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 .
DETAILED ACTION
This Office Action is in response to the application filed on 07/24/2024.
Claims 1-20 are pending.
Drawings
The drawings filed on 07/24/2024 are accepted.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US PGPUB 2022/0405758, hereinafter Gupta), in view of EIMeadawy et al. (US PGPUB 2021/0097512, hereinafter EIMeadawy).
As per as claim 1, Gupta discloses:
A system comprising:
a non-transitory memory (Gupta, e.g., [0031], “…a memory…”); and
one or more hardware processors coupled with the non-transitory memory and configured to execute instructions from the non-transitory memory (Gupta, e.g., [0031], “…. The memory 114 may be a non-transitory machine-readable storage medium, where the term “non-transitory…” to cause the system to:
receive, from a user device, a request to process a first transaction through a service provider (Gupta, e.g., [0023-0030], “…“transaction” or “payment transaction” may refer to a request for payment by a merchant 180 through a payment network 160. Such transactions may be initiated by an acquirer 170 on behalf of the merchant through an authorization request message transmitted by the acquirer 170 to the payment network 160, which in turn may request authorization from the issuer…”);
access (i) first data related to the first transaction and (ii) second data representing a first transaction pattern, the second data generated based on a plurality of second transactions conducted through the service provider (Gupta, e.g., [0023-0030], “…payment by a merchant 180 through a payment network 160. Such transactions may be initiated by an acquirer 170 on behalf of the merchant through an authorization request message transmitted by the acquirer …Cards 131 may refer to a payment card such as credit cards, debit cards, and other payment devices…, a request to cancel certain types of payment transactions, such as recurring payment transactions, originated from particular merchants…request with different transaction parameters…” (the examiner asserts, user/client allow to access/pay/interact different transactions of credits) and further see [0049], for patterns transactions;
generate embeddings representing the plurality of second transactions based on the second data (Gupta, e.g., [0015], “… recognize merchants based on an N-density aware transaction embedding learned from transaction data, train and use a merchant classifier to classify merchants as deceptive, and/or perform other AI-based tasks” and see [0020-0021], [0054-0055]);
generate a prompt for an artificial intelligence (AI) model based on the request, the first data related to the first transaction, and the embeddings (Gupta, e.g., fig. 1, associating with texts description, [0019-0022], “…input data is representative of the training data… payment transactions are classified as deceptive transactions, the system may identify merchants that submitted the payment transactions…” AI-based system 110 to identify deceptive merchants in payment transactions… and see [0036-0037] and [0047-0048]); and
obtain a response from the AI model based on the prompt, wherein the response indicates a classification of the first transaction (Gupta, e.g., fig. 1, associating with texts description, [0015-0022], “….train and use transaction classifiers to classify transactions as deceptive, recognize merchants based on an N-density aware transaction embedding learned from transaction data, train and use a merchant classifier to classify merchants as deceptive, and/or perform other AI-based tasks… Classification of a merchant into a merchant class, from among the N-classes, may refer to determining that the merchant is identified as a merchant represented by the merchant class…”).
To make records clearer regarding to the features of “access related to the first transaction and (ii) second data representing a first transaction pattern” (although as stated above, Gupta functional disclose access related to the first transaction and second data representation a first transaction patter (Gupta, e.g., [0023-0030]).
However EIMeadawy, in an analogous art, discloses “access related to the first transaction and (ii) second data representing a first transaction pattern” (EIMeadawy, e.g., fig. 1, associating with texts description, [0010], “… learning, by the server system, a pattern of classification of payment transactions based on a pre-defined number of classified payment transactions…” and [0040-0042], [0045], “…payment transactions may be related to personal expenses of the user 102 as well as business expenses of the enterprise…” and [0059], [0073-0075], “…frequently use the personal payment card for booking business trips related to the enterprise …”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of EIMeadawy and Gupta to determining the user input as the first gesture input, classifying the payment transaction into a personal transaction and upon determining the user input as the second gesture input, classifying the payment transaction into a business transaction in order to learn a pattern of classification of payment transactions based on a pre-defined number of classified payment transactions in order to determine future payment transaction is classified independent of the user input based on the pattern (EIMeadawy, e.g., [0010]).
