Microsoft Technology Licensing, LLC

Soohoon Cho

Arun Sacheti

Kun Wu

Yanfeng Sun

Parthasarathy Govindarajen

Karim Hasham

Aaron Chun Win Yuen

Malik Mehdi Pradhan

Avinash Vemuluru

Gautam Vishwas Vaidya

Vincent Kin-Wah Leung

Yuancao Cui

Alexandre Michelis

Non-limiting examples of the present disclosure describe processing by a contextual image analysis service that generates and surfaces contextually relevant data objects based on entities identified from image content. In one example, image content is analyzed and entity annotations are generated for the image content. The entity annotations may be converted to queries. Raw search results may be identified based on the converted queries. The raw search results may be filtered based on one or more specific content types. A contextual representation is generated based on the filtered raw search results, where the contextual representation comprises the image content and one or more contextually relevant data objects for at least one entity associated with the image content. The exemplary contextual representation of the image content and the one or more contextually relevant data objects may be surfaced through a user interface of an application/service.

Contextual data transformation of image content

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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Soohoon Cho