DETAILED ACTION
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 26, 2026 has been entered.
Status of Claims
• This action is in reply to the RCE filed on January 26, 2026.
• Claims 1, 4, 10-12, 15, and 19 have been amended and are hereby entered.
• Claims 2-3, 13-14, and 20 have been canceled.
• Claims 1, 4-12, and 15-19 are currently pending and have been examined.
• This action is made Non-FINAL.
Response to Arguments
Applicant’s arguments filed January 26, 2026 have been fully considered but they are not persuasive.
The Examiner is withdrawing the drawing objections due to Applicant’s amendments.
The Examiner is withdrawing the claim objections due to Applicant’s amendments.
The Examiner is withdrawing the 35 USC § 112 rejections due to Applicant’s amendments.
Applicant’s arguments with respect to 35 USC § 103 have been fully considered and are not persuasive.
Regarding Applicant’s argument on page 9, that the cited art of record does not teach a mean or median propensity score of a PAN set for multiple merchants each having been assigned to the first MCC, the Examiner respectfully disagrees. As discussed in the 103 rejection below, Abay teaches the limitation of determining a value using a PAN set for multiple merchants each having been assigned to the first MCC at least at [0095]-[0101], describing analyzing historical transaction data including MCC data to generate data for a merchant system, for example determining a difference value, and that the transaction data includes parameters such as PAN. And, Fidanza teaches the value is a mean or median propensity score at least at [0050]-[0052] and [0105], describing a mean or median value of a dataset, and that dataset for a plurality of customers include PAN for the customers accounts. The cited art of record therefore teaches this limitation.
For the reasons above, Applicant’s arguments are not persuasive.
Applicant’s arguments with respect to 35 USC § 101 have been fully considered and are not persuasive.
Regarding Applicant’s argument on page 11, that the claims are not directed to an abstract idea, the Examiner respectfully disagrees. As indicated in the 35 USC § 101 rejection below, the claimed inventions allows for using a model to determine whether a merchant was misclassified. The Specification at [0003]-[0006] states:
“The acquiring bank may use the MCC to determine interchange fees, the transaction fees paid by the acquiring bank, to cover the cost of risks involved in approving a payment transaction with the merchant. High-risk merchants are typically associated with high-risk industries, which pose a greater risk of chargebacks (e.g., the return of funds to a consumer initiated by an issuing bank), have an elevated fraud risk, and/or are heavily regulated in certain jurisdictions. For example, high-risk merchants may include online casinos, online pharmacies, adult content websites, and the sale of cryptocurrencies. High-risk merchants typically pay a higher interchange rate than lower risk merchants, and acquiring banks identify high-risk merchants based on their assigned MCC… for example a high-risk merchant may provide fraudulent or misleading documentation in order to be assigned to a lower risk MCC for the purpose of eliciting a lower interchange rate…”
The Specification and claims focus on an improvement to the process of mitigating risk and preventing fraud of a merchant misclassifying itself to avoid paying a higher interchange rate, which is a fundamental economic practice and a commercial and legal interaction including sales activities or behaviors which falls within the category of Certain Methods of Organizing Human Activity and therefore is an abstract idea.
Regarding Applicant’s arguments on pages 11-12, that the claims integrate a practical application and that the claims apply meaningful limits on the abstract idea, the Examiner respectfully disagrees. Under the Patent Subject Matter Eligibility analysis, Step 2A, prong two, integration into a practical application requires an additional element(s) or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Limitations that are not indicative of integration into a practical application are those that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.-see MPEP 2106.05(f). Here, the claims recite a computer-implemented method; a computer device comprising: at least one processor; and at least one memory in communication with the at least one processor, the at least one memory; a non-transitory computer-readable storage medium that includes computer-executable instructions executable by at least one processor; a machine learning tool; a computer device including at least one processor such that they amount to no more than mere instructions to apply the exception using generic computer components (see MPEP 2106.05(f)).
