DETAILED ACTION
This communication is a Non-Final Office Action on the merits in response to communications received on 08/19/2026. Claims 1, 6, 8, 11-13, 15 and 18-20 have been amended. Therefore, claims 1-2, 5-16, and 18-20 are pending and have been addressed below. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/19/2026 has been entered.
Claim Rejections - 35 USC § 101
2. 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.
3. Claims 1-2, 5-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the two-part analysis from Alice Corp, claim 1 recites a process (i.e., an act or step, or a series of acts or steps), claim 19 recites a machine (i.e., a concrete thing, consisting of parts, or of certain devices and combination of devices), and claim 20 recites a manufacture (i.e., an article that is given a new form, quality, property, or combination through man-made or artificial means). Thus, each of the claims fall within one of the four statutory categories.
4. Under Step 2A – [Prong One] of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
Claims 1, 19, and 20 recite:
“retrieving a training dataset comprising data relating to profiles of a plurality of clients and data relating to historical financial transactions associated with the plurality of clients;”, “extracting, from the training dataset, features representative of characteristics of the plurality of clients and the historical financial transactions;”, “…assign the plurality of clients to a plurality of clusters according to the extracted features, each client being associated with a respective cluster label based on its assigned cluster;”, “…, using the historical financial transactions and the cluster labels, to generate an anomaly score for a transaction identified as fraudulent…, the anomaly score indicating whether or not the transaction is anomalous;”, “…fraud claims transaction data comprising transaction data associated with a plurality of historical fraud claims, thereby providing a fraud claim dataset in which each historical fraud claim has an associated cluster label and an associated anomaly score;”, “…, using the fraud claim dataset, to generate a fraud score indicative of whether or not a given fraud claim provided…is legitimate;”, “receiving the fraud claim from the client, wherein the fraud claim is in respect of a potentially fraudulent transaction associated with the client;”, “retrieving client data associated with the client, wherein the client data comprises (i) data relating to historical financial transactions associated with the client and (ii) data relating to one or more characteristics of the client;”, “inputting the client data and one or more parameters of the potentially fraudulent transaction…;”, “generating…a fraud score associated with the received fraud claim;”, “determining, based on the fraud score, that the fraud claim is legitimate;” and “in response to determining that the fraud claim is legitimate, initiating an instruction so as to reverse the potentially fraudulent transaction.”
The limitations as drafted are processes under their broadest reasonable recite an abstract idea of determining whether a fraud claim is legitimate and reversing a potentially fraudulent transaction which encompasses fundamental economic principles or practices (i.e., mitigating risk), commercial or legal interactions (including, legal obligations, marketing or sales activities or behaviors; business relations), managing personal behavior or interactions between people (i.e., following rules or instructions), mental processes, (i.e., observations, evaluations, judgments, and opinions) subject matter that falls within the certain methods of organizing human activity and mental processes groupings enumerated in MPEP 2106.05(a)(2)
Applicant’s Specification emphasizes in at least [¶ 0002-0003] Credit card fraud is a major problem in the financial services industry. Despite the prevalence of credit card fraud, there exist many instances of credit card fraud claims that are, themselves, fraudulent or otherwise illegitimate. For example, it is possible that the victim of alleged credit card fraud may misidentify a transaction as being fraudulent, and may report the transaction as such. More typically, bad-faith clients may seek to defraud a credit card company by deliberately claiming that a genuine transaction, initiated by the client, is fraudulent. It can be difficult or at least time-consuming for credit card companies to efficiently and accurately determine whether credit card fraud claims, or fraud claims more generally, are legitimate (known as 1St-party fraud) or illegitimate (known as 3rd party fraud). There is therefore a need in the art for improved methods of determining whether a fraud claim is legitimate.
The limitations of “retrieving”, “extracting”, “assign”, “generating”, “receiving”, “inputting”, “determining”, initiating” in the context of the claim describe fraud claim scoring techniques and decision-making procedures that companies (such as banks/card issuers) perform using client and transactional data to resolve transaction disputes from their clients and to protect against false or fraudulent chargebacks. In this way, the series of steps cover concepts relating to fundamental economic practices, commercial interactions, business relations, and managing personal behavior or interactions between people. Also, the limitation of “determining” in the context of the claim covers mental processes for analyzing client/transactional data for fraud and making a decision related to resolving the fraud claim, which are steps that can be practically performed in the human mind or by a person with or without pen and paper. Accordingly, the claim recites an abstract idea.
