Microsoft Technology Licensing, LLC

Beibei Cheng

Jonathan Ross Hoof

Daniel Gilbert Kennett

Benjamin William Walker

Anis Ahmad

Michael Anthony Hall

Wei Wu

Siddhartha Cingh Arora

Kameron Wade Rausch

Dragomir Dimitrov Yankov

Abdelrehim Ahmed

Ben Walker

Alex Jordan Yuter

Senthil Kumar Palanisamy

Michael Robert Evans

Renzhong Wang

Described herein are technologies related to generating a predicted routine of a user of a mobile computing device. Location entries generated by the mobile computing device are processed to generate visit entries, wherein the visit entries are representative of visits made by the user to places over several days. An input sequence of states is constructed based upon the visit entries, wherein each state has a place identifier assigned thereto, and further wherein each state corresponds to a time interval of predefined length. A predicted routine of the user is generated based upon the input sequence of states.

Computing system that generates a predicted routine of a user

A facial skin mask may be generated based on isolating a head part in a captured image, removing a first pixel that is indicative of non-skin from the head part in the captured image, and removing a second pixel that is indicative of having a high velocity from the head part in the captured image. Heart rate may be detected based on the change of color of the pixels of the generated facial skin mask.

Facial skin mask generation for heart rate detection

An active illumination range camera that acquires a range and a picture image of a scene and provides a reflectance for a feature in the scene responsive to a distance for the feature provided by the range image, a registered irradiance for the feature provided by the picture image and registered irradiance for a region of a calibration surface corresponding to the feature provided by an image of the calibration surface acquired by the range camera.

Range camera

Changes in image exposure setting may be compensated by creating a table of aggregate differences between corresponding pixels in two images and applying the table of aggregate differences to a portion of one of the two images. The images may be in any color space for visible light, or images not of visible light, such as infrared or depth images. In various configurations, the differences may be aggregated by averaging the differences for pixels of a certain value in one of the two images.

Scene exposure auto-compensation for differential image comparisons

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Redmond, WA, United States of America

Beibei Cheng