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 11/12/2025.
Claims 13 and 19 have been amended.
Claims 1-12 have been canceled.
Claims 13-20 are currently pending and have been examined.
Response to Arguments
In response to Applicant Arguments /Remarks made in an amendment filled on 11/12/2025:
Claim Rejections - 35 USC § 101:
Applicant argument submitted under the title “The Rejection of Claims Under § 101” in pages 6-18.
Applicant's arguments have been fully considered but they are not persuasive.
Prong 1: Applicant states the claims “recite patent -eligible subject matter” because they are “specific technological improvements” for “fraud detection technology and computer-based transaction monitoring systems”. However, the claims expressly recite fundamental economic principles or practices (i.e., fraud detection) and following rules or instruction to detect fraud in cahier behavior (fraud detection; perform an analysis of the information obtained in relation to a possible fraud incident in the system), and a mental process performed in a computer environment (i.e., fraud detection), which fall squarely within the “methods of organizing human activity” and “mental process”.
Examples of product claims reciting mental processes include:
FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016).
computer readable medium containing program instructions for detecting fraud – CyberSource, 654 F.3d at 1368 n. 1, 99 USPQ2d at 1692 n.1.
Prong 2 and Step 2B: The additional elements are directed to using a generic computer to process information and perform the abstract idea. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to 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, as discussed in MPEP 2106.05(f).
Furthermore, utilizing a machine learning model (i.e., MLMs, Hidden Markov Model (HMM) that assumes an HMM process over time) by training the models with some data as inputs to generate outputs is mere recitation to a 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.
Moreover, automating the claims steps with “cloud server”, “processor”, “terminal”, “system”, “storage”, of a generic computer is similar to simply adding the words “apply it,” which is not enough to transform an abstract idea into eligible subject matter. See, Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976. Applicant's Specification acknowledges that nothing more than general purpose computers is needed to implement the invention. Thus, any improvement achieved by automating the claim steps (i.e., using generic computing devices/software) is not a technical improvement, but instead would come from the capabilities of a general-purpose computer rather than the sequence of steps/activities recited in the method itself, which does not materially alter the patent eligibility of the claim.
Even assuming, for the sake of argument, that the claims amount to an improvement over prior art techniques for fraud detection, such an improvement would be considered, at most, an improvement confined within the abstract idea itself, which is not enough to confer eligibility on the claim. For the reasons above, Applicant’s argument is not persuasive.
Applicant’s remaining arguments either logically depend from the above-rejected arguments, in which case they too are unpersuasive for the reasons set forth above arguments.
For the reasons above along with the reasons set forth in the updated §101 rejection set forth below, Applicant's amendments and arguments concerning the §101 rejection are not sufficient to overcome the rejection.
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.
Offending clauses include:
“training machine learning models (MLMs) on states transitions representing sequences of transactions based on transaction types”, “training machine learning models (MLMs) using a Hidden Markov Model (HMM) that assumes an HMM process over time”, “each MLM self trains and adjusts over time as more transactions and events and frequencies of state transitions are noted such that the MLM is self-learning, adaptable, and self- sustaining following initial training”
2) Further the independent claims 13 and 19 recite the abstract idea of catching cashier fraud (i.e., fraud detection), see specification [0002], which is considered a mental process performed in a computer environment (i.e., fraud detection), which recited limitations describing how the system would calculate probabilities; receive transaction events; selecting process to analyze received information; output results, evaluate results against threshold (i.e., the claimed abnormal transaction total), output alert.
Examples of product claims reciting mental processes include:
FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016).
computer readable medium containing program instructions for detecting fraud – CyberSource, 654 F.3d at 1368 n. 1, 99 USPQ2d at 1692 n.1.
No mathematical formulas are expressly recited; the exceptions arise from organizing human activity and mental-process style data handling/presentation.
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 claimed additional limitations are:
Claim 13: training machine learning models (MLMs) on state transitions representing sequences of transactions based on transaction types, each MLM adapted after training, a transaction terminal, fraud detection system comprises a set of executable instructions executed by a processor, each MLM self trains and adjusts over time as more transactions and events and frequencies of state transitions are noted such that the MLM is self-learning, adaptable, and self- sustaining following initial training,
Claim 19: a cloud server comprising at least one processor and a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium comprises executable instructions; the executable instructions when provided to and executed by the at least one processor from the non-transitory computer-readable storage medium cause the at least one processor to perform operations, training machine learning models(MLMs) using a Hidden Markov Model (HMM) that assumes an HMM process over time, training the HMM over numerous historical cashiers' sequences to gain optimal inference through processing the numerous historical cashiers' sequences, transaction terminal, fraud detection system comprises a set of executable instructions executed by the at least one processor, each MLM self trains and adjusts over time as more transactions and events and frequencies of state transitions are noted such that the MLM is self-learning, adaptable, and self- sustaining following initial training,
The additional elements are directed to using a generic computer to process information and perform the abstract idea. Therefore, the limitations merely amount to adding the words “apply it” (or an equivalent) to 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, as discussed in MPEP 2106.05(f).
Therefore, the recited exceptions are not integrated into a practical application.
Step 2B (Does the claim recite additional elements that amount to significantly more that the judicial exception?):
The claim does 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. As discussed above, the additional imitations amount to 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, as discussed in MPEP 2106.05(f).
Accordingly, the claims do not include “significantly more” than the judicial exceptions.
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
1. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
2. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Zoldi et al. (US-20170140384-A1) for system and method using archetype-based n-grams based on an event sequence of the real-time transactions.
Brandt et al. (US-20210067548-A1) for detecting malicious activity within a network.
Arrabothu et al. (US-20190385170-A1) for teaching automatically-updating fraud detection system configured to aid in fraud detection.
Bloom et al. (US-20210365922-A1) for teaching modeling and contextual information to prevent fraudulent charges.
3. Any inquiry concerning this communication or earlier communications from the examiner should be directed to AVIA SALMAN whose telephone number is (313)446-4901. The examiner can normally be reached Monday thru Friday; 9:00 AM to 5:00 PM EST.
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/AVIA SALMAN/Primary Patent Examiner, Art Unit 3627