Furthermore, and in response to Applicant’s arguments on pages 13-14 that the claims improve technology, in determining whether a claim integrates a judicial exception into a practical application, a determination is made of whether the claimed invention pertains to an improvement in the functioning of the computer itself or any other technology or technical field (i.e., a technological solution to a technological problem). Here, the claims recite generic computer components, i.e., a generic processor, a memory storing a computer program executable by the processor to perform the claimed method steps and system functions. The processor, memory and system are recited at a high level of generality and are recited as performing generic computer functions customarily used in computer applications.
Furthermore, the Specification describes a problem and improvement to a business or commercial process at least at [0003]-[0006], describing an improvement to the process of mitigating risk and preventing fraud of a merchant misclassifying itself to avoid paying a higher interchange rate, and stating:
“The acquiring bank may use the MCC to determine interchange fees, the transaction fees paid by the acquiring bank, to cover the cost of risks involved in approving a payment transaction with the merchant. High-risk merchants are typically associated with high-risk industries, which pose a greater risk of chargebacks (e.g., the return of funds to a consumer initiated by an issuing bank), have an elevated fraud risk, and/or are heavily regulated in certain jurisdictions. For example, high-risk merchants may include online casinos, online pharmacies, adult content websites, and the sale of cryptocurrencies. High-risk merchants typically pay a higher interchange rate than lower risk merchants, and acquiring banks identify high-risk merchants based on their assigned MCC… for example a high-risk merchant may provide fraudulent or misleading documentation in order to be assigned to a lower risk MCC for the purpose of eliciting a lower interchange rate…”
Regarding Applicant’s arguments on pages 12-13, that the claims are more than a drafting effort designed to monopolize the judicial exception, the argument has been considered and is not persuasive. In response to this argument, it is noted, “While preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility.” Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015). The instant application is reviewed within the framework of the Revised Guidance which specifies and particularizes the Mayo/Alice framework.
Regarding Applicant’s arguments on pages 14-15, that the claims recite significantly more than the abstract idea and that the claims recite more than what is well-understood, routine, conventional, the Examiner respectfully disagrees. The limitations are directed to an abstract idea and when determining if the claims are directed to significantly more, the additional limitations of the claims in addition to the abstract idea are analyzed. In the instant application, the additional elements of the claim include a computer-implemented method; a computer device comprising: at least one processor; and at least one memory in communication with the at least one processor, the at least one memory; a non-transitory computer-readable storage medium that includes computer-executable instructions executable by at least one processor; a machine learning tool; a computer device including at least one processor. The additional limitations, when considered both individually and in combination, do not affect an improvement to another technology or technological field; the claims do not amount to an improvement to the functioning of the computer itself; and the claims do not move beyond a general link of use of an abstract idea to a particular technological environment. Therefore, the claims merely amount to the application or instructions to apply the abstract idea using a computer, and is considered to amount to nothing more than requiring a generic computer merely to carry out the abstract idea itself. The specifics about the abstract idea do not overcome the rejection.
Applicant’s reliance upon claim 3 of Example 47, on pages 14-15, is misplaced. As an initial matter, with respect to USPTO Examples, the Examiner analyzes the claims under the two part framework under Alice/Mayo. The Examples provided in Office Guidance are hypothetical and intended to be illustrative only. While some of the fact patterns in the examples draw from U.S. Supreme Court and U.S. Court of Appeals for the Federal Circuit decisions, the examples do not carry the weight of court decisions. Claim 3 in hypothetical Example 47 were found to be eligible because the claim reflected an improvement “in the technical field of network intrusion. detection. Steps (d)-(f) provide for improved network security using the information from the detection to enhance security by taking proactive measures to remediate the danger by detecting the source address associated with the potentially malicious packets. Specifically, the claim reflects the improvement in step (d), dropping potentially malicious packets in step (e), and blocking future traffic from the source address in step (f). Turning to the instant application, the claims are not addressing a problem technical in nature, but are an improvement to a business process i.e., the process of mitigating risk and preventing fraud of a merchant misclassifying itself to avoid paying a higher interchange rate. The Examiner finds no parallel between the Applicant’s claims and the hypothetical, patent-eligible claim 3 described in Example 47.
