Using transaction data to present search results

Patent metadata

Field	Value
Publication number	US-12067574-B2
Application number	US-202318345466-A
Country	US
Kind code	B2
Filing date	Jun 30, 2023
Priority date	Sep 2, 2022
Publication date	Aug 20, 2024
Grant date	Aug 20, 2024

Abstract

Using transaction data to present search results is described. A payment service computing platform may receive transaction data associated with users of a payment service, and may receive a search query from a payment application associated with the payment service and executing on a device of a user. The payment service computing platform may then determine, based at least in part on a portion of the transaction data associated with the user, a user risk metric associated with the user, may generate a list of entities based at least in part on the user risk metric, and may cause a user interface of the payment application to present at least a portion of the list of entities as a search result to the search query.

First claim

What is claimed is:

1. A computer-implemented method comprising:
receiving, by a payment service computing platform, payment data associated with users of a payment service;
receiving, by the payment service computing platform, a search query from a payment application associated with the payment service and executing on a device of a user;
accessing, by the payment service computing platform, a portion of the payment data associated with the user;
determining, by the payment service computing platform, and using at least one machine-trained model to analyze the portion of the payment data, a user risk metric associated with the user,
generating, by the payment service computing platform, a personalized list of merchants based on the user risk metric; and
causing, by the payment service computing platform, a user interface of the payment application to present at least a portion of the personalized list of merchants as a search result to the search query.

2. The computer-implemented method of claim 1 , further comprising:
determining, by the payment service computing platform, that the user risk metric satisfies a first threshold indicating that the user is a high risk user,
wherein the merchants in the personalized list are associated with merchant risk metrics that do not satisfy a second threshold indicating that the merchants are low risk merchants.

3. The computer-implemented method of claim 1 , further comprising:
receiving, by the payment service computing platform, a first indication of a selection of a merchant in the personalized list;
causing, by the payment service computing platform, a second user interface of the payment application to display a subset of available payment options for purchasing an item from the merchant, wherein the subset is determined from a full set of payment options based on the user risk metric;
receiving, by the payment service computing platform, a second indication of a selection of a payment option of the subset of available payment options; and
completing, by the payment service computing platform, a transaction for the item using the payment option.

4. A system comprising:
one or more processors; and
computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving transaction data associated with users of a payment service;
receiving a search query from a payment application associated with the payment service and executing on a device of a user;
determining, based at least in part on a portion of the transaction data associated with the user, a user risk metric associated with the user;
generating a list of entities based at least in part on the user risk metric; and
causing a user interface of the payment application to present at least a portion of the list of entities as a search result to the search query.

5. The system of claim 4 , the operations further comprising:
determining that the user risk metric satisfies a first threshold indicating that the user is a high risk user,
wherein the entities in the list are associated with entity risk metrics that do not satisfy a second threshold indicating that the entities are low risk entities.

6. The system of claim 4 , the operations further comprising:
determining that the user risk metric does not satisfy a first threshold indicating that the user is a low risk user,
wherein the entities in the list include entities associated with entity risk metrics that satisfy a second threshold indicating that the entities are high risk entities.

7. The system of claim 4 , wherein the user risk metric is determined using a machine-trained model.

8. The system of claim 7 , wherein the machine-trained model is trained using:
at least some of the transaction data;
user interaction data associated with the user, the user interaction data indicating interactions of the user with other users of the payment service;
third party data associated with the user, the third party data having been received from one or more external services;
tenure data associated with the user, the tenure data indicating a length of time the user has been a registered user of the payment service;
demographic data associated with the user;
contact data associated with the user;
behavioral data associated with the user;
financial data associated with the user; or
user preference data associated with the user.

9. The system of claim 4 , wherein the user risk metric is determined prior to the receiving of the search query.

10. The system of claim 4 , the operations further comprising:
receiving a first indication of a selection of an entity in the list;
causing a second user interface of the payment application to display a subset of available payment options for purchasing an item from the entity, wherein the subset is determined from a full set of payment options based at least in part on the user risk metric;
receiving a second indication of a selection of a payment option of the subset of available payment options; and
completing a transaction for the item using the payment option.

11. The system of claim 4 , the operations further comprising:
accessing user data associated with the user; and
ranking the entities in the list to generate a personalized list of entities, wherein the ranking uses at least one machine-trained model to analyze the user data,
wherein the causing of the user interface to present at least the portion of the list of entities comprises causing the user interface to present at least a portion of the personalized list of entities.

12. The system of claim 11 , wherein at least some of the entities in the list are merchants, the operations further comprising:
accessing merchant data associated with the merchants,
wherein the ranking of the entities in the list is further based at least in part on the merchant data.

13. The system of claim 4 , the operations further comprising:
receiving updated transaction data associated with one or more of the entities in the list; and
causing, based at least in part on the updated transaction data, the user interface of the payment application to modify at least the portion of the list of entities being presented as the search result.

14. A computer-implemented method comprising:
receiving, by a payment service computing platform, transaction data associated with users of a payment service;
receiving, by the payment service computing platform, a search query from a payment application associated with the payment service and executing on a device of a user;
determining, by the payment service computing platform, and based at least in part on a portion of the transaction data associated with the user, a user risk metric associated with the user;
generating, by the payment service computing platform, a list of entities based at least in part on the user risk metric; and
causing, by the payment service computing platform, a user interface of the payment application to present at least a portion of the list of entities as a search result to the search query.

15. The computer-implemented method of claim 14 , further comprising:
determining, by the payment service computing platform, that the user risk metric satisfies a first threshold indicating that the user is at least one of a medium risk user or a high risk user; and
at least one of:
allowing, by the payment service computing platform, the user to conduct a number of transactions or a frequency of transactions that does not exceed a particular number of transactions or a particular fr

Assignees

• Block Inc

Inventors

• Sardari Mohsen
• Li Yi
• Lee Kin Ho
• Kleene Nic
• Dutta Ankit
• Choudhary Pooja
• Chen Yibo

Classifications

• G06Q20/40

  Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists · CPC title

• G06F16/24578 Primary

  using ranking · CPC title

• G06Q30/0256

  User search · CPC title

• G06Q30/0631

  Recommending goods or services · CPC title

• G06Q20/40145

  Biometric identity checks · CPC title