DETAILED ACTION
The present application is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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.
This Office Action is in response Applicant communication filed on 10/10/2025.
Claims
Claims 1, 8, 11, 17, and 20 have been amended.
Claims 10 and 19 has been cancelled.
Claims 1-9, 11-18, and 20 are currently pending in the application.
Response to Arguments
101
The examiner withdraws the previous 101 rejection and agrees that the amended claims recite an improvement to the functioning of a computer, or to any other technology or technical field. Independent claims 1, 11, and 20 recite the use of artificial intelligence to simulate transactions using parameters received for a first transaction. The AI simulations generate second parameters that are then compared to the first parameters to determine the likeliness of fraud for the first transaction. As recited in the specification in sections [0001], [0002], and [0014], the generative AI models can more efficiently and accurately identify indicators of error and/or fraud. This may result in lower amounts of bandwidth and fewer user impositions required in order to detect fraudulent transactions and/or errors associated with a legitimate transaction.
103
The applicant argues that the examiner acknowledged that Chamberlain does not teach “determining, by the provider computing system, a response to the first request based on the legitimacy value associated with the first transaction” (See pages 12-13 of applicant’s arguments/remarks) .
However the examiner respectfully disagrees. Chamberlain was used in the non-final rejection (date: 7/10/2025) to disclose this limitation. Chamberlain in sections [0056] and [0057] discloses that a risk score is calculated a transaction request and if the risk score is above a threshold then a notification is sent to the user.
The applicant further argues that Ranjan makes no mention of simulating newly requested transactions, much less using the stimulations of the newly requested transactions to approve or disapprove the newly requested transactions. Therefore, Ranjan does not teach, disclose, or suggest "receiving ... first request for initiating a first transaction having one or more first parameters," "simulating ... using at least one artificial intelligence (AI) system, one or more transactions based on the one or more first parameters of the first transaction included in the request," "identifying ... using the at least one AI system, one or more second parameters of the one or more simulated transactions, "comparing ... using the at least one AI system, the one or more second parameters to the one or more first parameters associated with the first transaction included in the request" "determining ... using the at least one AI system, a legitimacy value associated with the first transaction based on the comparison of the one or more second parameters to the one or more first parameters, "determining ... a response to the first request based on the legitimacy value associated with the first transaction, "approving .. .in response to the legitimacy value exceeding a predefined legitimacy value, the first request," "processing .. .in response to the approval of the first request, the first transaction as indicated by the first request," "transmitting ... to the user device based on the approval of the first request, the response to the first request including an indication that the first transaction is processed," as recited in amended claim 1 ( emphasis added). (see pages 13 and 14 of applicant’s arguments/remarks).
The applicant respectfully disagrees and believes that Ranjan does disclose some of these limitations as written. Ranjan discloses “simulating, by the provider computing system using at least one artificial intelligence (AI) system, one or more transactions based on the one or more first parameters of the first transaction included in the request” in sections [0090], [0106], [0121], and [0122]. Here Ranjan recites that machine learning fraud model simulations are used to determine fraudulent transactions based on historical payment transactions. Further, sections [0135]-[0138] recite that these machine learning models can be used on real-time payment transactions to determine fraudulent activity and transaction approval scores.
Ranjan further discloses “identifying, by the provider computing system using the at least one AI system, one or more second parameters of the one or more simulated transactions”, “comparing, by the provider computing system using the at least one AI system, the one or more second parameters to the one or more first parameters associated with the first transaction included in the request”, and “determining, by the provider computing system using the at least one AI system, a legitimacy value associated with the first transaction based on the comparison of the one or more second parameters to the one or more first parameters”. Ranjan discloses this in sections [0090] and [0123]-[0127] which recites that a difference between a second set of rank-ordered transaction features and a first set of rank-ordered payment transaction features are computed in order to simulate transaction approval rates and fraud transactions rates. Further, sections [0135]-[0139] recite that these transaction approval rates can be determined using real-time payment transaction data.