As per as claim 2, the combination of EIeadawy and Gupta disclose:
The system of claim 1, wherein executing the instructions further causes the system to:
determine, by the AI model, whether the first transaction corresponds to the first transaction pattern based on the first data and the embeddings (Gupta, e.g., fig. 1, associating with texts description, [0019-0022], “…input data is representative of the training data… payment transactions are classified as deceptive transactions, the system may identify merchants that submitted the payment transactions…” AI-based system 110 to identify deceptive merchants in payment transactions… and see [0036-0037] and [0047-0048]) and further see (EIMeadawy, e.g., fig. 1, associating with texts description, [0010], “… learning, by the server system, a pattern of classification of payment transactions based on a pre-defined number of classified payment transactions…” and [0040-0042], [0045], “…payment transactions may be related to personal expenses of the user 102 as well as business expenses of the enterprise…” and [0059], [0073-0075], “…frequently use the personal payment card for booking business trips related to the enterprise …”).
As per as claim 3, the combination of EIeadawy and Gupta disclose:
The system of claim 2, wherein the AI model is communicatively coupled with one or more machine learning (ML) models, and wherein executing the instructions further causes the system to:
in response to determining that the first transaction does not correspond to the first transaction pattern, provide, by the AI model, the first data to a particular ML model from the one or more ML models (EIMeadawy, e.g., fig. 1, associating with texts description, [0010], “… learning, by the server system, a pattern of classification of payment transactions based on a pre-defined number of classified payment transactions…” and [0040-0042], [0045], “…payment transactions may be related to personal expenses of the user 102 as well as business expenses of the enterprise…” and [0059], [0073-0075], “…frequently use the personal payment card for booking business trips related to the enterprise …”) (Gupta, e.g., fig. 1, associating with texts description, [0019-0022], “…input data is representative of the training data… payment transactions are classified as deceptive transactions, the system may identify merchants that submitted the payment transactions…” AI-based system 110 to identify deceptive merchants in payment transactions… and see [0036-0037] and [0047-0048]);
obtain, by the AI model, an output from the particular ML model (Gupta, e.g., [0019-0020], [0036], [0036], “… generate an output that introduces deviations from the training data (which represent deceptive transactions)…” and [0044-0046]) and (EIMeadawy, e.g., [0045], [0073-0077]); and
generate, by the AI model, the response based on the output (Gupta, e.g., [0019-0020], [0036], [0036], “… generate an output that introduces deviations from the training data (which represent deceptive transactions)…” and [0044-0046]) and (EIMeadawy, e.g., [0045], [0073-0077]).
As per as claim 4, the combination of EIeadawy and Gupta disclose:
The system of claim 2, wherein executing the instructions further causes the system to:
in response to determining that the first transaction corresponds to the first transaction pattern, generate, by the AI model, the response based on the request, the first data, and the embeddings (Gupta, e.g., fig. 1, associating with texts description, [0019-0022], “…input data is representative of the training data… payment transactions are classified as deceptive transactions, the system may identify merchants that submitted the payment transactions…” AI-based system 110 to identify deceptive merchants in payment transactions… and see [0036-0037] and [0047-0048]) and further see (EIMeadawy, e.g., fig. 1, associating with texts description, [0010], “… learning, by the server system, a pattern of classification of payment transactions based on a pre-defined number of classified payment transactions…” and [0040-0042], [0045], “…payment transactions may be related to personal expenses of the user 102 as well as business expenses of the enterprise…” and [0059], [0073-0075], “…frequently use the personal payment card for booking business trips related to the enterprise …”).
As per as claim 5, the combination of EIeadawy and Gupta disclose:
The system of claim 4, wherein the embeddings are first embeddings, and wherein generating the response comprises:
generating second embeddings based on the first transaction (Gupta, e.g., fig. 1, associating with texts description, [0019-0022], “…input data is representative of the training data… payment transactions are classified as deceptive transactions, the system may identify merchants that submitted the payment transactions…” AI-based system 110 to identify deceptive merchants in payment transactions… and see [0036-0037] and [0047-0048]); and comparing the second embeddings against the first embeddings (Gupta, e.g., [0037], [0048], [0056], “… compared to a threshold probability that may be predefined and/or configured based on empirical observations of known deceptive transactions and their corresponding probabilities of being representative of the training data…” and further see [0090-0091], “… comparing the merchant embedding with the multi-class density aware embedding…”).
