The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This following is in response to the amendments and arguments filed 07/07/2026. Claims 2-21 are pending.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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 2-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea. This judicial exception without significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 2-21 are directed to a method, device and product The claims fall within one of the four statutory categories of invention (processes, machines, manufactures and compositions of matter).
The Examiner has identified independent method Claim 2 as the claim that represents the claimed invention for analysis and is similar to independent device Claim 11 and product Claim 17.
The claim recites the steps of a method comprising:
Accessing evidence for past chargebacks and a designation of a plurality of evidence classifications that categorize the evidence for the past chargebacks in accordance with a plurality of evidence types and information for the evidence, wherein the plurality of evidence types include at least a primary evidence type and a secondary evidence type;
Extracting text data from the evidence for the past chargebacks using at least one of a text extraction process or an image extraction process;
classifying the evidence for the past chargebacks into the plurality of evidence classifications;
training a plurality of evidence recommendation …models to classify the evidence for the past chargebacks based on the classified evidence and the plurality of evidence classifications, wherein the plurality of evidence recommendation ML models are each further trained based on training data associated with a geographic area,
wherein the training data includes data compliance restrictions for the geographic area, and, wherein the training includes:
configuring the plurality of evidence recommendation … models to generate probabilities that the evidence for the past chargebacks for each of the plurality of evidence classifications resolves the past chargebacks for users or merchants submitting the evidence; for the past chargebacks;
receiving,…from a merchant, a dispute of a transaction between a user and the merchant, wherein the dispute comprises a chargeback associated with the transaction;
determining a plurality of attributes associated with the dispute and the chargeback;
determining, based on one or more characteristics of the merchant and the transaction and the data compliance restrictions, an evidence recommendation… model of the plurality of evidence recommendation…. models to use for the dispute;
determining, using the evidence recommendation… model based on the plurality of attributes and the plurality of evidence classifications for evidence submissible for a resolution of the dispute, at least the primary evidence type and the secondary evidence type recommended for the merchant to provide in response to the dispute;
computing, using the evidence recommendation… model, a probability that each of the primary evidence type and the secondary evidence type leads to a resolution for the merchant;
determining at least one data extraction process that extracts data from the evidence submissible for the resolution of the dispute based at least on the plurality of evidence classifications;
determining a submission process for the evidence submissible for the resolution of the dispute by the merchant for the dispute, wherein the submission process includes the at least one data extraction process and presents the probability in association with each of the primary evidence type and the secondary evidence type; and
configuring a display …to include the submission process for the merchant.
Under Step 2A Prong 1, the claim as a whole recites the series of steps for determining evidence for a dispute of a transaction between a user and the merchant, wherein the dispute comprises a chargeback associated with the transaction which falls within the abstract idea subgroup of commercial or legal interaction under the abstract idea grouping of Certain Method of Organizing Human Activity. Thus, the claim recites an abstract idea.
Under Step 2A prong 2, this judicial exception is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of determining evidence for a dispute of a transaction between a user and the merchant in a computer environment. The claimed computer components (machine learning models, interface; claim 11 processor, memory, device; claim 17 machine readable medium) are recited at a high level of generality and are merely invoked as tools to perform an existing dispute resolution process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A prong 2, the claim describes how to generally “apply” the concept of determining evidence for a dispute of a transaction between a user and the merchant in a computer environment. Thus, even when viewed separately and as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claim is ineligible.
The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it".
Dependent claims 8 and 18 recite additional elements of a dashboard for visualizing data. The claim as a whole merely describes how to generally “apply” the concept of processing insurance claims for a covered loss or policy event under an insurance policy in a computer environment. The claimed computer components are recited at a high level of generality and are merely invoked as tools to perform an existing medical claim integrity in healthcare revenue cycle management process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A prong 2, the claim describes how to generally “apply” the concept of processing insurance claims in a computer environment. Thus, even when viewed separately and as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claim is ineligible.
