaction record data object to a distributed ledger (e.g., a blockchain) for the internally-custodied digital asset, the distributed ledger being external to the closed-loop environment. In various instances, multiple closed-loop debits may be executed (e.g., in an off-chain manner) within a short amount of time for multiple digital asset conversion, and the account management system 102 is configured to preemptively execute an open-loop transaction (e.g., on-chain transaction) based at least in part on projecting and predicting the balance of the central operating account using one or more predictive models, projection models, optimization models, machine learning models, and/or the like.”
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 THOMAS Y. HO, whose telephone number is (571)270-7918. The examiner can normally be reached Monday through Friday, 9:30 AM to 5:30 PM Eastern.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor, can be reached at 571-272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/THOMAS YIH HO/Primary Examiner, Art Unit 3624
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