DETAILED ACTION
1. 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 communication filed on October 16, 2025. Claims 1-20 are pending and have been examined. The rejections and a statement of reasons for the indication of allowable subject matter over prior art are stated below.
Claim Rejections - 35 USC § 101
2. 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.
3. 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 claim(s) recite(s) performing an action associated with detecting criminal behavior using the trained machine learning model, which is considered a judicial exception because it falls under the category of “Certain Methods of organizing human activity” such as fundamental economic practice as well as commercial or legal interactions including agreements as discussed below. This judicial exception is not integrated into a practical application as discussed below. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception as discussed below.
Analysis
Step 1: In the instant case, exemplary claim 8 is directed to a system (apparatus).
Step 2A – Prong One: The limitations of “A system, comprising:
at least one processor programmed or configured to:
generate a base payment graph comprising a plurality of nodes and a plurality of edges connecting the plurality of nodes, wherein each node represents an entity and each edge represents a probability that a real-time payment transaction may be conducted involving two entities that are connected by the edge, wherein the real-time payment transaction is artificially created, such that transaction data associated with the real-time payment transaction is based on a payment transaction that took place in a real-world setting and the transaction data with the real-time payment transaction is not the same as transaction data associated with the payment transaction that took place in the real-world setting, and wherein, when generating the base payment graph, the at least one processor is programmed or configured to:
assign a plurality of account parameters to each of the plurality of nodes of the base payment graph; and
assign at least one interaction parameter to each edge of the plurality of edges of the base payment graph;
generate a plurality of dynamic payment graphs based on the base payment graph, wherein each dynamic payment graph of the plurality of dynamic payment graphs comprises a plurality of edges, wherein each edge of the plurality of edges comprises real-time-payment transaction parameters, and wherein, when generating the plurality of dynamic payment graphs, the at least one processor is programmed or configured to:
sample a first plurality of nodes and a first plurality of edges of the base payment graph to generate the plurality of dynamic payment graphs, wherein each dynamic payment graph is associated with a different discrete time period;
insert patterns representing adversarial activity into the plurality of dynamic payment graphs to provide a synthetic graph, wherein the patterns comprise a pattern representing fraud adversarial activity, a pattern representing money laundering adversarial activity, or any combination thereof;
generate a training dataset based on inserting the patterns representing adversarial activity into the plurality of dynamic payment graphs, wherein the training dataset comprises a plurality of transactions of the synthetic graph that represent payment transactions involving adversarial activity, and wherein the plurality of transactions is based on at least one dynamic payment graph of the plurality of dynamic payment graphs;
train a machine learning model based on the training dataset to provide a trained machine learning model; and
perform an action associated with detecting criminal behavior using the trained machine learning model” as drafted, when considered collectively as an ordered combination without the italicized portions, is a process that, under the broadest reasonable interpretation, covers the category of “Certain Methods of organizing human activity” such as fundamental economic practice as well as commercial or legal interactions including agreements.
Performing an action associated with detecting criminal behavior using the trained machine learning model is a fundamental economic practice such as mitigating risk.
The steps of the claim considered collectively, as an ordered combination, is also fulfilling agreements between parties to the transaction. Hence, the steps of the claim, considered collectively as an ordered combination without the italicized portions, covers the abstract category of “Certain Methods of organizing human activity”.
That is, other than, at least one processor, a base payment graph comprising a plurality of nodes and a plurality of edges connecting the plurality of nodes, a machine learning model and a training dataset, 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 recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of at least one processor, a base payment graph comprising a plurality of nodes and a plurality of edges connecting the plurality of nodes, a machine learning model and a training dataset to perform all the steps. A plain reading of Figures 1-6E and description in associated paragraphs reveals that the at least one processor may be a generic processor suitably programmed to execute the claimed steps. The base payment graph comprising a plurality of nodes and a plurality of edges connecting the plurality of nodes are broadly interpreted to include generic computer components suitably programmed to perform the associated functions. The training dataset is broadly interpreted to include a generic training dataset suitably programmed to store the associated data/information. The machine learning model is broadly interpreted to include generic computer components suitably programmed to perform the associated functions. Hence, the additional elements in the claims are all generic components suitably programmed to perform their respective functions. The additional elements in all the steps are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Hence, claim 8 is directed to an abstract idea.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements (identified above) to perform the claimed steps amounts to no more than mere instructions to apply the exception using a generic computer component. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, independent claim 8 is not patent eligible. Independent claims 1 and 15 are also not patent eligible based on similar reasoning and rationale.
Dependent claims 2-7, 9-14 and 16-20, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations only refine the abstract idea further.
