Microsoft Technology Licensing, LLC

Ken M Sadahiro

Christian Klein

Gregg Robert Wygonik

Axel Andrejs

Gregory R Slayden

Ronald S Ruzicka

Matthew D Johnson

Wen Li Looi

Aaron Michael Butcher

Anatolie Gavriliuc

Ron Mondri

Seung-Yup Chai

Alain Philippe Maillot

Roy Henry Berger, Jr

Jerin R Schneider

Robert David Steen

Roy H Berger

Nandini Bhattacharya

John D Malatras

Nicolas A Champagne-Williamson

Kangsan Lee

Hayden William McAfee

Rob Disano

Philippe Favre

Kofi S Opoku

Methods, systems, apparatuses, and computer program products are provided for enabling devices to determine the time zone in which they are located. A mobile device may receive location information from one or more sources. Based thereon, the current location of the mobile device may be determined in terms of latitude and longitude. The indication of the current location may be converted to an index value according to a Hilbert curve (or other space-filling curve), and the index value applied to a time zone index file to determine the local time zone. A time zone setting of the mobile device may be updated accordingly. Furthermore, 'geofencing' may be used by the mobile device to detect movement towards and through a time zone boundary, leading to a new time zone determination being initiated.

Automatic time zone detection in devices

Methods, systems, and computer program products are described herein for unsupervised clustering of browser history using web navigational activities. For example, correlation scores are calculated that indicate correlations between web pages indicated in a browsing history of a user. Moreover, the correlation scores are calculated based on web navigational activities determined from the browsing history. In addition, the web pages are clustered into a plurality of clusters based on the correlation scores and the clusters are ranked for relevancy to the user based on a relevancy algorithm. The relevancy algorithm determines a likelihood that a user will access a corresponding web page for each web page of a cluster. A cluster having a greatest ranking is identified and an indication of a web page of the identified cluster as a suggested web page to revisit is provided to a user.

Unsupervised clustering of browser history using web navigational activities

In a multi-user, collaborative environment, an operating system state change can occur by passing a stylus pen from one user to another. Upon detecting a change of the active user, a state machine transition can result in a change in a user experience. In one example, a change in the user experience includes a change in user interface (UI) functionality. Some examples include changing color of the digital ink being displayed, changing a handwriting pattern, and automatically switching a view displayed on the user interface. The switched view can be switching from a single-user model to a multi-user model. In another example, a state machine transition can result in establishing connections between devices (e.g., phones or laptops) associated with the users that used the pen.

Change of active user of a stylus pen with a multi user-interactive display

In one example, a method for detecting input can include detecting a capacitive pattern from a physical object and detecting that the physical object is a passive physical object. The method can also include determining a plurality of characteristics corresponding to the passive physical object based on the capacitive pattern, wherein the plurality of characteristics comprise a type of the physical object and a capability of the physical object. Additionally, the method can include modifying an application manager to detect input from the passive physical object based on the plurality of characteristics and modifying a user interface setting detected from the capacitive pattern.

Detecting input based on a capacitive pattern

In one example, a method for detecting input can include detecting a capacitive input pattern of an object in proximity of the system and comparing the capacitive input pattern to a sensed capacitive input profile of a known object, the sensed capacitive input profile comprising information about a plurality of characteristics of the known object. The method can also include determining a confidence that the object in proximity of the system corresponds to the known object based on the capacitive input pattern and at least one of the plurality of characteristics of the known object. Furthermore, the method can include assigning a functionality associated with the sensed capacitive input profile to the object in proximity of the system if the confidence exceeds a threshold and processing input received from the object in proximity of the system according to the functionality.

Detecting input based on a sensed capacitive input profile

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Redmond, WA, United States of America

Ken M Sadahiro