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 .
DETAILED ACTION
This Non-Final rejection is in response to the application filed on November 3, 2023, the response to the Restriction/Election requirement received on May 23, 2025, the amendments to the claims filed on September 30, 2025, and the Request for Continued Examination filed on February 18, 2026.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 February 18, 2026 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 7-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The recitation, “applying the output including the decision within the payment card network causing the at least one processor to isolate one or more subsequent transactions associated with the anomalous data pattern from processing on the payment card network, the isolation of the one or more subsequent transactions thereby reducing at least one of (i) a network traffic burden corresponding to network traffic associated with the one or more subsequent transactions within the payment card network and (ii) a computational burden corresponding to processing associated with the one or more subsequent transactions within the payment network” in lines 26-33 of claim 7, similarly recited in claim 14, is not supported by the specification. Paragraph [0046] of the specification sets forth, “resulting technical effect achieved by this system is at least one of: (i) reducing network-based fraud events through early detection; (ii) reducing network-based fraud events through multiple fraud detection methods; (iii) applying a cumulative fraud detection model to detect fraud; (iv) dynamically updating fraud models to substantially improve performance; and (vi) eliminating economic loss through, e.g., early detection and reaction to fraudulent activity. Thus, the system enables enhanced fraud detection on the payment card transaction network. Once a pattern of fraudulent activity is detected and identified, further fraudulent payment card transaction attempts may be reduced or isolated from further processing on the payment card interchange network, which results in a reduced amount of fraudulent network traffic and reduced processing time devoted to fraudulent transactions, and thus a reduced burden on the network[.]”. This is not commensurate with the scope of the claim language that recites that it is the decision that is applied and not the model as the specification sets forth; that it is the computational burden that is reduced and not the processing time devoted to fraudulent transactions. Computational burden is the demand placed on computing resources, such as processing power and memory to execute complex calculations or algorithms, while processing time is the actual duration taken to complete said task. Dependent claims 8-13 and 15-20 are consisted to be rejected by virtue of their dependencies. Appropriate correction is required.
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 7-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 7-20 are directed to a system, method, or product which are/is one of the statutory categories of invention. (Step 1: YES).
The Examiner has identified independent method Claim 14 as the claim that represents the claimed invention for analysis and is similar to independent system Claim 7. Claim 14 recites the limitations of receiving, at a machine learning-based model engine, from a database, an initial dataset including historical transaction data for a first time period; segmenting the initial dataset into a plurality of subsets, each subset associated with a second time period that is smaller than the first time period; incrementally training a machine learning model configured to detect data patterns on each subset of the plurality of subsets separately in accordance with one or more weighted increments of the second time period; receiving, from a merchant computing device, a payment card transaction request associated with a transaction being processed over the payment card network; analyzing the payment card transaction request using the trained machine learning model; assigning a score to the payment card transaction request based on the analysis; receiving, at a rules engine communicatively coupled to the machine learning-based model engine, the payment card transaction request and the corresponding score from the machine learning-based model engine; generating, based at least in part on the score, an output including a decision whether to approve or decline the transaction associated with the payment card transaction request including an indication that the payment card transaction request includes an anomalous data pattern; and applying the output including the decision within the payment card network causing isolation of one or more subsequent transaction associated with the anomalous data pattern on the payment card network, the isolation of the one or more subsequent transactions thereby reducing at least one of (i) a network traffic burden corresponding to network traffic associated with the one or more subsequent transactions within the payment card network and (ii) a computational burden corresponding to processing associated with the one or more subsequent transactions within the payment network.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. Detecting and preventing fraudulent network events in a payment card network recites a fundamental economic practice (mitigating risk) / commercial or legal interactions (sales activities). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a fundamental economic practice (mitigating risk) / commercial or legal interactions (sales activities), then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The database and merchant computing device in Claims 7 and 14 is just applying generic computer components to the recited abstract limitations. The machine learning-based model engine, machine learning-based model, and machine learning-based rules model in Claims 7 and 14 appears to be just software. Claim 7 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
