DETAILED ACTION
This communication is a Non-Final Office Action on the merits in response to communications received on 10/21/2025. Claims 1, 19, and 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/21/2025 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.
Claim 1 which represents claims 19 and 20 recites:
“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;”, “assigning…the client to a cluster, based on the data relating to the one or more characteristics of the client, and wherein the cluster is selected from among multiple clusters generated based on data associated with characteristics of multiple other clients;”, “generating…an anomaly score for the potentially fraudulent transaction based on the data relating to the historical financial transactions associated with the client, based on one or more parameters of the potentially fraudulent transaction, and based on the assigned cluster;”, “generating…a fraud score associated with the fraud claim based on the anomaly score, based on the data relating to the historical financial transactions associated with the client, based on the assigned cluster, and based on the one or more parameters of the potentially fraudulent transaction;”, “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 a series of steps for 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), 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 ¶ 0003 Real estate brokerages compete to provide faster, more accurate estimates for their clients to select properties for leasing/purchasing. Many factors affect the cost to a particular client for a particular property. A tool is needed to help brokers provide fast, accurate estimates and visualizations for clients to compare properties under consideration.
The limitations of “receiving”, “retrieving”, “assigning”, “generating”, “determining”, “initiating” in the context of the claim cover evaluation steps that banks typically perform when investigating potential fraud during the processing of chargeback requests from their customers. Thus, the series of steps recite concepts related to fundamental economic practices and commercial interactions - marketing or sales activities or behaviors; business relations. Also, the limitation of “determining” in the context of the claim cover mental processes for collecting and analyzing information client/transaction data which are steps that can be practically performed in the human mind 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: “being performed by one or more processors”, “using a clustering model”, “using a trained anomaly detection model”, “using a 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. Thus, 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. These 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, the method comprising:” 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: “being performed by one or more processors”, “using a clustering model”, “using a trained anomaly detection model”, “using a 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, 4-16, 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 used in conjunction with the abstract idea, but does not make the claimed invention any less abstract. Claim 4 recites “wherein: the client data further comprises data relating to one or more characteristics of the client; and determining the fraud score is further based on the data relating to the one or more characteristics of the client.” 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. 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 within the abstract idea, but does not make the claimed invention any less abstract. Claim 6 recites “wherein determining 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; and based on the comparison, determining 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 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 determining 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, determining 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 narrow how the abstract idea may be performed, but does not make the claim any less abstract. Claim 18 recites “further comprising, prior to determining 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.
Claim Rejections - 35 USC § 103
8. 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, 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.
9. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
10. Claim(s) 1-2, 5-7, 11, 15-16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Douglas (US 2021/0326884 A1) in view of Duan (US 2015/0095247 A1) in further view of Venturelli (US 11,288,673 B1).
With respect to claims 1, 19, and 20, Douglas discloses
a method (¶ 0073), system (abstract, ¶ 0006: discloses a system), and non-transitory computer-readable medium (¶ 0008: discloses a non-transitory computer readable medium) of determining whether a fraud claim initiated by a client is legitimate (¶ 0073: discloses a method for providing a dispute workflow.),
the method being performed by one or more processors (¶ 0033: discloses server 111 may include one or more processors 220) and comprising:
receiving the fraud claim from the client (¶ 0073: discloses server 111 may handle a dispute brought by user 120. For example, server 111 may receive an electronic message from user 120 who wishes to dispute a transaction made with the merchant 140.), wherein the fraud claim is in respect of a potentially fraudulent transaction associated with the client (¶ 0073: discloses user 120 may claim that the transaction is unauthorized, fraudulent, or that merchant 140 overcharged the user.);
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 (¶ 0035-0036, 0049-0050: discloses the server may receive data relating to an activity of the user 120. The data relating to the activity of the user may include data indicating that the user has made a purchase. The data may also include data indicating suspicious transactions. Data stored may include historical fraud or disputes data, transaction data, credit rating of user 120.);
determining, based on the fraud score, that the fraud claim is legitimate (¶ 0050, 0076: discloses if the transaction score is higher than a predetermined threshold, the transaction may be legitimate.)
