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 action is in response to the application filed May 7, 2025. Claims 1-20 are pending and examined.
Specification
Applicant is required to update the status (pending, allowed, etc.) of all parent priority applications in the first line of the specification. The status of all citations of US filed applications in the specification should also be updated where appropriate.
Information Disclosure Statement
An initialed and dated copy of Applicant’s IDS form 1449 filed May 7, 2025, is attached to the instant Office action.
Double patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,314,956. Although the conflicting claims are not identical, they are not patentably distinct from each other because the current application is slightly broader than the Patent therefore the application’s claims would be obvious to the Patent’s claims.
Current Application 19/201,088
Patent 12,314,956
1. A system to proactively identify fraudulent transactions, the system comprising:
at least one hardware processor;
a feature-selection module storing instructions, which, when executed by the at least one hardware processor, cause the system to:
receive a set of reports for one or more historical transactions, the set of reports describing, in natural language, evaluations regarding whether corresponding historical transactions were fraudulent; and
identify, from the set of reports for the historical transactions, one or more feature types relevant to the evaluations; and a fraud-prediction module storing instructions, which, when executed by the at least one hardware processor, cause the system to:
receive data associated with a set of requested transactions;
input the received data into a machine learning model to generate a fraud likelihood score for each requested transaction, the fraud likelihood score indicating a likelihood that the requested transaction is fraudulent, wherein the machine learning model is trained using features, from a data stream related to the one or more historical transactions, that correspond to the one or more feature types;
receive assessments, based on the evaluation of the set of requested transactions, regarding whether each of the requested transactions was fraudulent; and
compute a difference between the fraud likelihood scores for the set of requested transactions and the assessments, wherein the machine learning model is configured to be retrained using the computed difference.
1. A system to proactively identify fraudulent transactions, the system comprising:
at least one hardware processor;
a feature-selection module storing instructions, which, when executed by the at least one hardware processor, cause the system to:
receive a set of reports for each of a plurality of historical transactions, the set of reports describing, in natural language, evaluations regarding whether corresponding historical transactions were fraudulent;
identifying one or more feature types that are relevant to the evaluations by applying natural language processing to the set of reports; a training module storing instructions, which, when executed by the at least one hardware processor, cause the system to:
train a machine learning model using features, from a data stream related to the plurality of historical transactions, that correspond to the one or more feature types;
a fraud-prediction module storing instructions, which, when executed by the at least one hardware processor, cause the system to:
receive data associated with a set of requested transactions;
apply the trained machine learning model to the received data, wherein the trained machine learning model when applied to the received data is configured to output, for each requested transaction, a fraud likelihood score indicating a likelihood that the requested transaction is fraudulent, and wherein the fraud likelihood score for each requested transaction is output to an evaluation of the requested transaction;
receive assessments, based on the evaluation of the set of requested transactions, regarding whether each of the requested transactions was fraudulent; and
retrain the trained machine learning model based on a difference between the fraud likelihood scores for the set of requested transactions and the assessments.
2. The system of claim 1, wherein a first assessment of a first requested transaction is received prior to completion of the first requested transaction, and wherein the fraud-prediction module is further configured to cancel the first requested transaction when the first assessment indicates the first requested transaction is fraudulent.
2. The system of claim 1, wherein a first assessment of a first requested transaction is received prior to completion of the first requested transaction, and wherein the fraud-prediction module is further configured to cancel the first requested transaction when the first assessment indicates the first requested transaction is fraudulent.
3. The system of claim 1, wherein the assessments further comprise evaluations, in natural language, regarding whether the requested transaction is fraudulent.
3. The system of claim 1, wherein the assessments further comprise evaluations, in natural language, regarding whether the requested transaction is fraudulent.
4. The system of claim 1, wherein generating the fraud likelihood score for each requested transaction further comprises: inputting the received data into the machine learning model; and outputting the fraud likelihood score indicating the likelihood that the requested transaction is fraudulent.
4. The system of claim 1, wherein applying the trained machine learning model to the received data further comprises: inputting the received data into the trained machine learning model; and outputting the fraud likelihood score indicated the likelihood that the requested transaction is fraudulent.
5. The system of claim 1, further comprising:
receiving data associated with a second requested transaction;
inputting the received data associated with the second requested transaction into the retrained machine learning model to generate a second fraud likelihood score indicating a likelihood that the second requested transaction is fraudulent, and wherein the second fraud likelihood score for each requested transaction is output to an evaluation of the requested transaction;
receiving second assessments regarding whether the second requested transaction was fraudulent; and compute a difference between the second fraud likelihood score and the second assessments, wherein the machine learning model is configured to be retrained using the computed difference.
