Machine Learning to Select Input Language on a Software Keyboard

Generally, the present disclosure is directed to selecting an input language on a software keyboard. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an input language for a keyboard based on device usage data

Machine Learning to Select Media Playback Volume

Generally, the present disclosure is directed to setting a media playback volume of an electronic device. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an appropriate media playback volume based on device usage data

Configuring Alarm Setting Using Machine Learning

Generally, the present disclosure is directed to configuring the initial time set in an alarm system. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict a time to set an alarm based on historical alarm records and/or other contextual data

Machine Learning to Automatically Lock Device Screen at Opportune Time

Generally, the present disclosure is directed to determining when to lock a screen of an electronic device such as, for example, a smartphone. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an appropriate lock screen behavior based on device usage data

Machine Learning to Select Screen Brightness Level

Generally, the present disclosure is directed to setting a brightness of a screen of an electronic device (e.g., during media viewing or media playback). In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an appropriate screen brightness level based on device usage data

Machine Learning to Disable Applications from Using Background Resources Except at Appropriate Times

Generally, the present disclosure is directed to disabling computing applications from using background resources (e.g., processing or data transmission) except at appropriate times (e.g., times when the user is likely to use the electronic device). In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict an appropriate time for an application to use resources based on device usage data

Machine Learning to Select Best Network Access Point

Generally, the present disclosure is directed to selecting a best network access point when multiple are available (e.g., including cell towers, WiFi access points, etc.). In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict a best network access point based on device usage data