US-20260129262-A1 - RECOMMENDATIONS BASED ON EMBEDDED MODELS ON A TELEVISION

Abstract

According to an aspect, a method may include executing, by a computing device, a television application. A method may include gathering, by the computing device, information and data related to interactions of a user with a user interface of the television application. A method may store the information and data related to the interactions of the user locally on the computing device. A method may generate an on-device model associated with the user based on the information and data related to the interactions of the user with the user interface of the television application. A method may determine media content recommendations for the user utilizing the on-device model. A method may integrate the media content recommendations in the user interface of the television application.

Inventors

• Sundaramoorthy Murugesan
• Tamojit Chatterjee
• Shravan Nayak
• Kopal Niranjan
• Priyanshi Sharma
• Sujal Maheswari
• Kanishka Mishra

Assignees

• GOOGLE LLC

Dates

Publication Date

20260507

Application Date

20241104

Claims (20)

1. 1 . A method comprising: executing, by a computing device, a television application; gathering, by the computing device, information and data related to interactions of a user with a user interface of the television application; storing the information and data related to the interactions of the user locally on the computing device; generating an on-device model associated with the user based on the information and data related to the interactions of the user with the user interface of the television application; determining media content recommendations for the user utilizing the on-device model; and integrating the media content recommendations in the user interface of the television application.
2. 2 . The method of claim 1 , wherein the on-device model is embedded on the computing device.
3. 3 . The method of claim 2 , wherein the on-device model is a large language model.
4. 4 . The method of claim 1 , wherein the information and data associated with the user is not shared with any other computing devices.
5. 5 . The method of claim 1 , wherein the information and data include activities and interactions of the user with the television application.
6. 6 . The method of claim 5 , wherein the activities and interactions include at least one of selections or clicks by the user, a watch history of the user, a location of the computing device, a language used by the user when interacting with the television application, and a language of media content items watched or consumed by the user.
7. 7 . The method of claim 1 , further comprising: receiving additional training data; and fine-tuning the on-device model based on the received additional training data.
8. 8 . The method of claim 7 , wherein the fine-tuning uses one of a supervised fine-tuning process or a low rank optimization process.
9. 9 . The method of claim 1 , herein the computing device is a network-connected display device.
10. 10 . The method of claim 9 , wherein the network-connected display device is a smart television.
11. 11 . A non-transitory computer-readable medium storing executable instructions that when executed by at least one processor of a network-connected display device cause the at least one processor to execute operations, the operations comprising: executing a television application; gathering information and data related to interactions of a user with a user interface of the television application; storing the information and data related to the interactions of the user locally on the network-connected display device; generating an on-device model associated with the user based on the information and data related to the interactions of the user with the user interface of the television application; determining media content recommendations for the user utilizing the on-device model; and integrating the media content recommendations in the user interface of the television application.
12. 12 . The non-transitory computer-readable medium of claim 11 , wherein the on-device model is embedded on the network-connected display device.
13. 13 . The non-transitory computer-readable medium of claim 12 , wherein the on-device model is a large language model.
14. 14 . The non-transitory computer-readable medium of claim 11 , wherein the information and data associated with the user is not shared with any other computing devices.
15. 15 . The non-transitory computer-readable medium of claim 11 , wherein the information and data include activities and interactions of the user with the television application.
16. 16 . The non-transitory computer-readable medium of claim 11 , wherein the operations further comprise: receiving additional training data; and fine-tuning the on-device model based on the received additional training data.
17. 17 . The non-transitory computer-readable medium of claim 16 , wherein the fine-tuning uses one of a supervised fine-tuning process or a low rank optimization process.
18. 18 . A system comprising: at least one processor; and a non-transitory computer-readable medium storing instructions that when executed by the at least one processor cause the system to: execute a television application; gather information and data related to interactions of a user with a user interface of the television application; store the information and data related to the interactions of the user locally on the system; generate an on-device model associated with the user based on the information and data related to the interactions of the user with the user interface of the television application; determine media content recommendations for the user utilizing the on-device model; and integrate the media content recommendations in a user interface of the television application.
19. 19 . The system of claim 18 , wherein the on-device model is embedded on the system.
20. 20 . The system of claim 18 , wherein the information and data associated with the user is not shared with any other systems.

Description

BACKGROUND A television (TV) application may present various types of media content of interest to a user. The media content may have different formats such as streaming video and audio. The types of media content may include, but are not limited to, movies, television shows, sporting events, news items, short form videos, and music. In addition, or in the alternative, a variety of media content providers may deliver various types of media content for viewing by the user. The TV application may deliver a customized viewing experience to a user that spans the diverse types of media content provided by the variety of media content providers. SUMMARY In some non-limiting examples, a network-connected display device (e.g., a smart television (TV)) may execute a television (TV) application. The TV application may interface with an artificial intelligence (AI) module included on the network-connected display device. The TV application may use large language models (LLMs) embedded in the AI module to determine media content for recommending to a user of the network-connected display device. For example, the user may have an account with and/or is otherwise logged into the TV application running on the network-connected display device. The TV application may gather and/or store information and data related to the interactions of the user with the TV application for use in determining media content recommendations for the user. The TV application may provide the information and data to the AI module for training the LLMs. The interactions of the TV application with the AI module remain local to the network-connected display device as the obtained information and data related to the interactions of the user with the TV application are not sent, provided, or shared with computing devices outside of the network-connected display device. In some aspects, the techniques described herein relate to a method including: executing, by a computing device, a television application; gathering, by the computing device, information and data related to interactions of a user with a user interface of the television application; storing the information and data related to the interactions of the user locally on the computing device; generating an on-device model associated with the user based on the information and data related to the interactions of the user with the user interface of the television application; determining media content recommendations for the user utilizing the on-device model; and integrating the media content recommendations in the user interface of the television application. In some aspects, the techniques described herein relate to a method, wherein the on-device model is embedded on the computing device. In some aspects, the techniques described herein relate to a method, wherein the on-device model is a large language model. In some aspects, the techniques described herein relate to a method, wherein the information and data associated with the user is not shared with any other computing devices. In some aspects, the techniques described herein relate to a method, wherein the information and data include activities and interactions of the user with the television application. In some aspects, the techniques described herein relate to a method, wherein the activities and interactions include at least one of selections or clicks by the user, a watch history of the user, a location of the computing device, a language used by the user when interacting with the television application, and a language of media content items watched or consumed by the user. In some aspects, the techniques described herein relate to a method, further including: receiving additional training data; and fine-tuning the on-device model based on the received additional training data. In some aspects, the techniques described herein relate to a method, wherein the fine-tuning uses one of a supervised fine-tuning process or a low rank optimization process. In some aspects, the techniques described herein relate to a method, herein the computing device is a network-connected display device. In some aspects, the techniques described herein relate to a method, wherein the network-connected display device is a smart television. In some aspects, the techniques described herein relate to a non-transitory computer-readable medium storing executable instructions that when executed by at least one processor of a network-connected display device cause the at least one processor to execute operations, the operations including: executing a television application; gathering information and data related to interactions of a user with a user interface of the television application; storing the information and data related to the interactions of the user locally on the network-connected display device; generating an on-device model associated with the user based on the information and data related to the interactions of the user with the user interface of