As pe as claim 6, the combination of EIeadawy and Gupta disclose:
The system of claim 1, wherein the plurality of second transactions were conducted through the service provider over a first time period, and wherein executing the instructions further causes the system to:
analyze a plurality of third transactions conducted through the service provider over a second time period (EIMeadawy, e.g., [0045], [0049], “… analyze the classified payment transactions in the database 114 for generating one or more customized plans for the user 102. In an illustrative example scenario, the user 102 may frequently use the personal payment card for booking business trips related to the enterprise…” and [0072-0075]);
detect a second transaction pattern based on analyzing the plurality of third transactions (EIMeadawy, e.g., [0045], [0049], [0072-0075]); and
generate third data representing the second transaction pattern based on the plurality of third transactions (EIMeadawy, e.g., [0045], [0049], [0072-0076]).
As per as claim 7, the combination of EIeadawy and Gupta disclose:
The system of claim 6, wherein executing the instructions further causes the system to:
receive a second request to perform a third transaction for a second user (Gupta, e.g., [0023-0030], “…“transaction” or “payment transaction” may refer to a request for payment by a merchant 180 through a payment network 160. Such transactions may be initiated by an acquirer 170 on behalf of the merchant through an authorization request message transmitted by the acquirer 170 to the payment network 160, which in turn may request authorization from the issuer…”) (users submit a request to access different transaction) and further see (EIMeadawy, e.g., [0041], [0049], [0051-0052]);
select, between the second data and the third data, the third data for generating a second prompt for the AI model based on the second request (Gupta, e.g., fig. 1, associating with texts description, [0019-0022], “…input data is representative of the training data… payment transactions are classified as deceptive transactions, the system may identify merchants that submitted the payment transactions…” AI-based system 110 to identify deceptive merchants in payment transactions… and see [0036-0037] and [0047-0048]) and further see (EIMeadawy, e.g., [0041], [0049], [0051-0052]); and
generate the second prompt for the AI model based on the second request and the third data (Gupta, e.g., fig. 1, associating with texts description, [0019-0022], “…input data is representative of the training data… payment transactions are classified as deceptive transactions, the system may identify merchants that submitted the payment transactions…” AI-based system 110 to identify deceptive merchants in payment transactions… and see [0036-0037] and [0047-0048]) and further see (EIMeadawy, e.g., [0041], [0049], [0051-0052], [0072-0076], “… artificial intelligence or machine learning techniques are used for the checking. If the payment transaction can be classified independent of the user input…”)).
As per as claim 8, Gupta discloses:
A method comprising:
receiving, from a user device, a request to process a first transaction with a service provider (Gupta, e.g., [0023-0030], “…“transaction” or “payment transaction” may refer to a request for payment by a merchant 180 through a payment network 160. Such transactions may be initiated by an acquirer 170 on behalf of the merchant through an authorization request message transmitted by the acquirer 170 to the payment network 160, which in turn may request authorization from the issuer…”);
accessing, by a computer system of the service provider, data related to the first transaction based on the request (Gupta, e.g., [0023-0030], “…payment by a merchant 180 through a payment network 160. Such transactions may be initiated by an acquirer 170 on behalf of the merchant through an authorization request message transmitted by the acquirer …Cards 131 may refer to a payment card such as credit cards, debit cards, and other payment devices…, a request to cancel certain types of payment transactions, such as recurring payment transactions, originated from particular merchants…request with different transaction parameters…” (the examiner asserts, user/client allow to access/pay/interact different transactions of credits) and further see [0049], for patterns transactions;
determining, by the computer system, a transaction pattern unrecognizable by a machine learning (ML) model based on a plurality of second transactions conducted with the service provider (Gupta, e.g., [0014-0015], and [0020-0021], “…. Identifying a given transaction from among a large number of transactions as being deceptive to potentially inform whether or not a merchant that submitted the transaction is deceptive may be problematic when processed in isolation. Recognizing merchant identifiers involved in deceptive transactions may be difficult given the number of merchants, and determining whether the recognized merchants are deceptive versus exhibiting bad behavior presents a challenging classification task…” and further see [0031-0032] (determine recognize the merchant/transaction or not));