5. Under Step 2A – Prong Two of the two-part analysis from Alice Corp, this judicial exception is not integrated into a practical application because the additional elements of: “by one or more processors”, “using a clustering model”, “training an anomaly detection model”, “applying the clustering model and the trained anomaly detection model to” “training a classification model”, “using the trained classification model” – see claim 1, “a system”, “one or more processors”, ”one or more databases”– see claim 19, “a non-transitory computer-readable medium having stored thereon computer program code”, “one or more processors to cause the one or more processors”– see claim 20 are recited at a high-level of generality in light of the Specification. See [Fig. 2, ¶ 0037, 0040, 0045, 0055, 0071, 0080-0081, 0093-0094]. The original Specification does not provide support for technical improvement details related to the one or more processors, clustering, anomaly detection, and classification models. The training and applying steps recite result-oriented functional clam language and the operations being performed by each of the steps amounts to generic data processing to aid with performance of the abstract idea. The specification describes the additional elements in general terms, without describing the particulars, the claim limitations may be broadly but reasonably construed as reciting generic computer components and functionalities in light of the disclosure. The claimed additional elements merely recite the words "apply it" (or an equivalent) with the judicial exception, or merely include instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f)
The other additional element of: “a method of determining whether a fraud claim initiated by a client is legitimate” is merely indicating a field of use or technological environment in which to apply a judicial exception. See MPEP 2106.05(h)
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
6. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: “by one or more processors”, “using a clustering model”, “training an anomaly detection model”, “applying the clustering model and the trained anomaly detection model to” “training a classification model”, “using the trained classification model” – see claim 1, “a system”, “one or more processors”, ”one or more databases”– see claim 19, “a non-transitory computer-readable medium having stored thereon computer program code”, “one or more processors to cause the one or more processors”– see claim 20 amount to no more than mere instructions in which to apply the judicial exception and do not provide an inventive concept at Step 2B.
7. Claims 2, 5-16, 18 are the dependent claims.
Claim 2 recites “wherein the one or more parameters comprise one or more of: data indicating a type of merchant associated with the potentially fraudulent transaction; an amount associated with the potentially fraudulent transaction; a time of day associated with the potentially fraudulent transaction; and a day of a week associated with the potentially fraudulent transaction.” which further describes the type of data/information that may be recited within the abstract idea, but does not make the claimed invention any less abstract. Claim 5 recites “wherein the one or more characteristics comprise one or more of: an age of the client; an earning potential or a salary of the client; a gender of the client; an address of the client; and a credit score of the client.” which further describes the type of data/information that may be recited within the abstract idea, but does not make the claimed invention any less abstract. Claim 6 recites wherein generating the fraud score comprises: extracting, based on the data relating to the historical financial transactions associated with the client, one or more client transaction features; comparing the one or more client transaction features to stored client transaction features; based on the comparison, determining generating the fraud score” which further narrows how the abstract idea may be performed as it describes processes of parsing and comparing data at a high level of generality and merely uses generic computer components or machinery as a tool to perform the processes. Claim 7 recites “wherein the one or more client transaction features and the stored client transaction features are representative of one or more of: types of merchants; for each type of merchant from among multiple types of merchants, amounts associated with the type of merchant; one or more spending patterns; times of day; and days of a week.” which further describes the type of data/information that may be within the abstract idea, but does not make the claimed invention any less abstract. Claims 8 and 12 recite “further comprising, prior to receiving the fraud claim from the client, obtaining the stored client transaction features by: retrieving other client data associated with the multiple other clients, wherein the other client data comprises data relating to historical financial transactions associated with the other clients; extracting, based on the data relating to the historical financial transactions associated with the other clients, other client transaction features; and storing the other client transaction features” which further narrows how the abstract idea may be performed as it describes processes of parsing and comparing data at a high level of generality and merely uses generic computer components or machinery as a tool to perform the processes. Claims 9 and 13 recite “wherein retrieving the other client data comprises: retrieving a dataset of client data; extracting features from the dataset of client data; based on one or more similarities between the extracted features, assigning each feature to one of multiple groups; and retrieving the other client data from one of the groups.” which further narrows how the abstract idea may be performed as it describes processes of parsing and comparing data at a high level of generality and merely uses generic