The rest of the applicant’s arguments have been considered by the examiner but are moot because new art has been added as necessitated by the applicant’s claim amendments.
112
The applicant amended claims 8 and 17 to clarify that the parameters are “first” parameters. However it is unclear whether these first parameters are the same first parameters as recited in claims 1, 7, 11, and 16.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8 and 17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention. In this instant case,
Claims 8 and 17 recite “suggesting one or more first parameters associated with the first transaction…” (emphasis added). It is unclear whether “one or more first parameters” are referring to the same one or more first parameters recited in claims 1, 7, 11, and 16. The examiner recommends that the applicant amend claims 8 and 17 to recite “the one or more first parameters” (emphasis added).
Rejections under 35 § U.S.C. 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all
obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 4, 5, 7-9, 11, 14, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20190318358 A1 (“Chamberlain”) and US 20230111445 A1 (“Ranjan”) and US 20210334798 A1 (“Mossoba”).
Per claims 1, 11, and 20, Chamberlain discloses:
receiving, by a provider computing system and from a user device, a first request for initiating a first transaction having one or more first parameters (e.g. FIG. 4, the process 400 starts and a request to process an electronic transaction is received from a user device at block 405. The electronic transaction may correspond to a payment or transfer of funds from a user of the user device to a beneficiary of the payment or transfer) (Section [0055]);
analyzing, by the provider computing system, a transaction history comprising one or more previous transactions having at least one of the one or more first parameters (e.g. When processing of the electronic transaction, a risk associated with the electronic transaction is detected at block 410. In some arrangements, the risk may be detected if little or no previous transactional information corresponding to the beneficiary is known. For example, in some arrangements, transactional history data of the user and/or other customers are analyzed to determine if the beneficiary is a known or trusted beneficiary. In some arrangements, the risk is detected if a location of the beneficiary is associated with a hotspot of fraudulent activity. In some arrangements, the risk is detected if an amount of the transaction exceeds a user or automatically defined threshold. In some arrangements, the risk is detected by analyzing 3.sup.rd party data corresponding to the beneficiary or the electronic transaction. For example, in some arrangements, 3.sup.rd party data is analyzed to determine if adverse information corresponding to the beneficiary is available, or if the electronic transaction includes one or more attributes of a fraud trend) (Section [0055]);
determining, by the provider computing system, a response to the first request based on the legitimacy value associated with the first transaction (e.g. In response to identifying the risk, a risk score is calculated for the electronic transaction at block 415. The risk score can be calculated based on suitable models that consider context information, account/profile information, transaction history information, beneficiary information, previous fraud claims, and/or 3.sup.rd party data) (Section [0056] and [0057]);
Chamberlain further discloses at least one processing circuit having at least one processor coupled to at least one memory device (e.g. The “circuit” may also include one or more processors communicatively coupled to one or more memory or memory devices. In this regard, the one or more processors may execute instructions stored in the memory or may execute instructions otherwise accessible to the one or more processors. In some arrangements, the one or more processors may be embodied in various ways) (Section [0065]).
Although Chamberlain discloses receiving a transaction request and analyzing the transaction request to determine a legitimacy value associated with the transaction using Artificial Intelligence, Chamberlain does not specifically disclose:
simulating, by the provider computing system using at least one artificial intelligence (AI) system, one or more transactions based on the one or more first parameters of the first transaction included in the request;
identifying, by the provider computing system using the at least one AI system, one or more second parameters of the one or more simulated transactions;
comparing, by the provider computing system using the at least one AI system, the one or more second parameters to the one or more first parameters associated with the first transaction included in the request;
determining, by the provider computing system using the at least one AI system, a legitimacy value associated with the first transaction based on the comparison of the one or more second parameters to the one or more first parameters;
approving, by the provider computing system, in response to the legitimacy value exceeding a predefined legitimacy value, the first request;
processing, by the provider computing system in response to the approval of the first request, the first transaction as indicated by the first request;
transmitting, by the provider computing system to the user device based on the approval of the first request, the response to the first request including an indication that the first transaction is processed.