The Douglas reference does not explicitly the following limitations.
However, the Duan reference is related to fraud detection systems (¶ 0001) and teaches:
assigning, using a clustering model, the client to a cluster, based on the data relating to the one or more characteristics of the client (¶ 0018, 0024, 0046: discloses the event profile may include purchase information for the event. A purchase information may include a user’s name, payment information, credit card information, etc. The fraud detection system may access information associated with a set of event profiles. A clustering function may assign each sample from the set to a cluster.), and wherein the cluster is selected from among multiple clusters generated based on data associated with characteristics of multiple other clients (¶ 0018, 0024, 0046: discloses the first cluster may correspond to event profiles that have been previously classified as fraudulent. The second cluster may correspond to event profiles that have been previously classified as legitimate. Fraud detection system processes and arranges event profiles into a first or second cluster corresponding to their fraud classification.);
determining, based on the fraud score, that the fraud claim is legitimate (¶ 0038: discloses fraud-detection system 160 may approve or deny pay-out requests based on the fraud score of an event profile); and
in response to determining that the fraud claim is legitimate, initiating an instruction so as to reverse the potentially fraudulent transaction. (¶ 0038: discloses the fraud-detection system may receive a request to pay out funds. If the fraud score for the event profile is less than a threshold fraud score, the fraud detection system may approve the request to pay out the funds and facilitate the transfer of the requested funds.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have modified the system and methods of Douglas, to include assigning, using a clustering model, the client to a cluster, based on the data relating to the one or more characteristics of the client, and wherein the cluster is selected from among multiple clusters generated based on data associated with characteristics of multiple other clients; determining, based on the fraud score, that the fraud claim is legitimate; in response to determining that the fraud claim is legitimate, initiating an instruction so as to reverse the potentially fraudulent transaction, as disclosed by Duan to achieve the claimed invention. As disclosed by Duan, the motivation for the combination would have been to prevent users from violating the terms of services of the system and making requests for fraudulent financial transactions. (¶ 0024)
The combination of Douglas and Duan do not explicitly disclose the following limitations. In the same field of endeavor, the Venturelli reference is related to utilizing multiple machine learning models to detect fraud (col. 2:43-60) and teaches:
generating, using a trained anomaly detection model, an anomaly score for the potentially fraudulent transaction based on the data relating to the historical financial transactions associated with the client, based on one or more parameters of the potentially fraudulent transaction, and based on the assigned cluster (col. 2:43-60, col. 5:30-35, cols. 5-6:65-5, col. 8:13-21: discloses an unsupervised ML anomaly detector exists for each cluster. The unsupervised ML anomaly detector generates an anomaly score. The anomaly score represents how similar or different is with historic requests having similar fraud scores.);
generating, using a trained classification model, a fraud score associated with the fraud claim based on the anomaly score (col. 8:27-29: discloses processing based on comparisons between the fraud score and anomaly score with various thresholds.), based on the data relating to the historical financial transactions associated with the client, based on the assigned cluster, and based on the one or more parameters of the potentially fraudulent transaction (col. 2:43-60, col. 8:27-29);
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have modified the combination of Douglas and Duan, to include generating, using a trained anomaly detection model, an anomaly score for the potentially fraudulent transaction based on the data relating to the historical financial transactions associated with the client, based on one or more parameters of the potentially fraudulent transaction, and based on the assigned cluster; generating, using a trained classification model, a fraud score associated with the fraud claim based on the anomaly score, based on the data relating to the historical financial transactions associated with the client, based on the assigned cluster, and based on the one or more parameters of the potentially fraudulent transaction, as disclosed by Venturelli to achieve the claimed invention. As disclosed by Venturelli, the motivation for the combination would have been to provide benefits for identifying possible false positives and/or false negatives before deciding that fraud is or is not likely as expressly suggested by Venturelli (col. 1:6-16, col. 2:55-60)
With respect to claim 2, the combination of Douglas, Duan, and Venturelli discloses the method of claim 1,
wherein the one or more parameters comprise one or more of:
data indicating a type of merchant associated with the potentially fraudulent transaction (¶ 0036, 0073: Douglas discloses analysis of the historical fraud or disputes data associated with merchant 140.); an amount associated with the potentially fraudulent transaction (¶ 0050 – see Douglas); a time of day associated with the potentially fraudulent transaction; and a day of a week associated with the potentially fraudulent transaction. (¶ 0050 – see Douglas)
With respect to claim 5, the combination of Douglas, Duan, and Venturelli discloses the method of claim 1,
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 (¶ 0036, 0075: Douglas discloses credit rating of user 120.).