5. The system of claim 1, further comprising:
receiving data associated with a second requested transaction;
applying the retrained machine learning model to the data associated with the second requested transaction, wherein the retrained machine learning model, when applied to the received data, is configured to output a second fraud likelihood score indicating a likelihood that the second requested transaction is fraudulent, and wherein the second fraud likelihood score for each requested transaction is output to an evaluation of the requested transaction;
receiving second assessments regarding whether the second requested transaction was fraudulent; and retraining the retrained machine learning model based on the difference between the second fraud likelihood score and the second assessments.
6. The system of claim 1, wherein using natural language processing to identify one or more feature types that are relevant to the evaluation further comprises:
determining an extracted tone by performing tone analysis on the evaluation, wherein the extracted tone comprises a sentiment identified in the assessments; and identifying a feature type, wherein the feature type comprises the extracted tone.
6. The system of claim 1, wherein using natural language processing to identify one or more feature types that are relevant to the evaluation further comprises:
determining an extracted tone by performing tone analysis on the evaluation, wherein the extracted tone comprises a sentiment identified in the assessments; and identifying a feature type, wherein the feature type comprises the extracted tone.
7. The system of claim 1, wherein using natural language processing to identify one or more feature types that are relevant to the evaluation further comprises:
determining an extracted topic set by performing topic modeling on the evaluation, wherein the extracted topic set comprises a cluster of words that frequently co-occur together in reports of fraudulent transactions; and
identifying a feature type, wherein the feature type comprises the extracted topic set.
7. The system of claim 1, wherein using natural language processing to identify one or more feature types that are relevant to the evaluation further comprises:
determining an extracted topic set by performing topic modeling on the evaluation, wherein the extracted topic set comprises a cluster of words that frequently co-occur together in reports of fraudulent transactions; and
identifying a feature type, wherein the feature type comprises the extracted topic set.
8. The system of claim 1, wherein using natural language processing to identify one or more feature types that are relevant to the evaluation further comprises:
determining an extracted entity by performing entity extraction on the evaluation, wherein the extracted entity comprises data relevant to the requested transaction; and identifying a feature type, wherein the feature type comprises the extracted entity.
8. The system of claim 1, wherein using natural language processing to identify one or more feature types that are relevant to the evaluation further comprises:
determining an extracted entity by performing entity extraction on the evaluation, wherein the extracted entity comprises data relevant to the requested transaction; and identifying a feature type, wherein the feature type comprises the extracted entity.
9. The system of claim 1, wherein receiving the data associated with the requested transaction further comprises: receiving customer account data,
wherein the customer account data comprises metadata corresponding to a customer; and receiving a transaction data stream, wherein the transaction data stream comprises metadata corresponding to the requested transaction.
9. The system of claim 1, wherein receiving the data associated with the requested transaction further comprises: receiving customer account data,
wherein the customer account data comprises metadata corresponding to a customer; and receiving a transaction data stream, wherein the transaction data stream comprises metadata corresponding to the requested transaction.
10. The system of claim 1, further comprising: receiving assessments indicating the requested transaction was fraudulent; and generating recommended steps to obstruct the requested transaction from completing, wherein the recommended steps comprise actions necessary to prevent the requested transaction from occurring, based on the assessments.
10. The system of claim 1, further comprising: receiving assessments indicating the requested transaction was fraudulent; and generating recommended steps to obstruct the requested transaction from completing, wherein the recommended steps comprise actions necessary to prevent the requested transaction from occurring, based on the assessments.
11. A method performed by a fraud detection system for training a machine learning model, the method comprising: receiving, by at least one hardware processor, transaction data describing at least one requested transaction associated with an enterprise;
inputting the received transaction data into a machine learning model to generate a first assessment regarding whether the at least one requested transaction is fraudulent, wherein the machine learning model is trained using features from a data stream related to a first training set of transactions, the features corresponding to a first set of feature types that were identified based on natural language reports regarding whether the transactions in the training set were fraudulent;
receiving, by the at least one hardware processor, a second assessment regarding whether the at least one requested transaction is fraudulent; and computing, by the at least one hardware processor, feature differences between the first and the second assessments regarding whether the at least one requested transaction is fraudulent, wherein the machine learning model is configured to be retrained using the computed feature differences.
