Abstract: A method may include identifying, by one or more processors, an object in a field of view of a wearable display, where the object is identified for a presbyopic compensation. The presbyopic compensation is performed by the one or more processors on image data of the object to generate compensated image data of the object. The one or more processors render an image in response to the compensated image data of the object on a display of the wearable display.

Abstract: Signals representing local events and/or state are captured at a mobile device and utilized by a machine learning system to recognize patterns of user behaviors and make predictions to automatically launch an application, initiate within-application activities, or perform other actions. The local signals may include, for example, location information such as geofence crossings; alarm settings; use of network connections like Wi-Fi, cellular, and Bluetooth®; device state such as battery level, charging status, and lock screen state; device movement indicating that the device user may be driving, walking, running, or stationary; audio routing such as headphones being used; telemetry data from other devices; and application state including launches and within-application activities. A feedback loop is supported in which the machine learning system may utilize feedback from the user as part of a learning process to adapt and tune the system's predictions to improve the relevance of the predictions.