DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
2. This action is in reply to the responsive to communication(s) filed on 02/28/2025.
3. Claims 1-19 are currently pending and are rejected for the reasons set forth below.
Information Disclosure Statement
4. The Information Disclosure Statements (IDS) filed on 03/20/2025 and 08/19/2025 have been considered. Initialed copies of the Form 1449 are enclosed herewith.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
7. Analysis:
Step 1: Statutory Category?: (is the claim(s) directed to a process, machine, manufacture or composition of matter?) - YES: In the instant case, claims 1-13 are directed to a computer-implemented method (i.e., process) and claims 14-19 are directed to a server system (i.e., machine).
Regarding independent claim 1:
Step 2A - Prong 1: Judicial Exception Recited?: (is the claim(s) recited a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) – YES: Independent claim 1 recites the at least following limitations of “… receiving, …, an authorization request associated with an ongoing payment transaction initiated by a cardholder with a merchant, the authorization request comprising a plurality of ongoing payment transaction attributes; accessing, …, a historical payment transaction dataset …, the historical payment transaction dataset comprising a plurality of transaction attributes related to a plurality of historical payment transactions; generating, …, a plurality of features for the cardholder based, at least in part, on the plurality of ongoing payment transaction attributes and the plurality of transaction attributes; generating, …, a first return probability score associated with the ongoing payment transaction based, at least in part, on the plurality of features, the first return probability score indicating a likelihood of the ongoing payment transaction being associated with a return request within a predefined time interval; determining, …, a first return advice code for the ongoing payment transaction based, at least in part, on the first return probability score and a set of predefined threshold values; and facilitating, …, transmission of an authorization response message comprising the first return advice code….” These recited limitations of the claim, as drafted, under its broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of the limitations in commercial interactions (including sales activities for determining potential return transactions for products that may be returned in the future in exchange for a partial or full refund). Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application?: (is the claim(s) recited additional elements that integrate the exception into a practical application of the exception) - NO: This judicial exception is not integrated into a practical application. In particular, independent claim 1 further to the abstract idea includes additional elements of “a server system”, “a database”, “a return prediction model associated with the server system”, “a communication interface associated with the server system”, and “an acquirer server associated with the merchant”. However, the additional elements recite generic computer components such as a computer, computing devices, a server, and/or software programing that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f). The claim is directed to an abstract idea.
2B: Claim provides an Inventive Concept?: (is the claim(s) recited additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception) - NO: The claim does 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 elements of “a server system”, “a database”, “a return prediction model associated with the server system”, “a communication interface associated with the server system”, and “an acquirer server associated with the merchant” evaluated individually and in combination do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, or are not more than merely using a computer as a tool to perform an abstract idea. 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 (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more - See MPEP 2106.05(f)(2). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, the claim is patent-ineligible.
Regarding independent claim 10:
Step 2A - Prong 1: Judicial Exception Recited?: (is the claim(s) recited a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) – YES: Independent claim 10 recites the at least following limitations of “… receiving, …, a return prediction request for a checkout payment transaction from a merchant, the return prediction request comprising a plurality of checkout attributes; accessing, …, a historical payment transaction dataset …, the historical payment transaction dataset comprising a plurality of transaction attributes related to a plurality of historical payment transactions; generating, …, a plurality of features for a cardholder based, at least in part, on the plurality of checkout attributes and the plurality of transaction attributes; generating, …, a second return probability score associated with the checkout payment transaction based, at least in part, on the plurality of features, the second return probability score indicating a likelihood of the checkout payment transaction being associated with a return request within a predefined time interval; determining, …, a second return advice code for the checkout payment transaction based, at least in part, on the second return probability score and a set of predefined threshold values; and facilitating, …, transmission of a return prediction response message comprising the second return advice code to an acquirer server associated with the merchant….” These recited limitations of the claim, as drafted, under its broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of the limitations in commercial interactions (including sales activities for determining potential return transactions for products that may be returned in the future in exchange for a partial or full refund). Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application?: (is the claim(s) recited additional elements that integrate the exception into a practical application of the exception) - NO: This judicial exception is not integrated into a practical application. In particular, independent claim 10 further to the abstract idea includes additional elements of “a server system”, “a database”, “a return prediction model associated with the server system”, “a communication interface associated with the server system”, and “an acquirer server associated with the merchant”. However, the additional elements recite generic computer components such as a computer, computing devices, a server, and/or software programing that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f). The claim is directed to an abstract idea.
2B: Claim provides an Inventive Concept?: (is the claim(s) recited additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception) - NO: The claim does 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 elements of “a server system”, “a database”, “a return prediction model associated with the server system”, “a communication interface associated with the server system”, and “an acquirer server associated with the merchant” evaluated individually and in combination do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, or are not more than merely using a computer as a tool to perform an abstract idea. 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 (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more - See MPEP 2106.05(f)(2). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, the claim is patent-ineligible.
