DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
This action is in reply to Application 19/311,028 filed on 27 August 2025.
Claims 1-16 are currently pending and have been examined.
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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In the instant case, representative claim 1 is directed towards facilitating resolving disputed transactions and customer service in an automated manner. Claim 1 recites the abstract idea of using rules and/or instructions to facilitate a transaction comprising the steps of merely collect (“obtain training data”), preprocess (“format rules”), and analyze (“predict a resolution”) data/information associated with an financial transaction and/or activity, which is grouped under the certain methods of organizing human activity – fundamental economic principles, practices or concepts; sales activity; following set of instructions; commercial interactions (business relations); managing personal behavior of relationships or interactions between people (including social activities, teachings, following rules or instructions) grouping, in prong one of step 2A
Other than the mere nominal recitation of a computer-related device – nothing in the claim element precludes the steps from the organizing human interactions grouping, in prong one of step 2A. Accordingly, for these reasons, the claim recites an abstract idea.
Claim 1 recites:
“obtain training data indicative of payment network dispute resolution rules;
preprocess the training data to satisfy one or more of a set of formatting rules or a set of data completeness rules; and
train, using the training data, a machine learning model to predict a resolution to a dispute for a financial transaction”.
Based on the underlined elements above, abstract ideas and/or concepts are identified.
Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A, the additional elements of the claim such as a “circuitry”, “machine learning model”, represent the use of a computer as a tool to perform an abstract idea and/or does no more than generally apply the abstract idea to a particular field of use. Therefore, the additional elements do not integrate the abstract idea into a practical application as they do no more than represent a computer performing functions that correspond to (i.e. automate) implement the acts of using rules and/or instructions to facilitate a transaction comprising the steps of merely collect (“obtain training data”), preprocess (“format rules”), and analyze (“predict a resolution”) data/information associated with an financial transaction and/or activity.
When analyzed under step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception itself. Viewed as a whole, the combination of elements recited in the claims merely describe the concept of using rules and/or instructions to facilitate a transaction comprising the steps of merely collect (“obtain training data”), preprocess (“format rules”), and analyze (“predict a resolution”) data/information associated with an financial transaction and/or activity while utilizing computer computer-related technology and/or devices that merely perform as designed to function. Therefore, the use of these additional elements does no more than employ a computer as a tool to automate and/or implement the abstract idea, which cannot provide significantly more than the abstract idea itself (MPEP 2106.05(I)(A)(f) & (h)). Hence, claim 1 is not patent eligible.
Dependent claims 2-16 add further details and contain limitations that narrow the scope of the invention. However, these details do not result in significantly more than the abstract idea itself. As explained in the December 16, 2014 Interim Eligibility Guidance from the USPTO (in reference to the BuySAFE, Inc. v. Google, Inc. decision), further narrowing the details of an abstract idea does not change the § 101 analysis since a more narrow abstract idea does not make it any less abstract.
Viewed individually and in combination, these additional elements do not provide meaningful limitations to transform the abstract idea such that the claims amount to significantly more than the abstraction itself.
Accordingly, the present pending claims are not patent eligible and are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections – 35 USC § 103
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 of this title, 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.
Claims 1-16 are rejected under 35 U.S.C. 103 as being unpatentable over Ross et al., US 2022/0309507 A1 (“Ross”), in view of St. Pierre et al., US 11,916,927 B2 (“St. Pierre”).
Re Claim 1: Ross discloses a system comprising:
circuitry configured to: ([0034] “… evidence may be determined by transaction processor server 120 using ML engine 132”)
obtain training data indicative of payment network dispute resolution rules; ([0067] “Further, for recommended evidence 328, the merchant may be provided with an evidence guide 336 to view examples of the categories and/or learn more about each category”; FIG. 3B: {336} “See our Evidence Guide for examples on these categories”)
preprocess the training data to satisfy one or more of a set of formatting rules or a set of data completeness rules; ([0067] “Further, for recommended evidence 328, the merchant may be provided with an evidence guide 336 to view examples of the categories and/or learn more about each category”)
Regarding the limitation comprising:
train, using the training data, a machine learning model to predict a resolution to a dispute for a financial transaction.
St. Pierre makes these teachings in a related endeavor (C7 L4-13: “Specifically, the score API may function to interact with one or more computing servers that implement the ensembles of machine learning models used to predict a likelihood of digital fraud and/or digital abuse. The score API may function to return a value (e.g., a number, likelihood or probability, or other criterion) that indicates how likely it is that an actor involved or associated with digital events and/or activities is a malicious actor or may be perpetrating cyber fraud or digital abuse ( e.g., payment abuse, etc.)”; C12 L8-11: “S220 may function to implement one or more machine learning-based dispute classification models that may predict a dispute type for each distinct transaction dispute”; C5 L32-34: “generating the dispute response artifact based on the digital dispute …”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of St. Pierre with the invention of Ross as described above for the motivation of facilitating an enhanced detection of digital fraud or digital abuse.
