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 is in reply to communication filed on 05/28/2026.
Claims 13 and 19 have been amended.
Claims 1-12 have been canceled.
Claims 13-20 are currently pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/28/2026 has been entered.
Response to Arguments
In response to Applicant Arguments /Remarks made in an amendment filled on 05/28/2026:
Claim Rejections - 35 USC § 101:
Applicant argument submitted under the title “The Rejection of Claims Under § 101” in pages 7-13.
Applicant’s arguments have been fully considered but are not persuasive.
In response, the examiner respectfully disagrees and emphasizes that applicant contends that amended claims 13 and 19 are directed to a specific technical improvement in fraud detection technology and therefore integrate any recited judicial exception into a practical application. Applicant further relies on Ex parte Desjardins and Ex parte Kelley, asserting that the claimed machine learning operations improve the functioning of the machine learning model itself. The Examiner respectfully disagrees.
Although the claims recite training a machine learning model using transaction state transitions, optimizing transition probabilities, generating a non-fraud score, dynamically evaluating the score, and raising an alert, these limitations merely describe the use of mathematical models to analyze transaction information and produce a prediction regarding fraud. The claims do not recite any improvement to the operation of the computer, processor, memory, communication network, or machine learning architecture itself. Instead, the claims employ generic computing components as tools to perform mathematical analysis on transaction data for the purpose of identifying potentially fraudulent transactions, which also considered as a certain method of organizing human activity of commercial interaction and managing personal behavior or relationships or interaction between people by following rules or instructions to detect fraud.
Applicant argues that the claims improve fraud detection by evaluating transaction sequences rather than aggregated transaction totals. However, this purported improvement is directed to the quality or accuracy of the fraud analysis itself rather than to an improvement in computer technology. Improving the information produced by an algorithm or the accuracy of a prediction does not, without more, constitute an improvement in the functioning of a computer or other technology under MPEP § 2106.05(a). The claims merely apply mathematical techniques to a particular data set to obtain a more useful analytical result.
Applicant further argues that the fraud detection system performs additional operations after the non-fraud score is generated, including dynamically evaluating the score and raising an alert. These additional limitations likewise do not integrate the judicial exception into a practical application. Receiving the output of an analysis, comparing that output to a threshold, generating an alert, and providing the result for further evaluation merely constitute post-solution activity that uses the results of the abstract analysis. The claims do not recite any technological mechanism by which the fraud detection system itself is improved or operates differently as a result of these steps.
Applicant also relies on limitations directed to preprocessing transaction logs, differentiating between transaction types, combining suspended transactions with recall events, and self-training the machine learning model. These limitations describe the particular mathematical processing performed on the input data and the continued updating of model parameters. While such limitations may define how the mathematical model operates, they nevertheless remain part of the recited mathematical analysis itself. The claims do not recite an improvement to the underlying machine learning architecture, training framework, memory organization, data storage technique, processor operation, or other computer functionality independent of the mathematical model.
Applicant’s reliance on Ex parte Desjardins and Ex parte Kelley is not persuasive. Those decisions turned on the particular claim language before the Board and are not controlling on the facts of the present application. Here, the claimed invention remains focused on mathematically analyzing transaction-event data to generate a fraud likelihood score and using that score in a fraud detection workflow. Unlike claims directed to improvements in the operation of a machine learning model itself, the present claims do not recite modifications to the model architecture, training mechanism, computational efficiency, or computer functionality. Rather, the claims specify mathematical operations performed on transaction data to improve the accuracy of fraud prediction in a business context.
Accordingly, the additional claim elements, considered individually and as an ordered combination, merely apply the recited mathematical concepts and mental processes using generic computer technology in the field of fraud detection. The claims therefore do not integrate the judicial exception into a practical application under Step 2A, Prong Two, and remain directed to patent-ineligible subject matter under 35 U.S.C. § 101.
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 13-20 are rejected under 35 U.S.C. 101 for the following reasons:
are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1:
Claims 13-18 recite a method, which is directed to a process.
Claims 19-20 recite a system, which is directed to a machine.
Therefore, each claim falls within one of the four statutory categories.
Step 2A, Prong 1 (Is a judicial exception recited?):
1) The independent claims 13 and 19 recite the abstract idea of catching cashier fraud (i.e., fraud detection), see specification [0002], which is considered fundamental economic principles or practices (i.e., fraud detection) and following rules or instruction to detect fraud in cahier behavior by receiving information, follow instruction to select process for analyzing based on information type, output results, evaluate results against threshold (i.e., the claimed abnormal transaction total), output alert. Which considered a certain method of organizing human activity of fundamental economic principles or practices and managing personal behavior or relationships or interaction between people by following rules or instructions to detect fraud.
2) Further the independent claims 13 and 19 recites mathematical concepts by training a machine learning model on transaction state transitions, calculating and optimizing probabilities associated with state transitions, generating a non-fraud score based on those probabilities, and dynamically evaluating the generated score. These limitations recite mathematical calculations, probability modeling, statistical analysis, and machine-learning operations.
3) Additionally, the independent claims 13 and 19 recites mental processes, including receiving transaction information, selecting a machine learning model based on transaction type, evaluating whether a transaction is likely fraudulent based on the generated score, comparing the score to a threshold, identifying transactions requiring further auditing, and determining whether to raise an alert. These limitations describe evaluations and judgments that can practically be performed in the human mind or with the aid of pen and paper, except for the recited generic computer implementation.
