DETAILED ACTION
Acknowledgements
This action is in response to Applicant’s filing on Jul. 17, 2026, and is made Final. This action is being examined by James H. Miller, who is in the eastern time zone (EST), and who can be reached by email at James.Miller1@uspto.gov or by telephone at (469) 295-9082.
Interviews
Interviews are “indispensable to advance the prosecution of a patent application.” MPEP § 713. Accordingly, the following Examiner’s guidance and suggested workflow maximizes this benefit to Applicant by: (1) avoiding back and forth telephone calls for scheduling, (2) permitting Examiner out-of-office notifications to the Applicant when emailing the agenda, and (3) permitting real-time document collaboration and screen sharing.
Interviews are available by telephone or, preferably, by video conferencing using the USPTO’s web-based collaboration platform. Applicants are strongly encouraged to schedule via the USPTO Automated Interview Request (AIR) portal at http://www.uspto.gov/interviewpractice. If an interview is needed more quickly than permitted by the AIR scheduling tool, note this in the AIR remarks for consideration. The Examiner routinely considers such urgent requests when practicable.
An agenda submitted when filing the AIR is strongly encouraged, because Examiners use agendas when determining whether to grant an interview. The AIR has character limits, so send the agenda contemporaneously to James.Miller1@uspto.gov and reference the AIR.
After-Final Interviews Requests are granted only at the Examiner’s discretion and only if disposal or clarification for appeal may be accomplished with only nominal further consideration. MPEP § 713.09. An advance agenda explaining how the interview advances prosecution—e.g., through targeted arguments, identified Examiner error, or proposed claim amendments—is strongly suggested.
For GRANTED requests, expect an email within two (2) business days confirming a date/time slot and collaboration tool access instructions. For DENIED requests, the record will include an explanation for the denial.
The examiner is generally available for interviews, Monday through Friday, 10:00 a.m. to 4:00 p.m. ET.
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 .
Claim Status
The status of claims is as follows:
Claims 1–20 are pending and examined with Claims 1 and 11 in independent form.
Claims 1 and 11 are presently amended.
Terminal Disclaimer
The terminal disclaimer filed on Sept. 4, 2025, disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Pat. No. 11,941,599 has been reviewed and is accepted. The terminal disclaimer has been recorded. See Office acceptance filed Sept. 12, 2025.
Response to Amendment
Applicant's Amendment has been reviewed against Applicant’s Specification filed Mar. 25, 2024, [“Applicant’s Specification”] and accepted for examination.
Response to Arguments
35 U.S.C. § 101 Argument
Applicant argues that “[c]onsidered as a whole, amended claim 1 is not directed merely to mitigating commercial risk [because] [t]he claim requires a particular data-processing sequence [in which] [t]he processor reconciles separately generated request and verified records [using] multi-dimensional similarity scores for candidate record pairings … correct[s] the linkage attributes and populate an attribute with a user- specified value that was absent from the electronic activity request. Only after that reconciliation and correction does the processor encode the verified value, prior requested value, populated user- specified value, and numerical merchant label at predefined locations in a one-dimensional vector for machine-learning inference.” Applicant’s Reply at 10.
Applicant characterizes the absence of an authoritative source for user-specified values as a technological problem and contends that the claims produce corrected machine data for anomaly detection. Applicant’s Reply at 10–11. The Specification identifies the an authoritative source for user-specified values as the technological problem. Spec. ¶ 3; Applicant’s Reply at 10
Examiner respectfully disagrees. At Step 2A, Prong One, the Independent Claims explicitly recite generating similarity scores using a similarity metric and therefore recite mathematical concepts exception grouping. The commercial dispute/cancellation limitations also recite commercial interactions under the organizing human activity exception grouping. The mathematical concepts grouping provides an independent basis for proceeding to Prong Two, without treating all electronic activity or all machine learning as abstract. The claimed sequence changes the information being analyzed but not the basic character of the claim as mere data analysis for fraud/risk mitigation in a payment transaction setting. Additionally, although the Specification describes the absence of a source of truth for user-specified values, the benefit asserted is improving the information supplied to anomaly detection rather than improving the technology that performs it (i.e., improvement to the processor, memory, storage, networking, or model).
Applicant argues that the claims are integrated into a practical application because the Specification identifies a “particular problem in the technical field of data error detection,” citing Spec. ¶ 3, and amended Claim 1 reflects the disclosed improvement in the “reconciling,” “correcting,” “encoding,” and “inputting” limitations. Applicant’s Rely at 11–13.