The claims are not patent eligible.
For the reasons above, Applicant’s arguments are not persuasive.
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, 4-12, and 15-19 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 12, and 19 are directed to a method (claim 1), an apparatus (claims 12 and 19). Therefore, on its face, each independent claim 1, 12, and 19 are directed to a statutory category of invention under Step 1 of the Patent Subject Matter Eligibility analysis (see MPEP 2106.03).
Under Step 2A, Prong One of the Patent Subject Matter Eligibility analysis (see MPEP 2106.04), claims 1, 12, and 19 recite, in part, a system, a method, and an apparatus of organizing human activity. Using the limitations in claim 1 to illustrate, the claim recites using a tool for identifying and correcting a misclassified merchant category code (MCC) included within a request message, the method implemented using, the method comprising: storing a first propensity model that is trained with multiple account identifiers, including a personal account number (PAN), used to initiate multiple purchase transactions with multiple merchants each having been assigned to a first MCC; determining a mean or median propensity score of a PAN set for multiple merchants each having been assigned to the first MCC; inputting, into the first propensity model, an account identifier used to initiate a purchase transaction with a candidate merchant assigned to the first MCC and the determined mean or median propensity, wherein the candidate merchant is possibly being mis-assigned to a wrong MCC; outputting from the first propensity model a first score based on the inputted account identifier; comparing the outputted score to a threshold value; and based on the comparison to the threshold value, determining that the candidate merchant was mis-assigned to the first MCC; determining a correct MCC that more accurately classifies the candidate merchant than the first MCC: and automatically replacing the first MCC with the correct MCC for subsequent purchase transactions. The Specification at [0003]-[0006] states:
“The acquiring bank may use the MCC to determine interchange fees, the transaction fees paid by the acquiring bank, to cover the cost of risks involved in approving a payment transaction with the merchant. High-risk merchants are typically associated with high-risk industries, which pose a greater risk of chargebacks (e.g., the return of funds to a consumer initiated by an issuing bank), have an elevated fraud risk, and/or are heavily regulated in certain jurisdictions. For example, high-risk merchants may include online casinos, online pharmacies, adult content websites, and the sale of cryptocurrencies. High-risk merchants typically pay a higher interchange rate than lower risk merchants, and acquiring banks identify high-risk merchants based on their assigned MCC… for example a high-risk merchant may provide fraudulent or misleading documentation in order to be assigned to a lower risk MCC for the purpose of eliciting a lower interchange rate…”
The limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers fundamental economic principles or practices and commercial and legal interactions (certain methods of organizing human activity), but for the recitation of generic computer components. The claims as a whole recite a method of organizing human activity. The claimed inventions allows for using a model to determine whether a merchant was misclassified, which is a fundamental economic principle or practice of mitigating risk and a commercial and legal interaction including sales activities or behaviors. The mere nominal recitation of a computer device including at least one processor, do not take the claim out of the methods of organizing human activity grouping. Thus, the claims recite an abstract idea.
Under Step 2A, Prong Two of the Patent Subject Matter Eligibility analysis (see MPEP 2106.04), the judicial exception is not integrated into a practical application. In particular, the additional elements of a computer-implemented method; a computer device comprising: at least one processor; and at least one memory in communication with the at least one processor, the at least one memory; a non-transitory computer-readable storage medium that includes computer-executable instructions executable by at least one processor; a machine learning tool; a computer device including at least one processor are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of storing a model, inputting data into the model, outputting a score, comparing the score to a threshold value and determine whether a merchant was mis-assigned to an original MCC) such that they amount to no more than mere instructions to apply the exception using a generic computer components (see MPEP 2106.05(f)).
Accordingly, the combination of the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Under Step 2B of the Patent Subject Matter Eligibility analysis (see MPEP 2106.05), the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
The claims are not patent eligible.
