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 03/15/2023 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 rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Step 1 analysis:
Independent Claim 1 recites, in part, a method, therefore falling into the statutory category of process. Independent Claim 11 recites, in part, an apparatus, therefore falling into the statutory category of machine. Independent Claim 20 recites, in part, a computer program product, therefore falling into the statutory category of manufacture.
Regarding Claim 1:
Step 2A: Prong 1 analysis:
Claim 1 recites in part:
“generating an artificial scenario, wherein the artificial scenario comprises a node and an edge, wherein the node is associated with an action and the edge is associated with a decision weight”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses creating a node and an edge with associated weight and action values.
“determining a scenario discrimination score based on the artificial scenario”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses determining a score for the generated scenario.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“receiving first historical customer scenario data”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g)): i.e., pre-solution activity of gathering data for use in the claimed process.
“by scenario generator circuitry of a predictive outcome system”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning circuitry) (See MPEP 2106.05(f)).
“training a scenario generation model using the first historical customer scenario data”. This additional elements is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (training a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
“using the scenario generation model”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning model) (See MPEP 2106.05(f)).
“receiving second historical customer scenario data”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g)): i.e., pre-solution activity of gathering data for use in the claimed process.
“by scenario discriminator circuitry of the predictive outcome system”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning circuitry) (See MPEP 2106.05(f)).
“training a scenario discrimination model using the second historical customer scenario data”. This additional elements is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (training a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
“receiving the artificial scenario”. This additional element amounts to extra-solution activity of receiving data (MPEP 2106.05(g)): i.e., pre-solution activity of gathering data for use in the claimed process.
“using the scenario discrimination model”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning model) (See MPEP 2106.05(f)).
“wherein the scenario generation model is further trained with the scenario discrimination score and the artificial scenario”. This additional elements is recited at a high level of generality such that the claim recites only the idea of a solution or outcome (training a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished.
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “receiving first historical customer scenario data”, “receiving second historical customer scenario data”, and “receiving the artificial scenario” is/are recited at a high level of generality and amount(s) to extra-solution activity of receiving data i.e., pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
As discussed above, the additional element(s) of “by scenario generator circuitry of a predictive outcome system”, “using the scenario generation model”, “by scenario discriminator circuitry of the predictive outcome system”, and ““using the scenario discrimination model” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
As discussed above, the additional element(s) of “training a scenario generation model using the first historical customer scenario data”, “training a scenario discrimination model using the second historical customer scenario data”, and “wherein the scenario generation model is further trained with the scenario discrimination score and the artificial scenario” is/are recited at a high-level of generality such that the claim recites only the idea of a solution or outcome (training a model) i.e., the claim fails to recite details of how a solution to a problem is accomplished (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 2:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the second historical customer scenario data comprises transactions labeled as fraudulent or non-fraudulent”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (financial data) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of ““wherein the second historical customer scenario data comprises transactions labeled as fraudulent or non-fraudulent” is/are directed to particular field(s) of use (financial data) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 3:
Step 2A: Prong 1 analysis:
Claim 3 recites in part:
“determining a risk estimate for the node of the artificial scenario”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses estimating the risk of the action of the node of the scenario.
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“by the scenario discriminator circuitry”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning circuitry) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “by the scenario discriminator circuitry” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 4:
Step 2A: Prong 1 analysis:
Claim 4 recites in part:
“generating an outcome report comprising the artificial scenario, the scenario discrimination score, and the risk estimate”. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgement, or opinion) or with the aid of pencil and paper. For example, this limitation encompasses creating a report about the scenario(s).