Regarding independent claim 14:
Step 2A - Prong 1: Judicial Exception Recited?: (is the claim(s) recited a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon) – YES: Independent claim 14 recites the at least following limitations of “… receive an authorization request associated with an ongoing payment transaction initiated by a cardholder with a merchant, the authorization request comprising a plurality of ongoing payment transaction attributes; access a historical payment transaction dataset …, the historical payment transaction dataset comprising a plurality of transaction attributes related to a plurality of historical payment transactions; generate a plurality of features for the cardholder based, at least in part, on the plurality of ongoing payment transaction attributes and the plurality of transaction attributes; generate …, a first return probability score associated with the ongoing payment transaction based, at least in part, on the plurality of features, the first return probability score indicating a likelihood of the ongoing payment transaction being associated with a return request within a predefined time interval; determine a first return advice code for the ongoing payment transaction based, at least in part, on the first return probability score and a set of predefined threshold values; and facilitate … transmission of an authorization response message comprising the first return advice code to an acquirer server associated with the merchant….” These recited limitations of the claim, as drafted, under its broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they cover performance of the limitations in commercial interactions (including sales activities for determining potential return transactions for products that may be returned in the future in exchange for a partial or full refund). Accordingly, the claim recites an abstract idea.
Step 2A - Prong 2: Integrated into a Practical Application?: (is the claim(s) recited additional elements that integrate the exception into a practical application of the exception) - NO: This judicial exception is not integrated into a practical application. In particular, independent claim 14 further to the abstract idea includes additional elements of “a memory”, “a communication interface”, “a processor in communication with the memory”, “a database”, “a return prediction model”, “a communication interface”, and “an acquirer server associated with the merchant”. However, the additional elements recite generic computer components such as a computer, computing devices, a server, and/or software programing that are recited a high-level of generality that merely perform, conduct, carry out, implement, and/or narrow the abstract idea itself. Accordingly, the additional elements evaluated individually and in combination do not integrate the abstract idea into a practical application because they comprise or include limitations that are not indicative of integration into a practical application such as adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea -- See MPEP 2106.05(f). The claim is directed to an abstract idea.
2B: Claim provides an Inventive Concept?: (is the claim(s) recited additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception) - NO: The claim does 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 elements of “a memory”, “a communication interface”, “a processor in communication with the memory”, “a database”, “a return prediction model”, “a communication interface”, and “an acquirer server associated with the merchant” evaluated individually and in combination do not amount to more than a recitation of the words "apply it" (or an equivalent) or are not more than mere instructions to implement an abstract idea or other exception on a computer, or are not more than merely using a computer as a tool to perform an abstract idea. 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 (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more - See MPEP 2106.05(f)(2). None of the additional elements taken individually or when taken as an ordered combination amount to significantly more than the abstract idea. Accordingly, the claim is patent-ineligible.
Dependent claims 2-9, 11-13, and 15-19 have been given the full two-part analysis, analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually and in combination, are also held to be patent-ineligible under 35 U.S.C. 101.
Dependent claims 2 and 15: simply refine the abstract idea because they recite limitations (e.g., training, by the server system, the return prediction model based, at least in part, on performing a set of operations for a plurality of iterations until the performance of the return prediction model converges to a predefined criteria, the set of operations comprising: initializing the return prediction model based, at least in part, on one or more model parameters; generating a plurality of training features based, at least in part, on a training dataset, the training dataset comprising a plurality of training transaction attributes related to a plurality of training transactions; determining a training return probability score for each training transaction from the plurality of training transactions based, at least in part, on the plurality of training features; classifying each training transaction into one of a return transaction and a non-return transaction based, at least in part, on the training return probability score for each training transaction and a predefined threshold value; computing a classification loss for each training transaction based, at least in part, on a loss function and the training dataset; and optimizing the one or more model parameters based, at least in part, on back-propagating the classification loss), that fall under the category of organizing human activity as described above in independent claims 1 and 14. The dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claims 3 and 17: simply provide further definition to “the first return advice code” recited in claims 1 and 14. Simply stating that wherein the first return advice code is at least one of high risk, moderate risk, and low risk do not add any additional element or subject matter that provides a technological improvement that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claims 4 and 18: simply refine the abstract idea because they recite limitations (e.g., further comprising: determining, by the server system, a total return amount (R) for the cardholder, and a total purchase amount (P) for the cardholder based, at least in part, on the plurality of transaction attributes related to the plurality of historical payment transactions performed between a plurality of cardholders and a plurality of merchants; computing, by the server system, a set of ratios of R and P for the plurality of cardholders, each ratio of R and P of the set of ratios of R and P corresponding to each cardholder of the plurality of cardholders; and extracting, by the server system, a set of high ratios of R and P from the set of ratios of R and P based, at least in part, on a value associated with each ratio of R and P being at least equal to a threshold value), that fall under the category of organizing human activity as described above in independent claims 1 and 14. The dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claims 5 and 19: simply refine the abstract idea because they recite limitations (e.g., further comprising: generating, by the server system, a bipartite graph for the plurality of historical payment transactions based, at least in part, on the plurality of transaction