However Ranjan, in analogous art of approving payment transactions using Artificial Intelligence, discloses:
simulating, by the provider computing system using at least one artificial intelligence (AI) system, one or more transactions based on the one or more first parameters of the first transaction included in the request (e.g. determining, by the server system via a fraud model 304, a first set of rank-ordered payment transaction features based, at least in part, on a first set of shapley additive explanations (SHAP) values 306. In an example, each of the first set of rank-ordered payment transaction features may indicate towards the contribution of the corresponding payment transaction feature in predicting whether the payment transaction from the plurality of historical payment transactions is fraudulent or non-fraudulent) (Section [0090], [0106], [0121], [0122], and [0134]-[0136]);
identifying, by the provider computing system using the at least one AI system, one or more second parameters of the one or more simulated transactions (e.g. determining, by the server system via an approval model 308, a second set of rank-ordered payment transaction features based, at least in part, on a second set of SHAP values 310. In an example, each of the second set of rank-ordered payment transaction features may indicate towards the contribution of the corresponding payment transaction feature in predicting whether the payment transaction from the plurality of historical payment transactions is approved or declined) (Section [0090] and [0123]);
comparing, by the provider computing system using the at least one AI system, the one or more second parameters to the one or more first parameters associated with the first transaction included in the request (e.g. computing, by the server system, a difference in ranks of payment transaction features based, at least in part, on the first set of rank-ordered payment transaction features and the second set of rank-ordered payment transaction features) (Section [0124] and [0135]-[0139]);
determining, by the provider computing system using the at least one AI system, a legitimacy value associated with the first transaction based on the comparison of the one or more second parameters to the one or more first parameters (e.g. computing, by the server system, simulated transaction approval rate and a simulated fraud transaction rate for the simulated authorizing model based, at least in part, on the plurality of historical payment transactions) (Section [0125]-[0127] and [0135]-[0139]).
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the AI transaction system of Chamberlain to include the use of AI to run simulations and compare parameters of transactions, as taught by Ranjan, in order to achieve the predictable result of increasing transaction approval rates while also reducing fraudulent activities (See Ranjan Paragraphs [0004] and [0005]).
Although Chamberlain/Ranjan discloses receiving a transaction request and analyzing the transaction request to determine a legitimacy value associated with the transaction by comparing parameters of the transaction, Chamberlain/Ranjan does not specifically disclose:
approving, by the provider computing system, in response to the legitimacy value exceeding a predefined legitimacy value, the first request;
processing, by the provider computing system in response to the approval of the first request, the first transaction as indicated by the first request;
transmitting, by the provider computing system to the user device based on the approval of the first request, the response to the first request including an indication that the first transaction is processed.
However Mossoba, in analogous art of using machine learning to validate online payment transactions, discloses:
approving, by the provider computing system, in response to the legitimacy value exceeding a predefined legitimacy value, the first request (e.g. As another example, if the machine learning system were to predict a value of “approve” for the target variable of “decision,” then the machine learning system may provide a different recommendation (e.g., a merchant should approve an online transaction) and/or may perform or cause performance of a different automated action (e.g., generating data indicating approval of the online transaction). In some implementations, the recommendation and/or the automated action may be based on the target variable value having a particular label (e.g., classification, categorization, and/or the like), may be based on whether the target variable value satisfies one or more threshold (e.g., whether the target variable value is greater than a threshold, is less than a threshold, is equal to a threshold, falls within a range of threshold values, and/or the like)) (Section [0028] and [0051);
processing, by the provider computing system in response to the approval of the first request, the first transaction as indicated by the first request (e.g. the processing platform may process that transaction card number, with a fraud model, to determine whether to approve or deny the online transaction. For example, the processing platform may use a fraud model, such as one similar to that described in FIG. 1C, to determine whether to approve or deny the transaction. In some implementations, the processing platform identifies a previous determination that the network address was valid when determining whether to approve or deny the online transaction) (Section [0028] and [0029]);
transmitting, by the provider computing system to the user device based on the approval of the first request, the response to the first request including an indication that the first transaction is processed (e.g. the processing platform may provide, to the client device and/or the merchant server device, data indicating that the online transaction is approved. The merchant server device may perform one or more actions based on determining that the online transaction is approved. For example, the merchant server device may provide notification, to the client device, that the online transaction is approved. The client device may display the notification, indicating that the online transaction has been completed successfully) (Section [0028] and [0029]).