11. A method performed by a fraud detection system for training a fraud detection model, the method comprising: receiving, by at least one hardware processor, transaction data describing a plurality of requested transactions associated with an enterprise; for each requested transaction:
applying, by the at least one hardware processor, a fraud detection model to the received transaction data corresponding to the requested transaction, wherein the fraud detection model is trained using features from a data stream related to a first training set of transactions, the features corresponding to a first set of feature types that were identified based on natural language reports regarding whether the transactions in the training set were fraudulent; and
wherein the fraud detection model when applied to the received transaction data is configured to output a first assessment regarding whether the requested transaction is fraudulent; and
receiving, by the at least one hardware processor, a report containing a second assessment regarding whether the requested transaction is fraudulent; applying, by the at least one hardware processor, natural language processing to the received reports to identify a second set of feature types; and retraining, by the at least one hardware processor, the fraud detection model using features, corresponding to the second set of feature types, from a data stream related to a second training set of transactions.
12. The method of claim 11, wherein the first assessment of the at least one requested transaction is received prior to completion of the at least one requested transaction, and wherein the method further comprises cancelling the at least one requested transaction when the first assessment indicates the at least one requested transaction is fraudulent.
12. The method of claim 11, wherein a first assessment of a first
requested transaction is received prior to completion of the first requested transaction, and wherein the method further comprises cancelling the first requested transaction when the first assessment indicates the first requested transaction is fraudulent.
13. The method of claim 11, further comprising:
receiving, by the at least one hardware processor, data associated with a second requested transaction;
inputting the received data associated with the second requested transaction into the machine learning model to generate a third assessment regarding whether the second requested transaction is fraudulent;
receiving, by the at least one hardware processor, a fourth assessment regarding whether the second requested transaction was fraudulent; and computing, by the at least one hardware processor, feature differences between the third and the fourth assessments regarding whether the second requested transaction is fraudulent, wherein the machine learning model is configured to be retrained using the computed feature differences.
13. The method of claim 11, further comprising:
receiving data associated with a second requested transaction;
applying the retrained fraud detection model to the data associated with the second requested transaction, wherein the retrained fraud detection model, when applied to the received data, is configured to output a second fraud likelihood score indicating a likelihood that the requested transaction is fraudulent, and wherein the second fraud likelihood score for each requested transaction is output to an evaluation of the requested transaction; receiving second assessments regarding whether the second requested transaction was fraudulent; and
retraining the retrained fraud detection model based on a difference between the second fraud likelihood score and the second assessments.
14. The method of claim 11, wherein receiving the data associated with the at least one requested transaction further comprises:
receiving, by the at least one hardware processor, customer account data, wherein the customer account data comprises metadata corresponding to a customer; and receiving, by the at least one hardware processor,
a transaction data stream, wherein the transaction data stream comprises metadata corresponding to the at least one requested transaction.
14. The method of claim 11, wherein receiving the data associated with the requested transaction further comprises:
receiving customer account data,
wherein the customer account data comprises metadata corresponding to a customer; and receiving
a transaction data stream, wherein the transaction data stream comprises metadata corresponding to the requested transaction.
15. The method of claim 11, further comprising:
receiving, by the at least one hardware processor,
assessments indicating the at least one requested transaction was fraudulent; and generating, by the at least one hardware processor, recommended steps to obstruct the at least one requested transaction from completing, wherein the recommended steps comprise actions necessary to prevent the at least one requested transaction from occurring, based on the assessments.
15. The method of claim 11, further comprising:
receiving
assessments indicating the requested transaction was fraudulent; and generating recommended steps to obstruct the requested transaction from completing, wherein the recommended steps comprise actions necessary to prevent the requested transaction from occurring, based on the assessments.
16. A non-transitory computer-readable storage medium storing computing program instructions, execution of which by one or more processors causes the one or more processors to: receive, by the one or more processors, transaction data describing at least one requested transaction associated with an enterprise; input the received transaction data into a machine learning model to generate a first assessment regarding whether the at least one requested transaction is fraudulent, wherein the machine learning model is trained using features from a data stream related to a first training set of transactions, the features corresponding to a first set of feature types that were identified based on natural language reports regarding whether the transactions in the training set were fraudulent; receive, by the one or more processors, a second assessment regarding whether the at least one requested transaction is fraudulent; and compute, by the one or more processors, features from a data stream related to a second training set of transactions, the features corresponding to a second set of feature types identified from the received second assessment, wherein the machine learning model is configured to be retrained using the computed features.
