Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
The following NON-FINAL Office Action is in response to communication filed on 1/27/2026.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/27/2026 has been entered.
Status of Claims
Claims 1-8, 10-13, 15-22 are currently pending.
Claims 1-2, 6, 11-13, 15-17, 19-20 are amended.
Claims 21, 22 are newly added.
Claims 1-8, 10-13, 15-22 are currently under examination and have been rejected as follows.
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Response to Amendment
The previously pending rejections under 35 USC 101 will be maintained. The 101 rejection is updated in view of the amendments.
The previously pending rejections under 35 USC 103 are withdrawn in light of Applicant’s arguments and amendments.
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Response to Arguments
Regarding Applicant’s remarks pertaining to 35 USC 101:
Step 2A Prong One:
Applicant argues on page 14 of remarks 1/27/2026:
“In the present instance, the claims do not recite any of the activities (‘mitigating risk, advertising, marketing or sales activities or behaviors, or business relations’) mentioned by the Office. To the contrary, the combination of claimed elements define, with particularity, an improved information processing technology. This is seen by the various steps in which initial information (‘a historical transaction dataset ...’) is subjected to particular processing steps…. For this reason, it is respectfully asserted that, at most, the claims merely ‘involve’ rather than ‘recite’ the purported abstract idea.”
Examiner respectfully disagrees. Applicant specification ¶ [0006] states: “Various embodiments of the present disclosure provide methods and systems for classifying a plurality of merchants into different merchant categories.” Despite incorporating information processing technology, the claims as a whole still focus on segmenting merchants into categories based on historical transactions with cardholders, which falls within mitigating risk, advertising, marketing or sales activities or behaviors, and business relations as it pertains to commercial or legal interactions within the larger abstract grouping of Certain Methods of Organizing Human Activity (MPEP 2106.04(a)(2) II).
Step 2A Prong Two:
Applicant argues on page 15 of remarks 1/27/2026:
“The claims integrate the purported abstract idea into a practical application that improves the functioning of a computer or any other technology or technical field.
“Applicant's specification describes that there was, at the time of the claimed invention, a technological problem in connection with merchant classification that arose in the context of electronic payment systems.
“Some of the particular improvements enabled by the variously claimed embodiments are described in the specification at, for example, paragraph [0060]. For example, as mentioned in paragraph [0060], the technology is capable of producing outputs that are ‘more precise’ than conventional technology at the time of the claimed invention.
“In addition to the above, the claimed embodiments also provide a technical improvement in the claimed server system itself in that the hyperparameters that control how accurately the clustering will be performed are automatically generated by the server system itself using a rule-based algorithm rather than needing to be estimated by a human operator.”
Examiner respectfully disagrees. The problem addressed by the present invention appears to be incorrect, inconsistent, or otherwise inaccurate classification of merchants by transaction-acquiring banks, which is an entrepreneurial challenge. Technology is applied to improve the business process. Improving performance in output, i.e. more precise merchant classifications, and mere automation of a manual process (MPEP 2106.05(a)(I)(iii)), in this case the hyperparameter estimation, are not necessarily sufficient to demonstrate improvement in the computer technology itself.
Step 2B:
Applicant argues on page 19 of remarks 1/27/2026:
“…presently claimed embodiments provide a technology-based solution to the problem of categorizing merchants by defining a combination of operations in which merchant classification information ("labeling") is produced from a historical transaction dataset, the historical transaction dataset comprising transaction attributes corresponding to a plurality of payment transactions performed between a plurality of cardholders and a plurality of merchants.”
Examiner respectfully disagrees, with caveat. Applicant’s argument suggesting a unique combination of operations may approach eligibility. NLP, K-Nearest-Neighbor and DBSCAN are known, conventional concepts in machine learning. However, the combination of the three machine learning techniques to classify merchants on a large scale appears to be unconventional over the prior art. Examiner suggests amending the independent claims to:
define the data cleansing machine learning model as NLP as in claim 4
define the clustering machine learning model as DBSCAN as in claim 4
incorporate the functions of claim 6 (the DBSCAN method) as shown below:
generating, by the server system via the clustering machine learning model, a set of merchant clusters based, at least in part, on the set of key performance features for each merchant and the set of hyper-parameters;
>>>CLAIM 6 DBSCAN METHOD HERE<<<
labeling, by the server system, each merchant cluster of the set of merchant clusters as
one of a first merchant class and a second merchant class based, at least in part, on a classification threshold.
