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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08-15-2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under U.S.C 101 for containing an abstract idea without significantly more.
Regarding claim 1:
Step 1 – Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is a process.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites an abstract idea.
predicting, [by the at least one processor, via an entity category determining machine learning engine], at least one category associated with the at least one first activity performed by the user, - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.)
binding, [by the at least one processor], the unbound schema-specific identifier to the predicted at least one category to generate a category bound schema-specific identifier; and - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.)
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
by the at least one processor, – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)).
via an entity category determining machine learning engine – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)).
receiving, [by at least one processor], activity data related to at least one first activity performed by a user utilizing an unbound schema-specific identifier; – This limitation is directed to mere data gathering (see MPEP 2106.05(g))
wherein the entity category determining machine learning engine is trained to associate categories with entities based on at least one of: at least one entity data feature vector, at least one historical user activity data feature vector, and at least one historical user schema-specific identifier data feature vector; Adding the words "apply it" (or an equivalent) with the judicial exception, or 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)] and therefore fails to integrate the exception into a practical application.
instructing, [by the at least one processor], at least one second activity based on the category bound schema-specific identifier. Adding the words "apply it" (or an equivalent) with the judicial exception, or 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)] and therefore fails to integrate the exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, there are no additional elements that amount to significantly more than the judicial exception. The additional elements are:
by the at least one processor, – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)).
via an entity category determining machine learning engine – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)).
receiving, [by at least one processor], activity data related to at least one first activity performed by a user utilizing an unbound schema-specific identifier; – This limitation is directed to mere data gathering (see MPEP 2106.05(g))
wherein the entity category determining machine learning engine is trained to associate categories with entities based on at least one of: at least one entity data feature vector, at least one historical user activity data feature vector, and at least one historical user schema-specific identifier data feature vector; Adding the words "apply it" (or an equivalent) with the judicial exception, or 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)] and therefore fails to integrate the exception into a practical application.
instructing, [by the at least one processor], at least one second activity based on the category bound schema-specific identifier. Adding the words "apply it" (or an equivalent) with the judicial exception, or 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)] and therefore fails to integrate the exception into a practical application.
Regarding claim 2,
Claim 2 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations:
wherein the unbound schema-specific identifier is associated with a user profile of the user, the user profile being associated with an entity. This claim merely recites a further limitation on the receiving activity data related to at least one first activity performed by a user utilizing an unbound schema-specific identifier from Claim 1 which was directed to mere data gathering (see MPEP 2106.05(g))
Regarding claim 3,
Claim 3 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 2 which includes an abstract idea (see rejection for claim 2). The additional limitations:
wherein the at least one historical user activity data feature vector associated with the user profile comprises transaction information for one or more primary account number (PAN) transactions using a primary account number (PAN) of the user. This claim merely recites a further limitation on the wherein the entity category determining machine learning engine is trained to associate categories with entities based on at least one of: at least one entity data feature vector, at least one historical user activity data feature vector from Claim 1 which was directed to adding the words "apply it" (or an equivalent) with the judicial exception, or 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)] and therefore fails to integrate the exception into a practical application.
Regarding claim 4,
Claim 4 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations:
approving, [by the at least one processor], a request to perform the at least one second activity based on a determination that a second entity associated with the at least one second activity is also associated with the at least one category, - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.)
by the at least one processor– This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)).
declining, [by the at least one processor], the request to perform the at least one second activity based on a determination that the second entity associated with the at least one second activity is not associated with the at least one category. - This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed in the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) Ill. C.)
Regarding claim 5,
Claim 5 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 4 which includes an abstract idea (see rejection for claim 4). The additional limitations:
retraining, [by the at least one processor], based on approving or declining the request to perform the at least one second activity, the entity category determining machine learning engine. Adding the words "apply it" (or an equivalent) with the judicial exception, or 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)] and therefore fails to integrate the exception into a practical application.
by the at least one processor – This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)).