computer components or machinery as a tool to perform the processes. Claims 10 and 14 recite wherein extracting the other client transaction features comprises: inputting the other client data to a trained machine learning model; and outputting the other client transaction features using the trained machine learning model to which the other client data was input” which further describes the how the other client transaction features are analyzed, but does not make the claimed invention any less abstract. The additional element recited in the claim “a trained machine learning model” is no more than generic computer components and known techniques and/or algorithms used as tools to perform the recited abstract idea. See MPEP 2016.05(f). Claims 11 and 15 recite “wherein generating the fraud score further comprises: extracting, based on the data relating to the one or more characteristics of the client, one or more client characteristic features; comparing the one or more client characteristic features to stored other client characteristic features; and based on the comparison, generating the fraud score” which further narrows how the abstract idea may be performed as it describes processes of parsing and comparing data at a high level of generality and merely uses generic computer components or machinery as a tool to perform the processes. Claim 16 recites “wherein determining that the fraud claim is legitimate comprises: comparing the fraud score to a threshold; and based on the comparison, determining that the fraud claim is legitimate” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. Claim 18 recites “further comprising, prior to determining whether that the fraud claim is legitimate: determining a trust score associated with the client; and adjusting the fraud score based on the trust score, wherein determining that the fraud claim is legitimate is further based on the adjusted fraud score” which further narrow how the abstract idea may be performed, but does not make the claim any less abstract. Therefore, with respect to the dependent claims when viewed separately and in combination with the judicial exception, the recited limitations as whole fail to integrate the judicial exception into a practical application or provide an inventive concept.
Response to Arguments
Applicant's arguments filed 08/19/2026 have been fully considered but they are not persuasive.
With Respect to Rejections Under 35 USC 101
Applicant argues “The Examiner characterizes the claims as reciting "certain methods of organizing human activity," including fundamental economic practices and commercial interactions, and "mental processes." Applicant respectfully submits that this characterization does not account for the limitations now expressly recited in amended claim 1. While claim 1 concerns determining whether a fraud claim is legitimate, it now expressly recites, inter alia: "retrieving a training dataset comprising data relating to profiles of a plurality of clients and data relating to historical financial transactions associated with the plurality of clients;", “extracting, from the training dataset, features representative of characteristics of the plurality of clients and the historical financial transactions;", "using a clustering model to assign the plurality of clients to a plurality of clusters according to the extracted features, each client being associated with a respective cluster label based on its assigned cluster;", "training an anomaly detection model, using the historical financial transactions and the cluster labels ...;", "applying the clustering model and the trained anomaly detection model to fraud claims transaction data ... thereby providing a fraud claim dataset in which each historical fraud claim has an associated cluster label and an associated anomaly score;" and " "training a classification model, using the fraud claim dataset ...."
“These limitations recite particular operations for preparing training data, training machine- learning models, generating model-derived labels and scores for historical fraud claims, and using the resulting dataset to train another model. Regarding the asserted "mental process," the August 4, 2025 Memorandum ("Kim Memo") explains that the mental-process grouping applies to limitations that "can practically be performed in the human mind," and conversely does not encompass limitations that cannot practically be performed in the human mind. The Office has cautioned examiners not to expand that grouping beyond those limits.”
“The above-recited model-training operations, considered as actually claimed, cannot practically be performed in the human mind or with pencil and paper.
In particular, the claim is not merely directed to a person observing historical transactions and deciding whether a claim appears fraudulent. It requires training an anomaly detection model using historical financial transactions and cluster labels, applying trained models to historical fraud-claim transaction data so that each historical fraud claim has an associated cluster label and anomaly score, and then training a classification model using that resulting fraud claim dataset. Accordingly, Applicant respectfully submits that these limitations do not recite a mental process.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The response restates the limitations of the claim as amended and purports the model-training operations in the claim do not recite mental processes or cannot be performed in the human mind. Claims are not saved from abstraction because they recite components more specific than a generic computer. See BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1286 (Fed. Cir. 2018) It is important for Applicant to note, under Step 2A Prong One merely reciting computer components and machine learning technology (such as by one or more processors model training operations) in the claim does not preclude the limitations from falling within the certain methods of organizing human activity or mental processes groupings.