Examiner discussed this strategy with Attorney Ken Leffler on April 8, 2026 with tentative agreement. Subsequent attempts to reach Applicant have not been successful.
Accordingly, the previously pending rejections under 35 USC 101 will be maintained. The 101 rejection is updated in view of the amendments.
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Regarding Applicant’s remarks pertaining to 35 USC 103:
The previously pending rejections under 35 USC 103 are withdrawn in light of the amendments and Applicant’s arguments. Applicant’s arguments pages 19-21, filed 1/27/2026, with respect to the art rejection have been fully considered and are persuasive, the rejection under 35 USC 103 has been withdrawn. No art rejection has been put forth in the rejection for the reason found in the “Allowable Subject Matter” section found below.
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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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-10, 21 are directed to a method or process which is a statutory category.
Claims 11-18, 22 are directed to a system or machine which is a statutory category.
Claims 19-20 are directed to a non-transitory computer-readable storage medium or article of manufacture which is a statutory category.
Step 2A Prong One: The claims recite, describe, or set forth a judicial exception of an abstract idea (see MPEP 2106.04(a)). Specifically, the claims recite, describe or set forth commercial or legal interactions such as mitigating risk, advertising, marketing or sales activities or behaviors, or business relations, including: “accessing… a historical transaction dataset”, “the historical transaction dataset comprising transaction attributes corresponding to a plurality of payment transactions performed between a plurality of cardholders and a plurality of merchants”, “generating… a set of key performance features for each merchant”, “determining… a root word for each merchant”, “determine… merchant hierarchies… of sub-stores under a same merchant”, “generating… a set of key performance features for each merchant hierarchy”, “generating… a set of merchant clusters”, “labeling… each merchant cluster of the set of merchant clusters as one of a first merchant class and a second merchant class”. Segmenting merchants into categories based on historical transactions with cardholders falls within mitigating risk, advertising, marketing or sales activities or behaviors, and business relations as it pertains to commercial or legal interactions within the larger abstract grouping of Certain Methods of Organizing Human Activity (MPEP 2106.04(a)(2) II). Accordingly, the claims recite an abstract idea.
Step 2A Prong Two: Independent claims 1, 11, 19 recite the following additional elements: “server system”, “database”, “data cleansing machine learning model”, “clustering machine learning model”, “memory”, “communication interface”, “processor”, and “non-transitory computer-readable storage medium”. The functions of these additional elements include examples such as including accessing historical transaction data, storing transaction data, determining merchant hierarchies based on root words, generating key performance features for merchants, determining hyperparameters for the clustering model, and generating clusters of merchants. The additional elements are recited at a high level of generality (i.e. as a generic computer performing functions of mining and grouping data, calculating statistics, communicating and presenting data, etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components. Therefore, these functions can be viewed as not meaningfully different than a business method or mathematical algorithm being applied on a general-purpose computer as tested per MPEP 2106.05(f)(2)(i). Further, regarding the additional element “machine learning model”, this language merely requires execution of an algorithm that can be performed by a generic computer component and provides no detail regarding the operation of that algorithm. As such, the claim requirement amounts to mere instructions to implement the abstract idea on a computer, and, therefore, is not sufficient to make the claim patent eligible. See Alice, 573 U.S. at 226 (determining that the claim limitations “data processing system,” “communications controller,” and “data storage unit” were generic computer components that amounted to mere instructions to implement the abstract idea on a computer); October 2019 Guidance Update at 11–12 (recitation of generic computer limitations for implementing the abstract idea “would not be sufficient to demonstrate integration of a judicial exception into a practical application”). Such a generic recitation of “machine learning model” is insufficient to show a practical application of the recited abstract idea. The claims are directed to an abstract idea and the judicial exception does not integrate the abstract idea into a practical application.