attributes and the set of high ratios of R and P, the bipartite graph comprising a first plurality of nodes and a first plurality of edges, the first plurality of nodes corresponds to the plurality of cardholders and the plurality of merchants, the first plurality of edges corresponding to the set of high ratios of R and P; and generating, by the server system, a homogeneous merchant graph based, at least in part, on the bipartite graph, the homogeneous merchant graph comprising a second plurality of nodes, the second plurality of nodes corresponds to the plurality of merchants), that fall under the category of organizing human activity as described above in independent claims 4 and 14. The dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 6: simply refines the abstract idea because they recite limitations (e.g., further comprising: generating, by the server system, a preference vector for each cardholder based, at least in part, on the set of high ratios of R and P, the preference vector corresponding to each cardholder indicating a past return behavior of each cardholder at the plurality of merchants; and generating, by the server system, a merchant correspondence matrix based, at least in part, on the set of high ratios of R and P and the homogeneous merchant graph), that fall under the category of organizing human activity as described above in dependent claim 5. The dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 7: simply refines the abstract idea because they recite limitations (e.g., further comprising: computing, by the server system, serial returnee likelihood data for the cardholder based, at least in part, on the homogeneous merchant graph, the preference vector corresponding to the cardholder, and the merchant correspondence matrix, wherein the serial returnee likelihood data indicates a likelihood that the cardholder associated with the ongoing transaction is a serial returnee), that fall under the category of organizing human activity as described above in dependent claim 6. The dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 8: simply provides further definition to “the plurality of features” recited in claim 7. Simply stating that wherein the plurality of features comprises the serial returnee likelihood data for the cardholder does not add any additional element or subject matter that provides a technological improvement that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 9: simply provides further definition to “the plurality of features” recited in claim 1. Simply stating that wherein the plurality of features comprises a plurality of transaction amounts, a plurality of transaction types, a merchant category code, a time stamp associated with the transaction, a payment method, a plurality of previous transactions by the cardholder, a location data associated with the transaction does not add any additional element or subject matter that provides a technological improvement that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 11: simply provides further definition to “the return prediction request” recited in claim 10. Simply stating that wherein the return prediction request is an Application Programming Interface (API) request message and the return prediction response message is an API response message amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., an Application Programming Interface (API)).Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 12: simply provides further definition to “the plurality of checkout attributes” recited in claim 10. Simply stating that wherein the plurality of checkout attributes comprises serial returnee likelihood data, a cart value, a total amount of the transaction, a time stamp associated with the transaction, location data associated with the transaction, or a combination thereof does not add any additional element or subject matter that provides a technological improvement that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 13: simply refines the abstract idea because they recite limitations (e.g., further comprising: receiving, by the server system, an upcoming return prediction request from a particular merchant for one or more upcoming payment transactions to be initiated by a particular cardholder with the particular merchant; accessing, by the server system, a corresponding historical payment transaction dataset from a database associated with the server system, the corresponding historical payment transaction dataset comprising a plurality of corresponding transaction attributes related to a plurality of corresponding historical payment transactions; generating, by the server system, a plurality of corresponding features for the particular cardholder based, at least in part, on the plurality of corresponding transaction attributes; generating, by the return prediction model associated with the server system, a third return probability score associated with the one or more upcoming payment transactions based, at least in part, on the plurality of corresponding features, the third return probability score indicating a likelihood of the one or more upcoming payment transactions being associated with a return request within a corresponding predefined time interval; determining, by the server system, a third return advice code for the one or more upcoming payment transactions based, at least in part, on the third return probability score and a set of predefined corresponding threshold values; and facilitating, by the communication interface associated with the server system, transmission of a corresponding return prediction response message comprising the third return advice code to a corresponding acquirer server associated with the particular merchant), that fall under the category of organizing human activity as described above in dependent claim 10. The dependent claim does not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Dependent claim 16: simply provides further definition to “the server system” recited in claim 15. Simply stating that wherein the server system is further caused, at least in part, to: generate a preference vector for each cardholder based, at least in part, on the set of high ratios of R and P, the preference vector corresponding to each cardholder indicating a past return behavior of each cardholder at the plurality of merchants; generate a merchant correspondence matrix based, at least in part, on the set of high ratios of R and P and the homogeneous merchant graph; and compute serial returnee likelihood data for the cardholder based, at least in part, on the homogeneous merchant graph, the preference vector corresponding to the cardholder, and the merchant correspondence matrix, wherein the serial returnee likelihood data indicates a likelihood that the cardholder associated with the ongoing transaction is a serial returnee, wherein the serial returnee likelihood data is one of the plurality of features amounts to no more than merely applying generic computer components and/or software programing to implement the abstract idea on a computer (i.e., the server system).Thus, the dependent claim does not add any additional element or subject matter that provides a technological improvement that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Claim Rejections - 35 USC § 102
8. 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 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.
9. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
10. Claims 1-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dantu et al. (U.S. Pub. No. 2016/0140666), hereinafter, “Dantu”.
