Dynamic offers application programming interfaces

Patent metadata

Field	Value
Publication number	US-2025272714-A1
Application number	US-202519055141-A
Country	US
Kind code	A1
Filing date	Feb 17, 2025
Priority date	Feb 27, 2024
Publication date	Aug 28, 2025
Grant date	—

Abstract

Systems and methods are provided through which user online interactions are dynamically processed through an application programming interface (API) to identify and present a set of offers according to the user online interactions. In response to an API call to identify offers presentable to a user, the API obtains user interaction data associated with the user and processes this data, a set of available offers, and a set of parameters corresponding to an interface being accessed by the user through a machine learning algorithm to select a set of offers to be presented through the interface. The API continuously monitors user interactions with the set of offers to update the machine learning algorithm in real-time.

First claim

What is claimed is:

1 . A computer-implemented method, comprising:
receiving an application programming interface (API) call to identify one or more offers for rendering through an interface implemented through a landing page associated with a payment instrument service, wherein the API call includes identifying information associated with a user and with an ongoing online session, and wherein the API call is submitted during a request to access the landing page; obtaining user interaction data corresponding to interactions with different external online assets by the user, wherein the user interaction data is obtained using the identifying information; identifying a set of available offers, wherein the set of available offers corresponds to different entities associated with the payment instrument service; processing the user interaction data and the set of available offers through a machine learning algorithm to automatically select a set of offers from the set of available offers, wherein the machine learning algorithm is trained using a dataset of historical user interaction data and corresponding offers presented to different users through the interface; transmitting executable instructions that, as a result of being executed by a computing device of the user, cause the computing device to render the set of offers through the interface; monitoring in real-time and through the interface user interaction with the set of offers; and updating the machine learning algorithm according to the user interaction with the set of offers.

2 . The computer-implemented method of claim 1 , wherein identifying the set of offers further comprises:
identifying a set of rules corresponding to the set of available offers, wherein the set of rules defines different requirements for rendering of different offers from the set of available offers; and processing the set of rules with the user interaction data and the set of available offers through the machine learning algorithm to identify the set of offers.

3 . The computer-implemented method of claim 1 , wherein the identifying information is obtained from a cookie stored on a browser application implemented on the computing device.

4 . The computer-implemented method of claim 1 , wherein the identifying information includes a user identifier associated with the user and a session identifier corresponding to the ongoing online session, and wherein the user interaction data is obtained using the user identifier and the session identifier.

5 . The computer-implemented method of claim 1 , further comprising:
determining that the identifying information does not include a user identifier; and generating a cookie that encodes a new user identifier associated with the user, wherein the cookie is used to track ongoing user interactions with the interface and the set of offers.

6 . The computer-implemented method of claim 1 , wherein the set of offers is categorized according to a set of offer parameters, and wherein the set of offers is rendered through the interface according to the set of offer parameters.

7 . The computer-implemented method of claim 1 , wherein obtaining the user interaction data further comprises:
translating the identifying information into an external user identifier associated with the user, wherein the external user identifier corresponds to a user data connectivity platform that obtains the user interaction data from different external sources; and transmitting a query to obtain the user interaction data, wherein the query includes the external user identifier, and wherein when the query is received by the user data connectivity platform, the user data connectivity platform provides the user interaction data.

8 . A system, comprising:
one or more processors; and memory storing thereon instructions that, as a result of being executed by the one or more processors, cause the system to:
receive an application programming interface (API) call to identify one or more offers for rendering through an interface implemented through a landing page associated with a payment instrument service, wherein the API call includes identifying information associated with a user and with an ongoing online session, and wherein the API call is submitted during a request to access the landing page;
obtain user interaction data corresponding to interactions with different external online assets by the user, wherein the user interaction data is obtained using the identifying information;
identify a set of available offers, wherein the set of available offers corresponds to different entities associated with the payment instrument service;
process the user interaction data and the set of available offers through a machine learning algorithm to automatically select a set of offers from the set of available offers, wherein the machine learning algorithm is trained using a dataset of historical user interaction data and corresponding offers presented to different users through the interface;
transmit executable instructions that, as a result of being executed by a computing device of the user, cause the computing device to render the set of offers through the interface;
monitor in real-time and through the interface user interaction with the set of offers; and
update the machine learning algorithm according to the user interaction with the set of offers.

9 . The system of claim 8 , wherein the instructions that cause the system to identify the set of offers further cause the system to:
identify a set of rules corresponding to the set of available offers, wherein the set of rules defines different requirements for rendering of different offers from the set of available offers; and process the set of rules with the user interaction data and the set of available offers through the machine learning algorithm to identify the set of offers.

10 . The system of claim 8 , wherein the identifying information is obtained from a cookie stored on a browser application implemented on the computing device.

11 . The system of claim 8 , wherein the identifying information includes a user identifier associated with the user and a session identifier corresponding to the ongoing online session, and wherein the user interaction data is obtained using the user identifier and the session identifier.

12 . The system of claim 8 , wherein the instructions further cause the system to:
determine that the identifying information does not include a user identifier; and generate a cookie that encodes a new user identifier associated with the user, wherein the cookie is used to track ongoing user interactions with the interface and the set of offers.

13 . The system of claim 8 , wherein the set of offers is categorized according to a set of offer parameters, and wherein the set of offers is rendered through the interface according to the set of offer parameters.

14 . The system of claim 8 , wherein the instructions that cause the system to obtain the user interaction data further cause the system to:
translate the identifying information into an external user identifier associated with the user, wherein the external user identifier corresponds to a user data connectivity platform that obtains the user interaction data from different external sources; and transmit a query to obtain the user interaction data, wherein the query includes the external user identifier, and wherein when the query is received by the user data connectivity platform, the user data connectivity platform provides the user interaction data.

15 . A non-transitory, computer-readable storage medi

Assignees

• Synchrony Bank

Inventors

• Kumar Ravish
• Murphy Daniel

Classifications

• G06N5/025

  Extracting rules from data · CPC title

• G06N20/00

  Machine learning · CPC title

• G06Q30/02011

  Profiling or inferring profiles of users or market based on their behaviour · CPC title

• G06Q30/06311

  based on purchase or browsing history · CPC title

• G06Q30/0631

  Recommending goods or services · CPC title