DETAILED ACTION
1. The present application, filed on or after March 13, 2013, is being examined under the first inventor to file provisions of the AIA .
This application has no claim of priority.
Response to Amendment
2.. An Amendment was filed May 26, 2026 (hereinafter “Amendment”) and has been entered into the record and fully considered. The Amendment was filed in response to a Non-Final Rejection dated February 24, 2026.
Despite the Amendment to the Claims and Applicant’s remarks, the Rejections under §101 and §103 as set forth in the Non-Final Rejection are hereby maintained. However, the Rejections to the Claims under §103 are on NEW GROUNDS necessitated by the Amendment.
An explanation of the maintained Rejections and a response to Applicant’s arguments are set forth below. Please see the “Conclusion” section of this Action below for important information regarding responding to this Action.
The IDS filed June 5, 2026 has been considered in this Application.
NOTE: interviews are welcome at any stage of prosecution. Please use the AIR form, the link for which can be found at the end of this action, to schedule the interview.
While the previous interview in this case was helpful in advancing prosecution, despite the Amendment, issues remain under both §§101 and 103. A follow up interview would greatly help resolve such issues and the Examiner stands ready to propose amendments to the Claims that likely would render them eligible.
Status of the Claims:
Claims 1 – 8, 10 – 17, and 19 - 20 are pending in this Application.
Claims 9 and 18 are cancelled.
Independent Claims 1, 10, and 19 were amended in substantially identical fashion.
None of the dependent Claims were amended.
Therefore, the following explanation of the maintained rejections with regard to Claim 1 is considered explanatory of the Rejection as a whole.
With regard to the Amendment:
Claim 1 was amended as follows:
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Objection to the Claims:
Independent Claims 1, 10, and 19 are objected to as lacking clarity. For example, in Claim 1 of the Amendment, in the final limitation, there is reference to “the transactional edge” and “the payment node;” however, it is unclear which is the prior transaction. The Claim refers to a “plurality” of payment nodes and a “plurality” of funding nodes and yet the Claim does not recite nor provide a means for determining the prior transaction.
Correction or clarification is required.
Summary of the Amendment and Broadest Reasonable Interpretation:
Claim terminology is to be given its plain and ordinary meaning to a person of ordinary skill in the art, consistent with the specification. This is true, unless the terms are given a special meaning. See MPEP §2111.01
Here, no special meaning is detected. Respectfully, the Amendment does not advance prosecution in a material way. Indeed, some of the changes seem semantic and not substantive.
For example, as noted in the Amendment:
In a first instance, “include PII embeddings” is deleted and “embed the PII” is added
In a second instance, the “receive a subsequent transaction step” has been added; however, that step recites almost verbatim language that is deleted in the following step
The only substantive amendment appears to be the step of “identify a change in the PII” between a subsequent transaction and an unidentified/unidentifiable prior transaction.
These terms appear – subject to further consideration – to be defined in the specification based on their plain and ordinary meaning.
With regard to §101:
Respectfully, while the Office recognizes the good faith attempt to render the Claims eligible, the amendments to still lack the specificity required to incorporate a practical application into the abstract idea recited in the claims. Thus, the Claims remain directed to an abstract idea.
With regard to Claim 1, the concepts surrounding embedding PII into a graphic model a very common and abstract concept. It is a high level, conceptual idea. These concepts are well known and generic, as noted in the Non-Final Rejection. The change in the PII between the subsequent transaction and one of a plurality of prior transactions is not recited. What is the change? How is it determined? What has to be changed? Is there a threshold for change and what is the metric for measuring the change? If there is a score involved, it is not recited and neither is the means for measuring it.
The Claim provides no specificity in terms of the change in PII nor how it is detected and measured. Only the mere outcome or result of determining fraud is recited. No special functionality is recited. No new computerized components are recited. These limitations recite results or “outcome” of computer processing without specifying “how” a technical problem is solved. That is, the solution of a technical problem is not reflected in the Claim.
Without greater specificity as to “how” certain functions solve a technical problem, the currently recited limitations can be achieved by any general purpose computer without special programming. In short, each step does no more than require a generic computer to perform generic computer functions. Considered as an ordered combination, the computer components of the Claim add nothing that is not already present when the steps are considered separately.
The Claims do not, for example, purport to improve the functioning of the computer elements nor do the claims reflect how an improvement in any other technology or technical field is achieved. Thus, the Claims amount to nothing significantly more than instructions to “apply” the abstract idea of predicting fraud in certain transactions. Such is not sufficient to integrate a practical application in the abstract idea.