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 because for the same reasoning as above and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. Dependent claims 4-11 and 15-18 simply help to define the abstract idea. The additional limitations of the dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Viewing the claim limitations as an ordered combination does not add anything further than looking at the claim limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. Accordingly, claims 1, 4-12, and 15-19 are ineligible.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1, 4-5, 12, 15-16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20230214843 A1 (“Abay”) in view of US 20240169355 A1 (“Fidanza”), and in further view of US 11756020 B1 (“Stipech”).
Regarding claim 1, Abay discloses a computer-implemented method using a machine learning tool for identifying and correcting a misclassified merchant category code (MCC) included within a request message, the computer-implemented method implemented using a computer device including at least one processor, the method comprising (see at least [0020]-[0021] and FIG. 3A-3B. Machine learning tool, see at least [0102].):
storing a first propensity model that is trained with multiple account identifiers, including a personal account number (PAN), used to initiate multiple purchase transactions with multiple merchants each having been assigned to a first MCC (Obtaining historical transaction data associated with a merchant. For example, transaction service provider system may obtain historical transaction data associated with a time series of a plurality of historical transactions at merchant system over a historical period of time (e.g., over a prior year or years, etc.). The historical transaction data (e.g., clearing and settlement data, training data, etc.) may include a plurality of MCCs associated with the plurality of historical transactions and/or a plurality of transaction amounts associated with the plurality of transactions. The historical transaction data (e.g., clearing and settlement data, training data, etc.) may include a plurality of MCCs associated with the plurality of historical transactions, a plurality of transaction amounts associated with the plurality of historical transactions, and/or a plurality of merchant identifiers associated with the plurality of historical transaction. See at least [0095]-[0096]. Processing previous transaction data with a machine learning model. See at least [0108]. Storing data, see at least [0092]. Training a machine learning model using the historical transaction data, see at least [0101]-[0102]. Transaction data (e.g., historical transaction data, previous transaction data, current transaction data, etc.) may include parameters associated with a transaction, such as an account identifier (e.g., a PAN, etc.). See at least [0097].);
determining a value using a PAN set for multiple merchants each having been assigned to the first MCC (applying a difference transform to a time series of historical transaction data. For example, transaction service provider system 108 may apply a difference transform to the historical transaction data to generate transformed data. As an example, transaction service provider system 108 may apply, for each merchant system 102 of the plurality of merchant systems 102, a difference transform to the time series of the plurality of historical transactions at that merchant system 102 over the historical period of time, to generate transformed data associated with each merchant system. For example, a difference transform may subtract a value of previous observation, transaction parameters, or data point (e.g., a transaction amount, a percentage distribution of a transaction amount per MCC, etc.) from a value of a current observation transaction parameter, or data point in a time series to determine a difference therebetween (e.g., a transformed transaction parameter, etc.): difference(t)=observation(t)−observation(t−1). See at least [0100]-[0101]. Obtaining historical transaction data associated with a merchant. See at least [0095]-[0096]. Transaction data (e.g., historical transaction data, previous transaction data, current transaction data, etc.) may include parameters associated with a transaction, such as an account identifier (e.g., a PAN, etc.). See at least [0097].);
inputting, into the first propensity model, an account identifier used to initiate a purchase transaction with a candidate merchant assigned to the first MCC and the determined value, wherein the candidate merchant possibly being mis-assigned to a wrong MCC (Receiving current transaction data associated with a merchant. For example, transaction service provider system may receive, during processing of a current transaction at merchant system in transaction processing network, current transaction data associated with the current transaction. See at least [0113]. Transaction data (e.g., historical transaction data, previous transaction data, current transaction data, etc.) may include parameters associated with a transaction, such as an account identifier (e.g., a PAN, etc.). See at least [0097]. training a machine learning model with transformed data. See at least [0102].);