generating, by the computer system, a representation of the transaction pattern based on attribute values associated with the plurality of second transactions (Gupta, e.g., [0015], “… recognize merchants based on an N-density aware transaction embedding learned from transaction data, train and use a merchant classifier to classify merchants as deceptive, and/or perform other AI-based tasks” and see [0020-0021], [0054-0055]);
generating, by the computer system, a prompt for an artificial intelligence (AI) model based on the request, the data related to the first transaction, and the representation of the transaction pattern, wherein the AI model is communicatively coupled with the ML model and configured to provide data to the ML model that enables the ML model to classify the first transaction based on the prompt (Gupta, e.g., fig. 1, associating with texts description, [0019-0022], “…input data is representative of the training data… payment transactions are classified as deceptive transactions, the system may identify merchants that submitted the payment transactions…” AI-based system 110 to identify deceptive merchants in payment transactions… and see [0036-0037] and [0047-0048]); and
obtaining, by the computer system, a response from the AI model, wherein the response indicates a classification of the first transaction (Gupta, e.g., fig. 1, associating with texts description, [0015-0022], “….train and use transaction classifiers to classify transactions as deceptive, recognize merchants based on an N-density aware transaction embedding learned from transaction data, train and use a merchant classifier to classify merchants as deceptive, and/or perform other AI-based tasks… Classification of a merchant into a merchant class, from among the N-classes, may refer to determining that the merchant is identified as a merchant represented by the merchant class…”).
To make records clearer regarding to the features of “access related to the first transaction and (ii) second data representing a first transaction pattern” (although as stated above, Gupta functional disclose access related to the first transaction and second data representation a first transaction patter (Gupta, e.g., [0023-0030]).
However EIMeadawy, in an analogous art, discloses “access related to the first transaction and (ii) second data representing a first transaction pattern” (EIMeadawy, e.g., fig. 1, associating with texts description, [0010], “… learning, by the server system, a pattern of classification of payment transactions based on a pre-defined number of classified payment transactions…” and [0040-0042], [0045], “…payment transactions may be related to personal expenses of the user 102 as well as business expenses of the enterprise…” and [0059], [0073-0075], “…frequently use the personal payment card for booking business trips related to the enterprise …”). Thus, it would have been obvious to one of ordinary skill in the art BEFORE the effective filling date of the claimed invention to combine the teaching of EIMeadawy and Gupta to determining the user input as the first gesture input, classifying the payment transaction into a personal transaction and upon determining the user input as the second gesture input, classifying the payment transaction into a business transaction in order to learn a pattern of classification of payment transactions based on a pre-defined number of classified payment transactions in order to determine future payment transaction is classified independent of the user input based on the pattern (EIMeadawy, e.g., [0010]).
As per as claim 9, the combination of EIeadawy and Gupta disclose:
The method of claim 8, further comprising: authorizing or denying the request based on the classification (Gupta, e.g., [0028]-0029], “…determine that recurring payment transactions for the payment account 133 involving the acquirer identifier and merchant identifier are to be denied, and transmit an authorization decline message to the acquirer 170 with an appropriate response code that indicates reasons for the authorization decline message…” and [0044], “… payment transaction that should be denied based on the PCS listing 105 but may otherwise be errantly authorized, the AI-based system 110 may classify incoming payment transactions. In these instances, to determine whether or not to authorize the payment transaction involving a payment account in the PCS listing 105, the payment transaction may be classified as being “deceptive.” An example of a deceptive transaction may include a transaction submitted or resubmitted by a merchant 180 for payment from a payment account 133 when the payment account and a merchant identifier that identifies the merchant…”).
As per as claim 10, the combination of EIeadawy and Gupta disclose:
The method of claim 8, wherein the accessing the data comprises: querying a database for the data based on an identifier of a user associated with the user device (Gupta, e.g., [0093]) and further see (EIMeadawy, e.g., [0090-0091]).