Claim 1 –
Dantu disclose:
a computer-implemented method, comprising (See at least Dantu, [Abstract], “a method and a system are provided for indexing returns of goods by a payment card holder to a merchant”, see also Figures 1-3):
receiving, by a server system, an authorization request associated with an ongoing payment transaction initiated by a cardholder with a merchant, the authorization request comprising a plurality of ongoing payment transaction attributes (See at least Dantu, [0039], “The card issuer 160 approves or denies an authorization request, and then routes, via the payment card company network 150, an authorization response back to the acquirer 140”, see also Figure 1);
accessing, by the server system, a historical payment transaction dataset from a database associated with the server system, the historical payment transaction dataset comprising a plurality of transaction attributes related to a plurality of historical payment transactions (See at least Dantu, [0047], “data warehouse 200 integrates data from one or more disparate sources. Data warehouse 200 stores current as well as historical data and is used for creating reports, performing analyses on the network, merchant analyses, and performing predictive analyses”, see also Figure 2);
generating, by the server system, a plurality of features for the cardholder based, at least in part, on the plurality of ongoing payment transaction attributes and the plurality of transaction attributes (See at least Dantu, [0088], “At step 602, a payment card company (part of the payment card company network 150 in FIG. 1) retrieves, from one or more databases, information including purchasing and payment information attributable to one or more payment card holders. The information at 602 includes payment card transaction information, payment card holder information (e.g., payment card holder account identifier (likely anonymized), payment card holder geography (potentially modeled), payment card holder type (consumer/business), payment card holder demographics, and the like), and purchasing and payment activities attributable to payment card holders”, see also Figure 6);
generating, by a return prediction model associated with the server system, a first return probability score associated with the ongoing payment transaction based, at least in part, on the plurality of features, the first return probability score indicating a likelihood of the ongoing payment transaction being associated with a return request within a predefined time interval (See at least Dantu, [0108], “The activities and characteristics attributable to the payment card holders and based on the one or more predictive behavioral models are conveyed by the financial transaction processing entity to the entity (e.g., merchant) to take appropriate action, for example, modifying its return of goods policy in an attempt to better its index score. For example, a merchant may want to make its return of goods policy more strict or less strict in an attempt to improve one or more of the generated indices”, see also Figures 10, 11);
determining, by the server system, a first return advice code for the ongoing payment transaction based, at least in part, on the first return probability score and a set of predefined threshold values (See at least Dantu, [0108], “The activities and characteristics attributable to the payment card holders and based on the one or more predictive behavioral models are conveyed by the financial transaction processing entity to the entity (e.g., merchant) to take appropriate action, for example, modifying its return of goods policy in an attempt to better its index score. For example, a merchant may want to make its return of goods policy more strict or less strict in an attempt to improve one or more of the generated indices”, see also Figures 10, 11); and
facilitating, by a communication interface associated with the server system, transmission of an authorization response message comprising the first return advice code to an acquirer server associated with the merchant (See at least Dantu, [0095], “One or more indices based on the one or more benchmarks for rate of return of goods by the plurality of payment card holders to the plurality of merchants and the rate of return of goods by the plurality of payment card holders to the selected merchant are generated at 710. The rate of return of goods by the plurality of payment card holders to the selected merchant based on the one or more indices can then be assessed”, see also Figure 7).
Claim 2 –
Dantu disclose the computer-implemented method as claimed in claim 1, as shown above.
Dantu further disclose:
comprising: training, by the server system, the return prediction model based, at least in part, on performing a set of operations for a plurality of iterations until the performance of the return prediction model converges to a predefined criteria, the set of operations comprising: initializing the return prediction model based, at least in part, on one or more model parameters; generating a plurality of training features based, at least in part, on a training dataset, the training dataset comprising a plurality of training transaction attributes related to a plurality of training transactions; determining a training return probability score for each training transaction from the plurality of training transactions based, at least in part, on the plurality of training features; classifying each training transaction into one of a return transaction and a non-return transaction based, at least in part, on the training return probability score for each training transaction and a predefined threshold value; computing a classification loss for each training transaction based, at least in part, on a loss function and the training dataset; and optimizing the one or more model parameters based, at least in part, on back-propagating the classification loss (See at least Dantu, [0108], “The activities and characteristics attributable to the payment card holders and based on the one or more predictive behavioral models are conveyed by the financial transaction processing entity to the entity (e.g., merchant) to take appropriate action, for example, modifying its return of goods policy in an attempt to better its index score. For example, a merchant may want to make its return of goods policy more strict or less strict in an attempt to improve one or more of the generated indices”, see also Figures 10, 11).
Claim 3 –
Dantu disclose the computer-implemented method as claimed in claim 1, as shown above.
Dantu further disclose:
wherein the first return advice code is at least one of high risk, moderate risk, and low risk (See at least Dantu, [0095], “One or more indices based on the one or more benchmarks for rate of return of goods by the plurality of payment card holders to the plurality of merchants and the rate of return of goods by the plurality of payment card holders to the selected merchant are generated at 710. The rate of return of goods by the plurality of payment card holders to the selected merchant based on the one or more indices can then be assessed”, see also Figure 7).
Claim 4 –
Dantu disclose the computer-implemented method as claimed in claim 1, as shown above.
Dantu further disclose:
further comprising: determining, by the server system, a total return amount (R) for the cardholder, and a total purchase amount (P) for the cardholder based, at least in part, on the plurality of transaction attributes related to the plurality of historical payment transactions performed between a plurality of cardholders and a plurality of merchants; computing, by the server system, a set of ratios of R and P for the plurality of cardholders, each ratio of R and P of the set of ratios of R and P corresponding to each cardholder of the plurality of cardholders; and extracting, by the server system, a set of high ratios of R and P from the set of ratios of R and P based, at least in part, on a value associated with each ratio of R and P being at least equal to a threshold value (See at least Dantu, [0098], “an index can be a numerical value ranging from 0 to 500. An index of 0 indicates that the merchant does not have any returns of goods. An index between 1 and 99 indicates that the merchant is lower than their peers/competitors. An index of 100 indicates that the merchant is on par with other merchants. An index between 100 and 499 indicates that the merchant is higher than their peers/competitors. An index of 500 indicates that the merchant's return of goods is really high and immediate action is required”, see also Figure 9).
