Microsoft Technology Licensing, LLC

Aaron Chun Win Yuen

Arun Sacheti

Parthasarathy Govindarajen

Yanfeng Sun

Karim Hasham

Daniel Marantz

Kuansan Wang

Kun Wu

Yu-ting Kuo

Malik Mehdi Pradhan

Yandong Guo

Avinash Vemuluru

Hua Ding

Soohoon Cho

James Brian Lawson

Gautam Vishwas Vaidya

Alexandre B R Michelis

Yogesh Ajit Vaidya

Nan Wu

Deepak Santhanam

Alexandre Michelis

Guarang P Prajapati

Yin-Cheng Ting

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

Systems, methods, computer storage media, and user interfaces are provided for non-committal intent preview, disambiguation, and refinement of a search. A search prefix comprising one or more characters associated with an unexecuted search query is received. One or more intent suggestions are suggested to a user. For each of the one or more intent suggestions, one or more entity identifications associated with each of the one or more intent suggestions are received. Metadata corresponding to at least one entity associated with the one or more entity identifications is retrieved from an entity data store. Without retrieving search results for the unexecuted search query, an aggregated intent preview based on the retrieved metadata corresponding to the at least one entity is provided.

Search intent preview, disambiguation, and refinement

The technology described herein provides an efficient mechanism for generating image tags. Image data from a plurality of sources may be analyzed to identify relevant text items from the aggregated data. The relevant text items may be keywords describing a subject of an image, an entity of an image, a location of an image, or the like. From the aggregated image data, one or more image tags may be generated and stored as an offline dataset with an image identifier. Upon detecting a prompt such as a user issuing a search query for an image, the image identifier is used to perform a look up of the image and associated image tags to be provided.

Generating image tags

A user query may be evaluated to provide a result set. In case the results do not reflect the user's intent, the device may provide recourse options for adjusting the query in a manner that yields more desirable results, e.g., a suggestion at the top of the result set for a different spelling, or recommendations at the end of the results set for additional query techniques that may yield more accurate results. However, such presentation of recourse options may clutter the user interface and/or go unnoticed by the user. Instead, an adjusted query may be identified with an interpreted probability of reflecting the intent of the query. An adjustment option describing the adjusted query may be inserted into the result set, between a higher-probability first result and a lower-probability second result. Selection of the adjustment option may cause the adjusted query to be evaluated on behalf of the user.

Providing query recourse with embedded query adjustment options

Systems, methods and computer-readable storage media are provided for building long queries utilizing query suggestions and query suggestion entities. Upon receiving at least a portion of a search query, a query suggestion is provided. Upon receiving a selection of the query suggestion, the search query is updated. Prior to executing the updated search query, a user is permitted to edit the updated search query. Query suggestions may be presented in association with query suggestion entity representations. A query suggestion entity is a separate and distinct entity that may initiate performance of an action upon selection. A query suggestion entity representation may comprise various features and information associated with the search query.

Building long search queries

Growing community of inventors

Bellevue, WA, United States of America

Aaron Chun Win Yuen