outputting from the first propensity model a first score based on the inputted account identifier (Determining a risk score based on current transaction data and an anomaly score. For example, transaction service provider system may determine, based on the current transaction data and the anomaly score associated with merchant system, a risk score associated with the current transaction. As an example, transaction service provider system may input, to a risk prediction model (e.g., a fraud prediction model, etc.), one or more transaction parameters associated with the current transaction (e.g., merchant name, MCC, transaction amount, etc.) and the anomaly score associated with merchant system, and receive, as output from the risk prediction model, a risk score associated with the current transaction (e.g. a probability that the current transaction includes an incorrect or shifted MCC, a probability that the current transaction is a fraudulent transaction, etc.). See at least [0114]. Transaction data (e.g., historical transaction data, previous transaction data, current transaction data, etc.) may include parameters associated with a transaction, such as an account identifier (e.g., a PAN, etc.). See at least [0097].);
comparing the outputted score to a threshold value (Process includes comparing a risk score to a threshold risk score. For example, transaction service provider system may compare the risk score associated with the current transaction to at least one threshold risk score. See at least [0115].);
based on the comparison to the threshold value, determining that the candidate merchant was mis-assigned to the first MCC (Process includes approving or denying authorization of a current transaction. For example, in response to determining that the risk score satisfies the at least one threshold risk score, transaction service provider system may deny authorization of the current transaction. As an example, in response to determining that the risk score satisfies the at least one threshold risk score, transaction service provider system may determine that the current transaction includes an incorrect or shifted MCC. See at least [0116].).
While Abay discloses a value, Abay does not expressly disclose a mean or median propensity score. Furthermore, Abay does not expressly disclose determining a correct MCC that more accurately classifies the candidate merchant than the first MCC: and automatically replacing the first MCC with the correct MCC for subsequent purchase transactions.
However, Fidanza discloses a mean or median propensity score (inputting mean or median value into a machine learning model, see at least [0105]. Dataset for a plurality of customers include PAN for the customers accounts, see at least [0050]-[0052]. Analyzing BIN, see at least [0043].).
From the teaching of Fidanza, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the value of Abay to be a mean or median propensity score, as taught by Fidanza, in order to mitigate fraud (see Fidanza at least at [0003]-[0006] and [0043]), and in order to mitigate risk (see Fidanza at least at [0104]-[0105]).
Abay does not expressly disclose determining a correct MCC that more accurately classifies the candidate merchant than the first MCC: and automatically replacing the first MCC with the correct MCC for subsequent purchase transactions.
However, Stipech discloses determining a correct MCC that more accurately classifies the candidate merchant than the first MCC: and automatically replacing the first MCC with the correct MCC for subsequent purchase transactions ( determining a merchant is classified in the wrong class, and re-classifying the merchant. See at least col. 12, lines 6-39.).
From the teaching of Stipech, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify Abay to determine determining a correct MCC that more accurately classifies the candidate merchant than the first MCC, as taught by Stipech, and to modify Abay to automatically replacing the first MCC with the correct MCC for subsequent purchase transactions, as taught by Stipech, in order to improve ease and convenience of online transactions, see Stipech at least at col. 1, lines 8-25 and see col. 2, lines 36-60).
Regarding claim 4, the combination of Abay, Fidanza, and Stipech discloses the limitations of claim 1, as discussed above. Abay does not expressly disclose transmitting the correct MCC for the candidate merchant to an acquiring bank.
However, Stipech discloses transmitting the correct MCC for the candidate merchant to an acquiring bank (Classifying a merchant with the correct classification and transmitting the classification to the payment service system or another service (e.g., a credit card company). See at least col. 12, lines 40-50. Acquirer may be a credit card company or an acquiring bank, see at least col 5, lines 24-41.).
From the teaching of Stipech, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify Abay to transmit the correct MCC for the candidate merchant to an acquiring bank, as taught by Stipech, in order to improve ease and convenience of online transactions (see Stipech at least at col. 1, lines 8-25 and see col. 2, lines 36-60).
