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 .
Status of Claims
This action is in response to non-provisional patent application filed on 7/1/25 and IDS filed on 12/8/25. Claims 1-20 are pending and examined.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. IN202441050342, filed on 07/01/2024.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 12/8/25. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement was considered by the examiner.
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. (Step 1) The claims recite a process (claims 1-10), an apparatus (claims 11-19) and an article of manufacture (claim 20). For the purposes of this analysis, representative claim 11 (from claims 1, 11 and 20) is addressed. (Step 2A, prong 1) Abstract ideas are in bold below, and represent organizing human activity as a method of fraudulent transaction detection and associated prevention, as are all a form of commercial or legal interactions and managing personal behavior or relationships or interactions between people.
A server system, comprising:
a communication interface;
a memory comprising executable instructions; and
a processor communicably coupled to the communication interface and the memory, the processor configured to cause the server system to at least:
access a card candidate set from a database associated with the server system, the card candidate set comprising one or more relevant payment cards of a plurality of payment cards, each payment card of the one or more relevant payment cards being associated with a plurality of features;
segregate the plurality of features into a set of risk-related features and a set of transactional features;
generate, by a set of Machine Learning (ML) models associated with the server system, a riskiness score corresponding to each payment card of the card candidate set based, at least in part, on the set of risk-related features;
perform for each payment card to:
assign a particular risk category of one or more risk categories based, at least in part, on the riskiness score and risk categorization criteria; and
assign a particular transactional category of one or more transactional categories based, at least in part, on the set of transactional features and transaction behavior criteria; and
generate a recommendation message for an issuer based, at least in part, on the risk category and the transactional category assigned to each payment card, the recommendation message being indicative of a remedial action that the issuer needs to perform for fraud prevention.
(Step 2A prong 2) The additional elements are as follows:
“A server system, comprising”, “a communication interface”, “a memory comprising executable instructions”, “a processor communicably coupled to the communication interface and the memory, the processor configured to cause the server system to at least”. These additional elements do not integrate the abstract idea into a practical application as they are no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
“[access a card candidate set] from a database associated with the server system”. Receiving information from a database does not integrate the abstract idea into a practical application as they are no more than “apply it” because accessing the “database” is mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
“[generate], by a set of Machine Learning (ML) models associated with the server system, [a riskiness score …]”. The “set of Machine Learning (ML) models associated with the server system” does not integrate the abstract idea into a practical application as they are no more than “apply it” because the “ML models” are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2). Furthermore, use of the ML models does not integrate the abstract idea into a practical application as it is no more than “apply it” because the claim fails to recite the technological details of how the “ML models” generate the “riskiness score”, see MPEP 2106.05(f)(1).
“perform […] to”. This additional element does not integrate the abstract idea into a practical application as it is no more than “apply it” because it is mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
(Step 2B) 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 into a practical application, the additional elements do no more than provide mere instructions to apply the abstract idea using “a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2), and/or the claim fails to recite the technological details of “how a solution to a problem is accomplished”, see MPEP 2106.05(f)(1). Therefore, the claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of fraudulent transaction detection and associated prevention.
Continuing the analysis with dependent claims, claims 2 and 12 recite “wherein generating the riskiness score comprises”, “accessing, by the server system, the plurality of risk-related features for each payment card from the database”, “generating, by the server system, a training risk feature set for each payment card based, at least in part, on the plurality of risk-related features, wherein the training risk feature set is used to train the set of ML models” and “computing, by the server system, the riskiness score using the set of ML models”, additional details which further narrow the abstract idea and additional elements.
The additional elements are as follows:
“[generating], by the server system, [a training risk feature set … wherein the training risk feature set] is used to train the set of ML models”. Training set of “ML models” with an information set does not integrate the abstract idea into a practical application as it is no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2). Furthermore, training set of “ML models” with an information set does not integrate the abstract idea into a practical application as it is no more than “apply it” because the claim fails to recite the technological details of how the “ML models” are trained with “the training risk feature set”, see MPEP 2106.05(f)(1).
