Abstract

A method is disclosed for triggering context-sensitive audio notifications. Systems and devices according to the method also predict a least disruptive moment to trigger a notification within a given period of time. The method comprises two parts. In the first part, a machine learning (“ML”) subsystem determines a current context of a user (based on, e.g., surrounding audio environment, current location, user calendar, etc.) and assesses the potential disruptiveness of an audible notification in the current context. The ML subsystem predicts a time-series of scores denoting the disruptiveness of the audible notification if emitted at particular times in the future. The ML subsystem then determines the least disruptive time from among the particular times, based on importance of the notification, urgency of the notification, and interruptibility of the user (derived from the current context). In the second part, a system or device manages incoming notifications to assess their respective importance and urgency, and uses the output from the ML subsystem in the first part to determine when to trigger the notification.

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