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 with the RCE entered 12/31/2025. Claims 2-21 are pending.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/31/2025 has been entered.
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 recite the steps of a method comprising:
determining a plurality of evidence classifications that categorize evidence for 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;
training a plurality of evidence recommendation …models to classify the evidence for the past chargebacks based on the plurality of evidence classifications, 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 successfully resolves the past chargebacks for the users or the merchants submitting the evidence;
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, 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 successful 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 is 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.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2, 3, 10-12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Khare (US 2022/0230174) in view of Liao (US 2021/0390550) and further in view of Swaminathan (US 2019/0362347).
Specifically as to claims 2, 11 and 17, Khare et al. discloses a method (and related device and product) comprising: (Khare, Fig. 6a, [0074], process); receiving, via a dispute management interface from a merchant, a dispute of a transaction between a user and the merchant, wherein the dispute comprises a chargeback associated with the transaction; (Khare, Fig. 6a, [0074], submit a claim to the system regarding a disputed transaction 604); determining a plurality of attributes associated with the dispute and the chargeback; (Khare, Fig. 6a, [0074], [0075], summary of dispute reasoning 605); determining, based on one or more characteristics of the merchant and the transaction, an evidence recommendation machine learning (ML) model of a plurality of evidence recommendation machine learning (ML) model to use for the dispute; (Khare, [0078], “the machine learning engine utilizes a plurality of neural network models which are compared and selected, or combined to produce the most accurate pattern recognition or predictive capability based on available data.”); determining, using the evidence recommendation ML model based on the plurality of attributes and a plurality of evidence classifications for evidence submissible for a resolution of the dispute, at least a primary evidence type and a secondary evidence type recommended for the merchant to provide in response to the dispute; (Khare, Fig. 6b, [0080], determine if required information is available to take decision on claim, machine learning engine 156, classification).but does not specifically disclose determining a plurality of evidence classifications that categorize evidence for 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; training a plurality of evidence recommendation machine learning (ML) models to classify the evidence for the past chargebacks based on the plurality of evidence classifications, wherein the training includes: configuring the plurality of evidence recommendation ML models to generate probabilities that the evidence for the past chargebacks for each of the plurality of evidence classifications successfully resolves the past chargebacks for the users or the merchants submitting the evidence; determining a plurality of evidence classifications for evidence submissible for a resolution of the dispute, at least a primary evidence type and a secondary evidence type recommended for the merchant to provide in response to the dispute.; computing, using the evidence recommendation ML model, a probability that each of the primary evidence type and the secondary evidence type leads to a successful resolution for the merchant; determining at least one data extraction process that extracts data from the evidence submitted for the primary evidence type and the secondary evidence type based at least on the plurality of evidence classifications (Khare, [0077], [0078], data extraction from individual customer disputes. Liao, [0053]-[0061] discusses merchant integration to generate evidence automatically, including different types of evidence. Liao and Khare do not explicitly disclose determining a “data extraction process”. ); 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 of the dispute management interface to include the submission process for the merchant. (Khare, Fig. 6b, [0080], request for missing information 617. Liao, [0053]-[0061] discusses merchant integration to generate evidence automatically, including different types of evidence.).
Liao teaches determining evidence classifications that categorize evidence for past chargebacks in accordance with a plurality of evidence (see para 47 of Liao et al. “x, y, z” are within the scope of primary, secondary, etc.), training to classify the evidence for the past chargebacks and configure models to generate probabilities (see Liao et al. Para 47 “the automated representment may run multiple simulations by asking the model for the probability of success assuming particular evidence is available (e.g., Evidence “X”, “Y” or “Z”) and predicting the relevance of the evidence (e.g., relevance of the evidence to the resolution of a representment assertion).” discusses asking model for probability of success based on three types of evidence, x,y and z, Liao, [0040], notes merchant evidence available includes delivery confirmation, shipment tracking, prior user purchase history, and Liao, [0053], explicitly notes “A classification model to recommend action items to the merchant. Example action items including fighting chargebacks or not or what is the most valuable evidence.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning models and classification of Khare to include the model determining what evidence classification is most useful as discussed in Liao in order to use machine modeling to effectively handle transaction disputes as discussed in Liao, [0006], and Khare, [0040]. Further, it would have been obvious to one of ordinary skill in the art before the time of effective filing to include the system of Khare et the features as taught in Liao et al. since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, both are in the field of resolving transaction disputes and one of ordinary skill in the art would recognize the combination to be predictable.)
Swaminathan, [0045], discusses chargeback application that performs different algorithms (e.g. object recognition algorithms, parsing algorithms) on the evidence information to extract data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the data extraction and evidence generation of Khare and Liao to further include the specific algorithms of Swaminathan in order obtain evidence to dispute a chargeback or representment as discussed in Swaminathan, [0003], Liao, [0047], and Khare, [0078].