For instance, in claims 2, 9, and 16, the steps “wherein performing the action associated with detecting criminal behavior using the trained machine learning model comprises:
determining, with the trained machine learning model, that a pattern representing fraud adversarial activity, a pattern representing money laundering adversarial activity, or any combination thereof is present in another base payment graph; and
detecting a transaction, an account, an accountholder, or any combination thereof as being associated with criminal behavior based on determining that the pattern representing fraud adversarial activity, a pattern representing money laundering adversarial activity, or any combination thereof is present in the another base payment graph” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 3, 10, and 17, the steps “further comprising:
assigning a probability parameter to each edge of the plurality of edges” under the broadest reasonable interpretation, is a further refinement of methods of organizing human activity because this step describes an intermediate step of the underlying process.
In claims 4, 11, and 18, the steps “wherein generating the base payment graph comprises: generating the base payment graph based on a plurality of Barabasi-Albert graph structures” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 5, 12, and 19, the steps “wherein a number of nodes of the plurality of nodes in the base payment graph is a user selectable parameter and wherein the number of edges of the plurality of edges is based on the number of nodes of the plurality of nodes” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In claims 6-7, 13-14, and 20, the steps “wherein generating the plurality of dynamic payment graphs comprises:
assigning dynamic graph attributes to each edge of the plurality of edges of the plurality of dynamic payment graphs based on static graph attributes assigned to each node and each edge of the base payment graph; and
wherein generating the plurality of dynamic payment graphs comprises:
assigning dynamic graph attributes to each edge of the plurality of edges of the plurality of dynamic payment graphs based on a predefined statistical distribution” under the broadest reasonable interpretation, are further refinements of methods of organizing human activity because these steps describe the intermediate steps of the underlying process.
In all the dependent claims, the judicial exception is not integrated into a practical application because the limitations are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer components. Also, the claims do not affect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of a computer system itself; the claims do not affect a transformation or reduction of a particular article to a different state or thing; and the claims do not move beyond a general link of the use of an abstract idea to a particular technological environment. In addition, the dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of the instant underlying process, when taken in combination, together do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself. For these reasons, the dependent claims also are not patent eligible.
Allowable Subject Matter
4. Claims 1-20 would be allowable, over prior art, if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101, set forth in this Office action.
The following is a statement of reasons for the indication of allowable subject matter over prior art:
The closest prior art of record, (Harris et al. US Pub. 2019/0362263 A1 and Dewar et al. US Pub. 2019/0386888 A1), considered individually or in combination, fail to teach the steps of “inserting, with the at least one processor, patterns representing adversarial activity into the plurality of dynamic payment graphs to provide a synthetic graph, wherein the patterns comprise a pattern representing fraud adversarial activity, a pattern representing money laundering adversarial activity, or any combination thereof”. Page 3 of 13Appl. No.: 14/331,106For these reasons claims 1, 8 and 15 are deemed allowable over prior art. Dependent claims 2-7, 9-14 and 16-20 are allowable over prior art by virtue of dependency on an allowable claim.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(a) Williams; Jason et al. (US Pub. 2025/0086644 A1) discloses a method for payment transaction monitoring in a real-time payments system that includes receiving a payment transaction; assigning an identifier to the payment transaction; associating an event with the identifier at each step of processing the payment transaction; recording the event with the identifier in real-time as the event occurs; monitoring the events in real-time to determine whether a payment transaction stop condition exists; and stopping processing of the payment transaction on a condition that the payment transaction stop condition exists.
(b) Harish; Venkatesan et al. (US Pub. 2024/0062181 A1) discloses a method and system for routing payment transactions of a payment account. After the payment transaction is initiated, a payment server identifies flag information from the payment request indicating a payment account to be multi-configurable. When the payment server identifies that the payment request is from the POS device, the payment server retrieves primary configuration details corresponding to the payment account from a routing service and routes the payment transaction for the payment request to an issuer corresponding to the primary configuration details. Further, when the payment server identifies that the payment request is from a payment gateway, the payment server retrieves a list of names of configuration details corresponding to the payment account from the routing service for selection. Upon selection, the payment server retrieves configuration details corresponding to the selected issuer and routes the payment transaction to the issuer for completing the payment transaction.
6. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Narayanswamy Subramanian whose telephone number is (571) 272-6751. The examiner can normally be reached Monday-Friday from 9:00 AM to 5:00 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Abhishek Vyas can be reached at (571) 270-1836. The fax number for Formal or Official faxes and Draft to the Patent Office is (571) 273-8300.
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/Narayanswamy Subramanian/
Primary Examiner
Art Unit 3691
September 4, 2026
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