After further consideration, the original Specification [¶ 0001-0003, 0035-0038] does not indicate the focus of the invention was on machine learning or model-based training. The claim recites a method for determining whether a fraud claim is legitimate and an instruction for reversing a potentially fraudulent transaction. The operations being performed analyze client and transactional data to generate an anomaly score and a fraud score which helps determine if a disputed transaction is legitimate. In the instant case the inability for the human mind to perform each claim step does not alone confer patentability because claims can recite a mental process even if they are claimed as being performed on a computer. See MPEP 2106.04(a)(2)(III)(c) For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Even assuming, arguendo, that certain limitations relating to fraud-claim adjudication recite an abstract idea, amended claim 1 integrates any such concept into a practical application. The Specification identifies a problem that can be "difficult or at least time-consuming" to "efficiently and accurately determine whether credit card fraud claims, or fraud claims more generally, are legitimate," and accordingly identifies "a need in the art for improved methods of determining whether a fraud claim is legitimate." See the Specification, [0003].
“The Specification addresses this problem through a particular machine-learning training architecture in which information representing client characteristics and historical transactions is progressively processed by different models and incorporated into the training data for a downstream classification model. The Specification explains the technical operation underlying these limitations. In particular, it states that "the same historical data that was used in the training of clustering component 318a, together with the cluster labels determined at block 608, is used to train an unsupervised anomaly detection model." See the Specification, [0071]. The Specification further explains that the trained clustering and anomaly detection models "are applied to fraud claims transaction data" and that "[t]his will provide a dataset with each fraud claim therein having an associated cluster label and anomaly score. This dataset is then used to train classification model 318c." See the Specification, [0080].”
“Thus, the claimed subject matter addresses the identified problem through a particular way of preparing and enriching the training data used by the classification model. Rather than training the classification model only on the underlying fraud-claim transaction data, the claimed method provides a "fraud claim dataset in which each historical fraud claim has an associated cluster label and an associated anomaly score" and then trains the classification model "using the fraud claim dataset." The cluster label represents the result of processing client-profile and historical- transaction features through the clustering model, while the anomaly score represents whether a transaction is anomalous as determined by the trained anomaly detection model. The Specification similarly states that the fraud claim dataset "includes the cluster labels and anomaly scores generated through application of clustering model 318a and anomaly detection model 318b." See the Specification, [0081].”
“Accordingly, the claimed solution is not merely the result of determining whether a fraud claim is legitimate. It recites a particular model-training architecture by which model-generated information concerning client clustering and transaction anomalousness is incorporated into the dataset used to train the classification model. This provides a particular technical implementation for addressing the Specification's identified difficulty in efficiently and accurately determining fraud-claim legitimacy.
The foregoing is consistent with MPEP § 2106.04(d)(1), which addresses improvements in the functioning of a computer or another technology or technical field under Step 2A, Prong Two. The USPTO's current guidance expressly identifies improvements involving "learning models" as potentially relevant technological improvements and instructs Examiners to evaluate the claimed invention as a whole, including the technological advance described in the Specification. The Kim Memo likewise cautions against oversimplifying claim limitations and directs attention to whether a claim recites merely an outcome or instead "a particular solution to a problem or a particular way to achieve a desired outcome."
“The Examiner maintains that the technical improvements previously identified by Applicant are not discussed or otherwise sufficiently supported in the Specification. However, MPEP § 2106.04(d)(1) explains that "the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement," and further states that "[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art."
“Here, as discussed above, the Specification expressly describes the claimed training architecture, including use of the cluster labels in training the anomaly detection model and incorporation of the resulting cluster labels and anomaly scores into the fraud claim dataset used to train the classification model. See the Specification, [0071], [0080]-[0081]. The Specification therefore provides the technical disclosure from which the claimed improvement would be apparent to one of ordinary skill in the art.