This judicial exception is not integrated into a practical application. In particular, the claims only recite a database and merchant computing device in Claims 7 and 14 and fraud model engine, fraud scoring model, and fraud rules model in Claims 7 and 14. The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, claims 7 and 14 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. See Applicant’s specification para. [0047-0050] about implementation using general purpose or special purpose computing devices and MPEP 2106.05(f) where applying a computer as a tool is not indicative of significantly more. Even assuming there was a technical problem, the claims, as written, fail to recite the details of how a technical solution to the technical problem was accomplished. If there was a technical problem (e.g., existing technology was incapable of performing the claimed functions) then the claims should recite the details of the technical solution (e.g., how existing technology was improved to overcome this inability). However, the claims, as written, provide no such details and merely recite that the claimed functions (i.e., the outcome) are being performed. In addition, performing the judicial exception steps using ML merely confines the use of the abstract idea to a particular technological environment and thus fails to add an inventive concept to the claims. See MPEP 2105(h). Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus claims 7 and 14 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 8-13 and 15-20 further define the abstract idea that is present in their respective independent claims 7 and 14 and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above. Claims 8, 9, 13, 15, 16, and 20 further define the machine learning model; Claims 10, 11, 17, and 18 further define the configuring of the machine learning model training model; Claims 12 and 19 further define the time period of the dataset. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the claims 8-13 and 15-20 are directed to an abstract idea. Thus, the claims 7-20 are not patent-eligible.
Response to Arguments
Applicant’s arguments with respect to claim(s) 7-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments regarding the 35 USC 101 rejection of record (Remarks, pages 8-18) are acknowledged, however they are not persuasive. Specifically, applicant’s arguments that, “the Specification sets forth various technical problems in conventional techniques for accurately detecting data patterns via machine learning models and protecting computing networks from certain types of data/data elements” (Remarks, pages 9-14). However, applicant’s arguments about the “technical improvements” cite recitations of the claim language that are not supported by the specification, see the 35 USC 112(a) rejection as set forth above. Additionally, the improvements that are alleged throughout the specification are directing to the network burden and processing times in fraud detection. The claims are silent as to an improvement to the machine learning model in and of itself. Therefore, in the claimed invention, the computer has not been improved. The non-technological process that the software is performing may have been improved but, according to Alice, improving the process without any technological innovation is not statutory. The computer still operates according to its known and standard capabilities. A reduction of load on the computer does not bring about an improvement to the computer, it merely offers resources to other processes that are running on the computer.
Applicant’s arguments regarding Step 2A, Prong Two (Remarks, pages 14-16) are acknowledged, however they are not persuasive. The claims only recite a database and merchant computing device in Claims 7 and 14 and fraud model engine, fraud scoring model, and fraud rules model in Claims 7 and 14. The computer hardware is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. These additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Applicant’s arguments citing Example 47 (Remarks, pages 16-18), are not persuasive. Specifically, the Office Examples are meant to be for training purposes and do not have the force of legal precedent. Further, Example 47 is directed towards the use of specifically trained ANNs to detect anomalies and is found to integrate the abstract idea into a practical application because: “The claimed invention reflects this improvement in the technical field of network intrusion detection. Steps (d)-(f) provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. Specifically, the claim reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f). These steps reflect the improvement 12 described in the background. Thus, the claim as a whole integrates the judicial exception into a practical application such that the claim is not directed to the judicial exception. The current claims do not improve the functioning of a computer or technical filed of network intrusion. Instead, the instant claims perform the abstract idea of determining if a transaction should proceed. Therefore, Example 47 does not apply.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINDSAY M MAGUIRE whose telephone number is (571)272-6039. The examiner can normally be reached Monday to Friday 8:30 to 5:00.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at (571) 270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Lindsay Maguire
3/30/26
/LINDSAY M MAGUIRE/Primary Examiner, Art Unit 3619