Regarding claim 6,
Claim 6 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations:
retraining, [by the at least one processor], based on at least one user input that a second entity associated with the at least one second activity is not associated with the at least one category, the entity category determining machine learning engine. Adding the words "apply it" (or an equivalent) with the judicial exception, or 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)] and therefore fails to integrate the exception into a practical application.
by the at least one processor This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)).
Regarding claim 7,
Claim 7 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations:
wherein at least one historical user schema-specific identifier data feature vector comprises one or more second schema-specific identifiers created by the user. This claim merely recites a further limitation on the wherein the entity category determining machine learning engine is trained to associate categories with entities based on at least one of: at least one entity data feature vector, at least one historical user activity data feature vector from Claim 1 which was directed to adding the words "apply it" (or an equivalent) with the judicial exception, or 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)] and therefore fails to integrate the exception into a practical application.
Regarding claim 8,
Claim 8 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 7 which includes an abstract idea (see rejection for claim 7). The additional limitations:
wherein the schema-specific identifier further comprises transaction information for the one or more second schema-specific identifiers This claim merely recites a further limitation on the receiving activity data related to at least one first activity performed by a user utilizing an unbound schema-specific identifier from Claim 1 which was directed to mere data gathering (see MPEP 2106.05(g))
Regarding claim 9,
Claim 9 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations:
generating, [by the at least one processor], a real-time communication to the user regarding the binding of the unbound schema-specific identifier to the at least one category. Adding the words "apply it" (or an equivalent) with the judicial exception, or 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)] and therefore fails to integrate the exception into a practical application.
by the at least one processor This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)).
Regarding claim 10,
Claim 10 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations:
requesting and receiving, [by the at least one processor], entity data for the at least one second activity utilizing the bound schema-specific identifier. Adding the words "apply it" (or an equivalent) with the judicial exception, or 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)] and therefore fails to integrate the exception into a practical application.
by the at least one processor This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)).
Regarding claim 11,
Claim 11 is rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which includes an abstract idea (see rejection for claim 1). The additional limitations:
providing, [by the at least one processor], data related to the at least one second activity using the bound schema-specific identifier to a fraud algorithm to determine fraudulent activity. Adding the words "apply it" (or an equivalent) with the judicial exception, or 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)] and therefore fails to integrate the exception into a practical application.
by the at least one processor This limitation is directed to a computer merely used as a tool to perform an existing process (see MPEP 2106.05(f) (2)).
Regarding claim 12, this claim is rejected under the same rationale with claim 1 (as shown in the rejection above) because they are analogous claims.
Regarding claim 13, this claim is rejected under the same rationale with claim 4 (as shown in the rejection above) because they are analogous claims.
Regarding claim 14, this claim is rejected under the same rationale with claim 5 (as shown in the rejection above) because they are analogous claims.
Regarding claim 15, this claim is rejected under the same rationale with claim 6 (as shown in the rejection above) because they are analogous claims.
Regarding claim 16, this claim is rejected under the same rationale with claim 7 (as shown in the rejection above) because they are analogous claims.
Regarding claim 17, this claim is rejected under the same rationale with claim 8 (as shown in the rejection above) because they are analogous claims.
Regarding claim 18, this claim is rejected under the same rationale with claim 9 (as shown in the rejection above) because they are analogous claims.
Regarding claim 19, this claim is rejected under the same rationale with claim 10 (as shown in the rejection above) because they are analogous claims.
Regarding claim 20, this claim is rejected under the same rationale with claim 11 (as shown in the rejection above) because they are analogous claims.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kruse et al. (US 2024/0037560 A1) in view of Daruna et al. (US 2022/0207506 A1).
