EP-4736086-A1 - SYSTEMS AND METHODS FOR CUSTOMIZING USER INTERFACES USING ARTIFICIAL INTELLIGENCE

EP4736086A1EP 4736086 A1EP4736086 A1EP 4736086A1EP-4736086-A1

Abstract

Systems and methods are described herein for novel uses and/or improvements for designing user-specific interfaces using machine learning models. When a request to display certain data by an application is received, an application token and a user token may be retrieved and combined into a consolidated token. The consolidated token may be input into a machine learning model to obtain a user interface token for an application. The user interface token may indicate user interface settings/configuration desired/preferred by a user. The user interface token may then be sent to the application to cause the application to display the data using user interface configurations within the user interface token.

Inventors

Lewis, II, Kirk M.
DIN, SHAHZAD
THADESHWAR, AARVI
BYRNE, CHRISTOPHER JAMES

Assignees

Citibank, N.A.

Dates

Publication Date: 20260506
Application Date: 20240630

Claims (20)

1. A system for providing user interfaces using artificial intelligence, the system comprising: one or more processors; and one or more memories configured to store instructions that when executed by the one or more processors perform operations comprising: receiving a request to display application data to a user of an application, wherein the request comprises an application token, the application token comprising a plurality of interface configurations for the application, wherein the application token comprises the plurality of interface configurations encoded into a vector space of a machine learning model; retrieving a user token associated with the user, wherein the user token comprises a plurality of user configurations generated based on user interface data received from a plurality of sources, and wherein the user token comprises the user interface data encoded into the vector space of the machine learning model; merging the application token and the user token into a consolidated token, wherein the consolidated token comprises a first subset of the user interface data and a second subset of the plurality of interface configurations; inputting the consolidated token into the machine learning model to obtain a user interface token, wherein the machine learning model is trained to generate user interface tokens comprising unique user-application interface configurations; and sending a command to the application to display the application data in a user interface using the user interface token, wherein the command causes the application to initialize the user interface with a unique user-application interface configuration.
2. A method for providing user interfaces using artificial intelligence, the method comprising: receiving a request to display application data to a user of an application, wherein the request comprises an application token, the application token comprising a plurality of interface configurations for the application; retrieving a user token associated with the user, wherein the user token comprises a plurality of user configurations generated based on user interface data received from a plurality of sources; merging the application token and the user token into a consolidated token, wherein the consolidated token comprises a first subset of the user interface data and a second subset of the plurality of interface configurations; and inputting the consolidated token into a machine learning model to obtain a user interface token, wherein the machine learning model is trained to generate user interface tokens comprising unique user-application interface configurations; and sending a command to the application to display the application data in a user interface using the user interface token, wherein the command causes the application to initialize the user interface with a unique user-application interface configuration.
3. The method of claim 2, further comprising: generating a settings request for the application, wherein the settings request requests the plurality of interface configurations for the application; receiving, in response to the settings request, the plurality of interface configurations for the application; encoding the plurality of interface configurations into a vector within a vector space of the machine learning model; and generating the application token from the vector.
4. The method of claim 3, wherein encoding the plurality of interface configurations into the vector within the vector space of the machine learning model comprises: retrieving a plurality of fields corresponding to available interface configurations; matching each interface configuration of the plurality of interface configurations to a corresponding field of the plurality of fields; and encoding the plurality of interface configurations into the vector according to the plurality of fields, wherein predetermined values are added to fields that are not modifiable.
5. The method of claim 2, wherein merging the application token and the user token into the consolidated token further comprises: iterating through each user interface configuration of the application token to determine a type associated with each user interface configuration; determining, within the user token, a corresponding user configuration that matches each type; adding user interface configurations that have matching user configurations to the consolidated token; and encoding the consolidated token into a vector within a vector space of the machine learning model.
6. The method of claim 2, further comprising: receiving, from the machine learning model, the user interface token, wherein the user interface token comprises an output vector within a vector space of the machine learning model; decoding the output vector into a plurality of fields storing interface configuration settings, wherein the plurality of fields corresponds to a plurality of types; and generating the unique user-application interface configuration from the plurality of fields.
7. The method of claim 6, wherein generating the unique user-application interface configuration from the plurality of fields comprises: retrieving a user interface configuration template of a plurality of user interface configuration templates, wherein the user interface configuration template is associated with the application; and matching, based on the plurality of types, the plurality of fields storing the interface configuration settings with a plurality of template fields to generate the unique user-application interface configuration.
8. The method of claim 2, further comprising: receiving a first plurality of sets of user interface settings for a plurality of applications, wherein each set of the first plurality of sets has been configured by a corresponding user of a plurality of users; determining a plurality of environmental conditions associated with each set of user interface settings, wherein the plurality of environmental conditions comprises time of day, user location, and temperature at the user location at the time of day; retrieving a second plurality of sets of user characteristics, wherein each set of user characteristics is associated with the corresponding user of the plurality of users; generating a training dataset comprising the first plurality of sets of the user interface settings, the plurality of environmental conditions, and the second plurality of sets of the user characteristics; and inputting the training dataset into a training routine of a user token generation machine learning model to train the user token generation machine learning model to output user tokens, wherein each user token corresponds to a matching plurality of sets of the user interface settings.
9. The method of claim 8, further comprising: retrieving a user identifier of the user; determining, based on the user identifier, that the user token for the user has not been generated yet; determining a plurality of current environmental conditions and a set of user characteristics for the user; inputting the plurality of current environmental conditions and the set of user characteristics into the user token generation machine learning model to obtain a corresponding plurality of user interface settings; and generating the user token based on the corresponding plurality of user interface settings.
