Microsoft Technology Licensing, LLC

Ashish Vijay Gadre

Sanjib Saha

Janet Lynn Schneider

Frank Gorgenyi

Parmjeet Singh

Fernando Gonzalez

Mark A Inderhees

Stuart J Harper

Shaun C Hedrick

Gang Zhao

Hiep V Khuu

Lisa Hwei-Min Ong

Technologies are described herein for providing contextually-aware location sharing services for computing devices. In some configurations, the techniques disclosed herein can involve a number of computing devices configured to select and utilize location data from one or more resources based on one or more factors. An analysis of contextual data including, but not limited to, the capabilities of the individual devices, a status of one or more components, or the availability or cost of data, allows individual devices to dynamically select and utilize location data or a source of location data to accommodate a range of scenarios. Techniques disclosed herein can also detect the presence of a changed scenario and take one or more actions based, at least in part, on data defining the changed scenario.

Contextually-aware location sharing services

A computing device includes at least one processor and a memory, the memory storing computer-executable instructions for causing the device to be configured to load a subset of geo-fences from secondary storage to primary storage for tracking by the computing device, the subset of geo-fences selected from a set of available geo-fences stored in the secondary storage, based on a selection criteria. The computing device further creates a boundary geo-fence with a radius corresponding to a distance between a current location of the computing device and an edge of a geo-fence in the subset of geo-fences that is furthest from the current location of the computing device. Upon detecting a geo-fence event associated with a new location of the computing device in relation to the boundary geo-fence, a new subset of geo-fences is re-loaded, and a new boundary geo-fence is created based on the new location of the computing device.

Scalability and reliability of hardware geo-fencing with failover support

Systems and methods disclosed herein may include tracking one or more geo-fences using a GNSS hardware processor within a computing device. The tracking may use at least one GNSS signal. State changes of the one or more geo-fences during the tracking may be saved in a shared state database. The shared state database may be shared between the GNSS hardware processor and an application processor within the computing device. Upon detecting a deterioration of the at least one GNSS signal, tracking the one or more geo-fences may be switched from using the GNSS hardware processor to using the application processor. After the switching, an initial state of each of the one or more geo-fences may be set by using states currently stored in the shared state database prior to the switching.

Improving scalability and reliability of hardware geo-fencing with failover support

Techniques and tools for reducing power consumption of computing devices (e.g., mobile devices such as mobile phones and tablet computers) that perform position tracking operations are described. In described examples, a low-power processor calculates (e.g., in real time) position information (e.g., GPS position fixes) based on information received from a positioning system (e.g., GPS) and stores the position information for later use in a buffer associated with the low-power processor (e.g., in storage on the low-power processor). Described examples allow position information to be calculated in real time and stored while the device is in a low-power state, and can be used with location-based applications that do not require position information to be delivered to the application in real time.

Efficient power usage in position tracking operations

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Kirkland, WA, United States of America

Ashish Vijay Gadre