Step 2B: According to MPEP 2106.05(f)(1), considering whether the claim recites only the idea of a solution or outcome i.e., the claims fail to recite the technological details of how the actual technological solution to the actual technological problem is accomplished. The recitation of claim limitations that attempt to cover an entrepreneurial and thus abstract solution to an entrepreneurial problem with no technological details on how the technological result is accomplished and no description of the mechanism for accomplishing the result do not provide significantly more than the judicial exception.
Dependent claims 2, 4, 12, 20-22 recite the additional elements “data cleansing machine learning model” and “Natural Language Processing (NLP) model”. The functions of this additional element include examples such as “determining a root word for each merchant” and “remove stop words and alphanumeric or special characters from merchant database names”. Dependent claim 4 recites the additional elements “Density-Based Spatial Clustering of Applications with Noise (DBSCAN) based machine learning model” and “Natural Language Processing (NLP) machine learning model”. The functions of these additional elements include examples such as “generating a set of merchant clusters”, “generating a noise cluster”, “generating a feature vector”, “determining if the feature is a core point”, and “determining a root word for each merchant”. Dependent claim 10 recite the additional elements “payment server”, “payment network”, and “acquirer server”. The functions of these additional elements include examples such as “accessing a historical data transaction set”, “generating a set of key performance features”, “determining a set of hyper-parameters”, “generating a set of merchant clusters” and “labeling each merchant cluster based on a classification threshold.” The additional elements are also recited at a high level of generality (i.e. as a generic computer performing functions of calculating statistics; organizing, communicating and presenting data; etc.) such that they amount to no more than mere instructions to apply the exception using generic computer components.
Further, dependent claims 3, 5-10, 13-18 merely incorporate the additional elements recited in claims 1, 11, 19 along with further narrowing of the abstract idea of claims 1, 11, 19 along with their execution of the abstract idea. Specifically, the dependent claims narrow the “server system”, “clustering machine learning model”, “memory”, “communication interface”, “processor”, and “non-transitory computer-readable storage medium” to capabilities such as normalizing, scaling, generating, determining, initializing, selecting, adding, performing, and comprising various forms of data such as key performance features, clusters, vectors, spatial representations, merchants, epsilon values, MinPts values, operations, neighborhoods, cardholders, revenue, transactions, sales, etc. which, when evaluated per MPEP 2106.05(f)(2) represent mere invocation of computers to perform existing processes. Therefore, the additional elements recited in the claimed invention individually and in combination fail to integrate a judicial exception into a practical application (Step 2A prong two) and for the same reasons they also fail to provide significantly more (Step 2B). Thus, claims 1-22 are reasoned to be patent ineligible.
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Allowable Subject Matter
Claims 1-8, 10-13, 15-22 are allowable over the prior art in light of the amendments. However, these claims remain rejected under 35 USC 101.
Closest prior art to the invention includes Brosamer et al. US 10949825 B1, Adaptive merchant classification; Kellogg et al. US 20040054625 A1, Method and systems for providing merchant services with right time creation and updating of merchant accounts; Ester et al. 1996, A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise; Sahid et al. E-Commerce Merchant Classification using Website Information; and Jezewski US 10360631 B1, Utilizing artificial intelligence to make a prediction about an entity based on user sentiment and transaction history. None of the prior art of record, taken individually or in combination, teach or suggest the claimed invention as detailed in the independent claims. Specifically:
accessing [..] a historical transaction dataset from a database associated with the server system, the historical transaction dataset comprising transaction attributes corresponding to a plurality of payment transactions performed between a plurality of cardholders and a plurality of merchants;
generating [..] a set of key performance features for each merchant of the plurality of merchants by:
determining, [..] via a data cleansing machine learning model, a root word for each merchant of the plurality of merchants based, at least in part, on the historical transaction dataset and a merchant database name for each of the merchants;
determining [..] a plurality of merchant hierarchies present in the plurality of merchants based, at least in part, on the root word determined for each of the merchants, wherein a merchant hierarchy comprises a hierarchy of sub-stores under a same merchant; and
generating [..] the set of key performance features for each merchant hierarchy of the plurality of merchant hierarchies based, at least in part, on the historical transaction dataset; determining, [..] via a clustering machine learning model, a set of hyper-parameters for the clustering machine learning model, the set of hyper-parameters comprising an epsilon value and a Minimum points (MinPts) value, wherein determining the set of hyper-parameters comprises:
generating an estimated K-nearest neighbor (KNN) plot based, at least in part, on a KNN plot;
determining a slope of the estimated KNN plot; and
determining the epsilon value and the MinPts value based, at least in part, on a point of the estimated KNN plot where the slope approximates a unity value;
generating, [..] via the clustering machine learning model, a set of merchant clusters based, at least in part, on the set of key performance features for each merchant and the set of hyper-parameters; and
labeling [..] each merchant cluster of the set of merchant clusters as one of a first merchant class and a second merchant class based, at least in part, on a classification threshold.