Re Claim 2: Ross in view of St. Pierre discloses the system of claim 1. Ross further discloses:
wherein to obtain training data comprises to obtain training data from historical financial transaction disputes and resolutions. ([0048] “Transaction processing application 122 may process the payment and may provide a transaction history to merchant device 110 for transaction authorization, approval, or denial. The transaction history and other data may be associated with dispute 142 and may be used as evidence in resolving dispute 142”)
Re Claim 3: Ross in view of St. Pierre discloses the system of claim 2. Ross further discloses:
wherein to obtain training data comprises to obtain training data indicative of transaction parameters including a date, price, product, merchant, transaction type or payment network associated with each financial transaction. ([0034] “Dispute information for the dispute may be displayed through one or more of UIs 114, such as dispute details (e.g., issue raised by a customer, underlying transaction being dispute, and the like)”)
Re Claim 4: Ross in view of St. Pierre discloses the system of claim 2. Ross further discloses:
wherein to obtain training data comprises to obtain training data indicative of a reason for each dispute and a basis for each resolution. ([0060] “In list 300a, different disputes types are shown based on a reason 302 for submitting evidence categories in list 300a. Reason 302 may be associated with a reason code 304 for submitting the evidence, such as a dispute and/or evidence category or identifier with an internal or external system used in dispute claim resolution. Further, for each reason 302, a category 306 corresponding to the evidence submitted may be listed.”)
Re Claim 5: Ross in view of St. Pierre discloses the system of claim 1. Ross further discloses:
wherein to obtain training data comprises to obtain training data indicative of government regulations for financial transaction disputes. ([0069] “… evidence classification training data may be specific to a certain area or region, such as based on regulations, privacy laws, and the like.”)
Re Claim 6: Ross in view of St. Pierre discloses the system of claim 5. Ross further discloses:
wherein to obtain training data indicative of government regulations for financial transaction disputes comprises to obtain training data indicative of government regulations for one or more of debit transactions or credit transactions. ([0069] “… evidence classification training data may be specific to a certain area or region, such as based on regulations, privacy laws, and the like.”; [0015] “… For example, a claims resolution system for the service provider and/or other service providers (e.g., credit and/or debit card providers and card processor networks) may have multiple different categories for classifying evidence. A category of evidence may be evidence that provides proof of possession and/or proof of delivery, a device name or identifier (ID) used in a transaction, proof of signature to a transaction, and the like”)
Re Claim 7: Ross in view of St. Pierre discloses the system of claim 1. Ross further discloses:
wherein to obtain training data indicative of payment network dispute resolution rules comprises to obtain training data indicative of Visa payment network dispute resolution rules or MasterCard payment network dispute resolution rules. ([0015] “… For example, a claims resolution system for the service provider and/or other service providers (e.g., credit and/or debit card providers and card processor networks) may have multiple different categories for classifying evidence. A category of evidence may be evidence that provides proof of possession and/or proof of delivery, a device name or identifier (ID) used in a transaction, proof of signature to a transaction, and the like”)
Re Claim 8: Ross in view of St. Pierre discloses the system of claim 1. Ross further discloses:
wherein to obtain training data comprises to obtain training data indicative of merchant specific refund or exchange policies. ([0066] “Application 320 further includes a provide evidence 326 field that allows a merchant to submit evidence in response to the dispute, which may increase the merchant's chances of winning the dispute and/or avoiding a chargeback, refund, or the like. For example, based on dispute details 322, recommended evidence 328 is provided to the merchant in application 320”)
Re Claim 9: Ross in view of St. Pierre discloses the system of claim 1. Ross further discloses:
wherein to obtain training data comprises to obtain training data indicative of communication guidelines. (FIG. 3B: {324} “Have questions about this dispute? Email: disputes@abc.com”)
Re Claim 10: Ross in view of St. Pierre discloses the system of claim 1. Regarding the limitation comprising:
wherein to train a machine learning model comprises to train the machine learning model utilizing supervised learning.
St. Pierre makes these teachings in a related endeavor (C7 L25-27: “… digital event data processing decisions may be performed with manual input from one or more human analysts or the like”; C10 L24-28: “web (client) interface 120 may be used to collect manual decisions with respect to a digital
event processing decision, such as hold, deny, accept, additional review, and/or the like”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of St. Pierre with the invention of Ross as described above for the motivation of facilitating an enhanced detection of digital fraud or digital abuse.
Re Claim 11: Ross in view of St. Pierre discloses the system of claim 1. Ross further discloses:
wherein to train a machine learning model comprises to train the machine learning model utilizing unsupervised learning. ([0077] “FIG. 4B is a flowchart 420 for using a trained machine learning system for automated recommendations of evidence during dispute resolution”)
Re Claim 12: Ross in view of St. Pierre discloses the system of claim 1. Ross further discloses:
wherein to train a machine learning model comprises to adjust weights of a large language model. ([0042] “… the hidden layer may be trained with these attributes and corresponding weights using an AI algorithm, computation, and/or technique.”)
