DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to claims filed 05/05/2026 and Applicant’s communication regarding application 18/507044 filed 05/05/2026.
Claims 1-3, 5, 7-11, 13, and 15- 20 have been examined with this office action.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 has been entered.
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 1-3, 5, 7-11, 13, and 15- 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of performance of a downstream task (fraudulent transaction prediction) without significantly more.
Subject Matter Eligibility Standard
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v.CLS Bank International, et al., 573 U.S. _ (2014) as provided by the interim guidelines FR 12/16/2014 Vol. 79 No. 241.
Analysis
Step 1, the claimed invention must be to one of the four statutory categories. 35 U.S.C. 101 defines the four categories of invention that Congress deemed to be the appropriate subject matter of a patent: processes, machines, manufactures and compositions of matter. In this case independent claim 1 and all claims which depend from it are directed toward a method, and independent claim 10 and all claims which depend from it are directed toward a system and independent claim 17 all claims which depend from it are directed toward a computer program product comprising a computer readable storage medium storing instruction to perform functions/steps. As such, all claims fall within one of the four categories of invention deemed to be the appropriate subject matter.
Step 2A Prong 1, Under Step 2 A, Prong 1 of the 2019 Revised § 101 Guidance, it is determined whether the claims are directed to a judicial exception such as a law of nature, a natural phenomenon, or an abstract idea (See Alice, 134 S. Ct. at 2355) by identify the specific limitation(s) in the claim that recites abstract idea(s); and then determine whether the identified limitation(s) falls within at least one of the groupings of abstract ideas enumerated in the 2019 PEG.
Specifically, claim 1 comprises inter alia the functions or steps of “A computer-implemented method for task-guided graph augmentation and editing, the computer-implemented method comprising: receiving a label set and an input graph, wherein the input graph is received in an observed financial transaction network, and the label set is associated with a downstream task; extracting, using a task-guided context extractor, network contextual information from the input graph by sampling, for each class of the label set, a set of temporal random walk sequences conditioned on the downstream task, wherein the extracted network contextual information includes topological properties and temporal properties of the input graph, the topological properties are extracted based on first vertices having a high spatial frequency among a plurality of vertices of the input graph, and the temporal properties are extracted based on second vertices having a high temporal frequency among the plurality of vertices of the input graph; training a multi-resolution temporal generative model based on the set of temporal random walk sequences; learning, based on the training of the multi-resolution temporal generative model, a data augmentation function that maintains a true data distribution of the input graph; and generating, based on the data augmentation function, an augmented financial transaction network that enhances performance of the downstream task and preserves the topological properties and the temporal properties of the input graph, wherein the generating of the augmented financial transaction network includes: adding additional links to the input graph based on a probability associated with the additional links exceeding an add threshold, and removing existing links from the input graph based on a probability associated with the existing links falling below a prune threshold”.
Claim 10 comprises inter alia the functions or steps of “A system comprising: a processor; a data bus coupled to the processor; a memory coupled to the data bus; and a computer-usable medium embodying a computer program code, the computer program code comprising instructions executable by the processor and configured to: receive a label set and an input graph, wherein the input graph is received in an observed financial transaction network, and the label set is associated with a downstream task; extract, using a task-guided context extractor, network contextual information from the input graph by sampling, for each class of the label set, a set of temporal random walk sequences conditioned on the downstream task, wherein the extracted network contextual information includes topological properties and temporal properties of the input graph the topological properties are extracted based on first vertices having a high spatial frequency among a plurality of vertices of the input graph, and the temporal properties are extracted based on second vertices having a high temporal frequency among the plurality of vertices of the input graph; train a multi-resolution temporal generative model based on the set of temporal random walk sequences; learn, based on the training of the multi-resolution temporal generative model, a data augmentation function that maintains a true data distribution of the input graph; and generate, based on the data augmentation function, an augmented financial transaction network that enhances performance of the downstream task and preserves the topological properties and the temporal properties of the input graph, wherein the generation of the augmented financial transaction network includes: addition of additional links to the input graph based on a probability associated with the additional links exceeding an add threshold, and removal of existing links from the input graph based on a probability associated with the existing links falling below a prune threshold”.
Claim 17 comprises inter alia the functions or steps of “A computer program product for task-guided graph augmentation and editing, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform operations comprising: receiving a label set and an input graph, wherein the input graph is received in an observed financial transaction network, and the label set is associated with a downstream task; extracting, using a task-guided context extractor, network contextual information from the input graph by sampling, for each class of the label set, a set of temporal random walk sequences conditioned on the downstream task, wherein the extracted network contextual information includes topological properties and temporal properties of the input graph, the topological properties are extracted based on first vertices having a high spatial frequency among a plurality of vertices of the input graph, and the temporal properties are extracted based on second vertices having a high temporal frequency among the plurality of vertices of the input graph; training a multi-resolution temporal generative model based on the set of temporal random walk sequences; learning, based on the training of the multi-resolution temporal generative model, a data augmentation function that maintains a true data distribution of the input graph; and generating, based on the data augmentation function, an augmented financial transaction network that enhances performance of the downstream task and preserves the topological properties and the temporal properties of the input graph, wherein the generating of the augmented financial transaction network includes: adding additional links to the input graph based on a probability associated with the additional links exceeding an add threshold, and removing existing links from the input graph based on a probability associated with the existing links falling below a prune threshold”.
Those claim limits in bold are identified as claim limitations which recite the abstract idea, while those that are un-bolded are identified as additional elements.
