System, Method, and Computer Program Product to Determine Cuisine Type Classifiers

• US 20200134627A1
• Filed: 10/29/2019
• Published: 04/30/2020
• Est. Priority Date: 10/29/2018
• Status: Active Grant

• Current Assignee: Visa International Service Association (Visa, Inc.)
• Original Assignee: Visa International Service Association (Visa, Inc.)
• Inventors: Hamooni, Hossein, Dev, Himel
• Granted Patent: US 11,334,892 B2
• Time in Patent Office: Days
• CPC Class Codes: G06Q 20/0855 involving a third party
• CPC Class Codes: G06Q 20/12 specially adapted for electronic shopping systems
• CPC Class Codes: G06Q 20/145 Payments according to the detected use or quantity
• CPC Class Codes: G06Q 20/4014 Identity check for transactions
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 50/12 Hotels or restaurants

Abstract

Provided is a method that includes determining cuisine type classifiers based on merchant data, receiving transaction data associated with each payment transaction of a plurality of payment transactions involving a plurality of merchants and a plurality of consumers; determining, with at least one processor, a plurality of cuisine type labels based on a plurality of merchant names associated with the plurality of payment transactions or a portion of the plurality of payment transactions, wherein a cuisine type label identifies a cuisine type provided by a respective merchant of the plurality of merchants; generating a merchant cuisine type model from the transaction data to determine a cuisine type label of a merchant, the merchant cuisine type model based on the transaction data, and makes an inference based on a combination of merchant or consumer features associated with a payment transaction of the plurality of payment transactions; and automatically predicting one or more cuisine type classifications using the merchant cuisine type model based on one or more payment transactions associated with a consumer. A system and a computer program product are also disclosed.

Claims

1. A computer-implemented method comprising:

receiving, with at least one processor, transaction data of a plurality of payment transactions involving a plurality of merchants and a plurality of consumers;

determining, with at least one processor, a plurality of cuisine type labels based on a plurality of merchant names associated with the plurality of payment transactions or a portion of the plurality of payment transactions, wherein a cuisine type label identifies a cuisine type provided by a respective merchant of the plurality of merchants;

generating, with at least one processor, a cuisine type model from the transaction data to determine the cuisine type label of the merchant, wherein the cuisine type model is generated based on the transaction data to provide inferences based on one or more of merchant or consumer features associated with a payment transaction of the plurality of payment transactions; and

automatically predicting, with at least one processor, one or more cuisine type classifications using the cuisine type model based on one or more payment transactions associated with the consumer.

2. The computer-implemented method of claim 1, further comprising:

generating, with at least one processor, a recommendation based on the one or more cuisine type classifications.

3. The computer-implemented method of claim 1, further comprising:

detecting, with at least one processor, a payment fraud based at least partially on the one or more cuisine type classifications.

4. The computer-implemented method of claim 1, wherein the transaction data associated with the payment transaction comprises at least one of:

data associated with an identification of the consumer controlling an account associated with the payment transaction;

data associated with the merchant in the payment transaction;

data associated with a location of the merchant in the payment transaction;

data associated with a time interval at which the payment transaction occurred;

data associated with an authorized transaction amount of the payment transaction;

or

data associated with a settlement transaction amount of the payment transaction.

5. The computer-implemented method of claim 1, further comprising:

determining the cuisine type label of the plurality of cuisine type labels for at least one merchant for the payment transaction of the consumer based on one or more characteristics in the payment transaction associated with the at least one merchant and the consumer.

6. The computer-implemented method of claim 5, wherein making the inference based on the combination of the merchant or consumer features associated with the payment transaction of the plurality of payment transactions comprises:

determining merchant attributes by encoding a plurality of consumer embeddings of a respective plurality of consumers of the merchant;

determining consumer characteristics by encoding a plurality of merchant embeddings for the consumer; and

determining compatibility between the merchant and a consumer based on the similarity of characteristics of the consumer to the merchant attributes.

7. The computer-implemented method of claim 6, wherein the merchant or consumer are business features comprising at least one of:

an authorized amount percentile per payment transaction for the merchant;

a tip percentile per payment transaction for the merchant;

an hourly transaction percentile based on a number of payment transactions conducted in an hourly time interval for the merchant;

a party size distribution per payment transaction for the merchant;

a mean cost per payment transaction based on expected party size for the merchant;

a distribution of a number of payment transactions conducted based on days-of-week for the merchant;

a distribution of a number of transactions conducted based on hours-of-day during a weekday for the merchant;

or

a distribution of a number of transactions conducted based on hours-of-day during a weekend day for the merchant.

