This publication describes an operating system (OS) of a user equipment (UE), such as a smartphone, that can classify a wake-up alarm from other types of alarms. The importance of such classification stems from the fact that when a user sets a wake-up alarm on the smartphone, they may or may not turn on a do-not-disturb (DND) feature. In addition, the UE’s DND feature may differ, such that it may silence calls and alerts, but keep the luminosity of the UE’s screen the same, it may silence calls and alerts and lower the luminosity, or a combination thereof. To aid the user in classifying a wake-up alarm from other types of alarms, the UE’s OS autonomously analyzes the user’s behavior using a machine-learned model. The machine-learned model analyzes several inputs, such as location, day of the week, week, time of day, date, time duration until alarm, time elapsed since last alarm, alarm ringtone, barometer data, accelerometer data, recent user activity, user identity, and so forth. Then, the machine-learned model determines what is the probability of this alarm being a wake-up alarm to determine when and how the DND feature functions.
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Price, Thomas G., "Method for Classifying Wake-up Alarms Using Machine Learning (ML)", Technical Disclosure Commons, (June 05, 2019)