Examiner respectfully disagrees. At Step 2A, Prong Two, the issue is not merely if the claims supply useful corrected information but whether their particular implementation integrates the identified exception into a practical application. “[T]he feature extraction engine 130 may encode the features extracted from activity records … into a feature vector” and “the feature vector may include a one-dimensional vector of values representing each extracted feature. Accordingly, the feature vector may be efficiently ingested by a machine learning model for prediction. Spec. ¶ 47; see also ¶ 112 (“the feature vector generator 240 may generate a vector representation of the encoded attributes in, e.g., an n-dimensional vector, such as a one dimensional vector. For example, the vector representation may include a one-dimensional vector having a length corresponding to the number of the selected attributes. The encoding of each attribute may then be recorded in a predefined index location in the one dimensional vector to produce the activity feature vector 243 representative of the selected relevant attributes. As a result, the feature extraction engine 130 may encode an electronic activity and an associated user-specified data 212 (e.g., a user-specified value or quantity, or other data) into an activity feature vector 243 for use by a machine learning model to predict whether the user-specified data 212 is anomalous or accurate based on context as represented by the attributes of the electronic activity”). Spec. ¶¶ 47, 112 do not explain a particular ingestion mechanism or how predefined index locations improve model architecture, processor operation, memory access, or other computer functionality. To the extent Applicant relies on “ingestion,” the Specification identifies the ingestion mechanism at only a high level of generality. Spec. ¶ 49. The positions merely identify the values to be analyzed rather than implement a disclosed improvement in storage, retrieval, or model processing. Thus, the asserted efficiency is directed to preparing and organizing inputs for ingestion (i.e., the abstract idea) rather than to an improvement in computer technology. SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018) (“the focus of the claims is not any improved computer or network, but the improved mathematical analysis and indeed, the specification makes clear that off-the-shelf computer technology is usable to carry out the analysis”).
Considering the sequence as a whole, reconciliation and populating values merely prepare the information for prediction, while the dispute interface, cancellation, and feedback limitations merely use the prediction in activity verification. The asserted data accuracy benefit does not, on this record, identify a technical improvement in computer functionality or another technology.
Applicant argues that the ordered process produces “corrected [and] consistently organized data” which improves “the computer-based technology of data error and anomaly detection.” Applicant’s Reply at 13–15.
Examiner respectfully disagrees. The assertion of efficient ingestion in Spec. ¶ 47 must be read in light of numerical encoding and index vector generation. The mechanism organizes extracted information as model input, without describing how the index arrangement changes the operation of the ingestion or classification technology. Spec. ¶¶ 47, 112. The Specification disclosure must still connect the claimed mechanism to technological improvement rather than merely improved inputs or outputs. Further, Spec. ¶ 103 explains a different alleged efficiency benefit, i.e., a “percent difference” or “ratio” can represent “how the user-specified data 212 relates to the electronic activity” “while
maintaining insight into context,” which avoids the complexity of “using both the user-specified data 212 and the additional information as features.” Spec. ¶ 103. Even accepting a resulting computational saving, the disclosed mechanism is the mathematical calculation of the analyzed values and the abstract idea itself cannot be a practical application or significantly more. Spec. ¶ 103 further does not identify a separate improvement in how the model performs its computations. Thus, the disclosed benefit supports more efficient mathematical analysis of activity values but does not without further technical explanation establish an improvement in computer technology. Additionally, Independent Claims do not recite replacing the claimed inputs with the percent difference or ratio. Dependent Claims 4 and 14 determine percentage of the verified value associated with the user-specified value but do not require input replacement in the feature vector.
Applicant argues that SRI, Ancora, Enfish, and Ex Parte Desjardin support eligibility because amended claim 1 recites a specific implementation that improves automated electronic record reconciliation and data error anomaly detection. Applicant’s Reply at 13–15.
Examiner respectfully disagrees. Enfish concerned self-referential database structure, SRI concerned a particular network-security arrangement, and Ancora concerned verification using a specific BIOS memory implementation. Applicant presents no comparable improvement supplied by the claimed feature vector having predefined index locations for data.
Desjardins is not applicable. Desjardins concerned “improvements in the training [of] the machine learning model itself … to ‘effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Desjardins, p.8. The Desjardins specification “recite[d] that the claimed improvement allows artificial intelligence (AI) systems to ‘use less of their storage capacity’ and enables ‘reduced system complexity’ [and] independent claim 1 [ ] reflects the improvement.” Desjardins, p.9.
Here, the Specification does not describe “improvements in the training of the machine learning model itself.” Evaluating the reconciliation, missing value population, feature vector having predefined index locations, and feedback retraining, does not change the analysis. The Specification describes feedback parameter updates and loss function optimization but does not identify an improvement to the training mechanism from reconciliation, missing value population, feature vector having predefine index locations.
Second, the Specification does not identify a technological improvement in storage capacity or reduced system complexity. Even accepting Applicant’s reliance on Spec. ¶ 103 for reduced system complexity, ¶ 103 describes the benefits to using a percent difference or ratio to present how the user specified data relates to the electronic activity. This is still mathematical representation of the analyzed data and does not identify an improvement in how the model performs calculations. Thus, the disclosed benefit supports more efficient mathematical analysis of activity values but does not without further technical explanation establish an improvement in computer technology.