Claim 5 –
Dantu disclose the computer-implemented method as claimed in claim 4, as shown above.
Dantu further disclose:
further comprising: generating, by the server system, a bipartite graph for the plurality of historical payment transactions based, at least in part, on the plurality of transaction attributes and the set of high ratios of R and P, the bipartite graph comprising a first plurality of nodes and a first plurality of edges, the first plurality of nodes corresponds to the plurality of cardholders and the plurality of merchants, the first plurality of edges corresponding to the set of high ratios of R and P; and generating, by the server system, a homogeneous merchant graph based, at least in part, on the bipartite graph, the homogeneous merchant graph comprising a second plurality of nodes, the second plurality of nodes corresponds to the plurality of merchants (See at least Dantu, [0096], [0098], “The benchmarks are created for a rate of return of goods by a plurality of payment card holders to a plurality of merchants based on the first set of information and the second set of information. The benchmarks are created for a rate of return of goods by a plurality of payment card holders to a plurality of merchants across one or more merchant categories, merchant sales volumes, and merchant geographies … an index can be a numerical value ranging from 0 to 500. An index of 0 indicates that the merchant does not have any returns of goods. An index between 1 and 99 indicates that the merchant is lower than their peers/competitors. An index of 100 indicates that the merchant is on par with other merchants. An index between 100 and 499 indicates that the merchant is higher than their peers/competitors. An index of 500 indicates that the merchant's return of goods is really high and immediate action is required”, see also Figures 8, 9).
Claim 6 –
Dantu disclose the computer-implemented method as claimed in claim 5, as shown above.
Dantu further disclose:
further comprising: generating, by the server system, a preference vector for each cardholder based, at least in part, on the set of high ratios of R and P, the preference vector corresponding to each cardholder indicating a past return behavior of each cardholder at the plurality of merchants; and generating, by the server system, a merchant correspondence matrix based, at least in part, on the set of high ratios of R and P and the homogeneous merchant graph (See at least Dantu, [0096], [0098], “The benchmarks are created for a rate of return of goods by a plurality of payment card holders to a plurality of merchants based on the first set of information and the second set of information. The benchmarks are created for a rate of return of goods by a plurality of payment card holders to a plurality of merchants across one or more merchant categories, merchant sales volumes, and merchant geographies … an index can be a numerical value ranging from 0 to 500. An index of 0 indicates that the merchant does not have any returns of goods. An index between 1 and 99 indicates that the merchant is lower than their peers/competitors. An index of 100 indicates that the merchant is on par with other merchants. An index between 100 and 499 indicates that the merchant is higher than their peers/competitors. An index of 500 indicates that the merchant's return of goods is really high and immediate action is required”, see also Figures 8, 9).
Claim 7 –
Dantu disclose the computer-implemented method as claimed in claim 6, as shown above.
Dantu further disclose:
further comprising: computing, by the server system, serial returnee likelihood data for the cardholder based, at least in part, on the homogeneous merchant graph, the preference vector corresponding to the cardholder, and the merchant correspondence matrix, wherein the serial returnee likelihood data indicates a likelihood that the cardholder associated with the ongoing transaction is a serial returnee (See at least Dantu, [0096], [0098], “The benchmarks are created for a rate of return of goods by a plurality of payment card holders to a plurality of merchants based on the first set of information and the second set of information. The benchmarks are created for a rate of return of goods by a plurality of payment card holders to a plurality of merchants across one or more merchant categories, merchant sales volumes, and merchant geographies … an index can be a numerical value ranging from 0 to 500. An index of 0 indicates that the merchant does not have any returns of goods. An index between 1 and 99 indicates that the merchant is lower than their peers/competitors. An index of 100 indicates that the merchant is on par with other merchants. An index between 100 and 499 indicates that the merchant is higher than their peers/competitors. An index of 500 indicates that the merchant's return of goods is really high and immediate action is required”, see also Figures 8, 9).
Claim 8 –
Dantu disclose the computer-implemented method as claimed in claim 7, as shown above.
Dantu further disclose:
wherein the plurality of features comprises the serial returnee likelihood data for the cardholder (See at least Dantu, [0095], “One or more indices based on the one or more benchmarks for rate of return of goods by the plurality of payment card holders to the plurality of merchants and the rate of return of goods by the plurality of payment card holders to the selected merchant are generated at 710. The rate of return of goods by the plurality of payment card holders to the selected merchant based on the one or more indices can then be assessed”, see also Figure 7).
Claim 9 –
Dantu disclose the computer-implemented method as claimed in claim 1, as shown above.
Dantu further disclose:
wherein the plurality of features comprises a plurality of transaction amounts, a plurality of transaction types, a merchant category code, a time stamp associated with the transaction, a payment method, a plurality of previous transactions by the cardholder, a location data associated with the transaction (See at least Dantu, [0095], “One or more indices based on the one or more benchmarks for rate of return of goods by the plurality of payment card holders to the plurality of merchants and the rate of return of goods by the plurality of payment card holders to the selected merchant are generated at 710. The rate of return of goods by the plurality of payment card holders to the selected merchant based on the one or more indices can then be assessed”, see also Figure 7).