Accordingly, at Step 2A: Prong 1, the claim is directed to an abstract idea.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“by communications hardware”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (machine learning circuitry) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “by communications hardware” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 5:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the scenario discrimination score is related to a probability of fraudulent activity”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (fraud) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of ““wherein the scenario discrimination score is related to a probability of fraudulent activity” is/are directed to particular field(s) of use (fraud) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 6:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the artificial scenario further comprises one or more starting conditions comprising a credit score, a physical location, a debt-to-income ratio, a transaction amount, and an interest rate”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (financial data) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the artificial scenario further comprises one or more starting conditions comprising a credit score, a physical location, a debt-to-income ratio, a transaction amount, and an interest rate” is/are directed to particular field(s) of use (financial data) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 7:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“the scenario generation model and the scenario discrimination model are neural networks”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
“and the scenario generation model and the scenario discrimination model are part of a scenario outcome prediction generative adversarial network (scenario outcome prediction GAN) that further comprises an objective function, wherein the objective function is based on a difference between the second historical customer scenario data and a set of generated scenarios comprising the artificial scenario”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (adversarial neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “the scenario generation model and the scenario discrimination model are neural networks” and “and the scenario generation model and the scenario discrimination model are part of a scenario outcome prediction generative adversarial network (scenario outcome prediction GAN) that further comprises an objective function, wherein the objective function is based on a difference between the second historical customer scenario data and a set of generated scenarios comprising the artificial scenario” is/are directed to particular field(s) of use (neural networks and adversarial neural networks) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 8:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the scenario outcome prediction GAN causes the scenario generation model to minimize the objective function and the scenario discrimination model to maximize the objective function”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (objective functions) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the scenario outcome prediction GAN causes the scenario generation model to minimize the objective function and the scenario discrimination model to maximize the objective function” is/are directed to particular field(s) of use (objective functions) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 9:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the first historical customer scenario data and the second historical customer scenario data are different”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (customer data) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the first historical customer scenario data and the second historical customer scenario data are different” is/are directed to particular field(s) of use (customer data) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 10:
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“wherein the first historical customer scenario data and the second historical customer scenario data are identical”. This limitation merely indicates a field of use or technological environment in which the judicial exception is performed (customer data) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The additional element(s) of “wherein the first historical customer scenario data and the second historical customer scenario data are identical” is/are directed to particular field(s) of use (customer data) (MPEP 2106.05(h)) and therefore do not provide significantly more than the abstract idea, and thus the claim is subject-matter ineligible.
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Regarding Claim 11:
Due to claim language similar to that of Claim 1, Claim 11 is rejected for the same reasons as presented above in the rejection of Claim 1.
Regarding Claim 12:
Due to claim language similar to that of Claim 2, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 13:
Due to claim language similar to that of Claim 3, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 14:
Due to claim language similar to that of Claim 4, Claim 14 is rejected for the same reasons as presented above in the rejection of Claim 4.
Regarding Claim 15:
Due to claim language similar to that of Claim 5, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 5.
Regarding Claim 16:
Due to claim language similar to that of Claim 6, Claim 16 is rejected for the same reasons as presented above in the rejection of Claim 6.
Regarding Claim 17:
Due to claim language similar to that of Claim 7, Claim 17 is rejected for the same reasons as presented above in the rejection of Claim 7.
Regarding Claim 18:
Due to claim language similar to that of Claim 8, Claim 18 is rejected for the same reasons as presented above in the rejection of Claim 8.
Regarding Claim 19:
Due to claim language similar to that of Claim 9, Claim 19 is rejected for the same reasons as presented above in the rejection of Claim 9.
Regarding Claim 20:
Due to claim language similar to that of Claims 1 and 11, Claim 20 is rejected for the same reasons as presented above in the rejection of Claims 1 and 11, with the exception of the limitation(s) covered below.
Step 2A: Prong 2 analysis:
The judicial exception is not integrated into practical application. In particular, the claim recites the additional elements of:
“comprising at least one non-transitory computer-readable storage medium storing software instructions”. This additional element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (storage) (See MPEP 2106.05(f)).
Accordingly at Step 2A: Prong 2, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B analysis:
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
As discussed above, the additional element(s) of “comprising at least one non-transitory computer-readable storage medium storing software instructions” is/are recited at a high-level of generality such that it/they amount(s) to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional elements individually or in combination do not amount to significantly more than the judicial exception.
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.
Claim(s) 1-3, 5-9, 11-13, and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pandey et al (US 20210374756 A1, hereinafter Pandey) in view of Breen et al (US 20230289586 A1, hereinafter Breen).
Regarding Claim 1:
Pandey teaches
receiving first historical customer scenario data (Pandey [0031]: "The first GAN model may be trained to generate simulated customer fraud behaviors using payment transaction data associated with the customer that is accessed from a transactional database");
by scenario generator circuitry of a predictive outcome system (Pandey [0050]: “The processor 204 includes suitable logic, circuitry, and/or interfaces to execute operations for accessing various transaction data and utilize trained machine learning models”)
training, by the scenario generator circuitry, a scenario generation model using the first historical customer scenario data (Pandey [0031]: "The first GAN model may be trained to generate simulated customer fraud behaviors using payment transaction data associated with the customer that is accessed from a transactional database");
generating, by the scenario generator circuitry and using the scenario generation model, an artificial scenario (Pandey [0031]: "The first GAN model may be trained to generate simulated customer fraud behaviors using payment transaction data associated with the customer that is accessed from a transactional database")
receiving second historical customer scenario data (Pandey [0064]: "The discriminator neural network is configured to receive customer feature vectors associated with real customer fraud behaviors along with the simulated customer fraud behaviors and discriminate between real customer fraud behaviors and simulated customer fraud behaviors");
by scenario discriminator circuitry of the predictive outcome system (Pandey [0050]: “The processor 204 includes suitable logic, circuitry, and/or interfaces to execute operations for accessing various transaction data and utilize trained machine learning models”);
training, by the scenario discriminator circuitry, a scenario discrimination model using the second historical customer scenario data (Pandey [0030]: "another set of generator neural network model and discriminator neural network model may be trained using historical default data to detect default/credit risk in real-time");
receiving, by the scenario discriminator circuitry, the artificial scenario (Pandey [0064]: "The discriminator neural network is configured to receive customer feature vectors associated with real customer fraud behaviors along with the simulated customer fraud behaviors and discriminate between real customer fraud behaviors and simulated customer fraud behaviors");
determining, by the scenario discriminator circuitry and using the scenario discrimination model, a scenario discrimination score based on the artificial scenario (Pandey [0074]: "The scoring engine 214 includes a suitable logic and/or interfaces for generating confidence risk scores for the filtered simulated customer fraud/default behaviors using credit and fraud risk models. In one embodiment, the credit and fraud risk models 230 may be pre-trained models and generate confidence risk score such as fraud risk score and/or default risk scores.")