Last, even if a technological improvement is disclosed, the amended Independent Claims do not reflect that alleged improvement. As in SAP v. InvestPic, 898 F.3d 1161, 1170 (Fed. Cir. 2018) improved mathematical analysis of the selected information is merely “asserted advance in the realm of abstract ideas—an advance in mathematical techniques in finance” and was not patent eligible.
Applicant argues the “ordered combination also amounts to significantly more than merely instructing a generic computer to apply an abstract idea. The Office Action's conventionality findings concerning generic classifiers and ordinary transaction processing do not address the newly claimed combination of multi-dimensional candidate-pairing reconciliation, correction of linkage attributes to populate absent data, predefined-location vector encoding, and classification using the resulting corrected representation. These limitations must be evaluated collectively rather than reduced to generic record receipt, mathematical scoring, and model use in isolation.” Applicant’s Reply at 15.
Examiner respectfully disagrees. Applicant’s argument is persuasive to the extent that the amended ordered combination requires its own Step 2B analysis, but that requirement does not mean the ordered combination is nonconventional or inventive. The Specification describes the claimed operations, i.e., record matching using similarly metrics, determination of user specified values from differences, feature vector encoding, machine learning classifiers, and parameter updating, in conventional terms and with known algorithms or generic implementation. Spec. ¶¶ 47, 4978–80, 112, 115, 117, 118, 124. Considered together, the amended limitations still amount to preparing transaction data for mathematical analysis and using eh result in ordinary dispute processing activity.
Claim Interpretation
Under the broadest reasonable interpretation, the following claim terms are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. MPEP § 2111.
electronic activity is a payment transaction, Spec. ¶ 73.
linkage attributes are data attributes of the request and verified records used to associate records with the same electronic activity, such as activity identifier, (Spec. ¶ 77) and where that identifier is unavailable, a date, a location, a third-party entity, an entity type, or any other suitable information and combinations thereof from each record. Spec. ¶78; see also ¶¶ 100, 101. Stated another way, a label. The Specification does not explicitly define or recite “linkage attributes”.
reconcile an electronic activity request record corresponding to the electronic activity request with a verified electronic activity record corresponding to an electronic activity verification encompasses matching records associated with the same electronic activity. Spec. ¶¶ 77, 78.
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 1–20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Analysis
Step 1: Claims 1–20 are directed to a statutory category. Claims 1–10 recite a “method” and are therefore, directed to the statutory category of a “process.” Claims 11–20 recite a “system” and are therefore, directed to the statutory category of a “machine.”
Representative Claim
Claim 11 is representative [“Rep. Claim 11”] of the subject matter under examination and recites, in part, emphasis added by Examiner to identify limitations with normal font indicating the abstract idea exception, bold limitations indicating additional elements. Each limitation is identified by a letter for later use as a shorthand notation in referencing/describing each limitation. Portions of the claim use italics to identify intended use limitations1 and underline, as needed, in further describing the abstract idea exception:
[A] 11. A system comprising: at least one processor in communication with at least one computer readable storage medium having software instructions stored thereon, wherein the at least one processor, upon execution of the software instructions, is configured to:
[B] receive an electronic activity request associated with an electronic activity between a user account with an entity; wherein the electronic activity request comprises at least one value associated with the electronic activity;
[C] reconcile an electronic activity request record corresponding to the electronic activity request with a verified electronic activity record corresponding to an electronic activity verification by generating multi-dimensional similarity scores for candidate pairings of electronic activity request records and verified electronic activity records based on multi-dimensional data representative of respective linkage attributes and at least one multi-dimensional similarity metric;
[C1] correcting, by the at least one processor, the linkage attributes based at least in part on the verified electronic activity record and the electronic activity request record to populate at least one attribute with a user-specified value absent from the electronic activity request;
[D] encode the at least one verified value and the at least one value into a feature vector comprising a one-dimensional vector having predefined index locations for: the at least one verified value; the at least one value; the user-specified value; and a merchant identifier encoded as a numerical label; and
[E] input the feature vector into an anomalous attribute classification machine learning model to output an anomaly classification;
[F] wherein the anomalous attribute classification machine learning model comprises a plurality of trained machine learning model parameters trained to predict a likelihood of an error in the electronic activity verification based on the at least one verified value and the at least one value;
[G] cause to display in a dispute interface configured to enable a user to dispute electronic activities, an alert message to the user via a user computing device; wherein the alert message represents the anomaly classification of an incorrect value of the electronic activity verification;
[H] wherein the alert message comprises a user selectable element configured to enable a user to select to dispute the electronic activity or accept the electronic activity;
[I] receive a user selection of the user selectable element; wherein the user selection causes the at least one processor to:
[J] generate, in real-time, the electronic request to dispute the electronic activity to automatically issue and file the dispute;
[J1] generate, in real-time, an electronic activity invalidation removing the electronic activity verification from a user account of the user;
[J2] cause to cancel the electronic activity based on the user selection of the dispute interface element to prevent the execution of the electronic activity;
[K] determine a classification error of the anomalous attribute classification machine learning model based on the anomaly classification and the user selection; and
[L] retrain the plurality of trained machine learning model parameters of the anomalous attribute classification machine learning model based at least in part on the classification error to improve accuracy of the anomaly classification for the user; and
[M] input into the anomalous attribute classification machine learning model, a subsequent feature vector, comprising the one-dimensional vector having the predefined index locations, of a subsequent electronic activity verification to output a subsequent anomaly classification based at least in part on the plurality of trained machine learning model parameters having been retrained based on the user selection.