Claim 10 –
Dantu disclose:
a computer-implemented method, comprising (See at least Dantu, [Abstract], “a method and a system are provided for indexing returns of goods by a payment card holder to a merchant”, see also Figures 1-3):
receiving, by a server system, a return prediction request for a checkout payment transaction from a merchant, the return prediction request comprising a plurality of checkout attributes (See at least Dantu, [0039], “The card issuer 160 approves or denies an authorization request, and then routes, via the payment card company network 150, an authorization response back to the acquirer 140”, see also Figure 1);
accessing, by the server system, a historical payment transaction dataset from a database associated with the server system, the historical payment transaction dataset comprising a plurality of transaction attributes related to a plurality of historical payment transactions (See at least Dantu, [0047], “data warehouse 200 integrates data from one or more disparate sources. Data warehouse 200 stores current as well as historical data and is used for creating reports, performing analyses on the network, merchant analyses, and performing predictive analyses”, see also Figure 2);
generating, by the server system, a plurality of features for a cardholder based, at least in part, on the plurality of checkout attributes and the plurality of transaction attributes (See at least Dantu, [0088], “At step 602, a payment card company (part of the payment card company network 150 in FIG. 1) retrieves, from one or more databases, information including purchasing and payment information attributable to one or more payment card holders. The information at 602 includes payment card transaction information, payment card holder information (e.g., payment card holder account identifier (likely anonymized), payment card holder geography (potentially modeled), payment card holder type (consumer/business), payment card holder demographics, and the like), and purchasing and payment activities attributable to payment card holders”, see also Figure 6);
generating, by a return prediction model associated with the server system, a second return probability score associated with the checkout payment transaction based, at least in part, on the plurality of features, the second return probability score indicating a likelihood of the checkout payment transaction being associated with a return request within a predefined time interval (See at least Dantu, [0108], “The activities and characteristics attributable to the payment card holders and based on the one or more predictive behavioral models are conveyed by the financial transaction processing entity to the entity (e.g., merchant) to take appropriate action, for example, modifying its return of goods policy in an attempt to better its index score. For example, a merchant may want to make its return of goods policy more strict or less strict in an attempt to improve one or more of the generated indices”, see also Figures 10, 11);
determining, by the server system, a second return advice code for the checkout payment transaction based, at least in part, on the second return probability score and a set of predefined threshold values (See at least Dantu, [0108], “The activities and characteristics attributable to the payment card holders and based on the one or more predictive behavioral models are conveyed by the financial transaction processing entity to the entity (e.g., merchant) to take appropriate action, for example, modifying its return of goods policy in an attempt to better its index score. For example, a merchant may want to make its return of goods policy more strict or less strict in an attempt to improve one or more of the generated indices”, see also Figures 10, 11); and
facilitating, by a communication interface associated with the server system, transmission of a return prediction response message comprising the second return advice code to an acquirer server associated with the merchant (See at least Dantu, [0095], “One or more indices based on the one or more benchmarks for rate of return of goods by the plurality of payment card holders to the plurality of merchants and the rate of return of goods by the plurality of payment card holders to the selected merchant are generated at 710. The rate of return of goods by the plurality of payment card holders to the selected merchant based on the one or more indices can then be assessed”, see also Figure 7).
Claim 11 –
Dantu disclose the computer-implemented method as claimed in claim 10, as shown above.
Dantu further disclose:
wherein the return prediction request is an Application Programming Interface (API) request message and the return prediction response message is an API response message (See at least Dantu, [0039], “The card issuer 160 approves or denies an authorization request, and then routes, via the payment card company network 150, an authorization response back to the acquirer 140”, see also Figure 1).
Claim 12 –
Dantu disclose the computer-implemented method as claimed in claim 10, as shown above.
Dantu further disclose:
wherein the plurality of checkout attributes comprises serial returnee likelihood data, a cart value, a total amount of the transaction, a time stamp associated with the transaction, location data associated with the transaction, or a combination thereof (See at least Dantu, [0108], “The activities and characteristics attributable to the payment card holders and based on the one or more predictive behavioral models are conveyed by the financial transaction processing entity to the entity (e.g., merchant) to take appropriate action, for example, modifying its return of goods policy in an attempt to better its index score. For example, a merchant may want to make its return of goods policy more strict or less strict in an attempt to improve one or more of the generated indices”, see also Figures 10, 11).
Claim 13 –
Dantu disclose the computer-implemented method as claimed in claim 10, as shown above.
Dantu further disclose:
further comprising: receiving, by the server system, an upcoming return prediction request from a particular merchant for one or more upcoming payment transactions to be initiated by a particular cardholder with the particular merchant; accessing, by the server system, a corresponding historical payment transaction dataset from a database associated with the server system, the corresponding historical payment transaction dataset comprising a plurality of corresponding transaction attributes related to a plurality of corresponding historical payment transactions; generating, by the server system, a plurality of corresponding features for the particular cardholder based, at least in part, on the plurality of corresponding transaction attributes; generating, by the return prediction model associated with the server system, a third return probability score associated with the one or more upcoming payment transactions based, at least in part, on the plurality of corresponding features, the third return probability score indicating a likelihood of the one or more upcoming payment transactions being associated with a return request within a corresponding predefined time interval; determining, by the server system, a third return advice code for the one or more upcoming payment transactions based, at least in part, on the third return probability score and a set of predefined corresponding threshold values; and facilitating, by the communication interface associated with the server system, transmission of a corresponding return prediction response message comprising the third return advice code to a corresponding acquirer server associated with the particular merchant (See at least Dantu, [0108], “The activities and characteristics attributable to the payment card holders and based on the one or more predictive behavioral models are conveyed by the financial transaction processing entity to the entity (e.g., merchant) to take appropriate action, for example, modifying its return of goods policy in an attempt to better its index score. For example, a merchant may want to make its return of goods policy more strict or less strict in an attempt to improve one or more of the generated indices”, see also Figures 10, 11).