wherein the scenario generation model is further trained with the scenario discrimination score and the artificial scenario (Pandey [0075]: "The processor 204 is configured to retain a set of simulated customer fraud/default behaviors from the simulated customer fraud/default behaviors with fraud/default risk score lower than a threshold value"; [0076]: "The rule extraction engine 216 includes a suitable logic and/or interfaces for extracting fraud and credit risk rules which may be used to flag fraud/default customers in real-time using the set of simulated customer behaviors that are retained. In one embodiment, the fraud/credit risk rules may be extracted manually or automatically by learning a tree classifier resulting in rules to be extracted to classify an incoming real-time transaction as fraudulent or credit risky")
Pandey does not distinctly disclose
wherein the artificial scenario comprises a node and an edge, wherein the node is associated with an action and the edge is associated with a decision weight
However, Breen teaches
wherein the artificial scenario comprises a node and an edge, wherein the node is associated with an action and the edge is associated with a decision weight (Breen [0004]: "a group of directed entity relationship edges each associated with a source entity node for a source predictive entity and a destination entity node for a destination predictive entity, (iv) for each directed relationship edge, a direction-aware weight attribute that is determined based at least in part on a normalized historical contribution measure of the source predictive entity associated with the directed relationship edge and the destination predictive entity associated with the directed relationship edge")
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the methods and systems for detecting frauds in payment transactions made by payment instrument using spend patterns of multiple payment instruments associated with user of Pandey with the techniques for determining a graph-based prediction of Breen in order to provide a method for generating graph nodes with actions and weighted decision edges. The method presented in Breen is beneficial for Pandey in that it allows for machine learning frameworks for graph generation to be applied to the predictive actions relating to the customer financial history seen in Pandey (Breen [Abstract]: “the hybrid graph-based prediction machine learning framework is configured to generate the graph-based prediction based at least in part on a comprehensive representation of the cross-entity relationship graph data object that is generated based at least in part on output data of a graph convolutional neural machine learning model and an image-based graph convolutional neural network machine learning model”).
Regarding Claim 2:
Pandey teaches
The method of claim 1, wherein the second historical customer scenario data comprises transactions labeled as fraudulent or non-fraudulent (Pandey [0030]: "During execution, the discriminator neural network model is utilized to predict if the seen transactional behavior matches well against simulated transactional level behavior. Any deviations derived from discriminator neural network model are used to mark the transactional behavior as fraud.").
Regarding Claim 3:
Pandey teaches
The method of claim 1, further comprising: determining, by the scenario discriminator circuitry, a risk estimate for the node of the artificial scenario (Pandey [0060]: "The GAN models 228 are trained for determining simulated or virtual customer behaviors on fraud/non-fraud for fraud risk and default/non-default for credit risk").
Regarding Claim 5:
Pandey teaches
The method of claim 1, wherein the scenario discrimination score is related to a probability of fraudulent activity (Pandey [0074]: "The scoring engine 214 includes a suitable logic and/or interfaces for generating confidence risk scores for the filtered simulated customer fraud/default behaviors using credit and fraud risk models. In one embodiment, the credit and fraud risk models 230 may be pre-trained models and generate confidence risk score such as fraud risk score and/or default risk scores. In one example, if the fraud risk score for a simulated customer fraud behavior is high, it means that for a payment transaction being made by the customer in the future, there are high chances of it being a fraud").
Regarding Claim 6:
Pandey teaches
The method of claim 1, wherein the artificial scenario further comprises one or more starting conditions comprising a credit score, a physical location, a debt-to-income ratio, a transaction amount, and an interest rate (Pandey [0073]: "The predetermined filtering criterion may include constraints such as, but not limited to, a location, a product, time and amount of transaction, type of transaction, etc. In one example, the issuer 108 wants to detect customers with high dollar exposure values. In this scenario, the filtering engine 212 is configured to filter out simulated customer behaviors on the basis of their simulated exposure value").
