Microsoft Technology Licensing, LLC

Andrew Peter Oakley

Sean E Kollenkark

Cory J Cirrincione

Douglas Duane Berrett

Joseph Lloyd McClanahan

Christopher A Meyers

Hugh Evan Williams

Gopi Prashanth Gopal

Sanaz Ahari

Gaurav Sareen

Nitin Agrawal

Milad Shokouhi

Derrick Leslie Connell

Maxim V Stepin

Srinath Reddy Aaleti

Jay Goyal

Neil Sharman

Nicholas Craswell

Jason A Bolla

Nikolay A Glushnev

Joseph L Mcclanahan

Richard J Gruenhagen

Various embodiments provide a so-called companion experience in which content consumed on a primary screen can serve as a source for an automatic search that returns related content that can be presented on an auxiliary screen. The companion experience can be considered to reside in a layer that can be moved across different screens. The different screens can include different physical screens, such as those associated with different computing devices, or the same physical screen in which the companion experience would be rendered in a frame or sub-window.

Companion experience

Systems, methods, and computer storage media having computer-executable instructions embodied thereon that facilitate providing suggested sites in accordance with target sites are provided. Web-browsed data is locally stored on a user device and uploaded to a suggestion engine. Additional web-browsed data is communicated to the suggestion engine via a suggested site request that provides a request for suggested sites related to a target site. In embodiments, the web-browsed data contained in the suggested site request includes data not previously uploaded to the suggestion engine. The suggestion engine utilizes the web-browsed data uploaded to the suggestion engine and the web-browsed data communicated via the suggested site request to provide suggested sites in association with the target site.

Providing suggested sites associated with target sites

Methods, systems, and media are provided for identifying and clustering queries that are rising in popularity. Resultant clustered queries can be compared to other stored queries using textual and temporal correlations. Fresh indices containing information and results from recently crawled content sources are searched to obtain the most recent query activity. Historical indices are also searched to obtain temporally correlated information and results that match the clustered query stream. A weighted average acceleration of a spike can be calculated to distinguish between a legitimate spike and a non-legitimate spike. Legitimate clusters are combined with other stored clusters and presented as grouped content results to a user output device.

Detecting spiking queries

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Seattle, WA, United States of America

Andrew Peter Oakley