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the machine learning fraud detection system/process of Chamberlain/Ranjan to only approve and process the transaction when the risk score or approval score is above or below a certain threshold, as taught by Mossoba, in order to achieve the predictable result of increasing the security of the system by only allowing transactions to process when a certain level or certainty is achieved.
Per claims 4 and 14, Chamberlain/Ranjan/Mossoba disclose all the limitations of claims 1 and 11 above. Chamberlain further discloses:
wherein the one or more first parameters comprise at least one of: a transaction amount; one or more parties associated with the first transaction; and a transaction method (e.g. The electronic transaction may correspond to a payment or transfer of funds from a user of the user device to a beneficiary of the payment or transfer. When processing of the electronic transaction, a risk associated with the electronic transaction is detected at block 410. In some arrangements, the risk may be detected if little or no previous transactional information corresponding to the beneficiary is known. For example, in some arrangements, transactional history data of the user and/or other customers are analyzed to determine if the beneficiary is a known or trusted beneficiary) (Section [0055]).
Per claim 5, Chamberlain/Ranjan/Mossoba disclose all the limitations of claim 4 above. Chamberlain further discloses:
wherein the one or more parties associated with the first transaction further comprise at least one of: a sending party; and a receiving party (e.g. The electronic transaction may correspond to a payment or transfer of funds from a user of the user device to a beneficiary of the payment or transfer. When processing of the electronic transaction, a risk associated with the electronic transaction is detected at block 410. In some arrangements, the risk may be detected if little or no previous transactional information corresponding to the beneficiary is known. For example, in some arrangements, transactional history data of the user and/or other customers are analyzed to determine if the beneficiary is a known or trusted beneficiary) (Section [0055]).
Per claims 7 and 16, Chamberlain/Ranjan/Mossoba disclose all the limitations of claims 1 and 11 above. Chamberlain further discloses:
retrieving, by the provider computing system, contextual information related to the first transaction from at least one data source, wherein the contextual information is used to determine at least one of the one or more first parameters (e.g. For example, the AI system is configured to proactively determine risks of beneficiaries using context information, notify in real-time or near real-time the customer of the risks before approving the electronic transactions, and detecting mitigating factors from a customer's mitigation activities using context information. In some arrangements, to achieve benefits over conventional systems having databases, tables, and field definitions that are static, the databases described herein may be data-type agnostic and configured to store different information for different users, transaction types, and the like) (Sections [0014], [0055], and [0056]).
Per claims 8 and 17, Chamberlain/Ranjan/Mossoba disclose all the limitations of claims 7 and 16 above. Chamberlain further discloses:
suggesting one or more first parameters associated with the first transaction based on at least one of the transaction history, the one or more simulated transactions, and the contextual information (e.g. The risk score can be calculated based on suitable models that consider context information, account/profile information, transaction history information, beneficiary information, previous fraud claims, and/or 3.sup.rd party data) (Sections [0055] and [0056]).