Regarding claim 1, Kruse explicitly discloses:
A computer-implemented method, the method comprising:
receiving, by at least one processor, activity data related to at least one first activity
performed by a user utilizing an unbound schema-specific identifier; (Kruse, ¶[0008]: “In particular embodiments, the receiver processor may receive, from an external server, a transaction authorization request to authorize a transaction associated with a transfer of resources to a receiving entity from a user.”)
predicting, by the at least one processor, via an entity category determining machine learning engine, at least one category associated with the at least one first activity performed by the user, (Kruse, ¶[0120]: “In particular embodiments, the receiver processor 140 may determine, based on one or more machine-learning models trained on historical transaction data, one or more of the parameters of the transaction resource configuration.”, ¶[0115]: “In particular embodiments, the one or more parameters of the transaction resource configuration may comprise one or more of an identifier associated with the receiving entity, an identifier associated with a plurality of receiving entities, a category of receiving entities, or a geographic identifier associated with the receiving entity. As an example and not by way of limitation, the identifier associated with the receiving entity may correspond to an allowed receiving entity (e.g., a specific merchant). As another example and not by way of limitation, the category of receiving entities may indicate the merchant type, e.g., restaurant, retailer, and beauty, etc.”)
binding, by the at least one processor, the unbound schema-specific identifier to the predicted at least one category to generate a category bound schema-specific identifier; and (Kruse, ¶[0088]: “The receiver processor server 140b may also tag the individual card to one or more specific commerce entities 115… The receiver processor server 140b may also generate data indicating that the particular card is limited to use for the particular commerce entity 115. This data (referred to herein as a "tag") may be stored into and/or associated with the record of the new card or account profile 140c, and may be referenced by the receiver processor server 140b in the future to determine whether to authorize transactions attempting to use the new card at a commerce entity 115 as a method of payment.”)
instructing, by the at least one processor, at least one second activity based on the category bound schema-specific identifier. (Kruse, ¶[0097]: “During data verification 416, the receiver processor server 140b may determine whether to authorize the payment by comparing fields of the transaction data against fields stored in the account profiles 140c. As an example and not by way of limitation, the receiver processor server 140b may receive the card number and then cross-reference the card number against the corresponding record in account profiles 140c. Using the transaction data, the receiver processor server 140b may determine not only whether the data associated with the card number is generally correct (e.g., card number, CVV, name, address), and may also determine whether the transaction data identifies the commerce entity 115 as the particular commerce entity 115 tagged to the particular card number.”)
Kruse fails to disclose:
wherein the entity category determining machine learning engine is trained to associate categories with entities based on at least one of: at least one entity data feature vector, at least one historical user activity data feature vector, and at least one historical user schema-specific identifier data feature vector;
However, Daruna explicitly discloses:
wherein the entity category determining machine learning engine is trained to associate categories with entities based on at least one of: at least one entity data feature vector, at least one historical user activity data feature vector, and at least one historical user schema-specific identifier data feature vector; (Daruna, ¶[0045]: “In some embodiments, the feature extraction engine 130 may also extract features from a history of electronic activities, e.g., accessed via the verified history 116, activity history 117 and dispute history 118 of the account 114 associated with the electronic activity request 103. Using the history, the feature extraction engine 13 0 may generate training data for training a machine learning model to predict whether user-specified data of the electronic activity request 103 and electronic activity verification 104 is likely to be correct or incorrect according to the extracted features”, ¶[0047]: “In some embodiments, the feature extraction engine 130 may encode the features extracted from activity records (e.g., the electronic activity request 103, electronic activity verification 104, past verified activities in the verified history 116, past activities in the activity history 117 and past disputed activities in the dispute history 118) into a feature vector.”)
The combination of Kruse and Daruna are analogous art because they are in the same field of training time series data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention, having the teachings of Kruse and Daruna before them, to modify the teachings of Kruse to include the teachings of Daruna using feature-vector based training technique because representing historical transaction and identifier data as feature vectos would enable the model to efficiently process relevant account and transaction attributes and more accurately determine merchant categories for transaction authorization.
Regarding claim 2, the combination of Kruse and Daruna explicitly discloses all the limitations of claim 1 (as shown in the rejections above).
