Microsoft Technology Licensing, LLC

Malik Mehdi Pradhan

Paul Baecke

Alexander Leo Campbell

Robert David Frye

Gustavo M C Guerra

Kimberly A Wilton

Sean O Lyndersay

Arun Sacheti

Kun Wu

Gaurav S Anand

David Joel Sheldon

Yanfeng Sun

Parthasarathy Govindarajen

Aaron Chun Win Yuen

Karim Hasham

Avinash Vemuluru

Daniel Hill

Gaurav Sunil Anand

John Alton Price

Soohoon Cho

Nathan Novielli

Gautam Vishwas Vaidya

Subramanian Vuttravadium Venkata

Alfredo Augusto Menendez Sancho

Jacqueline Kay Killow

Yan Zhong

Carol Kumar Mekala

Anuja Milind Joshi

Dheeraj Mehta

Jacob G Danovitch-Tatoff

Richard Scott Cassan

Alexandre Michelis

Robert Ross Fenton

Hany Salaheldeen Khalil

Systems and methods are disclosed for dynamic search interfaces. In one implementation, a search query is received from a user within a content retrieval interface. The search query is processed with respect to a first content source to determine a relevance of the first content source with respect to the search query. Based on a determination that the first content source exceeds a defined relevance threshold, content from the first content source is requested and/or received. The content received from the first content source is presented within the content retrieval interface.

Dynamic search interfaces

Technology is disclosed for proactively enriching content of electronic communications through predicting user intents. User-composed content within an electronic communication is observed and used to predict one or more intents each associated with a task related to the communication and to be completed in creating the communication. Task-queries may be generated based on the detected intents and entities extracted from the user-composed content, contextual information, or other information related to the user, such as a user-knowledge database. User selection of a suggested task-query automatically executes the task-query, and the results are provided to the user. Each result may include an action involving adding content to or supplementing the content of the electronic communication. A conversational digital assistant may further be invoked to interact with the user to confirm and/or modify suggested task-queries and results of selected task-queries.

Composing rich content messages

Representative embodiments disclose mechanisms to improve the perceived responsiveness of a search engine. As a user types a query prefix into a browser or other interface to the search engine, the search engine returns query completion suggestions to the browser. The query completion suggestions, user history, user favorites and/or other information are presented to a trained machine learning model on the client device to predict a desired location that the user is attempting to navigate to. When the confidence level of the predicted location surpasses a threshold, content from the desired location is preloaded into a hidden tab in the browser. When the user submits a query, the browser submits feedback to a system responsible for updating and refining the machine learning model. Updated machine learning model coefficients can be received by the browser from the system to make predictions more accurate.

Machine learning model to preload search results

The technology described herein provides an efficient mechanism for quickly analyzing huge amounts of media content to find media content (hereafter 'content' or 'media content') that is relevant to a user. The technology analyzes features of a curator to classify curators by interest and/or find curators with similar content recommendations. The curator data can be used to make curator recommendations to users based on the user's interests. The technology described herein collects curator data from multiple content sites and analyzes the data to identify curators that recommend similar content on different content sites.

Recommending a content curator

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Kirkland, WA, United States of America

Malik Mehdi Pradhan