Abstract

This document describes a system in which a smart watch—also referred to as a wearable device, fitness tracker, or digital timepiece—displays different configurations of watch faces, widgets, app shortcuts, or complications, depending on different contexts. The system utilizes a context engine to monitor real-time data from sensors and system applications, such as temporal data, spatial data, schedule data, and activity data, to determine a user’s current situation and predict the user’s desired screen layout. The system uses a strict set of rules to resolve any conflicting information to ensure predictable and relevant information delivery. Based on the user’s current context state, a profile mapping service retrieves a complication profile that matches what the user is doing. This profile includes a selection of complications, widgets, or applications and may also include a watch layout with fixed slots for the widgets or a theme or appearance setting that changes the look or function of the display. According to the chosen widgets, the system identifies target data sources and routes live updates directly to the watch face. The context engine may also use artificial intelligence (AI) to learn from the user’s habits or preferences. For example, if the user has a pattern of manually checking a different widget, the system learns from this feedback to automatically show it in the future. In this way, helpful and relevant data is provided to the user throughout the day without the user ever having to manually swipe or switch between watch faces, profiles, or applications.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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