Regarding Claim 7:
Pandey teaches
The method of claim 1, wherein: the scenario generation model and the scenario discrimination model are neural networks (Pandey [0029]: "A setting of a generator neural network model and a discriminator neural network model where generator neural network model learns to simulate the transactional level behavior of a customer (for example, spend behavior) while the discriminator neural network model learns to distinguish simulated transactional level behavior against real transaction level behavior accessed from a transaction database");
and the scenario generation model and the scenario discrimination model are part of a scenario outcome prediction generative adversarial network (scenario outcome prediction GAN) (Pandey [0100]: "FIG. 4 illustrates a flow diagram of a method 400 for training generative adversarial network (GAN) models for generating unseen or unknown simulated customer behaviors associated with fraud and default behaviors")
that further comprises an objective function, wherein the objective function is based on a difference between the second historical customer scenario data and a set of generated scenarios comprising the artificial scenario (Pandey [0091]: "The objective of the triplet loss function is to minimize the distance between the simulated customer fraud behaviors and real customer fraud behaviors and simultaneously maximize the distance between the simulated customer fraud behaviors and real customer non-fraud behaviors").
Regarding Claim 8:
Pandey teaches
The method of claim 7, wherein the scenario outcome prediction GAN causes the scenario generation model to minimize the objective function and the scenario discrimination model to maximize the objective function (Pandey [0091]: "The objective of the triplet loss function is to minimize the distance between the simulated customer fraud behaviors and real customer fraud behaviors and simultaneously maximize the distance between the simulated customer fraud behaviors and real customer non-fraud behaviors").
Regarding Claim 9:
Pandey teaches
The method of claim 1 wherein the first historical customer scenario data and the second historical customer scenario data are different (Pandey [0064]: "The discriminator neural network is configured to receive customer feature vectors associated with real customer fraud behaviors along with the simulated customer fraud behaviors and discriminate between real customer fraud behaviors and simulated customer fraud behaviors"; (EN): it is noted that the first historical data is synthetic, while the second historical data is taken from real customer fraud behavior, therefore they are different)
Regarding Claim 11:
Due to claim language similar to that of Claim 1, Claim 11 is rejected for the same reasons as presented above in the rejection of Claim 1.
Regarding Claim 12:
Due to claim language similar to that of Claim 2, Claim 12 is rejected for the same reasons as presented above in the rejection of Claim 2.
Regarding Claim 13:
Due to claim language similar to that of Claim 3, Claim 13 is rejected for the same reasons as presented above in the rejection of Claim 3.
Regarding Claim 15:
Due to claim language similar to that of Claim 5, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 5.
Regarding Claim 16:
Due to claim language similar to that of Claim 6, Claim 16 is rejected for the same reasons as presented above in the rejection of Claim 6.
Regarding Claim 17:
Due to claim language similar to that of Claim 7, Claim 17 is rejected for the same reasons as presented above in the rejection of Claim 7.
Regarding Claim 18:
Due to claim language similar to that of Claim 8, Claim 18 is rejected for the same reasons as presented above in the rejection of Claim 8.
Regarding Claim 19:
Due to claim language similar to that of Claim 9, Claim 19 is rejected for the same reasons as presented above in the rejection of Claim 9.
Regarding Claim 20:
Due to claim language similar to that of Claims 1 and 11, Claim 20 is rejected for the same reasons as presented above in the rejection of Claims 1 and 11, with the exception of the limitation(s) covered below.
Pandey teaches
A computer program product comprising at least one non-transitory computer-readable storage medium storing software instructions (Pandey [0154]: “A computer-readable medium storing, embodying, or encoded with a computer program, or similar language, may be embodied as a tangible data storage device storing one or more software programs that are configured to cause a processor or computer to perform one or more operations”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12354139 B1 – Systems and methods for receiving an enterprise resource dataset associated with a customer from an enterprise application associated with the customer
US 20230029415 A1 – methods and systems for predicting and generating impacted scenarios based on a defined set of attributes
US 20220245643 A1 – apparatus and methods for identifying fraudulent transactions
US 20210174366 A1 – apparatus and methods for identifying fraudulent transactions
US 8768379 B2 – methods and systems that record the location of a user and determine the corresponding physical named location (e.g. business location) visited by the user
Any inquiry concerning this communication or earlier communications from the examiner should be directed to COREY M SACKALOSKY whose telephone number is (703)756-1590. The examiner can normally be reached M-F 7:30am-3:30pm EST.
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128