Prosecution Insights
Last updated: April 19, 2026
Application No. 19/118,717

SAMPLING FRAMEWORK FOR IMBALANCED TRANSACTIONS

Non-Final OA §101§103
Filed
Apr 04, 2025
Examiner
BUNKER, WILLIAM B
Art Unit
3691
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
VISA INTERNATIONAL SERVICE ASSOCIATION
OA Round
3 (Non-Final)
79%
Grant Probability
Favorable
3-4
OA Rounds
2y 11m
To Grant
99%
With Interview

Examiner Intelligence

Grants 79% — above average
79%
Career Allow Rate
171 granted / 216 resolved
+27.2% vs TC avg
Strong +94% interview lift
Without
With
+94.5%
Interview Lift
resolved cases with interview
Typical timeline
2y 11m
Avg Prosecution
24 currently pending
Career history
240
Total Applications
across all art units

Statute-Specific Performance

§101
42.4%
+2.4% vs TC avg
§103
48.6%
+8.6% vs TC avg
§102
2.9%
-37.1% vs TC avg
§112
3.4%
-36.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 216 resolved cases

Office Action

§101 §103
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 patent application with a claim of priority to a PCT Patent Publication Application filed November 1, 2023. Response to Amendment 2.. An RCE with accompanying Amendment was filed December 1, 2025 (hereinafter “Amendment”) and has been entered into the record and fully considered. The Amendment was filed in response to a Final Rejection dated October 1, 2025. Despite the Amendment to the Claims and Applicant’s remarks, the Rejections set forth in the Final Rejection are hereby maintained; although, the Rejection under §103 is based on a 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. OFFICE NOTE: The Examiner requests an interview to suggest a possible pathway to allowability. 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. While the previous interview was helpful, additional work will be necessary. The Examiner stands ready to assist the Applicant in this effort. Status of the Claims: Claims 1 and 14 are independent and amended in virtually identical fashion. Dependent Claim 11 was cancelled and only incorporated in part into Claim 1. It was not incorporated verbatim into Claim 1. The dependent Claims were not amended in any substantive manner. 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: PNG media_image1.png 674 694 media_image1.png Greyscale PNG media_image2.png 92 652 media_image2.png Greyscale 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, subject to further consideration of the Claims in light of the prior art and in light of the specification. As noted in the Amendment, the changes to Claim 1 relate generally to: Generating a cluster of unlabeled data points; and Calculating a probability of a data point being “selected” With regard to §101: Respectfully, the Amendment does not advance prosecution substantially. Thus, the amendments to the Claim do not alter the analysis set forth in the Final Rejection regarding §101. The only changes are summarized above. The above quoted recitations merely relate to the common step of clustering data and calculating a probability. These features relate generally to those previously recited in cancelled Claim 11. This is a very common economic activity. It is extremely common – in preparing training data for machine learning models – to use a clustering algorithm to find unlabeled data points that may be similar to labeled data in order to increase the size of the minority sample set, i.e., the fraudulent data points. These limitations are recited at a very high – extremely high – level of generality. There is nothing concrete or substantive about these recitations. The Claim provides no specificity in terms of “how” similarity is determined in terms of similar samples added to the sample set. There is no specificity on “how” the clustering is accomplished nor “how” the probability is calculated nor what it relates to. “Selected” for what? The claim lacks specificity. These are extremely high level concepts and ideas. 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 selecting training data, especially financial transactional data that may be indicative of fraud. Using a computer for these purposes 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 a reduced size sample set. 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 combination of the previously cited references teaches the features added in the Amendment and the Claim as a whole. For example, the Nia reference teaches the use of “fast” clustering-sampling: “[0042] In some embodiments, the fast clustering-sampling method is implemented as follows: [0043] 1. Randomly down sample the original dataset 208 of items to an initial subset D0 using the random sampler 214 with a first sampling factor. [0044] 2. Choose parameter values 218 for the clustering algorithm 222. [0045] 3. Cluster the data points in the initial subset D0 using the clustering algorithm 222 with the parameter values 218 obtained through step 2. [0046] 4. Draw random samples from each of the identified clusters using the uniform sampler 220 with a second sampling factor and add the drawn samples to