Moreover, claim 1 does not end at model training. Once the classification model has been trained using the enriched fraud claim dataset, the claim recites deploying that trained model to a real fraud claim: receiving a fraud claim from a client, inputting the client data and parameters of the potentially fraudulent transaction to the trained classification model, generating a fraud score, determining that the fraud claim is legitimate based on that fraud score, and - in response to that determination initiating an instruction so as to reverse the potentially fraudulent transaction. The claim therefore recites a complete pipeline from the identified problem (efficiently and accurately determining fraud-claim legitimacy) through to a concrete remedial action, rather than merely training models in the abstract.”
“Accordingly, Applicant respectfully submits that amended claim 1 recites a particular technical implementation for addressing the identified problem, rather than merely the idea or result of determining whether a fraud claim is legitimate. Considered as a whole, claim 1 therefore integrates any alleged judicial exception into a practical application under Step 2A, Prong Two.” The Examiner respectfully disagrees.
Applicant’s arguments are not persuasive. The claimed invention remains ineligible under Step 2A Prong Two of the analysis. The problem facing the Applicant as discussed in the original Specification was not on a particular machine-learning training architecture. Rather, the Applicant sought to provide an "efficient and accurate way to determine whether credit card fraud claims or fraud claims were legitimate”. See Specification [¶ 0003] At best, the claims describe the automation of the fundamental economic concept of detecting fraud in transaction dispute claims through the use of generic computer functions and machine learning technology. The Specification [Fig. 2, ¶ 0037, 0040, 0045, 0055, 0093-0094] does not describe use of a new processor or machine learning models. As best understood, these generic computing components and machine learning models behave in their ordinary or expected manner and are merely being used as a tool to aid in implementing the abstract idea. The Applicant relies upon the Specification [¶ 0071, 0080-0081] for support which also fails to provide any technical details to one of ordinary skill in the art for the tangible components but instead describes the methods or operations being performed in purely functional terms. Although the remarks attempt to limit the abstract idea to a particular environment (such as model-training architecture) the specificity of presently recited techniques does not automatically provide a technological solution to a technological problem. The remarks clearly indicate the improvements discussed by Applicant are within the abstract idea being claimed and not the generic computing equipment/machine learning technology being used to implement the abstract idea. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “For the sake of argument, even if the analysis proceeds to Step 2B, amended claim 1 should be considered as an ordered combination rather than as isolated references to generic machine- learning models. The ordered combination recites a particular inter-model training relationship and a particular construction of the training data used by the classification model. The Examiner has not established that this specific ordered combination, considered as a whole, amounts merely to well- understood, routine, and conventional activity. Accordingly, even under Step 2B, amended claim 1 recites significantly more than any alleged abstract idea.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The claimed invention remains ineligible under Step 2B of the analysis. The Applicant’s Specification [Fig. 2, ¶ 0037, 0040, 0045, 0055, 0093-0094] further demonstrates that the technological components recited in claimed invention were off-the-shelf computer components. Here, the response points to a particular inter-model training relationship and a particular construction of the training data used by the classification model but fails to identify what about the ordering of the steps in the claimed invention provides an inventive concept. It is well-settled that mere recitation of concrete tangible components in a claim is insufficient to confer patent eligibility and features recited within the abstract idea (such as training data) cannot be relied upon alone to provide the inventive concept. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “For at least these reasons, Applicant respectfully submits that amended claim 1, as well as the claims depending therefrom and amended claims 19 and 20, recite patent-eligible subject matter and respectfully requests withdrawal of the rejection under 35 U.S.C. § 101.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. In the instant case, claims 19 and 20 recite subject matter substantially similar to claim 1 and are therefore being rejected under the same grounds/rationales. As for the dependent claims, the remarks fail to comply with 37 CFR 1.111(b). The previous rejection presented findings for each of the dependent claims and explained why the additional limitations recited by these claims do not impart subject matter eligibility. For these reasons, the rejections under 101 are being maintained.
With Respect to Rejections Under 35 USC 103
Applicant’s arguments, see pgs. 3-7, filed 08/19/2026, with respect to claims 1-2, 5-16, and 18-20 have been fully considered and are persuasive. The rejection under 35 USC 103 over Douglas (US 2021/0326884 A1) in view of Duan (US 2015/0095247 A1) in further view of Venturelli (US 11,288,673 B1) of 05/26/2026 has been withdrawn.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EHRIN PRATT whose telephone number is (571)270-3184. The examiner can normally be reached 8-5 EST Monday-Friday.
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/EHRIN L PRATT/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
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