That said, the Examiner stands ready to assist the Applicant in revising the Claims to possibly recite eligible subject matter. If desired, please schedule an interview as mentioned above, the AIR form being found in the Conclusion section of this Action.
Accordingly, the Rejection is maintained.
With regard to §103:
Applicant’s amendments and arguments necessitated a new grounds of Rejection:
Claim Rejections - 35 USC § 103
4. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 8, 10 – 17, and 19 – 20 are rejected under 35 U.S.C. §103 as being unpatentable over U.S. Patent Publication No. 2022/0020026 to Wadhwa et al. (hereinafter “Wadhwa”) in view of Non-Patent Literature to Ghanem et al., “A Comparative Study of Knowledge Graph-to-Text Generation Architectures in the Context of Conversational Agents,” Springer, https://doi.org/10.1007/978-3-031-53468-3_35 2024 (hereinafter “Ghanem”) and further in view of Non-Patent Literature to Crone, “Vector Embedding 101: The New Building Blocks for Generative AI,” KX Systems, Medium, 2023 (hereinafter “Crone”) and still further in view of U.S. Patent Publication No. 2020/0320619 to Motaharian et al. (hereinafter “Motaharian”).
Motaharian is in the same field of endeavor as the claimed invention: the use of a graph structure to detect fraud. The Title reads as follows: Systems and methods for detecting and preventing fraud in financial institution accounts
The Abstract reads:
“Embodiments of the disclosure relate to systems and methods of detecting and preventing fraud in financial institution accounts. In various embodiments, data associated with tradelines may be received from credit reporting bureaus. The data may be used to generate a graph that represents a community of shared tradelines based on matches between attributes associated with tradelines such as account numbers or account type. A set of machine learning models can be trained using a training dataset to provide a set of rules that is optimized for evaluating the graph to detect synthetic identities. The set of rules can be evaluated against one or more nodes in the graph to determine whether an identity represented by each respective node in the graph is a synthetic identity.” (Emphasis Added)
The graph is organized in the same manner as the claimed invention – with nodes that represent an entity and edges that represent relationships between entities. The “rules” of Motaharian are used to analyze the graph and compare changes in the graph relative to PII and the detection of synthetic identities:
“[0024] A graph that represents a community of shared tradelines may be generated based on identifying one or more matches between attributes associated with different tradelines. Each node of the graph represents an identity, which may be a real identity or a synthetic identity. Each edge of the graph represents a tradeline that is shared between two identities.
[0025] A set of machine learning models can be trained to produce a set of rules that is optimized to detect synthetic identities in the graph. For example, a first machine learning model can be trained using a training dataset that includes attributes associated with tradelines, graph metrics relating to communities of shared tradelines, personal identity information relating to specific individuals, and default data. By training the first machine learning model, an ensemble of decision trees can be generated. A first set of rules can be extracted from the ensemble of decision trees and used as features to train a second machine learning model. By training the second machine learning model, important rules can be identified and extracted from the first set of rules and used to generate a second set of rules that is optimized to detect synthetic identities in the graph.
[0026] The second set of rules can be applied against the graph to efficiently determine whether an identity associated with a community of shared tradelines is or may be a synthetic identity. For example, the trained machine learning model may provide a second set of rules that includes the rule: Community size>=3 AND No individual mortgage tradeline AND FICO>700. The second set of rules can be applied to each node in the graph to determine if an identity represented by each respective node is a synthetic identity.
[0027] Once a synthetic identity is identified, a line of credit or loan associated with a financial institution account may be denied or restricted. For example, personal identity information associated with the synthetic identity may match personal identity information associated with an application for a line of credit or loan at a financial institution. Upon receiving information regarding the synthetic identity, a financial institution server may deny the application for the line of credit or loan or restrict a line of credit or loan on an existing financial institution account.” (Emphasis Added)
The “matching” taught in Motaharian is considered to constitute the recited term “identifying a change” in the PII.
Therefore, it would have been obvious to one of ordinary skill in the relevant art at the time of filing the claimed invention to have modified the combined system of Wadhwa in view of Ghanem in view of Crone to add the graph analysis of a change in the PII, as taught by Mataharian. The motivation to do so comes from Wadhwa. As quoted above, Wadhwa teaches the generation of vector embeddings. It would greatly enhance the efficiency and accuracy of the system of Wadhwa in view of Ghanem in view of Crone to use the matching teachings of Mataharian.
