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 . This office action is in response to Applicant’s RCE filed March 10, 2026 in which claims 1, 3, 4, 10, 12, 13, 15, 16, 19 and 20 are amended. Thus, claims 1-20 are pending in the application.
Continued Examination Under 37 CFR 1.114
2. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 03/10/2026 has been entered.
Claim Rejections - 35 USC § 101
3. 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.
The Examiner has identified independent system Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent Claims 10 and 19.
The claims 1-9 are directed to a system, claims 10-18 are directed to a method and claims 19-20 are directed to a non-transitory computer-readable storage medium which are one of the statutory categories of invention (Step 1: YES).
The claim 1 recites : receive transaction data for a plurality of transactions processed over electronic payment processing network, the plurality of transactions including a set of declined transactions and a set of approved transactions; parse the transaction data to identify each pair of a respective payment node and a respective funding node associated with each transaction of the plurality of transactions; build a machine learning (ML) graphical model configured to generate a result showing all transactions of the plurality of transactions initiated from each funding node to a corresponding payment node over a period of time; train the ML graphical model by labeling each transaction as either a completed transaction or a declined transaction; train the ML graphical model by applying fraud labels to each transaction; train the ML graphical model by mapping aggregated parameters to each payment node; train the ML graphical model by associating transaction features to each incoming or outgoing transaction for the payment nodes; output, as part of the result and from the trained ML graphical model, an indication of the payment nodes that are determined to be associated with data associated with early-stage suspicious activity based on the aggregated parameters, the fraud labels, and the associated features assigned to each payment node; based on the indication, cause dedicated computation resources to be deployed to identify, at an account level associated with the payment nodes determined to be associated with the data associated with early-stage suspicious activity, one or more future transactions as originating from the payment nodes determined to be associated with the data associated with early-stage suspicious activity, wherein the deployed dedicated computation resources enable improved tracing of funds to their respective originating accounts regardless of if the accounts are inside or outside of the electronic payment processing network; and prevent the one or more future transactions originating from the payment nodes determined to be associated with the data associated with the early-stage suspicious activity from being completed by blocking, based at least in part on the improved tracing of funds, the one or more future transactions while the one or more future transactions are in progress. These limitations (with the exception of italicized portions), under their broadest reasonable interpretation, is a process that covers Certain methods of organizing human activity such as Fundamental economic principles or practices. Detecting a suspicious activity in a transaction is a way of mitigating a risk and mitigating a risk is a Fundamental Economic Practice. The claim also recites the additional elements (as italicized above) such as a processor, a memory, electronic payment processing network, a payment node, a funding node, a machine learning (ML) graphical model, “train the ML graphical model…”, the trained ML graphical model and the computation resources which do not necessarily restrict the claim from reciting an abstract idea. That is, other than, the recited additional elements (as shown above in italics), nothing in the claim precludes the steps from being performed as a method of organizing human activity. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim 1 recites an abstract idea (Step 2A: Prong 1: YES).
This judicial exception is not integrated into a practical application. The additional elements of a processor, a memory, electronic payment processing network, a payment node, a funding node, a machine learning (ML) graphical model, “train the ML graphical model…”, the trained ML graphical model and the computation resources result in no more than simply applying the abstract idea using generic computer elements. The specification describes the additional elements of a processor, a memory, electronic payment processing network, a payment node, a funding node, a machine learning (ML) graphical model, “train the ML graphical model…”, the trained ML graphical model and the computation resources to be generic computer elements (see Fig. 1, 2, 5, [0072], [0080]). Hence, the additional elements in the claim are generic components suitably programmed to perform their respective functions. The additional elements (as shown above in italics) are recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computer arrangement. The presence of a generic computer arrangement is nothing more than mere instructions to implement the abstract idea on a computer (MPEP 2106.05(f)). 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. Hence, the claims as a whole are not integrated into a practical application. Therefore, the claim 1 is directed to an abstract idea (Step 2A - Prong 2: NO).
The claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements of a processor, a memory, electronic payment processing network, a payment node, a funding node, a machine learning (ML) graphical model, “train the ML graphical model…”, the trained ML graphical model and the computation resources are recited at a high level of generality in that it results in no more than simply applying the abstract idea using generic computer elements. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.05(f)). The additional elements, when considered separately and as an ordered combination, does not add significantly more (also known as an “inventive concept”) to the exception. The additional elements of the instant underlying process, when taken in combination, together do not amount to significantly more than the sum of the functions of the elements when each is taken alone. Thus, claim 1 is not patent eligible (Step 2B: NO).
Similar analysis can he extended to other independent claims 10 and 19 and hence the claims 10 and 19 are rejected on similar grounds as claim 1. In addition, claim 19 also recites a non-transitory computer-readable storage medium which amounts to generic computer implementation.