Dependent claims 3-10, 12-16, and 18-21 further define the abstract idea that is present in their respective independent claims 2, 11 and 17 (steps in training the models, determine availability, types of data, evidence types, location data, product lifecycle determination, characteristics of the merchant and transaction, for example). The dependent claims are abstract for the reasons presented above because there are no additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered as a whole, individually and as an ordered combination. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". Thus, the claims 1-20 are not patent-eligible.
Response to Arguments
Applicant's arguments filed 07/07/2025 have been fully considered but they are not persuasive..
Regarding the rejection under 35 U.S.C. 101, Applicant’s arguments have been fully considered but they are not persuasive.
Regarding applicant’s argument the claims are statutory under Step 2A, Prong Two as providing specific improvements in technology “the limitations relate to a specific usage (training machine learning (ML) models using text and image extraction processes for evidence classification with geographic-specific data compliance restrictions) in a specific situation (when managing dispute resolution for chargebacks requiring submission of different evidence types requiring data extraction) to provide a particular practical application that improves technology (e.g., to provide accurate data recommendations and improve success of dispute resolution intelligently by automated computing systems without manual efforts and unnecessary computing resources).” Rem 11, Examiner respectfully disagrees.
The instant is a technological solution to a business problem (see para 2 and 3 of the originally filed specification as directed to solving the business problems with transaction dispute resolution manually with the time it takes to provide the correct evidence and the back and forth to get the evidence and dispute properly submitted and resolved as a time intensive process). To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP § 2106.05(f) for more information about mere instructions to apply an exception.
While applicant has argued (Rem 12) this is an improved machine learning system to provide faster, more accurate, and more coordinated results for data submission recommendations, Examiner respectfully disagrees. The specification has not provided an improvement to the machine learning system, it is an improvement to the business problem. Specification paragraph [0017] as cited by the applicant, describes tagging data and sorting data (using structured data architecture to apply tax rules and adjust the rules as they change in the tax industry) but does not provide how the functioning of the machine learning is improved.
Regarding applicant's correlation to Example 47, claim 3 (rem 13), Examiner respectfully disagrees. In Example 47 claim 3, Steps (e) and (f) further specify remedial actions that are executed to remediate or prevent network intrusions. The claimed steps of (e), automatically dropping the one or more malicious network packets, and (f), blocking future traffic from the source address, provide specific computer solutions that use the output from the ANN to provide security solutions to the detected anomalies. As indicated in paragraph six of the background, the system may “automatically” perform dropping of malicious network packets and blocking future traffic without the need for any action by a network administrator. Instead, the ANN may make decisions about whether a network packet is potentially malicious and take action to drop malicious network packets and block future traffic." While in the instant claims "improving ML dispute resolution technology through the technical implementation of specifically trained ML modes…trained with geographic specific data compliance restriction and text image extraction processes for evidence classification. reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f)." therefore, the instant claim is not similar to example 47 claim 3.
In example 48 claim 2 and 3 (rem 13, the eligibility rest in "the disclosure explains that devices that capture audio cannot properly distinguish different speech sources belonging to the same class and that the current available solutions do not adequately address this problem because they require a target user, whose speech is to be recognized, to explicitly interact with the device to provide training data. The instant claims and specification do not provide for a similar level of solving a technical problem. The example reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f)." therefore, the instant claim is not similar to example 47 claim 3.
Regarding Applicant’s remarks comparing the claims to Ex Parte Desjardins, Examiner respectfully disagrees. The facts of the appeal case are not similar to the instant claimed invention. Specifically, in Ex Parte Desjardins, the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. In contrast, the instant specification does not identify an improvement to a machine learning technology but to a business problem of merchant may not locate and upload proper evidence… election and upload of evidence by merchant during a dispute. This can cause unnecessary time and computing resources …from notifying the merchant that the evidence is not proper, the merchant determining and locating other evidence …" (see Specification [0003] ). Applicant's argument (rem 13) is not found to be convincing.
Regarding Applicant’s remarks to the USPTO Memo of August 4, 2025, including at pages 4-5 ("Improvements consideration" and "Apply it" guidance) (Rem 14) is noted and was considered but not found to be convincing.