Claims are directed to an abstract idea exception.
Step 2A, Prong One: Rep. Claim 11, as drafted, recites “receive a user selection” in Limitation I to “dispute the electronic activity or accept the electronic activity” in Limitation H where disputing the electronic activity “represents the anomaly classification of an incorrect [fraud] value of the electronic activity verification” (Limitation G), which, as a whole, is a fundamental economic principle/practice under the organizing human activity exception because “mitigating electronic fraud” describes mitigating risk, a concept relating to the economy and commerce. MPEP § 2106.04(a)(2)(II)(A). Limitations B–I are the required steps to “mitigate fraud” and therefore recite the same exception. Id.
Alternatively, Limitations C, C1, D, E, K, L, M, as drafted, recite mathematical relationships under the mathematical concepts exception because those limitations expressly recite [C] “generating multi-dimensional similarity scores” from candidate pairings “based on … at least one multi-dimensional similarity metric,” [C1] correcting … the linkage attributes,” which employs mathematical algorithms. Spec. ¶¶ 78, 79; [D] “encod[ing] … into a feature vector comprising a one-dimensional vector,” [E] “input the feature vector into an anomalous attribute classification machine learning model”; [K] “determine a classification error” [L] “retrain the plurality of trained machine learning model parameters”; and [M] “input into the anomalous attribute classification machine learning model, a subsequent feature vector”. "[A] process that employs mathematical algorithms [i.e., Limitations C, C1, D, E, K, L, M] to manipulate existing information to generate additional information [“an anomaly classification” (Limitation E) and “improve accuracy” (Limitation L) of “subsequent anomaly classification” (Limitation M)] is an abstract idea.” Digitech Image Techs. LLC v. Elecs. For Imaging, Inc., 758 F.3d 1344, 1351 (Fed. Cir. 2014); MPEP § 2106.04(a)(2)(I)(A)(iv) (“The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula.”).
Regarding the “encode” limitation, Limitation D, Applicant’s Specification teaches encoding occurs by mathematical concepts. Spec. ¶ 105. Regarding the “input” limitations, Limitations E & M, Applicant’s Specification teaches the model is a mathematical model or algorithm. Spec. ¶ 52 (e.g., “nearest neighbor algorithm”). Regarding the “determine a classification error,” Limitation K, Applicant’s Specification teaches the “error” is determined by mathematical concepts. Spec., ¶ 72 (e.g., “cosine similarity”); see also ¶¶ 123, 124. Likewise, Applicant’s Specification teaches retaining, Limitation L, occurs by mathematical concepts. Spec. ¶ 124 (e.g., “gradient descent”). Whether the model is retrained (Limitation L) does not alter this determination of a mathematical process because retraining occurs by adjusting the model “[a]ccording to a predetermined period.” Spec. ¶ 15.
Step 2A, Prong Two: Rep. Claim 11 does not contain additional elements that integrate the abstract idea exception into a practical application because the additional elements are mere instructions to apply the abstract idea exception, MPEP § 2106.05(f), and are insignificant extra solution activity; MPEP § 2106.05(g). The additional elements are limited to the computer components and indicated in bold, supra. The additional elements are: a system comprising at least one processor; at least one computer readable storage medium having software instructions; “electronic” characterization of “activity request,” “activity,” and “activity verification” [computer]; a [trained] anomalous attribute classification machine learning model; a dispute interface configured to enable a user to dispute electronic activities; a user computing device; a user selectable element configured to enable a user to select to dispute the electronic activity or accept the electronic activity; Limitations J, J1, and J2.