Claim 14 –
Dantu disclose:
a server system, comprising: a memory configured to store instructions; a communication interface; and a processor in communication with the memory and the communication interface, the processor configured to execute the instructions stored in the memory and thereby cause the server system to perform at least in part to: (See at least Dantu, [Abstract], [0039], “a method and a system are provided for indexing returns of goods by a payment card holder to a merchant … The merchant's point of sale (POS) device communicates 132 with his acquiring bank or acquirer 140, which acts as a payment processor”, see also Figures 1-3)
receive an authorization request associated with an ongoing payment transaction initiated by a cardholder with a merchant, the authorization request comprising a plurality of ongoing payment transaction attributes (See at least Dantu, [0039], “The card issuer 160 approves or denies an authorization request, and then routes, via the payment card company network 150, an authorization response back to the acquirer 140”, see also Figure 1);
access a historical payment transaction dataset from a database associated with the server system, the historical payment transaction dataset comprising a plurality of transaction attributes related to a plurality of historical payment transactions (See at least Dantu, [0047], “data warehouse 200 integrates data from one or more disparate sources. Data warehouse 200 stores current as well as historical data and is used for creating reports, performing analyses on the network, merchant analyses, and performing predictive analyses”, see also Figure 2);
generate a plurality of features for the cardholder based, at least in part, on the plurality of ongoing payment transaction attributes and the plurality of transaction attributes (See at least Dantu, [0088], “At step 602, a payment card company (part of the payment card company network 150 in FIG. 1) retrieves, from one or more databases, information including purchasing and payment information attributable to one or more payment card holders. The information at 602 includes payment card transaction information, payment card holder information (e.g., payment card holder account identifier (likely anonymized), payment card holder geography (potentially modeled), payment card holder type (consumer/business), payment card holder demographics, and the like), and purchasing and payment activities attributable to payment card holders”, see also Figure 6);
generate by a return prediction model, a first return probability score associated with the ongoing payment transaction based, at least in part, on the plurality of features, the first return probability score indicating a likelihood of the ongoing payment transaction being associated with a return request within a predefined time interval (See at least Dantu, [0108], “The activities and characteristics attributable to the payment card holders and based on the one or more predictive behavioral models are conveyed by the financial transaction processing entity to the entity (e.g., merchant) to take appropriate action, for example, modifying its return of goods policy in an attempt to better its index score. For example, a merchant may want to make its return of goods policy more strict or less strict in an attempt to improve one or more of the generated indices”, see also Figures 10, 11);
determine a first return advice code for the ongoing payment transaction based, at least in part, on the first return probability score and a set of predefined threshold values (See at least Dantu, [0108], “The activities and characteristics attributable to the payment card holders and based on the one or more predictive behavioral models are conveyed by the financial transaction processing entity to the entity (e.g., merchant) to take appropriate action, for example, modifying its return of goods policy in an attempt to better its index score. For example, a merchant may want to make its return of goods policy more strict or less strict in an attempt to improve one or more of the generated indices”, see also Figures 10, 11); and
facilitate by a communication interface transmission of an authorization response message comprising the first return advice code to an acquirer server associated with the merchant (See at least Dantu, [0095], “One or more indices based on the one or more benchmarks for rate of return of goods by the plurality of payment card holders to the plurality of merchants and the rate of return of goods by the plurality of payment card holders to the selected merchant are generated at 710. The rate of return of goods by the plurality of payment card holders to the selected merchant based on the one or more indices can then be assessed”, see also Figure 7).
Claim 15 –
Dantu disclose the server system as claimed in claim 14, as shown above.
Dantu further disclose:
wherein training the return prediction model based, at least in part, on performing a set of operations for a plurality of iterations until the performance of the return prediction model converges to a predefined criteria, the server system is further caused, at least in part, to: initializing the return prediction model based, at least in part, on one or more model parameters; generating a plurality of training features based, at least in part, on a training dataset, the training dataset comprising a plurality of training transaction attributes related to a plurality of training transactions; determining a training return probability score for each training transaction from the plurality of training transactions based, at least in part, on the plurality of training features; classifying each training transaction into one of a return transaction and a non-return transaction based, at least in part, on the training return probability score for each training transaction and a predefined threshold value; computing a classification loss for each training transaction based, at least in part, on a loss function and the training dataset; and optimizing the one or more model parameters based, at least in part, on back-propagating the classification loss (See at least Dantu, [0108], “The activities and characteristics attributable to the payment card holders and based on the one or more predictive behavioral models are conveyed by the financial transaction processing entity to the entity (e.g., merchant) to take appropriate action, for example, modifying its return of goods policy in an attempt to better its index score. For example, a merchant may want to make its return of goods policy more strict or less strict in an attempt to improve one or more of the generated indices”, see also Figures 10, 11).
Claim 16 –
Dantu disclose the server system as claimed in claim 15, as shown above.