“computing, by the server system, [the riskiness score] using the set of ML models” does not integrate the abstract idea into a practical application as it is no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2). Furthermore, “computing, by the server system, [the riskiness score] using the set of ML models” does not integrate the abstract idea into a practical application as it is no more than “apply it” because the claim fails to recite the technological details of how the “riskiness score” is computed using the “ML models”, see MPEP 2106.05(f)(1).
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 into a practical application, the additional elements do no more than provide mere instructions to apply the abstract idea using “a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2), and/or the claim fails to recite the technological details of “how a solution to a problem is accomplished”, see MPEP 2106.05(f)(1). Therefore, the claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of fraudulent transaction detection and associated prevention.
Claim 10 recites “wherein the server system is a payment server associated with a payment network” which are additional elements.
The additional elements do not integrate the abstract idea into a practical application as they are no more than “apply it” because they are mere “[u]se of a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2).
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 into a practical application, the additional elements do no more than provide mere instructions to apply the abstract idea using “a computer or other machinery in its ordinary capacity for economic or other tasks”, see MPEP 2106.05(f)(2), and/or the claim fails to recite the technological details of “how a solution to a problem is accomplished”, see MPEP 2106.05(f)(1). Therefore, the claim elements when considered separately and in an ordered combination, do not add significantly more than implementing the abstract idea of fraudulent transaction detection and associated prevention.
Continuing the analysis with dependent claims 3-9 and 13-19, the claims recite additional details which only further narrow the abstract idea and do not add any additional features, alone or in combination, that would provide a practical application or provide significantly more.
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable US 20210073820 A1 (Kallas) in view of US 20180053188 A1 (Zoldi).
As to claims 1, 11 and 20,
Hu teaches,
accessing, by a server system, a card candidate set from a database associated with the server system, the card candidate set comprising one or more relevant payment cards of a plurality of payment cards, each payment card of the one or more relevant payment cards being associated with a plurality of features (para. 6 “receive, via a communication network, information regarding suspicious fraud activity at a first location involving a plurality of transaction cards”);
generating, by a set of Machine Learning (ML) models associated with the server system, a riskiness score corresponding to each payment card of the card candidate set based, at least in part, on the set of risk-related features (para. 105 “the reissue score model is built for each individual compromise event, all the cards in the event share the same POC & PAI values at the same interval … the system stacks all the sets together as a single training set”);
performing, by the server system, for each payment card (para. 5 “analysis of interactions between the fraud cards”):
segregating, by the server system, the plurality of features into a set of risk-related features and a set of transactional features (para. 105 “As the reissue score model is built for each individual compromise event, all the cards in the event share the same POC & PAI values at the same interval”);
assigning a particular risk category of one or more risk categories based, at least in part, on the riskiness score and risk categorization criteria (para. 105 “To obtain variations of the POC & PAI values, the system repeats the initial feature set 5-6 times, each time assigned with the POC & PAI values and the fraud labels of a different time interval”);
assigning a particular transactional category of one or more transactional categories based, at least in part, on the set of transactional features and transaction behavior criteria (para. 105 “To obtain variations of the POC & PAI values, the system repeats the initial feature set 5-6 times, each time assigned with the POC & PAI values and the fraud labels of a different time interval”).
