US-12623544-B2 - Generative artificial intelligence and cohesive experience for automotive applications

US12623544B2US 12623544 B2US12623544 B2US 12623544B2US-12623544-B2

Abstract

Apparatuses, systems, and methods relate to technology to receive first user data from an application and identify that the application is associated with a vehicle. The technology further generates, with a machine learning model, a parameter based on the first user data, where the parameter is associated with a display characteristic that controls a manner that information is presented on a user interface, further where the user interface is associated with the vehicle. The technology further provides the parameter to the vehicle based on the application being associated with the vehicle.

Inventors

Sergei Gage

Assignees

Toyota Motor North America, Inc.

Dates

Publication Date: 20260512
Application Date: 20231205

Claims (20)

1 . A computing system comprising: at least one processor; and at least one memory having a set of instructions, which when executed by the at least one processor, causes the computing system to: receive first user data from an application of a mobile device; identify that the application is associated with a vehicle; identify mobile display preferences of the application from the first user data, wherein the mobile display preferences include a background style of the mobile device including a mobile background selected by a user; identify second user data that indicates a location of the vehicle; generate, with a machine learning model, a parameter based on the first user data and the second user data by generating a new vehicle background for a vehicle graphical user interface of the vehicle based on the background style, the mobile background and the location, wherein the parameter is associated with a display characteristic that controls a presentation of the new vehicle background on the vehicle graphical user interface, wherein the new vehicle background is different from the mobile background; and provide the parameter to the vehicle based on the application being associated with the vehicle.
2 . The computing system of claim 1 , wherein the second user data further includes one or more of weather associated with the vehicle, a speed of the vehicle, a physical characteristic of the vehicle, a time associated with the vehicle, an illumination associated with the vehicle, a temperature associated with the vehicle, a driving condition of the vehicle, a season associated with the vehicle, or biometric data associated with a user.
3 . The computing system of claim 1 , wherein the instructions of the at least one memory, when executed, cause the computing system to: receive third user data that is a characteristic of an occupant of the vehicle; and wherein, to generate the parameter, the instructions of the at least one memory, when executed, cause the computing system to generate the parameter based on the third user data.
4 . The computing system of claim 1 , wherein the instructions of the at least one memory, when executed, cause the computing system to: set the display characteristic to synchronize the mobile display preferences with vehicle display preferences of the vehicle graphical user interface.
5 . The computing system of claim 1 , wherein the machine learning model is a generative artificial intelligence model.
6 . The computing system of claim 1 , wherein the display characteristic further includes one or more of a font style of the vehicle graphical user interface, or a menu of the vehicle graphical user interface.
7 . A vehicle comprising: a display that presents a vehicle graphical user interface; at least one processor; and at least one memory having a set of instructions, which when executed by the at least one processor, causes the vehicle to: receive a parameter from a computing device, wherein the parameter is generated with a machine learning model and based on mobile display preferences of first user data of an application and second user data that indicates a location of the vehicle, wherein the mobile display preferences include a background style of a mobile device including a mobile background selected by a user, wherein the machine learning model generates a new vehicle background for the vehicle graphical user interface based on the background style, the mobile background and the location, wherein the new vehicle background is different from the mobile background; identify a display characteristic based on the parameter; and control the vehicle graphical user interface based on the display characteristic by displaying the new vehicle background on the vehicle graphical user interface.
8 . The vehicle of claim 7 , wherein the second user data is one or more of weather associated with the vehicle, a speed of the vehicle, a physical characteristic of the vehicle, a time associated with the vehicle, an illumination associated with the vehicle, a temperature associated with the vehicle, a driving condition of the vehicle, a season associated with the vehicle, or biometric data associated with a user.
9 . The vehicle of claim 7 , wherein the instructions of the at least one memory, when executed, cause the vehicle to: provide third user data to the computing device, wherein the third user data includes a characteristic of an occupant of the vehicle; and wherein the parameter is generated based on the third user data.
10 . The vehicle of claim 7 , wherein the display characteristic synchronizes the mobile display preferences with vehicle display preferences of the vehicle graphical user interface.
11 . The vehicle of claim 7 , wherein the machine learning model is a generative artificial intelligence model.
12 . The vehicle of claim 7 , wherein the display characteristic includes one or more of a font style of the vehicle graphical user interface, or a menu of the vehicle graphical user interface.
13 . A method comprising: receiving first user data from an application of a mobile device; identifying that the application is associated with a vehicle; identifying mobile display preferences of the application from the first user data, wherein the mobile display preferences include a background style of the mobile device including a mobile background selected by a user; identifying second user data that indicates a location of the vehicle; generating, with a machine learning model, a parameter based on the first user data and the second user data by generating a new vehicle background for a vehicle graphical user interface of the vehicle based on the background style, the mobile background and the location, wherein the parameter is associated with a display characteristic that controls a presentation of the new vehicle background on the vehicle graphical user interface, wherein the new vehicle background is different from the mobile background; and providing the parameter to the vehicle based on the application being associated with the vehicle.
14 . The method of claim 13 , wherein the second user data further includes one or more of weather associated with the vehicle, a speed of the vehicle, a physical characteristic of the vehicle, a time associated with the vehicle, an illumination associated with the vehicle, a temperature associated with the vehicle, a driving condition of the vehicle, a season associated with the vehicle, or biometric data associated with a user.
15 . The method of claim 13 , further comprising: receiving third user data that is a characteristic of an occupant of the vehicle; and wherein the generating includes generating the parameter based on the third user data.
16 . The method of claim 13 , further comprising: setting the display characteristic to synchronize the mobile display preferences with vehicle display preferences of the vehicle graphical user interface.
17 . The method of claim 13 , wherein the machine learning model is a generative artificial intelligence model, and wherein the display characteristic further includes one or more of a font style of the vehicle graphical user interface, or a menu of the vehicle graphical user interface.
18 . The computing system of claim 1 , further comprising: at least one vehicle processor of the vehicle; and at least one vehicle memory of the vehicle having a set of instructions, which when executed by the at least one vehicle processor, causes the vehicle to: display the new vehicle background on the vehicle graphical user interface.
19 . A system comprising: the vehicle of claim 7 ; and a server comprising a memory and processor that executes the machine learning model to generate the new vehicle background.
20 . The method of claim 13 , further comprising: displaying the new vehicle background on the vehicle graphical user interface.