10. The method of claim 8, further comprising: determining that the user token for the user has not been generated yet; retrieving a set of user characteristics for the user; inputting the user characteristics into a similarity model to identify a similar user within a set of users having corresponding user tokens; and generating the user token based on a matching user token associated with the similar user.
11 . The method of claim 2, further comprising: receiving, from the application, a message indicating that the user updated one or more user interface settings, wherein the message comprises indications of the one or more user interface settings; inputting the one or more user interface settings into a user token generation machine learning model to obtain an updated plurality of user interface settings; and generating an updated user token based on the updated plurality of user interface settings.
12. The method of claim 2, further comprising: determining that the application token comprises a first hierarchy of settings, wherein the first hierarchy of settings comprises a first plurality of levels of detail for the plurality of interface configurations; determining that the user token comprises a second hierarchy of settings, wherein the second hierarchy of settings comprises a second plurality of levels of detail for the plurality of user configurations, and wherein the second plurality of levels has more levels than the first plurality of levels; and updating the user token to remove one or more levels to match the first plurality of levels of detail.
13. One or more non-transitory, computer-readable media for providing user interfaces using artificial intelligence, storing instructions thereon that cause one or more processors to perform operations comprising: receiving a request to display application data to a user of an application, wherein the request comprises an application token, the application token comprising a plurality of interface configurations for the application; retrieving a user token associated with the user, wherein the user token comprises a plurality of user configurations generated based on user interface data received from a plurality of sources; merging the application token and the user token into a consolidated token, wherein the consolidated token comprises a first subset of the user interface data and a second subset of the plurality of interface configurations; inputting the consolidated token into a machine learning model to obtain a user interface token, wherein the machine learning model is trained to generate user interface tokens comprising unique user-application interface configurations; and sending a command to the application to display the application data in a user interface using the user interface token, wherein the command causes the application to initialize the user interface with a unique user-application interface configuration.
14. The one or more non-transitory, computer-readable media of claim 13, wherein the instructions further cause the one or more processors to perform operations comprising: generating a settings request for the application, wherein the settings request requests the plurality of interface configurations for the application; receiving, in response to the settings request, the plurality of interface configurations for the application; encoding the plurality of interface configurations into a vector within a vector space of the machine learning model; and generating the application token from the vector.
15. The one or more non-transitory, computer-readable media of claim 14, wherein the instructions for encoding the plurality of interface configurations into the vector within the vector space of the machine learning model further cause the one or more processors to perform operations comprising: retrieving a plurality of fields corresponding to available interface configurations; matching each interface configuration of the plurality of interface configurations to a corresponding field of the plurality of fields; and encoding the plurality of interface configurations into the vector according to the plurality of fields, wherein predetermined values are added to fields that are not modifiable.
16. The one or more non-transitory, computer-readable media of claim 13, wherein the instructions for merging the application token and the user token into the consolidated token further cause the one or more processors to perform operations comprising: iterating through each user interface configuration of the application token to determine a type associated with each user interface configuration; determining, within the user token, a corresponding user configuration that matches each type; adding user interface configurations that have matching user configurations to the consolidated token; and encoding the consolidated token into a vector within a vector space of the machine learning model.
17. The one or more non-transitory, computer-readable media of claim 13, wherein the instructions further cause the one or more processors to perform operations comprising: receiving, from the machine learning model, the user interface token, wherein the user interface token comprises an output vector within a vector space of the machine learning model; decoding the output vector into a plurality of fields storing interface configuration settings, wherein the plurality of fields corresponds to a plurality of types; and generating the unique user-application interface configuration from the plurality of fields.
18. The one or more non-transitory, computer-readable media of claim 17, wherein the instructions for generating the unique user-application interface configuration from the plurality of fields further cause the one or more processors to perform operations comprising: retrieving a user interface configuration template of a plurality of user interface configuration templates, wherein the user interface configuration template is associated with the application; and matching, based on the plurality of types, the plurality of fields storing the interface configuration settings with a plurality of template fields to generate the unique user-application interface configuration.
19. The one or more non-transitory, computer-readable media of claim 13, further causing the one or more processors to perform operations comprising: receiving a first plurality of sets of user interface settings for a plurality of applications, wherein each set of the first plurality of sets has been configured by a corresponding user of a plurality of users; determining a plurality of environmental conditions associated with each set of user interface settings, wherein the plurality of environmental conditions comprises time of day, user location, and temperature at the user location at the time of day; retrieving a second plurality of sets of user characteristics, wherein each set of user characteristics is associated with the corresponding user of the plurality of users; generating a training dataset comprising the first plurality of sets of the user interface settings, the plurality of environmental conditions, and the second plurality of sets of the user characteristics; and inputting the training dataset into a training routine of a user token generation machine learning model to train the user token generation machine learning model to output user tokens, wherein each user token corresponds to a matching plurality of sets of user interface settings.
20. The one or more non-transitory, computer-readable media of claim 19, wherein the instructions further cause the one or more processors to perform operations comprising: retrieving a user identifier of the user; determining, based on the user identifier, that the user token for the user has not been generated yet; determining a plurality of current environmental conditions and a set of user characteristics for the user; inputting the plurality of current environmental conditions and the set of user characteristics into the user token generation machine learning model to obtain a corresponding plurality of user interface settings; and generating the user token based on the corresponding plurality of user interface settings.