Kallas does not teach,
generating, by the server system, a recommendation message for an issuer based, at least in part, on the risk category and the transactional category assigned to each payment card, the recommendation message being indicative of a remedial action that the issuer needs to perform for fraud prevention.
however, Zoldi teaches,
generating, by the server system, a recommendation message for an issuer based, at least in part, on the risk category and the transactional category assigned to each payment card, the recommendation message being indicative of a remedial action that the issuer needs to perform for fraud prevention (FIG. 7, items 706, 706, para. 67 “The total risk value obtain in 706 may be utilized to make a decision on the transaction. If the total risk exceeds some preset threshold, the transaction may be likely fraudulent, thus, the transaction may be placed under further review or investigation. If the total risk is small, the transaction may go through and the merchant may proceed to ship the merchandise to customers. The action after the risk evaluation is performed in 707 for each new transaction”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine card scoring based on transaction risk of Kalls with risk remediation of Zoldi because risk remediation improves card scoring based on transaction risk by remedying and stopping future fraudulent transactions.
additionally, with respect to claim 11,
Kallas teaches,
A server system (FIG. 12, item 1200, para. 113 “a computing system”), comprising:
a communication interface (FIG. 12, items 1225A-C, para. 113 “interfaces”);
a memory comprising executable instructions (FIG. 12, item 1210, para. 113 “memory device”); and
a processor (FIG. 12, items 1230A-N, para. 115 “processors”) communicably coupled to the communication interface and the memory, the processor configured to cause the server system to at least (para. 115-116).
additionally, with respect to claim 20,
Kallas teaches,
A non-transitory computer-readable storage medium (para. 110, “SSD”) comprising computer-executable instructions that, when executed by at least a processor of a server system, cause the server system to perform a method (para. 110, 115-116) comprising.
As to claims 2 and 12, combination of Kallas and Zoldi teach all limitations of claims 1 and 11.
Kallas teaches,
wherein generating the riskiness score (para. 105) comprises:
accessing, by the server system, the plurality of risk-related features for each payment card from the database (para. 105 “each individual compromise event,”);
generating, by the server system, a training risk feature set for each payment card based, at least in part, on the plurality of risk-related features, wherein the training risk feature set is used to train the set of ML models (para. 105 “the system stacks all the sets together as a single training set”); and
computing, by the server system, the riskiness score using the set of ML models (FIG. 9A, para. 106 “The two plots show the reissue score model can predict the probability of a card used in fraud transactions in the coming time intervals. Thus, the card issuer can reissue new cards to those card holders to stop potential losses, and the cards with low reissue scores won't get a unnecessary new card”).
As to claims 3 and 13, combination of Kallas and Zoldi teach all limitations of claims 1-2 and 11-12.
Kallas teaches,
wherein training the set of ML models (para. 105) comprises:
accessing, by the server system, the training risk feature set for each payment card from the database (para. 105);
initializing, by the server system, each ML model of the set of ML models with one or more model parameters for the corresponding ML model (para. 105); and
performing iteratively, by the set of ML models, for each payment card, until convergence criteria are met, a set of operations (para. 105 “ the system repeats the initial feature set 5-6 times”) comprising:
generating a set of predicted probability scores based, at least in part, on the training risk feature set and the one or more model parameters of the set of corresponding ML models, each ML model generates each predicted probability score of the set of probability scores, and each predicted probability score indicating a likelihood of each payment card being risky (para. 105-106);
computing a set of losses based, at least in part, on the set of corresponding probability scores, true labels for the corresponding set of ML models, and a loss function (para. 106 “the probability of a card used in fraud transactions in the coming time intervals”); and
optimizing the one or more model parameters of each ML model of the set of ML models based, at least in part, on back-propagation of the set of losses of the corresponding set of ML models (para. 105 “the system repeats the initial feature set 5-6 times”).
As to claims 4 and 14, combination of Kallas and Zoldi teach all limitations of claims 1-3 and 11-13.
Kallas teaches,
in response to meeting the convergence criteria, obtaining, by the server system, a set of final predicted probability scores using the set of ML models (para. 105-106);
generating, by the server system, the riskiness score using the set of ML models based, at least in part, on normalizing and performing an ensemble measure on the set of final predicted probability scores (para. 106).
As to claims 5 and 15, combination of Kallas and Zoldi teach all limitations of claims 1 and 11.