Regarding the system comprising at least one processor; at least one computer readable storage medium having software instructions; “electronic” characterization of “activity request,” “activity,” and “activity verification” [computer]; a [trained] anomalous attribute classification machine learning model; a dispute interface configured to enable a user to dispute electronic activities; a user computing device; a user selectable element configured to enable a user to select to dispute the electronic activity or accept the electronic activity; Limitations J, J1, and J2, Applicant’s Specification does not otherwise describe them or describes them using exemplary language as a general-purpose computer, as a part of a general-purpose computer, or as any known and exemplary (generic) computer component known in the prior art. Thus, Applicant takes the position that such hardware/software is so well known to those of ordinary skill in the art that no explanation is needed under 35 U.S.C. § 112(a). Lindemann Maschinenfabrik GMBH v. Am. Hoist & Derrick Co., 730 F.2d 1452, 1463 (Fed. Cir. 1984) (citing In re Meyers, 410 F.2d 420, 424 (CCPA 1969) (“[T]he specification need not disclose what is well known in the art”). E.g., Spec. ¶ 26 (“the electronic activity request 103 may include any user activity performed via electronic computing systems”); ¶ 28 (“the processor 112 may include any type of data processing capacity”); ¶ 31 (any exemplary and known hardware elements); ¶ 32 (any exemplary and known software with functions implement with any programming language); ¶ 32 (any known combination of hardware and software); ¶ 52 (“the attribute accuracy model engine 140 may be configured to utilize one or more exemplary AI or machine learning techniques”); ¶¶ 52, 53 (any known neural network technique); ¶ 58 (“the user computing device 102 may be any suitable computing device”); ¶ 59 (“the activity verification system 110 may generate a notification including the prediction of the anomalous user-specified data 105 to deliver to the user computing device 102 … [using] any other suitable messaging technique”); ¶ 63 (“the dispute may also send a request to the activity execution computing device 101 to modify or correct the electronic activity verification 104, or any other suitable response to the dispute 107”); ¶ 72 (“the user-specified value calculator 210 may extract, e.g., a date, a location, a third-party entity, an entity type, or any other suitable information and combinations thereof from each record”); ¶¶ 81, 102 (“the predefined attributes can be adjusted at any time, e.g., by the administrative user, or may be fixed”); ¶ 90 (“the batches may be provided periodically … according to any other suitable period”); ¶¶ 109, 118 (“any other suitable algorithm for a classification model”); ¶ 127 (“any computing device capable of receiving and sending a message over a network”); ¶ 127 (“virtually any mobile computing device, and the like”); ¶ 127 (“any other device that is equipped to communicate over a wired and/or wireless communication medium”); ¶ 127 (“exemplary specifically programmed browser application of the present disclosure may be configured to receive and display graphics, text, multimedia, and the like, employing virtually any web based language” and programmed by any “suitable programming language”); ¶ 128 (“the exemplary network 405 may provide network access, data transport and/or other services to any computing device coupled to it”); ¶ 128 (any known exemplary network 405 transmitted based on any known communication mode); ¶ 130 (any known server specially programmed with software to send process and receive information in any known way); ¶ 132 (“any other medium from which a computer processor can read instructions … compris[ing] code from any computer-programming language”); ¶ 133 (“any type of processor-based platforms that are connected to a network 506”); ¶ 133 (“member computing devices 502a through 502n may operate on any operating system capable of supporting a browser or browser-enabled application”); ¶ 134 (“any type of database”); ¶ 140 (“any medium and/or mechanism for storing or transmitting information in a form readable by a machine”); ¶ 142 (“any other microprocessor or central processing unit (CPU)”); ¶ 143 (“any combination of hardware and software”); ¶ 144 (“any appropriate hardware and/or computing software languages”); ¶ 147 (“one or more of the computer-based systems of the present disclosure may obtain, manipulate, transfer, store, transform, generate, and/or output any digital object and/or data unit (e.g., from inside and/or outside of a particular application) that can be in any suitable form such as, without limitation”); ¶ 147 (“not limited to any specific combination of hardware circuitry and software”); ¶ 152 (“the term "mobile electronic device," or the like, may refer to any portable electronic device that may or may not be enabled with location tracking functionality”); ¶ 153 (“the terms "proximity detection," "locating," "location data," "location information," and "location tracking" refer to any form of location tracking technology or locating method that can be used to provide a location”); ¶ 153 (“any reasonable form of wireless and non-wireless communication”); and ¶ 159 (“the various steps may be carried out in any desired order ( and any desired steps may be added, and/or any desired steps may be eliminated”).
The generic processor, here, appears to perform calculations (functions) that are programmed by software. Spec. ¶¶ 32, 130, 143, 144, 147. This is a computer doing what it is designed to do—performing directions it is given to follow.
The displaying step, Limitation G, fails to transform the claims into patent eligible material, as this is part of the field of use and technical environment in which the abstract idea is being implement and does not result in an improvement to additional elements, a practical application, or inventive concept. MPEP 2106.05(h) (citing Electric Power Group). Further, requiring the use of software to tailor information and provide it to the user on a generic computer also does not provide a practical application or inventive concept. MPEP § 2106.05(f)(2) (citing Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)).
Regarding the user selectable element configured to enable a user to select, requiring the use of software to tailor information and provide it to the user on a generic computer also does not provide a practical application or inventive concept. MPEP § 2106.05(f)(2) (citing Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015)). Likewise, a web browser’s back and forward button functionality is not a practical application or inventive concept. MPEP § 2106.05(d)(II)(vi) (citing Internet Patent Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015)).