8. The computer-implemented method of claim 1, wherein determining the plurality of cuisine type labels comprises:

determining a label of a merchant name associated with the payment transaction by satisfying criteria including at least one of support, confidence, or a significance of a word within the merchant name of the plurality of merchants.

9. The computer-implemented method of claim 1, predicting the one or more cuisine type classifications using the cuisine type model, further comprises:

determining a plurality of merchant features based on transaction data associated with a portion of payment transactions of the plurality of payment transactions associated with the consumer.

12. The computer-implemented method of claim 1, further configured to:

detect a payment fraud based at least partially on the one or more cuisine type classifications.

10. A computing system comprising:

one or more processors programmed or configured to;

receive transaction data of a plurality of payment transactions involving a plurality of merchants and a plurality of consumers;

determine a plurality of cuisine type labels based on a plurality of merchant names associated with the plurality of payment transactions or a portion of the plurality of payment transactions, wherein a cuisine type label identifies a cuisine type provided by a respective merchant of the plurality of merchants;

generate a cuisine type model from the transaction data to determine the cuisine type label of the merchant, wherein the cuisine type model is generated based on the transaction data to provide inferences based on one or more of merchant or consumer features associated with a payment transaction of the plurality of payment transactions; and

automatically predict one or more cuisine type classifications using the cuisine type model based on one or more payment transactions associated with the consumer.

11. The computing system of claim 10, further configured to:

generate a recommendation based on the one or more cuisine type classifications.

13. The computing system of claim 10, wherein the transaction data associated with the payment transaction comprises at least one of:

data associated with an identification of the consumer controlling an account associated with the payment transaction;

data associated with the merchant in the payment transaction;

data associated with a location of the merchant in the payment transaction;

data associated with a time interval at which the payment transaction occurred;

data associated with an authorized transaction amount of the payment transaction;

or

data associated with a settlement transaction amount of the payment transaction.

14. The computing system of claim 10, further configured to:

determine the cuisine type label of the plurality of cuisine type labels for at least one merchant for the payment transaction of the consumer based on one or more characteristics in the payment transaction associated with the at least one merchant and the consumer.

15. The computing system of claim 14, wherein the one or more processors making the inference based on the combination of the merchant or consumer features associated with the payment transaction of the plurality of payment transactions are further configured to:

determine merchant attributes by encoding a plurality of consumer embeddings of a respective plurality of consumers of the merchant;

determine consumer characteristics by encoding a plurality of merchant embeddings for the consumer; and

determine compatibility between the merchant and a consumer based on the similarity of characteristics of the consumer to the merchant attributes.

16. The computing system of claim 15, wherein the merchant or consumer are business features comprising at least one of:

an authorized amount percentile per payment transaction for the merchant;

a tip percentile per payment transaction for the merchant;

an hourly transaction percentile based on a number of payment transactions conducted in an hourly time interval for the merchant;

a party size distribution per payment transaction for the merchant;

a mean cost per payment transaction based on expected party size for the merchant;

a distribution of a number of payment transactions conducted based on days-of-week for the merchant;

a distribution of a number of transactions conducted based on hours-of-day during a weekday for the merchant;

or

a distribution of a number of transactions conducted based on hours-of-day during a weekend day for the merchant.

17. The computing system of claim 10, wherein the one or more processors when determining the plurality of cuisine type labels are further configured to:

determine a label of a merchant name associated with the payment transaction by satisfying criteria including at least one of support, confidence, or a significance of a word within the merchant name of the plurality of merchants.

18. The computing system of claim 10, wherein the one or more processors predicting the one or more cuisine type classifications using the cuisine type model are further configured to:

determine a plurality of merchant features based on transaction data associated with a portion of payment transactions of the plurality of payment transactions associated with the consumer.

19. A computer program product including at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to:

receive transaction data of a plurality of payment transactions involving a plurality of merchants and a plurality of consumers;

determine a plurality of cuisine type labels based on a plurality of merchant names associated with the plurality of payment transactions or a portion of the plurality of payment transactions, wherein a cuisine type label identifies a cuisine type provided by a respective merchant of the plurality of merchants;

generate a cuisine type model from the transaction data to determine the cuisine type label of the merchant, wherein the cuisine type model is generated based on the transaction data to provide inferences based on one or more of merchant or consumer features associated with a payment transaction of the plurality of payment transactions; and

automatically predict one or more cuisine type classifications using the cuisine type model based on one or more payment transactions associated with the consumer.

20. The computer program product of claim 19, wherein the at least one non-transitory computer-readable medium further includes one or more instructions that, when executed by at least one processor, cause the at least one processor to:

determine the cuisine type label of the plurality of cuisine type labels for at least one merchant for the payment transaction of the consumer based on one or more characteristics in the payment transaction associated with the at least one merchant and the consumer.