Therefore, the Rejection of these Claims under §103 is also maintained.
Response to Arguments
3. Applicant's arguments set forth in the Remarks section of the Amendment have been fully considered but they are not persuasive.
With regard to section 101 rejection, Applicant argues as follows:
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While the Desjardins decision may be helpful to Applicant’s claiming the training of a machine learning model, such is not the case here. That case focused on the detailed steps implemented in training a model. The only training step here has been amended to recite an “embedding” step. Thus, Desjardins is inapplicable.
In its detailed arguments, Applicant points to various technical problems addressed in the specification and asserts:
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However, it is clear that the so-called computational complexities are not reflected in the Claim, given that nowhere in the claim is reflected limitations relating to treating an edge attribute as a category nor the use of “one hot embedding,” etc.
Therefore, these arguments are not persuasive.
With regard to §103:
Applicant’s arguments with respect to the Rejections under §103 are moot in view of the new grounds of Rejection.
A follow up interview is encouraged to discuss the merits of this Application.
Conclusion
5. Applicant should carefully consider the following in connection with this Office Action:
A. Finality
THIS ACTION IS MADE FINAL. Applicant’s amendments to the Claims necessitated the new grounds of Rejection. See MPEP § 706.07. 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 extension fee 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 date of this final action.
B. Search and Prior Art
The search conducted in connection with this Office Action, as well as any previous Actions, encompassed the inventive concepts as defined in the Applicant’s specification. That is, the search(es) included concepts and features which are defined by the pending claims but also pertinent to significant although unclaimed subject matter. Accordingly, such search(es) were directed to the defined invention as well as the general state of the art, including references which are in the same field of endeavor as the present application as well as related fields (e.g. the use of graphical models in fraud detection). Indeed, there is a plethora of prior art in these fields.
Therefore, in addition to prior art references cited and applied in connection with this and any previous Office Actions, the following prior art is also made of record but not relied upon in the current rejection:
U.S. Patent Publication No. 2022/0327541 to Seguratan et al. This reference relates to the concept of generating fraud scores from graphical models.
Chinese Patent Publication No. CN 115660814 TO Xu. This reference relates to the concept of embedding PII into a graph model for detecting fraud.
Non-Patent Literature to Khan et al., “Synthetic Identity Detection using Inductive Graph Convolutional Networks,” 978-93-80544-47-2/23New Delhi, India 2023
C. Responding to this Office Action
In view of the foregoing explanation of the scope of searches conducted in connection with the examination of this application, in preparing any response to this Action, Applicant is encouraged to carefully review the entire disclosures of the above-cited, unapplied references, as well as any previously cited references. It is likely that one or more such references disclose or suggest features which Applicant may seek to claim. Moreover, for the same reasons, Applicant is encouraged to review the entire disclosures of the references applied in the foregoing rejections and not just the sections mentioned.
D. Interviews and Compact Prosecution
The Office strongly encourages interviews as an important aspect of compact prosecution. Statistics and studies have shown that prosecution can be greatly advanced by way of interviews. Indeed, in many instances, during the course of one or more interviews, the Examiner and Applicant may reach an agreement on eligible and allowable subject matter that is supported by the specification.
Interviews are especially welcomed by this examiner at any stage of the prosecution process. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool (e.g. TEAMS).
To facilitate the scheduling of an interview, the Examiner requests the use of the AIR form as follows:
USPTO Automated Interview Request http://www.uspto.gov/interviewpractice.
Other forms of interview requests filed in this application may result in a delay in scheduling the interview because of the time required to appear on the Examiner's docket. Thus, the use of the AIR form is strongly encouraged.
E. Communicating with the Office
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM BUNKER whose telephone number is (571)272-0017. The examiner can normally be reached on M - F 8:30AM - 5:30PM, Pacific.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas, can be reached at 571-270-1836. Information regarding the status of an application, whether published or unpublished, may be obtained from the “Patent Center” system. For more information about the Patent Center system, https://patentcenter.uspto.gov/
/William (Bill) Bunker/
U.S. Patent Examiner
AU 3691
william.bunker@uspto.gov
(571) 272-0017
June 9, 2026
/ABHISHEK VYAS/Supervisory Patent Examiner, Art Unit 3691
Prosecution Insights