Regarding claim 5, the combination of Abay, Fidanza, and Stipech discloses the limitations of claim 1, as discussed above, and Abay further discloses training the first propensity model with a plurality of primary account number (PAN) sets (Obtaining historical transaction data associated with a merchant. For example, transaction service provider system may obtain historical transaction data associated with a time series of a plurality of historical transactions at merchant system over a historical period of time (e.g., over a prior year or years, etc.). The historical transaction data (e.g., clearing and settlement data, training data, etc.) may include a plurality of MCCs associated with the plurality of historical transactions and/or a plurality of transaction amounts associated with the plurality of transactions. The historical transaction data (e.g., clearing and settlement data, training data, etc.) may include a plurality of MCCs associated with the plurality of historical transactions, a plurality of transaction amounts associated with the plurality of historical transactions, and/or a plurality of merchant identifiers associated with the plurality of historical transaction. See at least [0095]-[0096]. Transaction data (e.g., historical transaction data, previous transaction data, current transaction data, etc.) may include parameters associated with a transaction, such as an account identifier (e.g., a PAN, etc.). See at least [0097].).
Claim 12 has similar limitations found in claim 1 above, and therefore is rejected by the same art and rationale.
Claim 15 has similar limitations found in claim 4 above, and therefore is rejected by the same art and rationale.
Claim 16 has similar limitations found in claim 5 above, and therefore is rejected by the same art and rationale.
Claim 19 has similar limitations found in claim 1 above, and therefore is rejected by the same art and rationale.
Claims 6, 8-9, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Abay in view of Fidanza, in further view of Stipech, and in further view of US 20240013211 A1(“Edwards”).
Regarding claim 6, the combination of Abay, Fidanza, and Stipech discloses the limitations of claim 1, as discussed above. Abay does not expressly disclose using the first propensity model to calculate the threshold value.
However, Edwards discloses using the first propensity model to calculate the threshold value (Model trained to output a threshold, see at least [0074] and [0076]).
From the teaching of Edwards, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the using of the propensity model of Abay to use the model to calculate a threshold value, as taught by Edwards, in order to improve safety of financial accounts (see Edwards at least at [0002]-[0005]).
Regarding claim 8, the combination of the combination of Abay, Fidanza, and Stipech discloses the limitations of claim 1, as discussed above, and Abay further discloses selecting the candidate merchant from among a plurality of merchants (Receiving current transaction data associated with a merchant. For example, transaction service provider system may receive, during processing of a current transaction at merchant system in transaction processing network, current transaction data associated with the current transaction. See at least [0113]. Transaction data (e.g., historical transaction data, previous transaction data, current transaction data, etc.) may include parameters associated with a transaction, such as an account identifier (e.g., a PAN, etc.). See at least [0097].).
While Abay discloses selecting the candidate merchant, Abay does not expressly disclose using a natural language processing model.
However, Edwards discloses selecting using a natural language processing model (using a natural language processing with a model, see at least [0064].).
From the teaching of Edwards, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the selecting of Abay to select using a natural language processing model, as taught by Edwards, in order to improve safety of financial accounts (see Edwards at least at [0002]-[0005]).
Regarding claim 9, the combination of the combination of Abay, Fidanza, and Stipech discloses the limitations of claim 1, as discussed above, and Abay further discloses selecting the candidate merchant from among a plurality of merchants (Receiving current transaction data associated with a merchant. For example, transaction service provider system may receive, during processing of a current transaction at merchant system in transaction processing network, current transaction data associated with the current transaction. See at least [0113]. Transaction data (e.g., historical transaction data, previous transaction data, current transaction data, etc.) may include parameters associated with a transaction, such as an account identifier (e.g., a PAN, etc.). See at least [0097].).
While Abay discloses selecting, Abay does not expressly disclose randomly selecting a merchant.
However, Edwards discloses randomly selecting a merchant (randomly selecting a merchant, see at least [0055].).