the down-sampled dataset 216. [0047] 5. Finally, obtain an explanation 224 on the down-sampled dataset 216 using the model explainer 212. .” (Emphasis Added) Furthermore, the new reference to U.S. Patent Publication No. 2023/0419402 to Ghelichi et al. (hereafter “Ghelichi”), as noted below – in combination with the previously cited references – teach all of the newly added limitations. The title is: Systems and methods of optimizing machine learning models for automated anomaly detection The Abstract is as follows: “There is provided methods, systems and techniques for optimized anomaly prediction using machine learning. A data set is obtained which corresponds to a query for anomaly detection. Feature classification is performed along with anomaly labelling using an unsupervised clustering technique based on determining similar groups of data and behaviours and determining a distribution for a particular feature of interest in each cluster such as to apply a threshold to each cluster to extract the anomaly data and label same. Once the labelled dataset is generated, a tree classification model is trained based on the labelled data set for detecting anomaly. Once trained, a set of computing model rules may be extracted from the tree classification model to generate a rules executable for anomaly spotting to define combinations of feature characteristics resulting in outlier data so that the rules executable may be applied to new data.” (Emphasis Added) Thus, Ghelichi is clearly on point with the claimed invention and the previous combination of cited references. NEW GROUNDS OF REJECTION: Claim Rejections - 35 USC § 103 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, and 13 – 19 are rejected under 35 U.S.C. §103 as being unpatentable over U.S. Patent Publication No. 2025/0087313 to O’Conchuir et al. (hereinafter “O’Conchuir”) in view of U.S. Patent Publication No. 2022/0172105 to Nia et al. (hereinafter “Nia”) and further in view of U.S. Patent Publication No. 2020/0175418 to Pham et al. (hereinafter “Pham”) and still further in view of Ghelici, as noted above. Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined sampling system of O’Conchuir in view of Nia and further in view of Pham to add the clustering and sampling features of Ghelichi. The motivation to make this modification comes from O’Conchuir. It teaches that the similarity-based sampling can be used to reduce the size of the sample dataset. It would greatly enhance the efficiency and accuracy of the combined system of O’Conchuir in view of Nia in view of Pham to add the sampling and labelling teachings of Ghelichi. Thus, the existing Rejection applies to the Amendment. 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: PNG media_image3.png 512 694 media_image3.png Greyscale The problem with this argument is that the Rejection under §101 is NOT based on the Berkheimer standard, but rather the “apply it” standard. As noted above, the claim limitations are recited without the specificity required for eligibility. Applicant describes several technical advantages of the clamed invention. The problem is that the technical solution is not reflected in the Claim with specificity. The important aspect of “how” the problem is solved is only recited at a very high level. Accordingly, the Claim is a classic example of an “apply it” situation, as explained in the Final Rejection in more detail. Applicant’s remaining arguments are likewise not persuasive. The Rejection must be maintained. As to §103: Applicant’s arguments are moot in view of the new grounds of Rejection. Accordingly, the Rejections are maintained. However, a follow up interview is encouraged. 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. applying similarity-based labelling in unbalanced datasets). 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. 2023/0076083 to Mishra et al. This reference relates to the concept of clustering. U.S. Patent Publication No. 2023/0350880 to Shreve et al. This reference relates to the concept of simplifying data labelling efforts. 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 use of 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 December 27, 2025 /ABHISHEK VYAS/Supervisory Patent Examiner, Art Unit 3691
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Prosecution Timeline

Apr 04, 2025
Application Filed
Jun 13, 2025
Non-Final Rejection — §101, §103
Sep 04, 2025
Interview Requested
Sep 12, 2025
Examiner Interview Summary
Sep 16, 2025
Response Filed
Sep 27, 2025
Final Rejection — §101, §103
Nov 13, 2025
Interview Requested
Nov 19, 2025
Examiner Interview Summary
Dec 01, 2025
Request for Continued Examination
Dec 05, 2025
Response after Non-Final Action
Dec 27, 2025
Non-Final Rejection — §101, §103 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

3-4
Expected OA Rounds
79%
Grant Probability
99%
With Interview (+94.5%)
2y 11m
Median Time to Grant
High
PTA Risk
Based on 216 resolved cases by this examiner. Grant probability derived from career allow rate.

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