As per as claim 11, the combination of EIeadawy and Gupta disclose:
The method of claim 8, further comprising:
performing a clustering operation on the plurality of second transactions, wherein a plurality of clusters is generated based on the performing the clustering operation (Gupta, e.g., [0034], [0040], “… cluster together merchant identifiers 212 that correspond to a merchant 180 identified by an aggregate merchant identifier 210. The aggregate merchant matcher 120 may also keep track of the acquirer identifier 214 and the merchant identifier 212 in a given transaction…” and [0044-0045], “… transaction may be classified as being “deceptive.” An example of a deceptive transaction may include a transaction submitted or resubmitted by a merchant 180 for payment from a payment account 133 when the payment account and a merchant identifier that identifies the merchant…”) and further see (EIMeadawy, e.g., [0010], [0044-0046] and [0072-0077], “… payment transaction is classified independent of the user input using the machine learning techniques. In an example embodiment, a pattern of classification may be learnt to classify the payment transaction using the machine learning techniques. The pattern may be obtained based on learning a pre-defined number of classified payment transactions. Further, the pattern may be used to classify the payment transactions independent of the user input. In an illustrative example scenario, payment transactions related to a merchant ‘XYZ’ for purchasing employee management software and anti-virus software may be classified as business transaction…”);
identifying, from the plurality of clusters, a particular cluster that corresponds to the transaction pattern (Gupta, e.g., [0034], [0040], “… cluster together merchant identifiers 212 that correspond to a merchant 180 identified by an aggregate merchant identifier 210. The aggregate merchant matcher 120 may also keep track of the acquirer identifier 214 and the merchant identifier 212 in a given transaction…” and [0044-0045], “… transaction may be classified as being “deceptive.” An example of a deceptive transaction may include a transaction submitted or resubmitted by a merchant 180 for payment from a payment account 133 when the payment account and a merchant identifier that identifies the merchant…”) and further see (EIMeadawy, e.g., [0010], [0044-0046] and [0072-0077], “… payment transaction is classified independent of the user input using the machine learning techniques. In an example embodiment, a pattern of classification may be learnt to classify the payment transaction using the machine learning techniques. The pattern may be obtained based on learning a pre-defined number of classified payment transactions. Further, the pattern may be used to classify the payment transactions independent of the user input. In an illustrative example scenario, payment transactions related to a merchant ‘XYZ’ for purchasing employee management software and anti-virus software may be classified as business transaction…”); and
accessing the plurality of second transactions from the particular cluster (EIMeadawy, e.g., [0010], [0044-0046] and [0072-0077], “… payment transaction is classified independent of the user input using the machine learning techniques. In an example embodiment, a pattern of classification may be learnt to classify the payment transaction using the machine learning techniques. The pattern may be obtained based on learning a pre-defined number of classified payment transactions. Further, the pattern may be used to classify the payment transactions independent of the user input. In an illustrative example scenario, payment transactions related to a merchant ‘XYZ’ for purchasing employee management software and anti-virus software may be classified as business transaction…”).
As per as claim 12, the combination of EIeadawy and Gupta disclose:
The method of claim 8, wherein the generating the representation comprises: extracting, from the plurality of second transactions, one or more second transactions based on a set of criteria (Gupta, e.g., [0093]) and further see (EIMeadawy, e.g., [0072-0077], [0090-0091]); and
generating embeddings based on a portion of the attribute values associated with the one or more second transactions (Gupta, e.g., [0034], [0040], “… cluster together merchant identifiers 212 that correspond to a merchant 180 identified by an aggregate merchant identifier 210. The aggregate merchant matcher 120 may also keep track of the acquirer identifier 214 and the merchant identifier 212 in a given transaction…” and [0044-0045], “… transaction may be classified as being “deceptive.” An example of a deceptive transaction may include a transaction submitted or resubmitted by a merchant 180 for payment from a payment account 133 when the payment account and a merchant identifier that identifies the merchant…”) and further see (EIMeadawy, e.g., [0010], [0044-0046] and [0072-0077], “… payment transaction is classified independent of the user input using the machine learning techniques. In an example embodiment, a pattern of classification may be learnt to classify the payment transaction using the machine learning techniques. The pattern may be obtained based on learning a pre-defined number of classified payment transactions. Further, the pattern may be used to classify the payment transactions independent of the user input. In an illustrative example scenario, payment transactions related to a merchant ‘XYZ’ for purchasing employee management software and anti-virus software may be classified as business transaction…”).
