Microsoft Technology Licensing, LLC

Qian Liu

Hari R Pulapaka

Emily Nicole Wilson

Jon Robert Berry

Alain Franck Gefflaut

Rahul Anantha Padmanabha Udipi

Justin James Wagle

Syed Farhan Raza

Nathaniel Gunther Roth

Pnina Eliyahu

Timothy Edward Bellay

John Robert Berry

A technique is described herein for automatically logging journeys taken by a user, and then automatically classifying the purposes of the journeys. In one implementation, the technique obtains journey data from one or more movement-sensing devices as a user travels from a starting location to an ending location in a vehicle. The technique generates a set of features based on the journey data, and then uses a machine-trainable model (such as a neural network) to make its classification based on the features. The machine-trainable model accepts at least one feature that is based on statistical information regarding at least one aspect of prior journeys that the user has taken. Overall, the technique provides a resource-efficient solution that rapidly provides personalized results to individual respective users. In some implementations, the technique performs its personalization without sharing journey data with a remote server.

Automated personalized classification of journey data captured by one or more movement-sensing devices

Controlling background activity in a computing device or system during a low-power mode is described. In some example techniques, when a computing device or system is in a low power mode, a determination is made whether to block one or more trigger events from causing an activation of one or more respective background task operations. Based at least in part on the determination, at least one trigger event may be allowed to cause an activation of a respective background task operation during the low power mode.

Selective blocking of background activity

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Qian Liu