Kruse in view of Daruna further discloses:
wherein the unbound schema-specific identifier is associated with a user profile of the user, the user profile being associated with an entity. (Kruse, ¶[0070]: “During a process for generating a new card/account, after the receiver processor server 140b is instructed to generate a new card/account, the receiver processor server 140b may determine the authenticity of the request based on any number of factors about the account holder/client, account holder's personal computing device, account processor 120, and other factors, which may include thousands of data points. The receiver processor server 140b may store data/one or more records indicating the commerce entity 115 involved/associated with an ongoing transaction (referred to herein as a "tag" or "merchant tag"), for which the account holder 105 or account processor 120 has requested a new card/account.”, ¶[0044]: “In particular embodiments, the account processor 120 may comprise account holder profiles 120b”)
Regarding claim 3, the combination of Kruse and Daruna explicitly discloses all the limitations of claim 2 (as shown in the rejections above).
Kruse in view of Daruna further discloses:
wherein the at least one historical user activity data feature vector associated with the user profile comprises transaction information for one or more primary account number (PAN) transactions using a primary account number (PAN) of the user. (Kruse, ¶[0173]: “In particular embodiments, the cardholder activity may comprise the following detailed transaction information. The cardholder activity may comprise cardholder number, which may be a primary account number (PAN), e.g., up to 21 characters. The cardholder activity may comprise a plastic number, which may indicate the card used for transaction if there are multiple cards per primary account number.”)
Regarding claim 4, the combination of Kruse and Daruna explicitly discloses all the limitations of claim 1 (as shown in the rejections above).
Kruse in view of Daruna further discloses:
approving, by the at least one processor, a request to perform the at least one second activity based on a determination that a second entity associated with the at least one second activity is also associated with the at least one category, or (Kruse, ¶0128]: “The method may begin at step 710, where the receiver processor 140 may receive, from an external server, a transaction authorization request to authorize a transaction associated with a transfer of resources to a receiving entity from a user… At step 730, the receiver processor 140 may determine whether one or more parameters of the transaction resource configuration are satisfied based on metadata associated with the transaction authorization request, wherein the one or more parameters of the transaction resource configuration comprise one or more of a type of payment, a type of currency, a type of transaction, an identifier associated with the receiving entity, an identifier associated with a plurality of receiving entities… At step 740, the receiver processor 140 may transmit, to the external server, responsive to determining whether the one or more parameters of the transaction resource configuration are satisfied, a transaction authorization response indicating whether the transaction is authorized”)
declining, by the at least one processor, the request to perform the at least one second activity based on a determination that the second entity associated with the at least one second activity is not associated with the at least one category. (Kruse, ¶[0145]: “The method may begin at step 910, where the receiver processor 140 may receive, from an external server, a transaction authorization request to authorize a transaction associated with a transfer of resources to a receiving entity from a user…. At step 930, the receiver processor 140 may determine that one or more parameters of the transaction resource configuration are not satisfied based on metadata associated with the transaction authorization request… At step 950, the receiver processor 140 may transmit, to a client device associated with the user, instructions for presenting a notification indicating a transaction authorization failure”)
Regarding claim 5, the combination of Kruse and Daruna explicitly discloses all the limitations of claim 4 (as shown in the rejections above).
Kruse in view of Daruna further discloses:
retraining, by the at least one processor, based on approving or declining the request to perform the at least one second activity, the entity category determining machine learning engine. (Kruse, ¶[0100]: “Then, BOW A and BOW B may be input to a function (e.g., Bayesian probability function, k-means clustering algorithm, etc.) that returns a percentage confidence score that BOW A is related to BOW B. If the probability is over a specified threshold T, then the receiver processor server 140b may communicate that the charge is accepted, else the receiver processor server 140b may communicate that the charge is declined. In some embodiments, threshold T may change as more transactions are processed and/or as a probability function (e.g., Bayes function) and/or cluster analysis (e.g., k-means clustering algorithm) is trained and/ or updated with historical data”, ¶[0101]: “The receiver processor server 140b may, in some embodiments, retrain the k-means clustering on each card-create request, to retrain the k-means cluster with the new, additional cluster added.”)