The reason to withdraw the 35 USC 103 rejection of claims 1, 11, 19 in the instant application is because the prior art of record fails to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Upon further searching the examiner could not identify any prior art to teach these limitations. The prior art on record, alone or in combination, neither anticipates, reasonably teaches, not renders obvious the Applicant’s claimed invention.
However, these claims remain rejected under 35 USC 101.
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Conclusion
The following art is made of record and considered pertinent to Applicant’s disclosure:
Brosamer et al. US 10949825 B1, Adaptive merchant classification.
Kellogg et al. US 20040054625 A1, Method and systems for providing merchant services with right time creation and updating of merchant accounts.
Ester et al. 1996, A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
Sahid et al. 2019, E-Commerce Merchant Classification using Website Information.
Jezewski US 10360631 B1, Utilizing artificial intelligence to make a prediction about an entity based on user sentiment and transaction history.
Lin et al. CN 116562952 A, False transaction order detection method and device.
Bishnoi et al. IN 202041043790 A, Server systems and methods for identifying ambiguous merchant data based on artificial intelligence models.
T. K. Behera and S. Panigrahi, "Credit Card Fraud Detection: A Hybrid Approach Using Fuzzy Clustering & Neural Network," 2015 Second International Conference on Advances in Computing and Communication Engineering, Dehradun, India, 2015, pp. 494-499, doi: 10.1109/ICACCE.2015.33.
D. Deng, "DBSCAN Clustering Algorithm Based on Density," 2020 7th International Forum on Electrical Engineering and Automation (IFEEA), Hefei, China, 2020, pp. 949-953, doi: 10.1109/IFEEA51475.2020.00199.
K. Khan, S. U. Rehman, K. Aziz, S. Fong and S. Sarasvady, "DBSCAN: Past, present and future," The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014), Bangalore, India, 2014, pp. 232-238, doi: 10.1109/ICADIWT.2014.6814687.
Giri, K., Biswas, T.K. (2022). Determining Optimal Epsilon (eps) on DBSCAN Using Empty Circles. In: Sanyal, G., Travieso-González, C.M., Awasthi, S., Pinto, C.M.A., Purushothama, B.R. (eds) International Conference on Artificial Intelligence and Sustainable Engineering. Lecture Notes in Electrical Engineering, vol 836. Springer, Singapore. https://doi.org/10.1007/978-981-16-8542-2_21
Hosseinali; Massoud et al. US 20240211965 A1, Systems and methods for merchant level fraud detection based in part on merchant cohort clustering.
Fotso; Stephane et al. US 20220327583 A1, Artificial intelligence based service recommendation.
Martins; Victoria US 20240013220 A1, Embedding analysis for entity classification detection.
Arora; Ankur et al. US 20220374927 A1, Methods and systems for predicting panic states of merchants.
Bhatt; Deepak et al. US 20220301049 A1, Artificial intelligence based methods and systems for predicting merchant level health intelligence.
Jacoby; Brandon et al. US 11488195 B1, Reward offer redemption for payment cards.
Zeng et al. US 20050234879 A1, Term suggestion for multi-sense query.
Wright et al. US 20110047044 A1, Method and apparatus for evaluating fraud risk in an electronic commerce transaction.
Matlick et al. US 20210073661 A1, Machine learning techniques for internet protocol address to domain name resolution systems.
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/REED M. BOND/Examiner, Art Unit 3624 April 30, 2026
/HAMZEH OBAID/Primary Examiner, Art Unit 3624
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