With respect to claim 6, the combination of Douglas, Duan, and Venturelli discloses the method of claim 1, wherein determining 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 ¶ 0049-0050: Douglas discloses server may receive data relating to the activity of user 120. The data may include the type of transaction, the frequency of the transaction, the deviation of the amount of transaction, the location of the transaction.);
comparing the one or more client transaction features to stored client transaction features ¶ 0050: Douglas discloses server 111 may determine the type of transaction is out of the scope of a user’s normal purchases.); and based on the comparison, determining the fraud score. (¶ 0050: Douglas discloses server 111 may assign a transactional score based on any one or a combination of factors such as the type of transaction, the location of the transactions, etc.)
With respect to claim 7, the combination of Douglas, Duan, and Venturelli discloses the method of claim 6,
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. (¶ 0050: Douglas discloses server 111 may detect that three transactions for TV’s were made within one week involving different types of merchants.)
With respect to claims 11 and 15, the combination of Douglas, Duan, Venturelli discloses the method of claim 1,
wherein determining 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 (¶ 0049-0050: Douglas discloses server may receive data relating to the activity of user 120. The data may include the type of transaction, the frequency of the transaction, the deviation of the amount of transaction, the location of the transaction.); comparing the one or more client characteristic features to stored other client characteristic features (¶ 0050, 0057: Douglas discloses server may detect a charge has been made at a merchant located over 250 miles from where the user typically makes purchases.); and based on the comparison, determining the fraud score. (¶ 0050, 0077: Douglas discloses the transaction score may be based on data indicating suspicious transactions relating to activities of the user.)
With respect to claim 16, the combination of Douglas, Duan, Venturelli discloses the method of claim 1,
wherein determining that the fraud claim is legitimate (¶ 0038: Duan discloses fraud-detection system 160 may approve or deny pay-out requests based on the fraud score of an event profile) comprises:
comparing the fraud score to a threshold; and based on the comparison, determining that the fraud claim is legitimate. (¶ 0038: Duan discloses the fraud-detection system may receive a request to pay out funds. If the fraud score for the event profile is less than a threshold fraud score, the fraud detection system may approve the request to pay out the funds and facilitate the transfer of the requested funds.)
With respect to claim 18, the combination of Douglas, Duan, Venturelli discloses the method of claim 1, further comprising, prior to determining whether the fraud claim is legitimate: determining a trust score associated with the client (¶ 0075: Douglas discloses a user reputation score may identify whether the user has a history of disputing transactions, whether those disputes were resolved in favor of user 120 or merchant 140, whether the user is considered by FSP 110 to be in good standing, or whether the user is a participant in program(s) that include expedited dispute resolution as a benefit, etc.); and adjusting the fraud score based on the trust score (¶ 0075-0077: Douglas discloses the server may process the dispute with the expedited dispute resolution process when it determines that the user has a high reputation score.), wherein determining whether the fraud claim is legitimate is further based on the adjusted fraud score. (¶ 0075-0077: Douglas discloses the server may determine whether the transactional score is higher than a predetermined threshold score which may indicate the transactions is more likely valid.)