Regarding the [trained] anomalous attribute classification machine learning model, Applicant’s Specification explains it can be created in any known way using any known, AI, machine learning, or neural network technique, such that it could be almost anything. Spec. ¶ 52 (“the attribute accuracy model engine 140 may be configured to utilize one or more exemplary AI or machine learning techniques”); ¶¶ 52, 53 (any known neural network technique); ¶¶ 109, 118 (“any other suitable algorithm for a classification model”). The trained model itself is created outside the scope of the claims and as claimed, like any model, receives inputs and provides an output, in this case an anomaly classification. Therefore, the “[trained] anomalous attribute classification machine learning” characterization of the “model” is merely a label describing its intended use and imparts no functional aspect to the claimed “model.” This describes a solution merely at the level of a “generic black box” for determining an anomaly classification and reads neatly on “use a computer” to do it in any way. MPEP § 2106.05(f); see also, Recentive Analytics, Inc. v. Fox Corp., 2025 U.S.P.Q.2d 628 (Fed. Cir. 2025) (holding “that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101”).
Limitations J, J1, and J2 are recited at a high level of generality, are purely functional, and amount to routine post solution financial activities that merely implement the result of anomaly classification in a conventional transaction processing environment using generic computer components. The Specification teaches “the notification may include, e.g., an email, a simple message service (SMS) message, a multi-media message service (MMS) message, a rich communication service (RCS) message, an internet messaging message (e.g., Apple iMessage, WhatsApp, Facebook Messenger, Signal, Telegram, Skype, or other internet-based messaging service), social media message, telephone call, or any other suitable messaging technique.” ¶ 65. “the dispute interface 106 may include user interface elements.” ¶ 68. “the dispute may automatically invalidate the electronic activity verification 104 and remove the electronic activity verification 104 from the verified history 116 of the account 114.” ¶ 69. Thus, Limitations J, J1, and J2 are insignificant extra-solution activity. MPEP 2106.05(g).
To the extent Applicant contends that the newly added reconciliation and value population limitations integrate the abstract idea into a practical application, the Specification teaches those functions using known matching and comparison techniques rather than a technological improvement. Spec. ¶¶, 78.
Limitation A describes the processor communicating with the storage medium and executing stored software instructions to perform the steps of the claimed invention. This takes a generic piece of hardware and describe the functions of transmitting, receiving, and storing data (software instructions) between the processor and storage medium, which merely invokes computers or other machinery in its ordinary capacity to receive, store, or transmit data. MPEP § 2106.05(f)(2).
Limitations B–M describe the processor, medium, and software instructions, performing the steps of the claimed invention, which represents the abstract idea exception itself. Performing the steps of the abstract idea exception itself simply adds a general-purpose computer after the fact to an abstract idea exception, MPEP § 2106.05(f)(2), or generically recites an effect of the judicial exception. MPEP § 2106.05(f)(3).
Therefore, the claim as a whole, looking at the additional elements individually and in combination, are no more than mere instructions to apply the exception using generic computer components and is not a practical application. MPEP § 2106.05(f). The additional elements do not integrate the abstract idea exception into a practical application because they do not impose any meaningful limits on the abstract idea exception. They do not improve computer technology—they improve fraud-detection outcomes (a business result). Accordingly, Rep. Claim 11 is directed to an abstract idea.
Rep. Claim 11 is not substantially different than Independent Claim 1, which includes all the limitations of Rep. Claim 11. Independent Claim 11 contains no additional elements. Therefore, Independent Claim 1 is also directed to the same abstract idea.
The claims do not provide an inventive concept.
Step 2B: Rep. Claim 11 fails Step 2B because the claim as whole, looking at the additional elements individually and in combination, are not sufficient to amount to significantly more than the recited judicial exception. As discussed with respect to Step 2A, Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer and/or generic computer components. MPEP § 2106.05(f). The same analysis applies here in Step 2B. Mere instructions to apply an exception using a generic computer and/or generic computer components cannot provide an inventive concept. MPEP § 2106.05(I).
The additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the identified judicial exception.
The pending claims in their combination of additional elements is not inventive. First, as explained above, the claims are directed to an abstract idea. Second, each additional element represents a currently available generic computer technology, used in the way in which it is commonly used (individually generic). Last, Applicant’s Specification discloses that the combination of additional elements is not inventive, but instead relies on conventional computer components and functions. Spec. ¶ 159 (steps/functions may be performed in any order); ¶¶ 26, 28, 31, 32, 52, 53, 58, 59, 63, 72, 81, 90, 102, 109, 118, 127, 128, 130, 132, 133, 134, 140, 142, 143, 144, 147, 152, 153, 159 (describing exemplary, known and generic computer equipment and networks)
Accordingly, Examiner finds the additional elements of Rep. Claim 11 are elements that have been recognized as well-understood, routine, and conventional (“WURC”) activity in the particular field of this invention based on Applicant’s own disclosure2. Spec. ¶¶ 26, 28, 31, 32, 52, 53, 58, 59, 63, 72, 81, 90, 102, 109, 118, 127, 128, 130, 132, 133, 134, 140, 142, 143, 144, 147, 152, 153, 159; MPEP § 2106.05(d). Specifically, Applicant’s Specification describes the recited additional elements (i.e., a system comprising at least one processor; at least one computer readable storage medium having software instructions; “electronic” characterization of “activity request,” “activity,” and “activity verification” [computer]; a [trained] anomalous attribute classification machine learning model; a dispute interface configured to enable a user to dispute electronic activities; a user computing device; a user selectable element configured to enable a user to select to dispute the electronic activity or accept the electronic activity) as being implemented using generic, off-the-shelf computing technology. These elements do no more than “apply” the recited abstract idea(s) on a known computer and computer-related components. The Examiner also finds the functions of receiving, storing, transmitting, displaying, and processing (e.g., performing mathematical operations on) data are normal, well-understood operations of genric computer systems known in the art.