Description

SYSTEMS AND METHODS FOR CUSTOMIZING USER INTERFACES USING ARTIFICIAL INTELLIGENCE BACKGROUND [0001] This application claims benefit of priority of U.S. Patent Application No. 18/345,705 filed June 30, 2023. The content of the foregoing application is incorporated herein it its entirety by reference. BACKGROUND [0002] User interface development has been an important branch of computer science for many years. Engineers have been developing user interfaces that enable easy consumption of visual and audio information. In recent years, engineers have been developing interfaces that are more and more flexible, enabling users to customize many interface settings so that users are able to digest information in the best and most efficient way possible. However, as user interfaces become more and more complex, it is very difficult and time consuming for a user to find and configure each user interface for each and every application that the user may use. In addition, in recent years the use of artificial intelligence, including but not limited to machine learning, deep learning, etc. (referred to collectively herein as artificial intelligence), has exponentially increased. Broadly described, artificial intelligence refers to a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. Key benefits of artificial intelligence are its ability to process data, find underlying patterns, and/or perform real-time determinations. Thus, it may be desirable to use artificial intelligence (e.g., machine learning) to design user interfaces that are user-specific and that will help each user digest application data in a more efficient manner. SUMMARY [0003] Accordingly, systems and methods are described herein for designing user-specific interfaces using machine learning models. An interface configuration system may be used to perform operations for designing user-specific interfaces. In many instances, users may be interacting with a user device (e.g., a smartphone, an electronic tablet, or another suitable user device). Furthermore, an ability to interact with wearable devices, such as virtual reality devices, augmented reality (AR) devices (e.g., AR glasses), smart watches provide an opportunity for the user to have environmental and biological data collected as well as intellectual and preferential data for further analysis. The interface configuration system may reside (at least partially) on that device and perform operations described below. [0004] The interface configuration system may receive a request to display application data to a user. The request may include an application token with a multitude of interface configurations for the application. For example, a user may launch a particular application on the user device. When the application is launched, the interface configuration system may receive an application token that stores various user interface configurations for the application (e.g., a default configuration including colors, fonts, etc.). In some embodiments, the application token may include the plurality of interface configurations encoded into a vector space of a machine learning model. Furthermore, the application token may include one or more rules indicating which application configurations are available for update and which are not. Furthermore, the application token may include a number of rules indicating which configuration settings should be linked to other configuration settings. For example, a text color setting may be linked to a background color setting. [0005] The interface configuration system may also use a user token for interface configuration. Thus, the interface configuration system may retrieve a user token associated with the user. The user token may include a multitude of user configurations generated based on user interface data received from a plurality of sources. In some embodiments, the user token may include the user interface data encoded into the vector space of the machine learning model. For example, the user token may include user preferences for text size, text color, background color, etc. In some embodiments, the user token may include user preference and historical preference information. In another example, the user preference information may include interface preferences from other applications. Historical preference information may include information regarding interface settings or configurations that the user changed in the past. In some embodiments, the user token may include environmental and temporal information. For example, the user token may include user location, time of day, weather data (e.g., temperature, rain/snow, wind), and/or other suitable environmental information. In some embodiments, the interface configuration system may collect the environmental information in response to receiving the request. [0006] Once the two tokens are available (the applicatio