35 USC 103
Applicant's arguments (rem 15-16) are convincing and overcome the prior rejection under 35 USC 103. Specifically, the combination of Khare et al., Liao, and Swaminathan do not teach ...extracting text data from the evidence for the past chargebacks using at least one of a text extraction process or an image extraction process; classifying the evidence for the past chargebacks into the plurality of evidence classifications; training a plurality of evidence recommendation machine learning (ML) models to classify the evidence for the past chargebacks based on the classified evidence and the plurality of evidence classifications, wherein the plurality of evidence recommendation ML models are each further trained based on training data associated with a geographic area, wherein the training data includes data compliance restrictions for the geographic area... Khare teaches processes for analyzing and resolving disputed data records using an ML engine for pattern recognition, which may correspond to neural network models that
are compared and selected to produce accurate pattern recognition or predictive capability
based on available data. See Khare, paragraph [0078]. Khare further teaches additional
processes for determining if required information is available to take a decision on a claim.
Id. at FIG. 6B, paragraph [0080]. However, Khare is silent with regard to "extracting text
data from the evidence for the past chargebacks using at least one of a text extraction
process or an image extraction process" for the purpose of training ML models, as recited
by amended claim 2. Moreover, Khare does not disclose, teach, or suggest training ML models "based on training data associated with a geographic area, wherein the training
data includes data compliance restrictions for the geographic area" and determining an ML
model to use for a dispute "based on . . .the data compliance restrictions," as recited by
amended claim 2.
Liao teaches a model-based chargeback representment system that predicts the probability of chargeback success based on the availability of particular evidence, as well as predicting the relevance of the evidence. See Liao, paragraph [0047]. Liao further teaches a classification model that recommends action items to the merchant for chargebacks, such as fighting chargebacks or determining what is the most valuable evidence for successful chargeback resolution. Id. at paragraph [0053]. However, Liao is similarly silent with regard to "extracting text data from the evidence for the past chargebacks using at least one of a text extraction process or an image extraction process" for training purposes, as recited by amended claim 2. Further, Liao makes a general reference to enabling collection and aggregation of information "in compliance with applicable laws, regulations, and rules," at paragraph [0062]. However, Liao does not disclose, teach, or suggest that ML models are "each further trained based on training data associated with a geographic area, wherein the training data includes data compliance
restrictions for the geographic area," nor does Liao disclose, teach, or suggest determining an ML model to use for a dispute based on "the data compliance restrictions," as recited
by amended claim 2.
Swaminathan teaches a chargeback application that performs different algorithms on evidence information to extract data relevant to a chargeback, such as object recognition algorithms and parsing algorithms to extract data from information provided to the application. See Swaminathan, paragraph [0045]. Swaminathan further teaches a GUI that walks a user through submission of data files that are requested for chargeback resolution and provides instructions for uploading the data files. Id. at paragraphs [0066]- [0071]. However, the processes for data extraction in Swaminathan are performed on evidence submitted by a merchant during representment of a current chargeback, not on evidence for past chargebacks as part of training ML models, as recited by amended claim 2. Furthermore, Swaminathan does not teach training ML models "based on training data associated with a geographic area, wherein the training data includes data compliance restrictions for the geographic area," much less determining an ML model to use based on "the data compliance restrictions," as recited by amended claim 2.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Jia et al. disclose an integrated machine learning models are jointly trained for taking control actions on a transaction to maximize an objective function based on a probability of the control actions matching corresponding target control actions. In an aspect, the integrated machine learning models are periodically re-trained, for example, re-trained every day, every week, every month, etc., based on historical control action data. Krammer et al. disclose a system for discriminating fraud disputes with machine learning. Baker et al. disclose a fraud detection system using models for scoring.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 Kelly Campen whose telephone number is (571)272-6740. The examiner can normally be reached Monday-Thursday 6am-3pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abhishek Vyas can be reached at 571-270-1836. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Kelly S. Campen
Primary Examiner
Art Unit 3691
/KELLY S. CAMPEN/Primary Examiner, Art Unit 3691