Regarding claim 6, the combination of Kruse and Daruna explicitly discloses all the limitations of claim 1 (as shown in the rejections above).
Kruse in view of Daruna further discloses:
retraining, by the at least one processor, based on at least one user input that a second entity associated with the at least one second activity is not associated with the at least one category, the entity category determining machine learning engine. (Kruse, ¶[0100]: “Then, BOW A and BOW B may be input to a function (e.g., Bayesian probability function, k-means clustering algorithm, etc.) that returns a percentage confidence score that BOW A is related to BOW B. If the probability is over a specified threshold T, then the receiver processor server 140b may communicate that the charge is accepted, else the receiver processor server 140b may communicate that the charge is declined. In some embodiments, threshold T may change as more transactions are processed and/or as a probability function (e.g., Bayes function) and/or cluster analysis (e.g., k-means clustering algorithm) is trained and/ or updated with historical data”, ¶[0101]: “The receiver processor server 140b may, in some embodiments, retrain the k-means clustering on each card-create request, to retrain the k-means cluster with the new, additional cluster added.”)
Regarding claim 7, the combination of Kruse and Daruna explicitly discloses all the limitations of claim 1 (as shown in the rejections above).
Kruse in view of Daruna further discloses:
wherein at least one historical user schema-specific identifier data feature vector comprises one or more second schema-specific identifiers created by the user. (Daruna, ¶[0045]: “Using the history, the feature extraction engine 13 0 may generate training data for training a machine learning model to predict whether user-specified data of the electronic activity request 103 and electronic activity verification 104 is likely to be correct or incorrect according to the extracted features.”, ¶0047]: “In some embodiments, the feature extraction engine 130 may encode the features extracted from activity records (e.g., the electronic activity request 103, electronic activity verification 104, past verified activities in the verified history 116, past activities in the activity history 117 and past disputed activities in the dispute history 118) into a feature vector. In some embodiments, the feature vector may include a one-dimensional vector of values representing each extracted feature.”)
Regarding claim 8, the combination of Kruse and Daruna explicitly discloses all the limitations of claim 7 (as shown in the rejections above).
Kruse in view of Daruna further discloses:
wherein the schema-specific identifier further comprises transaction information for the one or more second schema-specific identifiers. (Kruse, ¶[0173]: “The cardholder activity may comprise a plastic number, which may indicate the card used for transaction if there are multiple cards per primary account number… The cardholder activity may also comprise transaction type ( e.g., 2-3 characters), tied to settlement process code or transaction type code. The cardholder activity may also comprise debit information, which may indicate amount if transaction is a debit (e.g., deposit), amounts not followed by rejection which are settled transactions, and additional possible values which are a mix of rejected and accepted.”)
Regarding claim 9, the combination of Kruse and Daruna explicitly discloses all the limitations of claim 1 (as shown in the rejections above).
Kruse in view of Daruna further discloses:
generating, by the at least one processor, a real-time communication to the user regarding the binding of the unbound schema-specific identifier to the at least one category. (Kruse, ¶[0089]: “In particular embodiments, the receiver processor server 140b may send a card generation response 370 to the receiver processor API 140a, which may then transmit a notification of card generation 375 to the account processor server 120a, indicating the requested card has been created for them. The account processor server 120a may download, access, and/or otherwise receive/retrieve related software program(s )/module(s ), that, when activated/instantiated/run, can monitor the account holder's 105 actions/behaviors/ interaction, such as using the card at commerce entities 115, browsing, transactions initiated by the account holder's 105 personal computing device (and associated programs), and interact with the receiver processor 140. In particular embodiments, the account processor server 120a may optionally send a notification of card data 380 to the account holder 105. As an example and not by way of limitation, the card data may comprise card number, authorization code (CVV), expiration date, merchant tag, etc.”)