As per as claim 13, the combination of EIeadawy and Gupta disclose:
The method of claim 8, further comprising: determining, by the AI model, whether the first transaction corresponds to the transaction pattern based on the data related to the first transaction and the representation associated with the transaction pattern (EIMeadawy, e.g., fig. 1, associating with texts description, [0010], “… learning, by the server system, a pattern of classification of payment transactions based on a pre-defined number of classified payment transactions…” and [0040-0042], [0045], “…payment transactions may be related to personal expenses of the user 102 as well as business expenses of the enterprise…” and [0059], [0073-0075], “…frequently use the personal payment card for booking business trips related to the enterprise …”).
As per as claim 14, the combination of EIeadawy and Gupta disclose:
The method of claim 13, further comprising:
in response to determining that the first transaction does not correspond to the transaction pattern, providing, by the Al model, the data to the ML model, wherein the ML model is configured to generate an output based on the data (EIMeadawy, e.g., fig. 1, associating with texts description, [0010], “… learning, by the server system, a pattern of classification of payment transactions based on a pre-defined number of classified payment transactions…” and [0040-0042], [0045], “…payment transactions may be related to personal expenses of the user 102 as well as business expenses of the enterprise…” and [0059], [0073-0075], “…frequently use the personal payment card for booking business trips related to the enterprise …”) (Gupta, e.g., fig. 1, associating with texts description, [0019-0022], “…input data is representative of the training data… payment transactions are classified as deceptive transactions, the system may identify merchants that submitted the payment transactions…” AI-based system 110 to identify deceptive merchants in payment transactions… and see [0036-0037] and [0047-0048]); and
generating, by the AI model, the response based on the output (Gupta, e.g., [0019-0020], [0036], [0036], “… generate an output that introduces deviations from the training data (which represent deceptive transactions)…” and [0044-0046]) and (EIMeadawy, e.g., [0045], [0073-0077]).
As per as claim 15, the combination of EIeadawy and Gupta disclose:
The method of claim 13, further comprising: in response to determining that the first transaction corresponds to the transaction pattern, generating, by the Al model, the response based on the data and the representation (Gupta, e.g., fig. 1, associating with texts description, [0019-0022], “…input data is representative of the training data… payment transactions are classified as deceptive transactions, the system may identify merchants that submitted the payment transactions…” AI-based system 110 to identify deceptive merchants in payment transactions… and see [0036-0037] and [0047-0048]) and further see (EIMeadawy, e.g., fig. 1, associating with texts description, [0010], “… learning, by the server system, a pattern of classification of payment transactions based on a pre-defined number of classified payment transactions…” and [0040-0042], [0045], “…payment transactions may be related to personal expenses of the user 102 as well as business expenses of the enterprise…” and [0059], [0073-0075], “…frequently use the personal payment card for booking business trips related to the enterprise …”).
Claims 16-20 are essentially the same as claims 1-7 except that they set forth the claimed invention as a non-transitory computer readable medium rather a system, respectively and correspondingly, therefore is rejected under the same reasons set forth in rejections of claims 1-7.
Additional Art Considered
The prior art made of record and not relied upon is considered pertinent to the Applicants’ disclosure.
The following patents and papers are cited to further show the state of the art at the time of Applicants’ invention with respect to providing an artificial intelligence (AI) model framework that uses an AI model to assist a machine learning (ML) model in classifying data, such that data corresponding to emerging patterns unrecognizable by the ML model can be classified accurately.
a. Kayathwal et al. (US PGPUB 2023/0206241, hereinafter Kayathwal); “Methods and Systems For Evaluating Vulnerability Risks Of Issuer Authorization System” discloses “accessing a set of payment transaction data including subset of fraudulent transaction data and generating via a machine learning model, set of synthetic transaction data based on the subset of fraudulent transaction data. Method includes accessing set of historical card velocity features and collating the set of synthetic transaction data and the set of historical card velocity features to generate set of enriched synthetic transaction data”.
Kayathwal also teaches artificial intelligence-based model configured to generate the set of synthetic transaction data from a set of payment transaction data [0050].
Kayathwal further teaches authorization model is a transaction authorization model used by the issuer server to classify payment transactions as fraudulent or non-fraudulent transactions.
Conclusion
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/TUAN A PHAM/Primary Examiner, Art Unit 2163
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