Kallas teaches,
assigning the particular risk category for each payment card (para. 105) comprises:
accessing, by the server system, the riskiness score of each payment card of the card candidate set from the database (para. 105);
sorting, by the server system, the card candidate set in a predefined order of the riskiness score of each payment card of the card candidate set to obtain a sorted card candidate set (para. 103 “the system can reissue score to each of the compromised cards based on the severity level of the compromise event and the transaction characteristics of the cards so that the card issuer can only reissue new cards to those with high scores”);
segregating, by the server system, the sorted card candidate set into one or more card groups based at least on the riskiness score of each payment card of the sorted card candidate set and a set of grouping thresholds (para. 105), wherein each payment card of each card group of the one or more card groups is assigned the particular risk category of the one or more risk categories based at least on the risk categorization criteria (para. 105)
As to claims 6 and 16, combination of Kallas and Zoldi teach all limitations of claims 1 and 11.
Kallas teaches,
wherein assigning the particular transactional category for each payment card (para. 105) comprises:
accessing, by the server system, the set of transactional features for each payment card of the card candidate set from the database (para. 105);
identifying, by the server system, a spending behavior of each payment card of the card candidate set based, at least in part, on the set of transactional features and a set of transactional thresholds (para. 105), wherein each payment card of the card candidate set is assigned the particular transactional category of the one or more transactional categories based at least on the spending behavior of the corresponding payment card and the transaction behavior criteria (para. 105).
As to claims 7 and 17, combination of Kallas and Zoldi teach all limitations of claims 1 and 11.
Kallas teaches,
accessing, by the server system, an input dataset from the database, the input dataset comprising historical information corresponding to a plurality of payment transactions performed between a plurality of cardholders and a plurality of merchants using the plurality of payment cards (para. 105);
generating, by the server system, the plurality of features for each payment card of the plurality of payment cards based, at least in part, on the input dataset (para. 105);
storing, by the server system, the plurality of features in the database (para. 105 “a single training set”).
As to claims 8 and 18, combination of Kallas and Zoldi teach all limitations of claims 1 and 11.
Kallas teaches,
accessing, by the server system, the plurality of features and a set of predetermined risk scores corresponding to each payment card of the plurality of payment cards from the database (para. 105);
extracting, by the server system, a risky card set from the plurality of payment cards based, at least in part, on the plurality of features, the set of predetermined risk scores, and segregation criteria (para. 105);
accessing, by the server system, a set of cardholder behavioral attributes associated with each payment card of the risky card set from the database (para. 105);
generating, by the server system, the card candidate set based, at least in part, on the risky card set and the set of cardholder behavioral attributes, wherein the card candidate set is a subset of the risky card set (para. 106).
As to claims 9 and 19, combination of Kallas and Zoldi teach all limitations of claims 1 and 11.
Kallas teaches,
generating, by the server system, a reason code () based at least on the risk category and the transactional category assigned to each payment card of the card candidate set (para. 103, 104 “quantify the severity level”), wherein the reason code is indicative of a reason for generating the corresponding recommendation message (para. 103, 104).
As to claim 10, combination of Kallas and Zoldi teach all limitations of claim 1.
Kallas teaches,
wherein the server system is a payment server associated with a payment network (para. 35 “point-of-compromise detection server”).
Conclusion
References made of record, not relied upon, pertinent to Applicant’s disclosure, include:
NPL: Bello, Oluwabusayo Adijat, et al. "Machine learning approaches for enhancing fraud prevention in financial transactions." International Journal of Management Technology 10.1 (2023): 85-108.
US 20200211020 A1 (Allbright) disclosing improved detection of network fraud events,
US 20230111445 A1 (Ranjan) disclosing neural network based methods and systems for increasing approval rates of payment transactions.
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/BROCK E TURK/Examiner, Art Unit 3692 /DAVID P SHARVIN/ Primary Examiner, Art Unit 3692
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