Per claims 9 and 18, Chamberlain/Ranjan/Mossoba disclose all the limitations of claims 1 and 11 above. Chamberlain further discloses:
wherein comparing the one or more second parameters to the one or more first parameters further comprises: receiving an accepted range associated with the one or more first parameters (e.g. In some arrangements, the risk is detected if an amount of the transaction exceeds a user or automatically defined threshold) (Section [0040] and [0055]);
determining whether the one or more second parameters fall within the accepted range (e.g. In some arrangements, the risk is detected if an amount of the transaction exceeds a user or automatically defined threshold) (Sections [0040] and [0055]).
Claims 2, 3, 12, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Chamberlain/Ranjan/Mossoba, as applied to claims 1 and 11 above, in further view of US 12347283 B1 (“Mattison”).
Per claims 2 and 12, although Chamberlain/Ranjan/Mossoba discloses using an AI system to determine fraudulent transactions, Chamberlain/Ranjan/Mossoba do not specifically disclose:
wherein the at least one AI system comprises a generative AI model.
However Mattison, in analogous art of financial transactions using AI, discloses:
wherein the at least one AI system comprises a generative AI model (e.g. Generative AI engine 112e may host, train, execute, update and/or validate one or more generative AI models that may receive inputs such as image and/or measurement data of an ATM, user requests for transactions or other user input, or the like, and, upon execution of the one or more models, may output audio script data describing a position of components of the ATM, user interface or audio data in a preferred language or dialect of a user to facilitate transaction processing, and the like) (Column 5, Ln 51 – Column 6, Ln 4).
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself that is in the substitution of the Generative AI of Mattison for the Machine Learning AI of Chamberlain/Ranjan/Mossoba. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Per claims 3 and 13, Chamberlain/Ranjan/Mossoba/Mattison discloses all the limitations of claims 2 and 12 above. Ranjan further discloses:
wherein the provider computing system is further configured to store information associated with the first request to serve as training data for the generative AI model (e.g. In one embodiment, the data pre-processing engine 220 is configured to randomly select all historical payment transaction data (i.e., payment authorization request and payment authorization response messages of past payment transactions) associated with the issuer 112 and/or the acquirer 114 for training the neural network engine 224. The past transaction-level data associated with the issuer 112 and/or the merchant 106 is stored in the transaction database 118) (Section [0063]).
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the AI fraud detection models of Chamberlain/Mossoba/Mattison to store transaction request data, as taught by Ranjan, in order to achieve the predictable result of constantly improving the AI fraud detection system by utilizing additional transaction data to train the model.
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Chamberlain/Ranjan/Mossoba, as applied to claims 1 and 11 above, in further view of US 20180109386 A1 (“Khan”).
Per claims 6 and 15, although Chamberlain/Ranjan/Mossoba discloses receiving a request for a first transaction having one or more first parameters, Chamberlain/Ranjan/Mossoba do not specifically disclose:
generating, by the provider computing system, a key regarding the first transaction, wherein the key regarding the first transaction is configured to authenticate the first transaction.
However Khan, in analogous art of transaction authentication, discloses:
generating, by the provider computing system, a key regarding the first transaction, wherein the key regarding the first transaction is configured to authenticate the first transaction (e.g. Among other things and to facilitate such a transaction, the digital signature may be accompanied by the contextual data (e.g., custom user approval text, etc.) and configured to validate the private key by decrypting the contextual data and/or transaction data using a public key associated with the user device and/or application running via the user device) (Section [0093]).
It would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the first transaction having one or more first parameters of Chamberlain/Ranjan/Mossoba to include the use of a key to authenticate the first transaction, as taught by Khan, in order to achieve the predictable result of increasing the security and privacy of the transactions (See Khan Paragraph [0093]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to TIMOTHY SAX whose telephone number is 571-272-2935. The Examiner can normally be reached on M-F 8-4:30. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Patrick McAtee can be reached at (571) 272-7575.
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/TS/
Examiner, Art Unit 3698
/PATRICK MCATEE/Supervisory Patent Examiner, Art Unit 3698
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