Abstract
This publication describes intelligently enabling or disabling various capabilities (e.g., health tracking, GPS tracking, voice calling, messaging, etc.) of a computing device (e.g., mobile devices, wearable devices, etc.). This publication may enable a mobile device (e.g., cellular phone, smartwatch, etc.) to increase battery life by using machine learning (ML) techniques to provide recommendations on which device capabilities should be disabled. The computing device may monitor the usage of each device capability to determine a set of historical actions associated with the device capability. For instance, the computing device may monitor the foreground usage of various applications (e.g., services and/or processes of the application triggered by direct user interactions with the application), wherein each application may be associated with one or more device capabilities based on the capabilities the application utilizes. The computing device may also monitor the status (e.g., enabled, disabled, etc.) of various device capabilities. The computing device may determine usage patterns and predicted battery discharge rates (e.g., battery drain) for each device capability by using ML techniques to analyze the set of historical actions associated with the device capability. The computing device may provide the user with the ability to select a desired battery life. The computing device may use the predicted battery discharge rates and usage patterns to determine what capabilities and functions to disable to achieve the desired battery life. In this way, the computing device may enhance user experience by intelligently disabling features to increase battery life.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Edhara, Prem, "INTELLIGENT USER TAILORED BATTERY SAVING BASED ON USAGE PATTERNS", Technical Disclosure Commons, (March 13, 2025)
https://www.tdcommons.org/dpubs_series/7895