Swaminathan, Figs. 2 and 7, [0066], discusses chargeback application 179, presents a GUI includes a request for data files and “walks the user through which data files are requested and including instructions for uploading the data files” (See Swaminathan, [0045] and [0067]-[0071]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the request for information of Khare and generation of evidence of Liao to further include the GUI to submit evidence and explain the process of Swaminathan in order obtain evidence to dispute a chargeback or representment as discussed in Swaminathan, [0003], Liao, [0047], and Khare, [0078].)
Specifically as to claims 11 and 17 correspond to claim 2 and are rejected on the same grounds. Regarding device claim 11, Khare, Fig. 1, [0036], processing device 138, memory device 140. Regarding CRM claim 17, Khare, [0087], CRM.
. Specifically as to claims 3, and 12, wherein the primary evidence type comprises a higher percentage of a successful resolution of the dispute for the merchant than the secondary evidence type, and (Khare, Fig. 6b, [0080], determine if required information is available to take decision on claim. Khare does not specifically disclose the primary evidence type comprises a higher percentage of a successful resolution of the dispute for the merchant than the secondary evidence type; wherein the method further comprises: determining a plurality of tertiary evidence types recommended for the merchant to provide in response to the dispute, wherein two or more of the plurality of tertiary evidence types from the merchant improve chances for the successful resolution of the dispute for the merchant.
Liao, [0047], discusses relative relevance of evidence including percentage success rates for evidence and discusses asking model for probability of success based on three types of evidence, x,y and z..
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning models of Khare to include the model determining what evidence is most useful as discussed in Liao in order to use machine modeling to effectively handle transaction disputes as discussed in Liao, [0006], and Khare, [0040].)
Specifically as to claim 10, determining, from the plurality of attributes, one or more data variables associated with requesting the chargeback by the user; (Khare, Fig. 6a, [0075], neural network based text summarization 605 based on user’s submitted claim)
classifying, using the evidence recommendation ML model trained from a plurality of previous chargeback results, the chargeback as one of a plurality of dispute types based on the one or more data variables; and (Khare, Fig. 6a, [0075], machine learning engine 146 tags dispute according to given category in system claim generation 606) but does not specifically disclose determining dispute evidence for the chargeback from a plurality of dispute evidence tiers that enables a resolution of the chargeback by the merchant, wherein the plurality of dispute evidence tiers each have a corresponding threshold probability of the resolution of the chargeback, wherein the dispute evidence comprises the at least the primary evidence type and the secondary evidence type. (Khare, Fig. 6b, [0080], determine if required information is available to take decision on claim.).
Liao, [0047], discusses asking model for probability of success based on types of evidence, x,y and z.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the machine learning models of Khare to include the model determining what evidence is most useful as discussed in Liao in order to use machine modeling to effectively handle transaction disputes as discussed in Liao, [0006], and Khare, [0040].)
Response to Arguments
Applicant's arguments filed 12/31/2025 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 “ overcome the subject matter eligibility rejection for at least the reasons discussed during the Examiner Interview, i.e., the claims are statutory under Step 2A, Prong Two of the current guidance” Rem 11, it is respectfully noted no agreement was reached with regards to subject matter eligibility.
With regards to applicant’s arguments with respect to an improvement to technology because "a specific usage (more accurate classification of data and data extraction) in a specific situation (when managing dispute resolution for chargebacks requiring submission of different data 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)" 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 is 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 is 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 but have not provided an improvement to the machine learning system, it is an improvement to the business problem.
Regarding Applicant’s remarks to the USPTO Memo of August 4, 2025, including at pages 4-5 ("Improvements consideration" and "Apply it" guidance) (Rem 12) is noted and was considered but not found to be convincing.
35 USC 103
Regarding applicant’s argument the references do not teach “determining a plurality of evidence classifications that categorize evidence for 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; training a plurality of evidence recommendation machine learning (ML) models to classify the evidence for the past chargebacks based on the plurality of evidence classifications, wherein the training includes: configuring the plurality of evidence recommendation ML models to generate probabilities that the evidence for the past chargebacks for each of the plurality of evidence classifications successfully resolves the past chargebacks for the users or the merchants submitting the evidence… [and] ...computing, using the evidence recommendation ML model, a probability that each of the primary evidence type and the secondary evidence type leads to a successful resolution for the merchant...” Examiner respectfully disagrees.
Liao et al, as reasoned in the above rejection, teaches in an example at para 47, “ he model can be used to determine what evidence is most useful in asserting representment and predict, using identified evidence, the relative relevance of the respective evidence. The model can further cause the transaction processing system or merchant to actively acquire this evidence, or recommend acquiring the evidence, during future transactions. Because the model's features include available evidence, the automated representment may run multiple simulations by asking the model for the probability of success assuming particular evidence is available (e.g., Evidence “X”, “Y” or “Z”) and predicting the relevance of the evidence (e.g., relevance of the evidence to the resolution of a representment assertion). These simulations may be used to reduce operational cost. For example, if having Evidence X leads to a 95% success rate, whereas having Evidence Y and Z leads to a 90% success rate, a recommendation may be for the merchant to provide just the one piece of Evidence X.”
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.
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