Dantu further disclose:
wherein the server system is further caused, at least in part, to: generate a preference vector for each cardholder based, at least in part, on the set of high ratios of R and P, the preference vector corresponding to each cardholder indicating a past return behavior of each cardholder at the plurality of merchants ; generate a merchant correspondence matrix based, at least in part, on the set of high ratios of R and P and the homogeneous merchant graph; and compute serial returnee likelihood data for the cardholder based, at least in part, on the homogeneous merchant graph, the preference vector corresponding to the cardholder, and the merchant correspondence matrix, wherein the serial returnee likelihood data indicates a likelihood that the cardholder associated with the ongoing transaction is a serial returnee, wherein the serial returnee likelihood data is one of the plurality of features (See at least Dantu, [0096], [0098], “The benchmarks are created for a rate of return of goods by a plurality of payment card holders to a plurality of merchants based on the first set of information and the second set of information. The benchmarks are created for a rate of return of goods by a plurality of payment card holders to a plurality of merchants across one or more merchant categories, merchant sales volumes, and merchant geographies … an index can be a numerical value ranging from 0 to 500. An index of 0 indicates that the merchant does not have any returns of goods. An index between 1 and 99 indicates that the merchant is lower than their peers/competitors. An index of 100 indicates that the merchant is on par with other merchants. An index between 100 and 499 indicates that the merchant is higher than their peers/competitors. An index of 500 indicates that the merchant's return of goods is really high and immediate action is required”, see also Figures 8, 9).
Claim 17 –
Dantu disclose the server system as claimed in claim 14, as shown above.
Dantu further disclose:
wherein the first return advice code is at least one of high risk, moderate risk, and low risk (See at least Dantu, [0095], “One or more indices based on the one or more benchmarks for rate of return of goods by the plurality of payment card holders to the plurality of merchants and the rate of return of goods by the plurality of payment card holders to the selected merchant are generated at 710. The rate of return of goods by the plurality of payment card holders to the selected merchant based on the one or more indices can then be assessed”, see also Figure 7).
Claim 18 –
Dantu disclose the server system as claimed in claim 14, as shown above.
Dantu further disclose:
wherein the server system is further caused, at least in part, to: determine a total return amount (R) for the cardholder, and a total purchase amount (P) for the cardholder based, at least in part, on the plurality of transaction attributes related to the plurality of historical payment transactions performed between a plurality of cardholders and a plurality of merchants; compute a set of ratios of R and P for the plurality of cardholders, each ratio of R and P of the set of ratios of R and P corresponding to each cardholder of the plurality of cardholders; and extract a set of high ratios of R and P from the set of ratios of R and P based, at least in part, on a value associated with each ratio of R and P being at least equal to a threshold value (See at least Dantu, [0098], “an index can be a numerical value ranging from 0 to 500. An index of 0 indicates that the merchant does not have any returns of goods. An index between 1 and 99 indicates that the merchant is lower than their peers/competitors. An index of 100 indicates that the merchant is on par with other merchants. An index between 100 and 499 indicates that the merchant is higher than their peers/competitors. An index of 500 indicates that the merchant's return of goods is really high and immediate action is required”, see also Figure 9).
Claim 19 –
Dantu disclose the server system as claimed in claim 14, as shown above.
Dantu further disclose:
wherein the server system is further caused, at least in part, to: generate a bipartite graph for the plurality of historical payment transactions based, at least in part, on the plurality of transaction attributes and the set of high ratios of R and P, the bipartite graph comprising a first plurality of nodes and a first plurality of edges, the first plurality of nodes corresponds to the plurality of cardholders and the plurality of merchants, the first plurality of edges corresponding to set of high ratio of R and P; andP12089-US-UTIL generate a homogeneous merchant graph based, at least in part, on the bipartite graph, the homogeneous merchant graph comprising a second plurality of nodes, the second plurality of nodes corresponds to the plurality of merchants (See at least Dantu, [0096], [0098], “The benchmarks are created for a rate of return of goods by a plurality of payment card holders to a plurality of merchants based on the first set of information and the second set of information. The benchmarks are created for a rate of return of goods by a plurality of payment card holders to a plurality of merchants across one or more merchant categories, merchant sales volumes, and merchant geographies … an index can be a numerical value ranging from 0 to 500. An index of 0 indicates that the merchant does not have any returns of goods. An index between 1 and 99 indicates that the merchant is lower than their peers/competitors. An index of 100 indicates that the merchant is on par with other merchants. An index between 100 and 499 indicates that the merchant is higher than their peers/competitors. An index of 500 indicates that the merchant's return of goods is really high and immediate action is required”, see also Figures 8, 9).
Relevant Prior Art
11. The prior art made of record and not relied upon are considered pertinent to applicant's disclosure:
Banipal et al. (U.S. Pub. No. 2022/0215400) teach dynamic return optimization for loss prevention based on customer return patterns.
Conclusion
12. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Liz Nguyen whose telephone number is (571) 272-5414. The examiner can normally be reached on Monday to Friday 8:00 A.M to 5:00 P.M.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Gart, can be reached on (571) 272-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Center system (visit: https://patentcenter.uspto.gov). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call (800) 786-9199 (USA or CANADA) or (571) 272-1000.
/LIZ P NGUYEN/
Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
Prosecution Insights