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. Dependent claims 2-9, 11-18 and 20 are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations narrow the abstract idea further and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above. Claims 2, 3, 5, 11, 12, 14 and 20 recite a new additional element that is not present in independent claim 1, 10 or 19 and require further analysis under Prong Two of Step 2A and Step 2B.
Claims 2 and 11 recite the additional element of the graphical multi-layer model. The graphical multi-layer model, recited in the claims, is recited at a high level of generality and amounts to generic computer implementation. Hence, it does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea when considered individually and as an ordered combination.
Claims 3, 12 and 20 recite the additional elements of a receiver embedding layer and a card embedding layer. A receiver embedding layer and a card embedding layer, recited in the claims, is recited at a high level of generality and amounts to generic computer implementation. Hence, it does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea when considered individually and as an ordered combination.
Claims 5 and 14 recite the additional element of a neural network. A neural network, recited in the claims, is recited at a high level of generality and amounts to generic computer implementation. Hence, it does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea when considered individually and as an ordered combination.
Viewing the claim limitations as an ordered combination does not add anything further than looking at the claim limitations individually. When viewed either individually, or as a combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. Accordingly, claim(s) 1-20 are ineligible.
No Prior Art Rejections
3. Based on the prior art search results, the prior art of record fails to anticipate or render obvious the claimed subject matter of claims 1-20. While some individual features of claims 1-20 may be shown in the prior art of record, no known reference, alone or in combination, would provide the invention of claims 1-20. The prior art most closely resembling the applicant’s claimed invention are :
Drapeau (US 11704673 B1) – This invention relates generally to fraud detection during transactions using identity graphs. T The method may include receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method may also include determining, by the commerce platform system, an identity associated with the user, wherein the identity is associated with additional transaction attributes not received with the transaction. Furthermore, the method may include accessing, by the commerce platform system, a feature set associated with the initial transaction attributes and the additional transaction attributes, wherein the feature set comprises machine learning (ML) model features for detecting transaction fraud. The method may also include performing, by the commerce platform system, a machine learning model analysis using the feature set and the transaction data to determine a likelihood that the transaction is fraudulent, and performing, by the commerce platforms system, the transaction when the likelihood that the transaction is fraudulent does not satisfy a transaction fraud threshold.
Mohammed (US 2024/0161106 A1) - This invention generally relates to automated anomaly identification solutions and, in particular, systems and methods for identification of an anomaly of a block transactions graph of a blockchain over time using artificial intelligence solutions. An artificial intelligence (AI) tool includes a graph neural network (GNN) tool that uses deep learning models and graphics processing units (GPUs) and is trained to analyze graphics for irregular graph patterns based on block(s) over time to detect and classify anomalies to determine whether fraud or other invalidate transaction is associated with the block at a transaction and an address level.
Wang (US 2009/0018940 A1) - This invention relates to monitoring past customer account transactions conducted with a selected one or more transaction devices, and generating a predictive model that combines customer account transaction profiles with transaction device profiles related to the one or more transaction devices, and storing a representation of the predictive model in a storage. A system for detecting fraud in financial transaction includes a fraud detection computer that receives, through a communications network, customer account transaction data obtained by a monitoring device of a transaction device according to one or more transaction device variables of a transaction device profile.
Response to Arguments
4. Applicant's arguments filed dated 03/10/2026 have been fully considered but they are not persuasive due to the following reasons:
5. With respect to the rejection of all claims under 35 U.S.C. 101 with regards to Step 2A, Prong 1 (pages 14-16), Applicant argues that, “the pending claims do not recite an abstract idea in any of the three permissible groupings listed above, and in particular do not recite the alleged groupings of certain methods of organizing human activity as alleged.”