Re Claim 13: Ross in view of St. Pierre discloses the system of claim 1. Ross further discloses:
wherein to train a machine learning model comprises to train an ensemble of multiple machine learning models. ([0040] “… one or more of ML models 134 are used to perform evidence recommendation, the training data may be evidence categories or types and results of their corresponding dispute (e.g., success or failure to win a dispute, including win likelihood or probability over a group of disputes).”)
Re Claim 14: Ross in view of St. Pierre discloses the system of claim 13. Ross further discloses:
wherein to train an ensemble of multiple machine learning models comprises to train a separate machine learning model for each of multiple types of financial transactions including debit financial transactions and credit financial transactions. ([0040] “… one or more of ML models 134 are used to perform evidence recommendation, the training data may be evidence categories or types and results of their corresponding dispute (e.g., success or failure to win a dispute, including win likelihood or probability over a group of disputes).”)
Re Claim 15: Ross in view of St. Pierre discloses the system of claim 13. Ross further discloses:
wherein to train an ensemble of multiple machine learning models comprises to train a separate machine learning model for each of multiple sets of payment network dispute resolution rules. ([0040] “… one or more of ML models 134 are used to perform evidence recommendation, the training data may be evidence categories or types and results of their corresponding dispute (e.g., success or failure to win a dispute, including win likelihood or probability over a group of disputes).”)
Re Claim 16: Ross in view of St. Pierre discloses the system of claim 13. Ross further discloses:
wherein to train an ensemble of multiple machine learning models comprises to train a separate machine learning model for communicating with parties associated with a financial transaction based on a set of communication guidelines. ([0040] “… one or more of ML models 134 are used to perform evidence recommendation, the training data may be evidence categories or types and results of their corresponding dispute (e.g., success or failure to win a dispute, including win likelihood or probability over a group of disputes).”; [0035] Dispute resolution application 112 may be utilized by the merchant to view one or more of UIs 114, for example, via graphical UIs (GUIs) presented using an output display device of merchant device 110. UIs 114 may enable the merchant associated with merchant device 110 to access the dispute resolution portal and/or application of transaction processor server 120 and provide the evidence in response to the dispute, including the recommended evidence. Additionally, APIs and API integrations through calls, requests, and responses may be utilized between dispute resolution application 112 and one or more applications, platforms, and/or services of transaction processor server 120. Such API interactions may cause UIs 114 to present the recommended evidence though the output display device or component of merchant device 110”)
Conclusion
The prior art(s) made of record and not relied upon is/are considered pertinent to applicant's disclosure.
Richey et al., (US 2010/0169194 A1) discloses a method and system for facilitating electronic dispute resolution. A system for facilitating payment transaction disputes is provided. According to one aspect of the system, a user, such as an issuer, is allowed to use the system to resolve a disputed transaction. Based on information provided by a cardholder, the issuer is able to use the system to retrieve transactional information relating to the disputed transaction reported by the customer for review. When the issuer uses the system to retrieve information relating to the disputed transaction, a case folder is created. The case folder is a repository for storing all the relevant information and documentation relating to the disputed transaction. Using the information retrieved by the system, the issuer then determines whether to initiate a dispute. Alternatively, the system can also be used by an acquirer to respond to a dispute, usually on behalf of one of its merchant. If a dispute is responded to, a questionnaire is then created by the system. Alternatively, the issuer may decline to initiate a dispute and either seek additional information from the cardholder or deny the cardholder's inquiry.
Vijayaraghavan, (US 2023/0098747 A1) discloses systems and methods for payment transactions, alerts, dispute settlement, and settlement payments, using multiple block chains. Systems and methods are disclosed for payment transactions, alerts, dispute settlement, and settlement payments,
using multiple blockchains. One method includes: entering, in a first blockchain, a transaction identifier indicating the initiation of and identification of a transaction; receiving an identifier of a currency or cryptocurrency account for participants of the payment transaction; performing one or more iterations of: identifying a new transaction event in the series of transaction events stored in the first blockchain; presenting the new transaction event to participants of the transaction, wherein the presentation enables a participant to indicate a dispute of an attribute of the transaction; relaying one
or more attributes of the transaction to a second blockchain for processing the transaction; and receiving, from the second blockchain, an indication of a transfer of funds between the two or more participants, using the identifiers of the currency or cryptocurrency accounts of the two or more
participants.
Claims 1-16 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Clifford Madamba whose telephone number is 571-270-1239. The examiner can normally be reached on Mon-Thu 7:30-5:00 EST Alternate Fridays.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ryan Donlon, can be reached at 571-272-3602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CLIFFORD B MADAMBA/Primary Examiner, Art Unit 3692
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