From the teaching of Edwards, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the selecting of Abay to randomly select, as taught by Edwards, in order to improve safety of financial accounts (see Edwards at least at [0002]-[0005]).
Regarding claim 11, the combination of the combination of Abay, Fidanza, and Stipech discloses the limitations of claim 1, as discussed above, and Abay further discloses selecting the candidate merchant from among a plurality of merchants using a machine learning model (Receiving current transaction data associated with a merchant. For example, transaction service provider system may receive, during processing of a current transaction at merchant system in transaction processing network, current transaction data associated with the current transaction. See at least [0113]. Transaction data (e.g., historical transaction data, previous transaction data, current transaction data, etc.) may include parameters associated with a transaction, such as an account identifier (e.g., a PAN, etc.). See at least [0097].).
While Abay discloses selecting, Abay does not expressly disclose selecting to determine merchant names that exhibit semantic differences than a set keywords associated with merchant names for merchants having the first MCC.
However, Edwards discloses selecting to determine merchant names that exhibit semantic differences than a set keywords associated with merchant names for merchants having the first MCC (The computing device may use a fuzzy search or match algorithm to translate certain key words into entity names. The computing device may take a key word that might look like a merchant name, and do a fuzzy search or match by removing the capitalization, or removing “'s” at the end of the word Pizzeria's. If a word is mis-spelled, the computing device may account for the mis-spellings and recognize the name “Pizzeria,” even it is spelled as “Pizeria.”).
From the teaching of Edwards, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the selecting of Abay to select to determine merchant names that exhibit semantic differences than a set keywords associated with merchant names for merchants having the first MCC, as taught by Edwards, in order to improve safety of financial accounts (see Edwards at least at [0002]-[0005]).
Claim 18 has similar limitations found in claim 8 above, and therefore is rejected by the same art and rationale.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Abay in view of Fidanza, in further view of Stipech, and in further view of US 11144923 B1 (“Griffith”).
Regarding claim 7, the combination of Abay, Fidanza, and Stipech discloses the limitations of claim 1, as discussed above. Abay does not expressly disclose selecting the candidate merchant from among a plurality of merchants based on an issue referral of cardholder complaints.
However, Griffith discloses selecting the candidate merchant from among a plurality of merchants based on an issue referral of cardholder complaints (The progressive authorization determination system can increase a risk (e.g., the risk score described above) associated with a merchant server based on complaints received from users regarding the merchant server or merchant, or information indicative of the merchant server improperly utilizing received user profile information. See at least col. 12, line 63 to col 13, line 26. Users are cardholders, see at least col. 2, lines 11-26.).
From the teaching of Griffith, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify Abay to select selecting the candidate merchant from among a plurality of merchants based on an issue referral of cardholder complaints, as taught by Griffith, in order to improve security of transactions (see Griffith at least atcol. 1, lines 7-16 and col. 12, line 63 to col 13, line 26).
Claim 17 has similar limitations found in claim 7 above, and therefore is rejected by the same art and rationale.
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 claim 10. While some individual features of claim 10 may be shown in the prior art of record: no known reference, alone or in combination, would provide the invention of claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20230060331 A1 (“Kraus”) discloses receive a request for processing a transaction; identify a merchant-specific identifier for a merchant associated with the transaction; determine, in real-time and using a machine trained model, whether the merchant-specific identifier is a valid merchant-specific identifier or not; and process the transaction based on whether the machine trained model indicates that the merchant-specific identifier is valid or not.
US 20230079865 A1 (“Rolfs”) discloses identifying merchant category code misclassifications includes at least one processor in communication with a transaction database and a merchant database. The transaction database stores transaction records by a plurality of account holders. The processor generates a first MCC profile including at least one transaction characteristic representative of merchants properly classified as the first MCC and comparing the first MCC profile to a second set of transaction records. If the comparison satisfies a comparison threshold for the first MCC the processor identifies the corresponding selected merchant as being MCC misclassified.
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/RAVEN E YONO/Primary Examiner, Art Unit 3694
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