Microsoft Technology Licensing, LLC

Other

Microsoft Corporation

Christopher A Meyers

Nitin Agrawal

Andrew Peter Oakley

Kieran Richard McDonald

Kumaresh Pattabiraman

Tapas Kanungo

Kuansan Wang

Hugh Evan Williams

Gopi Prashanth Gopal

Sanaz Ahari

Ran Gilad-Bachrach

Gaurav Sareen

Milad Shokouhi

Derrick Leslie Connell

Maxim V Stepin

Nipoon Malhotra

Srinath Reddy Aaleti

Jay Goyal

Neil Sharman

Nicholas Craswell

Luke Evans Simon

Jason A Bolla

Ashok Ponnuswami

Richard J Gruenhagen

Nikolay A Glushnev

Andrew P Oakley

Srinath R Aaleti

A vehicle automated audio reminder apparatus for reminding parents to check for children and pets when exiting a vehicle includes a housing defining a housing cavity. A front side of the housing has a speaker aperture and a button aperture each extending through to the housing cavity. A first and second engagement member couple the housing under a dash panel of a vehicle. A speaker is coupled to the housing within the speaker aperture and a microphone is coupled within the speaker. A record button is coupled within the button aperture. A digital recorder is coupled within the housing cavity to stores an audio message through the microphone when the record button is depressed and reproduce the audio message through the speaker when activated. A microprocessor activates the digital recorder when a door of the vehicle is opened.

Vehicle automated audio reminder apparatus

This patent application pertains to answer model comparison. One implementation can determine a first frequency at which an individual answer category appears in an individual slot on a query results page when utilizing a first model. The method can ascertain a second frequency at which the individual answer category appears in the individual slot on the query results page when utilizing a second model. The method can calibrate the second model so that the second frequency approaches the first frequency.

Answer model comparison

Methods and computer-storage media having computer-executable instructions embodied thereon that facilitate generating a machine-learned model for ranking search results using click-based data are provided. Data is referenced from user queries, which may include search results generated by general search engines and vertical search engines. A training set is generated from the search results and click-based judgments are associated with the search results in the training set. Based on click-based judgments, identifiable features are determined from the search results in a training set. Based on determining identifiable features in a training set, a rule set is generated for ranking subsequent search results.

Ranking search results using click-based data

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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Redmond, WA, United States of America

Christopher A Meyers