Accordingly, claims 13 and 19 recite judicial exceptions in the form of a certain method of organizing human activity, mathematical concepts and mental processes.
Step 2A, Prong 2 (Is the exception integrated into a practical application?):
This judicial exception is not integrated into a practical application because the claims satisfy the following criteria, which indicate that the claims do not integrate the abstract idea into practical application.
The additional elements include receiving transaction events from an event agent of a transaction terminal, providing transaction data to a machine learning model, providing a generated non-fraud score to a fraud detection system, dynamically evaluating the score, raising an alert when the score falls below a threshold, and using a processor, memory, and transaction terminal to perform the recited operations.
These additional elements merely use generic computer technology as tools for collecting transaction information, executing the mathematical analysis, communicating analytical results, and displaying or acting upon those results. The claims do not recite an improvement to the operation of the computer, processor, memory, communication network, machine learning architecture, or other technological component. Rather, the claims improve only the information produced by the mathematical analysis, namely the determination of whether a transaction is likely fraudulent.
Although the claims recite training a machine learning model using transaction sequences, preprocessing transaction event logs, optimizing probabilities, combining suspended transactions with recall events, and self-training over time, these limitations remain part of the recited mathematical analysis itself. They define how the mathematical model analyzes data but do not improve the functioning of the computer or another technology. Likewise, receiving the generated score, comparing it with a threshold, and raising an alert merely use the analytical result in a fraud detection workflow and constitute insignificant post-solution activity rather than a technological improvement.
Viewed as an ordered combination, the claim describes collecting transaction information, mathematically analyzing the information to generate a fraud score, evaluating the score, and reporting the result for further action. The claims therefore applies the judicial exception using generic computing technology without imposing a meaningful limit on the exception.
Accordingly, claims 13 and 19 are directed to a judicial exception that is not integrated into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?):
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As for Step 2B analysis, knowing the consideration is overlapping with Step 2A, Prong 2. The Step 2B considerations have already been substantially addressed under Step 2A Prong 2, see Step 2A Prong 2 analysis above. The additional elements, individually and as an ordered combination, merely provide a generic computing environment in which the certain method of organizing human activity, mathematical concepts and mental processes are performed. The processor, memory, transaction terminal, event agent, and fraud detection system are recited at a high level of generality and perform their ordinary functions of receiving data, processing data, storing data, transmitting information, and generating outputs. The claims do not recite a particular machine, manufacture, or transformation that meaningfully limits the judicial exception, nor do it recite any technological mechanism that improves the operation of the computer or another technology.
Accordingly, the additional elements do not amount to significantly more than the judicial exception itself, and claims 13-20 remain directed to patent-ineligible subject matter under 35 U.S.C. § 101.
In addition, the dependent claims recite:
Claims 14-17 further narrowing the abstract idea found in the independent claims 13 and 19 by reciting such as, identifying an operator identifier, preprocessing the transaction events to filter out some events and to aggregate other events, providing the operator identifier with the current non-fraud score, providing the current non-fraud score batching the current non-fraud score with other non-fraud scores when the current non-fraud score and the other non-fraud scores are above a threshold score, the fact the steps of dependent claims are tied to fraud detection system, transaction terminal is an instruction to use a generic computer, as was addressed for claim 13 and 19, to which applicant is referred, as the recitation of generic computer technology that is being used as a tool to execute the steps that define the abstract idea do not provide for integration at the 2nd prong and do not provide for significantly more at step 2B.
Claims 18 and 20 further narrowing the abstract idea found in the independent claims 13 and 19 by reciting such as, processing the method to a retailer that processes the current transaction. Claims 18 and 20 executing the abstract idea utilizing Software-as-a-Service (SaaS) and transaction terminal; however, the mere recitation to a plurality of computers claimed in a generic and non-limiting manner, for the same reasons that are set forth for claims 13 and 19, the recitation of generic computer technology that is being used as a tool to execute the steps that define the abstract idea do not provide for integration at the 2nd prong and do not provide for significantly more at step 2B.
Therefore, the limitations on the invention of claims 13-20, when viewed individually and in ordered combination are directed to in-eligible subject matter.
Distinguished Over Prior Art
The claims, in present form, render the claimed invention allowable over the prior art. The prior art found by the examiner, alone or in combination, neither anticipates, reasonably teaches, nor renders obvious the applicant's claimed invention. After updating the search, the closest prior art found by the examiner is MORRIS J (WO-2021198640-A1), which teaches detection of fraudulent return transaction at a retailer. MORRIS further teaches return transaction recognition logic (12) and return transaction assessment logic (14). The return transaction recognition logic (12) is configured to receive transaction data relating to retail transactions from at least one point of sale device, to recognize transaction data relating to a return transaction, and to pass return transaction data relating to the return transaction to the return transaction assessment logic. The return transaction assessment logic (14) is configured to obtain security data by reference to the return transaction data and to process the referenced security data in order to attribute to the return transaction a fraud risk score. Yet, MORRIS does not teach the claimed invention as the independent claims 13 and 19 recited. A detailed reasons of allowance would be issued by the examiner based on further responses from the applicant.
Conclusion
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/AVIA SALMAN/Primary Patent Examiner, Art Unit 3627
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