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 is a regular utility application with no claim of priority.
Claims 1 - 18 are pending and examined as follows:
Response to Amendment
2.. An RCE with accompanying Amendment was filed June 11, 2026 (hereinafter “Amendment”) and has been entered into the record and fully considered. The Amendment was filed in response to a Final Rejection dated December 11, 2025.
Despite the Amendment to the Claims and Applicant’s remarks, the Rejections set forth in the Non-Final Rejection are hereby maintained.
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.
Status of Claims:
Claims 1 – 18 remain pending in this Application.
None have been cancelled.
Only independent Claims 1 and 10 are pending and they were amended in the Amendment in substantially identical fashion and are structurally similar and have essentially the identical scope.
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.
OFFICE NOTE: Interviews are always welcome at any stage of prosecution. Please use the AIR form for scheduling an interview if such is desired. The link for the AIR form is found at the end of this Action.
With regard to the Amendment:
Claim 1 was amended as follows:
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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. The claim term “mule” or “mule account” clearly relates to the use of such accounts by individuals engaged in money laundering. (See at least [0002])
With regard to §101:
Respectfully, the changes to Claim 1 set forth in the Amendment make only a minor impact on the analysis set for the Final Rejection regarding §101. The only changes are summarized as follows:
The Amendment clarifies that the previously recited term “hop” relates to a graph-based traversal of distance. This has only a minor effect on the eligibility of the Claim as it was already clear that the claimed system was a graph-like structure.
The Amendment adds a few high-level, general features upon which the similarity score is based.
The algorithm used for clustering is clarified to be “unsupervised.”
The training dataset is used to “define” a “relation” which represents potential mule-hood based on “shared cluster membership and transaction linkages.”
Finally, the link prediction model predicts these relations
It is clear that these features are recited at an extremely high level of generality. They fail to add the specificity required under §101. Broad concepts of “hops” and traversal of a graph are very common. This is the purpose of a graph – to define links (often referred to as “edges”) between accounts, persons, or other objects. These are generic concepts in computer-based graph processing. High level score parameters are also recited. These are very broad and abstract concepts.
Similarly, a person of ordinary skill in the art would understand that clustering algorithms can be of any type, including supervised or unsupervised or semi-supervised. These are also common concepts and reciting merely one form of clustering algorithm barely adds specificity to the claim. The final two features listed above are also conceptual ideas and actually closely related.
The Claim amendments do not recite “how” a technical problem is solved. They do not reflect how the computer-based system is improves or how the field of detecting money laundering is improved.
The recited limitations relate to very common economic activity. These limitations are recited at a very high – extremely high – level of generality. There is nothing concrete or substantive about these recitations. The Claims lack the specificity required for eligibility. The questions listed in the Final Rejection remain unclarified in the Claim. Thus, the Claim provides little specificity in terms of how the model is trained nor how the training data is prepared for training (e.g. how dimensionality reduction is accomplished.) Only the mere outcome or result that the a mule list is generated. 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.
Taking the claim elements separately, the function performed by the computer elements at each step of the process is purely typical of processing data and especially financial transactional data. Using a computer to receive information, cluster it, calculate scores, established linked pairs, and the like - are among the most basic functions of a computer. 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.
Claim 1 does not, for example, purport to improve the functioning of the computer elements nor does the claim reflect how an improvement in any other technology or technical field is achieved. Thus, Claim 1 amounts to nothing significantly more than instructions to “apply” the abstract idea of generating an AI chatbot to provide an estimate of a home for the purpose of some insurance product using some unspecified, generic algorithm and computer components. Such is not sufficient to integrate a practical application in the abstract idea.
Accordingly, the Rejection is maintained.
With regard to §103:
It is respectfully submitted that the features listed above are taught by the combination of the cited references and, in particular, the reference to Juban.
The specifics of this reference to Juban were set forth in the Final Rejection. It teaches a graph-based money laundering detection system with “hops” that define a graph-traversal distance. (See at least 0118), Clustering techniques are applied to provide for the generation of labels for clusters of similar natures, i.e. associated with money laundering. This is shown in Fig. 17:
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See related description at 0053 and 0124.