The cited limitations as drafted are systems and methods that, under their broadest reasonable interpretation, covers performance of a method of organizing human activity, but for the recitation of the generic computer components. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Performance of a downstream task (fraudulent transaction prediction) is a fundamental economic practice long prevalent in commerce systems. If a claim limitation, under its broadest reasonable interpretation, covers a fundamental economic principle or practice but for the general linking to a technological environment, then it falls within the organizing human activity grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2, Next, it is determined whether the claim is directed to the abstract concept itself or whether it is instead directed to some technological implementation or application of, or improvement to, this concept, i.e., integrated into a practical application. See, e.g., Alice, 573 U.S. at 223, discussing Diamond v. Diehr, 450 U.S. 175 (1981). The mere introduction of a computer or generic computer technology into the claims need not alter the analysis. See Alice, 573 U.S. at 223—24. “[T]he relevant question is whether the claims here do more than simply instruct the practitioner to implement the abstract idea on a generic computer.” Alice, 573 U.S. at 225.
In the present case, the judicial exception is not integrated into a practical application. The claim limitations are not indicative of integration into a practical application by claiming an improvement to the functioning of the computer or to any other technology or technical field. Further, the claim limitations are not indicative of integration into a practical application by applying or using the judicial exception in some other meaningful way.
In particular, the claims contain the following additional elements: a system; a processor; a data bus; a memory; a computer-usable medium; a computer program code / program instructions / a task-guided context extractor; a computer program product; a computer readable storage medium; training / learning. However, the specification description of the additional elements a system ([Figure 9, element 900] [0068]); a processor ([Figure 9, element 910] [0068]); a data bus ([0006]); a memory ([0067]); a computer-usable medium ([0006]); a computer program code / program instructions / a task-guided context extractor ([0006]); a computer program product ([0066-0067]); a computer readable storage medium ([0067] [0088]); training / learning ([0004] [0025] [0065]) are at a high level of generality using exemplary language or as part of a generic technological environment and are functions any general purpose computer performs such that it amount no more than mere instruction to apply the exception to a particular technological environment. Further, none of the limitations recite technological implementations details for any of the steps but, instead, only recite broad functional language being performed by the generic use of at least one processor. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaning limits on practicing the abstract idea. Thus, the claim is directed toward an abstract idea.
Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more that the abstract idea(s). As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the abstract idea(s) amounts to no more than mere instructions to apply the exaction using a generic computer component. Mere instruction to apply an exertion using a generic computer component cannot provide an inventive concept. These generic computer components are claimed at a high level of generality to perform their basic functions which amount to no more than generally linking the use of the judicial exception to the particular technological environment of field of use (Specification as cited above for additional elements) and further see insignificant extra-solution activity MPEP § 2106.05 I. A. iii, 2106.05(b), 2106.05(b) III, 2106.05(g). Thus, the claims are not patent eligible.
As for dependent claims 2-3, 5, 7-9, 11, 13, 15-16, and 18- 20 these claims recite limitations that further define the same abstract idea using previously identified additional elements noted from the respective independent claims from which they depend. Therefore, the cited dependent claims are considered patent ineligible for the reasons given above.
Prior Art
The claims overcome the prior art of record such that none of the cited prior art reference’s disclosures can be applied to form the basis of a 35 USC § 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC § 103 rejection when the limitations are read in the particular environment of the claims. The prior art of record, Rossi (US Patent No. 10728104), Shekhar (US Patent No. 11403643), and Sun (PGPub No. 20230351215) all teach the use temporal random walk algorithm used in fraud detection. However, none of the prior art teaches the use of the terms “true data distribution” and that they “preserve topological and temporal properties of the input graph”. The examiner notes that these functional objectives are merely claimed at a high level of generality without implementation details of how the functional objectives are achieved. Therefore, the claims may be allowable if amended to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph and 35 U.S.C. 101, set forth in this Office action.
Response to Arguments
Applicant's arguments with regards to claims have been fully considered but they are not persuasive.
EXAMINER’S RESPONSE TO APPLICANT REMARKS CONCERNING Claim Rejections - 35 USC § 101: Applicant's arguments with regards to 35 USC § 101 have been fully considered but are not persuasive. Regarding applicant’s argument that “claimed features are inextricably tied to a machine and a computer technology. The claimed method augments a task-guided graph while preserving true properties of the graph by using labels and multi-resolution properties to enhance performance of machine learning model performing downstream tasks. Therefore, the Applicant respectfully submits that the features of the amended independent claim 1 cannot describe an abstract concept, or a concept similar to those found by the Courts to be Abstract, such as certain methods of organizing human activity”, the examiner maintains that the machine and a computer technology are merely applied to the abstract idea of the claims. The claim limitations are replete with functional objectives are merely claimed at a high level of generality without implementation details of how the functional objectives are achieved within the claims to include the use of the terms “true data distribution” and that they “preserve topological and temporal properties of the input graph”. The examiner maintains that performance of a downstream task (fraudulent transaction prediction) is a fundamental economic practice long prevalent in commerce systems. If a claim limitation, under its broadest reasonable interpretation, covers a fundamental economic principle or practice but for the general linking to a technological environment, then it falls within the organizing human activity grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Applicant’s argument directed toward “data sparsity”, “data noisiness”, and “data dynamics”, are moots since these features are not claimed. Again, the claims contain functional objectives without implementations technical improvement details as to how the functional objective is achieved beyond merely applying a technology. Thus, Desjardins is readily distinguishable from the present claims. As such, the examiner maintains the rejection.
Conclusion
For prior art made of record and not relied upon is considered pertinent to applicant's disclosure see Notice of References Cited items A-E submitted 09/10/2025 used as prior art and in the conclusion section in the office action submitted 09/10/2025.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Gregory A Pollock whose telephone number is (571) 270-1465. The examiner can normally be reached M-F 8 AM - 4 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas can be reached on 571 270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Gregory A Pollock/Primary Examiner, Art Unit 3691
07/11/2026
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