11. Claim(s) 8-10 and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Douglas in view of Duan in view of Venturelli in further view of Kramme (US 2021/0374764 A1).
With respect to claims 8 and 12, the combination of Douglas, Duan, and Venturelli discloses the method of claim 6, further comprising,
prior to receiving the fraud claim from the client (¶ 0024: Douglas discloses the FSP may identify suspicious transactions before, during, or after the FSP customers conduct such transactions.),
The combination of Douglas, Duan, and Venturelli does not explicitly disclose the following limitations. However, Kramme discloses:
obtaining the stored client transaction features by: retrieving other client data associated with the multiple other clients (¶ 0041), wherein the other client data comprises data relating to historical financial transactions associated with the other clients (¶ 0043: discloses obtaining cardholder related or other customer related information.); extracting, based on the data relating to the historical financial transactions associated with the other clients, other client transaction features (¶ 0043: discloses analyzes information obtained from account records to identify spending patterns associated with different cardholders.); and storing the other client transaction features. ¶ 0043: discloses data indicative of the behavior patterns identified may be stored in an account holder behaviors database.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to have modified the combination of Douglas, Duan, and Venturelli to include the steps for obtaining the stored client transaction features, as disclosed by Kramme to achieve the claimed invention. As disclosed by Kramme, the motivation for the combination would have been to learn from other financial accounts which types of data tend to be indicative of different classifications which then may be used to facilitate a more in-depth analysis or investigation. (¶ 0026-0027)
With respect to claims 9 and 13, the combination of Douglas, Duan, and Venturelli, Kramme discloses the method of claim 8,
wherein retrieving the other client data comprises:
retrieving a dataset of client data (¶ 0054: Kramme discloses multi-account data 82 may represent data associated with multiple financial accounts.); extracting features from the dataset of client data ¶ 0044-0045: Kramme discloses analyzes a transaction in view of past spending patterns of a particular cardholder.); based on one or more similarities between the extracted features, assigning each feature to one of multiple groups (¶ 0045: Kramme discloses utilizes the individual spending patterns when detecting and/or classifying fraud); and retrieving the other client data from one of the groups. (¶ 0043: Kramme discloses analyzes information obtained from account records to identify spending patterns associated with different cardholders).
With respect to claims 10 and 14, the combination of Douglas, Duan, Venturelli, Kramme discloses the method of claim 8, wherein extracting the other client transaction features comprises:
inputting the other client data to a trained machine learning model (¶ 0024, 0026: Kramme discloses a machine learning program may be trained using past dispute resolution interactions with customers and the associated outcomes.); and
outputting the other client transaction features using the trained machine learning model to which the other client data was input. ¶ 0026, 0058: Kramme discloses the machine learning program provides fraud classifications made in connection with multiple other financial accounts.)
Response to Arguments
Applicant's arguments filed 10/21/2025 have been fully considered but they are not persuasive.
With Respect to Rejections Under 35 USC 101
Applicant argues “The Examiner Applies an Over-Generalized Characterization of the Claims. The Advisory Action dated August 25, 2025, characterizes the claims as nothing more than "determining/adjusting a fraud score and reversing a transaction," classifying the subject-matter as a "fundamental economic practice" and a "method of organizing human activity." This abstraction strips away the concrete, technological particulars that the claims actually recite: 1. Ingestion of historical transaction data and client data; 2. Assignment of the client to a cluster using a clustering model, based on the client data; 3. Generation of an anomaly score using a trained anomaly detection model, based on the assigned cluster, historical transaction data, and one or more parameters of the potentially fraudulent transaction; 4. Generation of a fraud score using a trained classification model, based on the anomaly score, the assigned cluster, the historical transaction data, and the one or more parameters of the potentially fraudulent transaction; and 5. An automated reversal instruction transmitted in response to the target transaction being identified as fraudulent. By ignoring these technical limitations, the Advisory Action departs from the requirement, reaffirmed in the USPTO's August 2025 Memorandum ("Reminders on Evaluating Subject Matter Eligibility"), to evaluate the claim as a whole.” The Examiner respectfully disagrees.