Examiner respectfully disagrees and notes that as explained in the 101 analysis above, the steps of the claim, is a process that, under their broadest reasonable interpretation, covers Certain methods of organizing human activity such as Fundamental economic principles or practices. Detecting a suspicious activity in a transaction is a way of mitigating a risk and mitigating a risk is a Fundamental Economic Practice. The claim also recites the additional elements such as a processor, a memory, electronic payment processing network, a payment node, a funding node, a machine learning (ML) graphical model, “train the ML graphical model..”, the trained ML graphical model and the computation resources which do not necessarily restrict the claim from reciting an abstract idea. That is, other than the recited additional elements, nothing in the claim precludes the steps from being performed as a method of organizing human activity. If the claim limitations, under the broadest reasonable interpretation, covers methods of organizing human activity but for the recitation of generic computer components, then it falls within the “Certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
6. With respect to the rejection of all claims under 35 U.S.C. 101 with regards to Step 2A, Prong 2 (pages 16-21), Applicant argues that, “the claims include recitations that integrate any abstract idea into a practical application”
The Examiner respectfully disagrees. The Examiner would like to point out that according to 2019 Patent Eligibility Guidelines (2019 PEG), limitations that are indicative of integration into a practical application include:
• Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
• Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition - see Vanda Memo
• Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b)
• Effecting a transformation or reduction of a particular article to a different state or thing -see MPEP 2106.05(c)
• Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo
In the instant case, the judicial exception is not integrated into a practical application, because none of the above criteria is met. The amended limitations of the claims do not result in computer functionality improvement or technical/technology improvement when the underlying abstract idea is implemented using technology. The amendments to the claims only further define the data being used however a specific abstract idea is still an abstract idea. All the features in the Applicant’s claims can at best be considered an improvement in the abstract idea. Reduction in computation resources may improve the abstract idea, however, it is not a technical improvement. The advantages over conventional systems are directed towards improving the abstract idea. The specification describes the additional elements of a processor, a memory, electronic payment processing network, a payment node, a funding node, a machine learning (ML) graphical model, “train the ML graphical model..”, the trained ML graphical model and the computation resources to be generic computer elements (see Fig. 1, 2, 5, [0072], [0080]). Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. Unlike in Desjardins, there is no improvement in the machine learning model itself. The machine learning graphical model is merely used as a tool to implement the abstract idea. The additional elements are recited at a high level of generality and under their broadest reasonable interpretation comprises a generic computer arrangement. The presence of a generic computer arrangement is nothing more than mere instructions to implement the abstract idea on a computer (MPEP 2106.05(f)). 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. Hence, the claims as a whole are not integrated into a practical application.
7. Applicant further argues that (pages 18-20), “amended Claim 1 is submitted to be eligible for reasons similar to eligible Claim 3 of Example 47 ("Anomaly Detection") of the 2024 AI SME Update”
The Examiner does not see the parallel between the claims of the instant case and those of Claim 3 of Example 47. Claim 3 is eligible because the claim as a whole integrates the judicial exception into a practical application by improving network security. Claim 3 of Example 47 provides 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. 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. As discussed above and in the rejection, the Applicant’s claims deal with detecting the fraud and are not directed to any improvements to another technology, technical field, or improvements to the functioning of the computer itself. Looking at the limitations of Applicant’s claimed invention as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Thus, Claim 3 of Example 47 is not applicable.
8. Applicant argues that (pages 21-23), “the recitations in the claims represent an inventive concept with recitations that amount to "significantly more" and are non-conventional, non-routine, and not well-understood, thereby rendering the claims eligible under Section 101.”
One of the guidelines issued by the Office to determine if the claims recite additional elements which are not well understood, routine or conventional and hence, amount to significantly more than an abstract idea, is the USPTO guidelines of April 19, 2018 incorporating the Berkheimer memo (Berkheimer memo, hereinafter).
According to the Berkheimer memo,
In a step 2B analysis, an additional element (or combination of elements) is not well understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing with, one or more of the following:
1. A citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s).
2. A citation to one or more of the court decisions discussed in MPEP § 2106.05(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s).
3. A citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s).
4. A statement that the examiner is taking official notice of the well-understood, routine, conventional nature of the additional elements). This option should
be used only when the examiner is certain, based upon his or her personal knowledge, that the additional elements) represents well-understood, routine, conventional activity engaged in by those in the relevant art, in that the additional elements are widely prevalent or in common use in the relevant field, comparable to the types of activity or elements that are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).
The claim simply applies the abstract idea using generic computer elements as a tool (see MPEP 2106.05(f)). The additional elements in the claim are a processor, a memory, electronic payment processing network, a payment node, a funding node, a machine learning (ML) graphical model, “train the ML graphical model…”, the trained ML graphical model and the computation resources. As per the rejection above, the specification describes the additional elements of a processor, a memory, electronic payment processing network, a payment node, a funding node, a machine learning (ML) graphical model, “train the ML graphical model”, the trained ML graphical model and the computation resources to be generic computer elements (see Fig. 1, 2, 5, [0072], [0080]). Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. There is no indication in Applicants’ claims that any specialized hardware or other inventive computer components are required. The fact that a general purpose computing system, suitably programmed, may be used to perform the claimed method and the fact that the claims at issue do not require any nonconventional computer, network, or other components, or even a “non-conventional and non-generic arrangement of known, conventional pieces” but merely call for performance of the claimed functions “on a set of generic computer components, satisfies the Berkheimer memo requirement that the additional elements are conventional elements (as outlined in criterion 1 of the Berkheimer memo). The additional elements of the instant underlying process, when taken in combination, together do not amount to substantially more than the sum of the functions of the elements when each is taken alone. Hence, the claims do not recite significantly more than an abstract idea.
For these reasons and those discussed in the rejection, the rejections under 35 U.S.C. 101 are maintained.
Examiner Request
9. The Applicant is request to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Conclusion
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BHAVIN D SHAH whose telephone number is (571)272-2981. The examiner can normally be reached on 8:00-5:00 M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Bennett Sigmond can be reached on 303-297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BHAVIN D SHAH/Examiner, Art Unit 3694
May 08, 2026
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