Microsoft Technology Licensing, LLC

Just-evotec Biologics, Inc.

Fernando Garcia

Randal Robert Ketchem

Ethan Joseph Bernstein

Patrick Tousignant

Joe Keng Yap

Tali Roth

Gurashish Brar

Dmitri Gavrilov

Aditi Desai

Umut Alev

Jonathan Lecroy

Victor Wang

Vamsi Kanamaluru

Mike Borysenko

John Michael Winn

James Edelen

Tore L Sundelin

Jeremy Martin Shaver

Tileli Amimeur

Ron Gillespie

A cloud service stores and aggregates content that is obtained from different content sources using different protocols. For example, the content may include electronic messages, calendar information, contacts, social network content, feed data, search results, and the like. The cloud service aggregates content for each user based on the user's configuration settings. The cloud service may perform processing on the aggregated content before it is delivered and displayed to the user on the client device. For example, one or more views may be generated and stored by the cloud service before the view is requested from the client. Changes made to the aggregated content on the client may be synchronized with the content source. Searches may be made across all or a portion of the different content sources that are aggregated for a user.

Aggregating content from different content sources at a cloud service

A system is provided for automatically predicting actions a user is likely to take in response to receiving data. The system may be configured to monitor and observe a user's interactions with incoming data and to identify patterns of actions the user may take in response to the incoming data. The system may enable a trainer component and a classifier component to determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user and the identified pattern of the user's actions. The system may also be configured to continuously observe the user's actions to fine-tune and adjust the identified patterns of user actions and to update the probabilities of likely user actions in order increase the accuracy of the predicted user action in response to incoming data.

Prediction of user response actions to received data

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Seattle, WA, United States of America

Fernando Garcia