Description

TECHNICAL FIELD Embodiments generally relate to a cohesive user interface experience across platforms. In detail, examples unify user preferences across platforms and personalize a graphical user interface (GUI) based on factors specific to a user. BACKGROUND Infotainment systems may include a touch-enabled screen that allows drivers and passengers (e.g., users) to access various functionalities, such as navigation, media playback, and communication. The infotainment system may present a user interface (UI) that a user may view. The UI may be a visual portion of an infotainment system (e.g., GUI) which users interact with. The infotainment system may also include a control portion that controls an input for the user interface through touch screen, knobs, steering wheel controls, voice commands etc. The infotainment system may include various functionalities such as Global Positioning System (GPS) navigation, media playback, hands-free calls, and internet services. BRIEF SUMMARY A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions. In some aspects, the examples described herein relate to a computing system including at least one processor, and at least one memory having a set of instructions, which when executed by the at least one processor, causes the computing system to receive first user data from an application, identify that the application is associated with a vehicle, generate, with a machine learning model, a parameter based on the first user data, where the parameter is associated with a display characteristic that controls a manner that information is presented on a user interface, where the user interface is associated with the vehicle, and provide the parameter to the vehicle based on the application being associated with the vehicle. In some aspects, the examples described herein relate to a vehicle including a display that presents a user interface, at least one processor, and at least one memory having a set of instructions, which when executed by the at least one processor, causes the vehicle to receive a parameter from a computing device, where the parameter is generated with a machine learning model and based on first user data of an application, identify a display characteristic based on the parameter, and control a manner that information is presented on the user interface based on the display characteristic. In some aspects, the techniques described herein relate to a method including receiving first user data from an application, identifying that the application is associated with a vehicle, generating, with a machine learning model, a parameter based on the first user data, where the parameter is associated with a display characteristic that controls a manner that information is presented on a user interface, where the user interface is associated with the vehicle, and providing the parameter to the vehicle based on the application being associated with the vehicle. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced. FIG. 1 illustrates a diagram of an enhanced configuration and preference generation process in accordance with an example; FIG. 2 is a flowchart of a method of unifying user experiences and building enhanced GUIs according to an example; FIG. 3 illustrates a diagram of a vehicle in accordance with an example; FIG. 4 is a flowchart of a method of generating an enhanced GUI according to an example; FIG. 5 is a flowchart of a method of generating visual elements based on biometric data according to an example; FIG. 6 is a flowchart of a method of generating prompts for a generative AI model according to an example; FIG. 7 is a flowchart of a method of providing user feedback to a generative AI model according to an example; and FIG. 8 is a block diagram of an example of a unified user experience system according to an embodiment. DETAILED DESCRIPTION Existing examples fail to provide a unified visual experience across a visual display of a vehicle (e.g., the infotainment screen) and applications (e.g., mobile applications). Currently, such platforms (e.g., infotainment screens and applications) often have different interfaces, different backgrounds, and different font styles. Existing examples lead to a disjointed user experience across electronic devices, duplicative acts to recreate settings and preferences across the different electronic devices and po