16. A non-transitory computer-readable storage medium storing computing program instructions, execution of which by one or more processors causes the one or more processors to: receive, by a fraud detection system, transaction data describing a plurality of requested transactions associated with an enterprise; for each requested transaction: apply, by the fraud detection system, a fraud detection model to the received transaction data corresponding to the requested transaction, wherein the fraud detection model is trained using features from a data stream related to a first training set of transactions, the features corresponding to a first set of feature types that were identified based on natural language reports regarding whether the transactions in the training set were fraudulent; and wherein the fraud detection model when applied to the received transaction data is configured to output a first assessment regarding whether the requested transaction is fraudulent; and receive a report containing a second assessment regarding whether the requested transaction is fraudulent; apply, by the fraud detection system, natural language processing to the received reports to identify a second set of feature types; and retrain, by the fraud detection system, the fraud detection model using features, corresponding to the second set of feature types, from a data stream related to a second training set of transactions.
17. The non-transitory computer-readable storage medium of claim 16, wherein the first assessment of the at least one requested transaction is received prior to completion of the at least one requested transaction, and wherein the instructions further cause the one or more processors to cancel the at least one requested transaction when the first assessment indicates the at least one requested transaction is fraudulent.
17. The non-transitory computer-readable storage medium of claim 16, wherein a first assessment of a first requested transaction is received prior to completion of the first requested transaction, and wherein the instructions further cause the one or more processors to cancel the first requested transaction when the first assessment indicates the first requested transaction is fraudulent.
18. The non-transitory computer-readable storage medium of claim 16, further causing the one or more processors to: receive, by the one or more processors, data associated with a second requested transaction; input the received data associated with the second requested transaction into the machine learning model to generate a third assessment regarding whether the second requested transaction is fraudulent; receive, by the one or more processors, a fourth assessment regarding whether the second requested transaction was fraudulent; and compute, by the one or more processors, feature differences between the third and the fourth assessments regarding whether the second requested transaction is fraudulent,wherein the machine learning model is configured to be retrained using the computed feature differences.
18. The non-transitory computer-readable storage medium of claim 16, wherein the execution of the computing program instructions by the one or more processors further causes the one or more processors to: receive data associated with a second requested transaction; apply the retrained fraud detection model to the data associated with the second requested transaction, wherein the retrained fraud detection model, when applied to the received data, is configured to output a second fraud likelihood score indicating a likelihood that the requested transaction is fraudulent, and wherein the second fraud likelihood score for each requested transaction is output to an evaluation of the requested transaction; receive second assessments regarding whether the second requested transaction was fraudulent; and retrain the retrained fraud detection model based on a difference between the second fraud likelihood score and the second assessments.
19. The non-transitory computer-readable storage medium of claim 16, wherein receiving the data associated with the at least one requested transaction further causes the one or more processors to: receive, by the one or more processors, customer account data,wherein the customer account data comprises metadata corresponding to a customer; and receive, by the one or more processors, a transaction data stream,wherein the transaction data stream comprises metadata corresponding to the at least one requested transaction.
19. The non-transitory computer-readable storage medium of claim 16, wherein receiving the data associated with the requested transaction further comprises: receiving customer account data, wherein the customer account data comprises metadata corresponding to a customer; and receiving a transaction data stream, wherein the transaction data stream comprises metadata corresponding to the requested transaction.
20. The non-transitory computer-readable storage medium of claim 16, further causing the one or more processors to: receive, by the one or more processors, assessments indicating the at least one requested transaction was fraudulent; and generate, by the one or more processors, recommended steps to obstruct the at least one requested transaction from completing, wherein the recommended steps comprise actions necessary to prevent the at least one requested transaction from occurring, based on the assessments.
20. The non-transitory computer-readable storage medium of claim 16, wherein the execution of the computing program instructions by the one or more processors further causes the one or more processors to: receive assessments indicating the requested transaction was fraudulent; and generate recommended steps to obstruct the requested transaction from completing, wherein the recommended steps comprise actions necessary to prevent the requested transaction from occurring, based on the assessments.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Hansen et al. U.S. 11,544,713 B1 – Natural language processing where data is ingested. Was the closest to “the set of reports describing, in natural language, evaluations regarding whether corresponding historical transactions were fraudulent” but it just did not seem obvious. Evaluations of records regarding fraud would probably either be labeled fraudulent or have some kind of score regarding a likelihood of being fraudulent attached to them.
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/SCOTT S TROTTER/Primary Examiner, Art Unit 3696
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