A computing device (e.g., a mobile phone, camera, tablet computer, etc.) uses an application preload prediction model (e.g., an artificial intelligence model) for preloading an application. The computing device may execute an application that employs a user interface to facilitate human-machine interaction. The computing device may collect contextual user data and use a prediction model (e.g., an application preload prediction module) to analyze the collected contextual data to determine and predict an application to preload in background mode even with the computing device’s screen locked. For example, a user device (e.g. mobile phone) with a touch screen predicts a user’s intention to use the device and intelligently preloads applications (apps) (e.g., camera app, browser, terminal application, map, virtual assistant, emergency dialer, etc.) in the background of the device, even when the screen is off. The device includes a machine learned model that predicts the user’s intention to use the device based on the information from one or more sensors (such as light sensor, accelerometer, gyroscope, GPS, and proximity sensor) and location information, and preloads the necessary app before the user unlocks the screen or opens the app. This allows for rapid launching of the preloaded app in response to the user selecting the app.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Irwin, Collin; Hausmann, Rachel; and Rothlin, Joe, "PREDICTED APPLICATION PRELOAD BASED ON CONTEXT", Technical Disclosure Commons, (November 18, 2019)