Regarding claim 10, the combination of Kruse and Daruna explicitly discloses all the limitations of claim 1 (as shown in the rejections above).
Kruse in view of Daruna further discloses:
requesting and receiving, by the at least one processor, entity data for the at least one second activity utilizing the bound schema-specific identifier. (Kruse, ¶[0093]: “The originator processor 125 may in turn transmit a transaction verification request 406 including the transaction data (typically via the payment network 135) to the receiver processor server 140b.”, ¶[0094]: “particular embodiments, the transaction data that is provided by the commerce entity 115 and goes through to the receiver processor server 140b may comprise one or more of card information, payment/charge amount, billing information, or card acceptor name and location.”, ¶[0097]: “the receiver processor server 140b may receive the card number and then cross-reference the card number against the corresponding record in account profiles 140c. Using the transaction data, the receiver processor server 140b may determine not only whether the data associated with the card number is generally correct (e.g., card number, CVV, name, address), and may also determine whether the transaction data identifies the commerce entity 115 as the particular commerce entity 115 tagged to the particular card number”)
Regarding claim 11, the combination of Kruse and Daruna explicitly discloses all the limitations of claim 1 (as shown in the rejections above).
Kruse in view of Daruna further discloses:
providing, by the at least one processor, data related to the at least one second activity using the bound schema-specific identifier to a fraud algorithm to determine fraudulent activity. (Kruse, ¶[0124]: “In particular embodiments, the receiver processor server 140b may further use one or more machine-learning models, e.g., a risk model, to identify fraudulent transactions that otherwise satisfy parameters of a VBAN, thereby achieving risk/fraud controls. The receiver processor server 140b may determine, based on a risk model for determining whether a transaction is fraudulent, a likelihood that the transaction authorization request is fraudulent. The receiver processor server 140b may then transmit, based on the likelihood that the transaction authorization request is fraudulent, a transaction authorization response indicating that the transaction is not authorized. In particular embodiments, the likelihood that the transaction authorization request is fraudulent may be determined based on one or more patterns identified in metadata associated with one or more previous fraudulent transactions. As an example and not by way of limitation, the risk model may search for transaction patterns that indicate high-risk usage (credit or otherwise) or attempts to defraud. Utilizing a transaction risk model trained based on patterns identified in metadata of previous fraudulent transactions, in which the identified patterns are correlated with high-risk usage or fraudulent transactions or attempted transactions, may be an effective solution for addressing the technical challenge of effective risk/fraud controls.”)
Regarding claim 12, this claim is rejected under the same rationale with claim 1 (as shown in the rejections above), because they are analogous claims.
Regarding claim 13, this claim is rejected under the same rationale with claim 4 (as shown in the rejections above), because they are analogous claims.
Regarding claim 14, this claim is rejected under the same rationale with claim 5 (as shown in the rejections above), because they are analogous claims.
Regarding claim 15, this claim is rejected under the same rationale with claim 6 (as shown in the rejections above), because they are analogous claims.
Regarding claim 16, this claim is rejected under the same rationale with claim 7 (as shown in the rejections above), because they are analogous claims.
Regarding claim 17, this claim is rejected under the same rationale with claim 8 (as shown in the rejections above), because they are analogous claims.
Regarding claim 18, this claim is rejected under the same rationale with claim 9 (as shown in the rejections above), because they are analogous claims.
Regarding claim 19, this claim is rejected under the same rationale with claim 10 (as shown in the rejections above), because they are analogous claims.
Regarding claim 20, this claim is rejected under the same rationale with claim 11 (as shown in the rejections above), because they are analogous claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMY TRAN whose telephone number is (571)270-0693. The examiner can normally be reached Monday - Friday 7:30 am - 5:00 pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AMY TRAN/Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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