The anomalous attribute classification machine learning model is well-understood and merely operates on the generic components. The Specification discloses, “the attribute accuracy model engine 140 may be configured to utilize one or more exemplary AI or machine learning techniques chosen from, but not limited to, decision trees, boosting, support-vector machines, neural networks, nearest neighbor algorithms, Naive Bayes, bagging, random forests, and the like” (Spec. ¶ 52) and further describes generic neural network training and optional continued training with “a predetermined periodicity.” Spec. ¶ 58. NPL Leskovec is prior art that discloses that classification loss, training, and loss functions are conventional in the ML arts. NPL Leskovec. pp. 537–39 (Section 13.2.9 “Classification Loss”), pp. 464–65 (Example 12.1.2) (cited herein on PTO-892). The claims do not recite a particular unconventional model architecture, training objective, loss fucntion, update rule, or technical mechanism by which retraining improves the computer or model itself. See Recentive Analytics, Inc. v. Fox Corp., 2025 U.S.P.Q.2d 628 (Fed. Cir. 2025) (holding “that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101”). Using the anomalous attribute classification machine learning model and retraining it are also well understood at the time of filing. NPL Leskovec, pp. 464–67 (Section 12.1.1. “Training Sets), pp. 535–40 (“Loss Functions).
The user selectable element configured to enable a user to select is well-understood and merely operates on the generic components. MPEP § 2106.05(d)(II)(vi) (citing Internet Patent Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015)).
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, Limitations J, J1, and J2 were considered to be insignificant extra-solution activity in Step 2A because they merely implement the financial consequences of the anomaly determination using generic computer functions. Reevaluated under Step 2B, these same limitations are found to be ni more than well-understood, routine, and conventional post-solution activity in the field of electronic transaction processing and do not provide an inventive concept. As discussed above, Limitations J, J1, and J2, merely recite the processor generating electronic messages and updating account records in response to the anomaly classification and user input. Applicant’s Specification describes these operations as occurring within a conventional electronic account-management and transaction-processing environment implemented using standard client devices, servers, cloud services, and databases, and does not indicate that these functions are performed in an unconventional manner. Spec. ¶¶ 22, 23, 25, 27, 37–41, 45–47, 60–63, 100–103, 140–144. For example, the Specification explains that the processor generates a dispute GUI and, upon user interaction, causes an electronic request to dispute the electronic activity verification and cancels the electronic activity to prevent its execution, in the context of an ordinary computing system. Spec. ¶¶ 4–7, 22, 25, 33, 35, 36, 60–63).Moreover, neither the Specification nor the record identifies Limitations J, J1, and J2 as providing any improvement to the functioning of the computer itself, to the underlying network or storage technology, or to the machine-learning model; nor has Applicant provided any evidence that such operations were not well-understood, routine, and conventional in the field at the time of the invention. Spec. ¶¶ 26, 28, 31–33, 52, 53, 58, 59, 63, 72, 81, 90, 102, 109, 118, 127, 128, 130, 132–134, 140, 142–144, 147, 152, 153, 159). In view of this disclosure and the absence of contrary evidence, and consistent with MPEP § 2106.05(d) and USPTO guidance interpreting Berkheimer, these limitations are found to be well-understood, routine, and conventional post-solution activity in the field of electronic transaction processing and do not supply an inventive concept.
There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements in combination adds nothing that is not already present when looking at the elements individually. Their collective functions merely provide conventional computer implementation of the abstract idea at a high level of generality. Thus, Rep. Claim 11 does not provide an inventive concept.
Rep. Claim 11 is not substantially different than Independent Claim 1, which includes all the limitations of Rep. Claim 11. Independent Claim 11 contains no additional elements. Therefore, Independent Claim 1 also does not recite an inventive concept.