A person of ordinary skill in the art would readily understand that either supervised or unsupervised clustering could be applied. See 0109.
Juban teaches that various features can be used to detect money laundering, including patterns and behaviors of the bad actors/mules. See at least 0109-0110. It would be readily apparent to a person of ordinary skill in the art that these features are used to generate the similarity score. See 0008-0017. These sections also make it clear that the scores – similarity or risk – are related to the risk of fraud and money laundering. Juban teaches that the risk can be related to a pair of accounts or “nodes.” See 0018-0120.
Thus, the Claims remain rejection under §103 on the grounds set forth in the Final Rejection:
Claims 1 - 18 are rejected under 35 U.S.C. §103 as being unpatentable over U.S. Patent Publication No. 2021/0334822 to Pati et al. (hereinafter “Pati) in view of U.S. Patent No. 2021/0264318 to Butvinik (hereinafter “Butvinik”) and further in view of U.S. Patent Publication No. 2020/0394707 to Guo (hereinafter “Guo) and still further in view of U.S. Patent Publication No. 2022/0405860 to Juban et al. (hereinafter “Juban”).
Therefore, the Rejection under §103 is 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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Here, the Office has not alleged that the abstract idea relates to a mental process – rather, the abstract idea is clearly a list of common generic steps for carrying out a method of organizing human behavior, namely, fundamental economic principles or practices such as fraud and money laundering detection.
While the Amendment is helpful, the Claim lacks specificity. The recited steps are generic and high level. They recite a series of steps that are performed by all computers.
An interview is encouraged.
The Rejection must be maintained.
As to §103, Applicant argues as follows:
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This argument is puzzling. While using different terminology, this teaching seems on point with the claim limitations which read as follows:
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The claimed invention contemplates that certain accounts are “known” to be mule accounts. The type of graph-based clustering technique recited in the Claim is well-understood by persons of ordinary skill in the art. It results in dimensionality reduction in generating a smaller, labelled training dataset. It can result in what is sometimes referred to as “semi-supervised” training, in that “pseudo-labels” are generated and used for training.
These techniques are well known. Applicant is encouraged to refer to the publication set forth below in the Conclusion section of this Non-Final Rejection and entitled:
Shrivastava et al., “Identifying Linked Fraudulent Activities Using Graph Convolution Network,” arXiv:2106.04513v1 [cs.SI] 5 June 2021
That is, graph structures and clustering can be used to generate or enrich labeled training data for supervised learning, often in hybrid approaches that combine unsupervised and supervised techniques. It is well known that clustering can be used to generate labels. The clustering can use supervised algorithms or unsupervised algorithms. Either approach can produce pseudo-labels for unlabeled data, which can then be used in supervised training.
Thus graph structures and clustering can be integrated. By combining graph structures and clustering, the resulting clusters can discover meaningful subgraphs or communities, which can be used as candidate classes. While clustering and graph structures are traditionally unsupervised, they can be used in supervised clustering and graph-based label propagation to generate or refine labeled training data, making them valuable tools for creating richer datasets for supervised learning
Accordingly, the Rejections are maintained.
Conclusion
4. Applicant should carefully consider the following in connection with this Office Action:
A. 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. using clustering and other dimensionality reduction techniques – such as linked pair or link prediction analysis, to detect money laundering). 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. 2023/0376594 to Dalal et al. This reference relates to the concept of integrating graph and cluster techniques.
Non-Patent Literature:
Shrivastava et al., “Identifying Linked Fraudulent Activities Using Graph Convolution Network,” arXiv:2106.04513v1 [cs.SI] 5 June 2021
Blumenfeld, “Graph Machine Learning: An Overview,” Data Science, April 4, 2023
B. 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.
C. 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 AIR form is strongly encouraged.
D. 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, see https://patentcenter.uspto.gov/
/William (Bill) Bunker/
U.S. Patent Examiner
AU 3691
(571) 272-0017 - office
william.bunker@uspto.gov
June 27, 2026
/ABHISHEK VYAS/Supervisory Patent Examiner, Art Unit 3691
Prosecution Insights