How Android Uses Machine Learning for Adaptive Battery?

From app usage prediction to background throttling and smarter Doze Mode.

How does Android implement Adaptive Battery using Machine Learning?

The Android system categorizes your apps into the following categories:

• Active: Currently in use.

• Working Set: Your daily essentials.

• Frequent: Regular but not daily.

• Rare: Apps you rarely use.

• Restricted: Heavy battery drainers.

The system analyzes your unique usage patterns by:

• Time-series analysis to predict when the apps will be used.

• Pattern recognition learns your daily and weekly routines.

• Contextual learning that considers factors like time of day, location, and user habits.

• Feature extraction from multiple data points, including CPU usage, screen time, and network activity.

With all the above in place, the model can predict and do the following:

• Preloads frequently used apps into memory.

• Throttles background services for predicted unused apps.

• Enhancement in the Doze Mode as ML helps optimize when and how aggressively to enter Doze Mode by predicting periods of non-use.

All processing happens on-device. No personal data leaves your phone.

This is how Android implements Adaptive Battery using Machine Learning.

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Thanks

Amit Shekhar

Founder, Outcome School