Contrary to the remarks, the previous rejection was proper because the Examiner identified the claims that recite an abstract idea and explained why the limitations fall within certain methods of organizing human activity and mental processes groupings under Step 2A Prong One of the analysis. The specificity of the presently recited techniques does not automatically make the claimed invention eligible. In the instant case, the amendments to the claim further narrow how the abstract idea may be performed, but do not make the claim any less abstract. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Under the 2024 and 2025 USPTO guidance, a claim is not "directed to" a judicial exception when it recites a particular technological solution to a technological problem. Here, the problem is the latency, inaccuracy, and manual overhead historically associated with post-authorization fraud resolution in electronic payment systems. The solution is a computer-implemented architecture that leverages multiple machine learning models operating in a structured pipeline to generate and act upon a fraud score in real time. The Examiner analogizes the claims to Bozeman Financial LLC. That decision involved rule-based notifications instructing a bank simply to "authorize" or "not authorize" a payment: an archetypal business method performed on generic computing components. In contrast, the present claims recite a clustering model, a trained anomaly detection model, and a trained classification model working together to generate a fraud score by ingesting historical transaction data and data specific to the client under question, as well as their assigned cluster. These distinctions demonstrate that the claims embody a technological arrangement whose novelty lies in the interplay of specifically configured computational elements, not in a pure economic concept. Accordingly, the claims are not directed to an abstract idea, and the § 101 analysis should conclude here.” The Examiner respectfully disagrees.
Contrary to the remarks, the additional elements of “a clustering model”, “a trained anomaly detection model”, “a trained classification model” are recited at a high-level of generality and amount to mere instructions to be performed by a computer as opposed to a technological solution to a technological problem. While the machine learning models may aid in performing the fraud analysis, merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008). See MPEP 2106.05(b) For these reasons, the rejections under 101 are being maintained.
Applicant further argues “If the Office nevertheless proceeds to Prong Two, the claims still satisfy eligibility because they integrate any putative abstract idea into a practical application that "imposes a meaningful limit on the judicial exception" (2024 Guidance Update). For example, by reducing false-positive fraud holds and eliminating manual chargeback workflows, the claimed subject-matter may reduce network bandwidth consumption, decrease database write-rollback events, and minimize processor cycles expended on downstream dispute processing. This is precisely the type of technological improvement distinguished from the "mere automation" condemned in FairWarning, Credit Acceptance, LendingTree, and Intellectual Ventures. Those cases involved simply moving an existing manual process onto a computer; here, the three-stage, machine learning pipeline introduces capabilities impossible to perform manually with equivalent speed or scale. For these reasons, the claims pass Step 2A, Prong Two.” The Examiner respectfully disagrees.
Contrary to the remarks, the claims remain ineligible under Step 2A Prong Two of the analysis. Upon further review the features being relied on such as “reducing false-positive fraud holds and eliminating manual chargeback workflows, the claimed subject-matter may reduce network bandwidth consumption, decrease database write-rollback events, and minimize processor cycles expended on downstream dispute processing” are not discussed in the present Specification. Thus, the improvements purported by Applicant cannot be relied upon to integrate judicial exception into a practical application or provide an inventive concept. See MPEP 2106.05(a) It is also important to note the courts have previously held "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In the instant case, the improvement may be in the abstract idea recited within the claim not any of the generic computer components or machinery being used to carry out the abstract idea. For these reasons, the rejections under 101 are being maintained.
With Respect to Rejections Under 35 USC 103
Applicant’s arguments with respect to claim(s) 1-2, 5-16, and 18-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached at 571-272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EHRIN L PRATT/Examiner, Art Unit 3629
/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629
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