Dependent Claims Not Significantly More
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. § 101. Dependent claims are dependent on Independent Claims and include all the limitations of the Independent Claims. Therefore, all dependent claims recite the same Abstract Idea. Dependent claims do not contain additional elements that integrate the abstract idea exception into a practical application or recite an inventive concept because the additional elements: (1) are mere instructions to apply the abstract idea exception; and/or (2) further limit the abstract idea exception of the Independent Claims. The abstract idea itself cannot provide the inventive concept or practical application. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claims 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18, 19, and 20, all recite “wherein” clauses or limitations that further limit the abstract idea of the Independent Claims and contain no additional elements. Regarding Dependent Claim 12, “to train model parameters of a machine learning model to obtain the learned model parameters” is interpreted as intended use because it describes the purpose of the function being claimed (i.e., utilize the history of verified electronic activities and the history of disputed electronic activities). Accordingly, statements of intended use fail to limit the scope of the claim under BRI. MPEP § 2103(I)(C). On the merits, training model parameters may be performed by hand. NPL Leskovec, pp. 464–67 (Section 12.1.1. “Training Sets), pp. 535–40 (“Loss Functions). An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Dependent Claims 2, 3, 4, 5, and 6 recite additional limitations that form part of the same abstract idea exception as recited in Independent Claims and contain no additional elements. Dependent Claim 2 recites intended use for the same reason as Dependent Claim 12 above. An inventive concept or practical application cannot be furnished by an abstract idea exception itself. MPEP §§ 2106.05(I), 2106.04(d)(III).
Conclusion
Claims 1–20 are therefore drawn to ineligible subject matter as they are directed to an abstract idea without significantly more. The analysis above applies to all statutory categories of invention. As such, the presentment of Rep. Claim 11 otherwise styled as another statutory category is subject to the same analysis.
Examiner Statement of Prior Art—No Prior Art Rejections
Based on the prior art search results, the prior art of record fails to anticipate or render obvious the claimed subject matter of the instant application. While some individual features of Claims 1–20 may be shown in the prior art of record—no known reference, alone or in combination, would provide the invention of Claims 1–20. The prior art most closely resembling the applicant’s claimed invention are:
Das et al. (U.S. Pat. Pub. No. 2021/0192641) is pertinent because it discloses receiving a clearing record including at least one key field, comparing a value associated with a first key field of the clearing record to a value associated with a first key field of one or more authorization records associated with one or more payment transactions, and determining that the clearing record corresponds to an authorization record from among the one or more authorization records based on comparing the value associated with the first key field of the clearing record to the value associated with the first key field of the one or more authorization records.
Barry et al. (U.S. Pat. Pub. No. 2021/0383391) is pertinent because it discloses a system for fraud dispute of pending transactions comprising receiving data corresponding to a pending transaction between the user and a merchant; analyzing the transaction data to determine whether the transaction data comprises at least one indicator of a fraudulent transaction; pausing an initiation to provide funds for the pending transaction, providing the user at least one questionnaire relating to the received transaction data or a set of stored user data, receiving a response from the user for the questionnaire, comparing the received response to the received transaction data or the stored user data, determining whether to validate the user based on the comparison, rejecting the pending transaction when the user is not validated, and removing the indicator when the user is validated.
Kolman et al. (U.S. Pat. No. 8,875,267) is pertinent because it discloses active learning-based fraud detection techniques in adaptive authentication systems and a closed loop active learning process.
FOR: A.U. Pat. App. No. 2015201865 A1 is pertinent because it discloses initiating a payment transaction with a payment device by a consumer with a merchant, wherein the merchant generates an authorization request message for the payment transaction that is transmitted to a payment processing system, wherein the payment processing system determines if data contained in the authorization request message satisfies a condition specified by the consumer; and after initiating the payment transaction, receiving, from the payment processing system, a transaction alert message containing information associated with a gratuity for the payment transaction at a communication device operated by the consumer.
NPL: Leskovec, Jure, Anand Rajaraman, and Jeffrey David Ullman. "Mining of Massive Datasets." (2019) is pertinent because it discloses computational approaches to modeling.
NPL: J. Álvarez Cid-Fuentes, C. Szabo and K. Falkner, "Adaptive Performance Anomaly Detection in Distributed Systems Using Online SVMs," in IEEE Transactions on Dependable and Secure Computing, vol. 17, no. 5, pp. 928-941, 1 Sept.-Oct. 2020 is pertinent because it discloses a framework for identification of anomalies in distributed systems with feature extraction into a vector, two-step classification, evaluate classification with user manual feedback, and updating a machine learning model. See, Fig. 1.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES H MILLER whose telephone number is (469)295-9082. The examiner can normally be reached M-F: 10- 4 PM (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bennett M Sigmond can be reached at (303) 297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES H MILLER/Primary Examiner, Art Unit 3694
1 Statements of intended use fail to limit the scope of the claim under BRI. MPEP § 2103(I)(C).
2 See Changes in Examination Procedure Pertaining to Subject Matter Eligibility, Recent Subject Matter Eligibility Decision (Berkheimer v. HP, Inc.), 3-4, https://www.uspto.gov/sites/default/files/documents/memo-berkheimer-20180419.PDF (April, 18, 2018) (That additional elements are well-understood, routine, or conventional may be supported by various forms of evidence, including "[a] citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates the well-understood, routine, conventional nature of the additional element(s).").
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