Microsoft Technology Licensing, LLC

Yahoo! Inc.

Jake M Hofman

Daniel Gray Goldstein

Duncan James Watts

Miroslav Dudik

Winter Mason

Omar Rogelio Alonso

Vasileios Kandylas

Serge-Eric Tremblay

Sharad Goel

Siddhartha Sen

Robert Warren Gruen

Kellan Elliott-McCrea

Siddharth Suri

Utilizing graphical elements representing human bodies to estimate physical measurements of a user is described. In at least one example, a service provider can access a database storing a plurality of data items. The service provider can cause a set of data items of the plurality of data items to be presented to the user. Data items in the set of data items are associated with at least one graphical element representing a human body with individual magnitudes corresponding to individual dimensions of a plurality of dimensions. The service provider can receive data indicating a selection of a data item associated with a first magnitude associated with a first dimension and a second magnitude associated with a second dimension. The service provider can estimate physical measurements associated with the user based partly on a first magnitude and/or the second magnitude.

Interactive graphical system for estimating body measurements

Various techniques for providing perspective annotation to numerical representations are disclosed herein. For example, a method includes detecting a numerical representation in an original content and retrieving one or more perspectives from a database based on the detected numerical representation. The one or more perspectives individually include a restatement of information contained in the numerical representation. The method can also include annotating the original content with the retrieved one or more perspectives to form an annotated content.

Perspective annotation for numerical representations

Techniques for designing an awareness engine that organizes and serves popularly discussed and viral online content in response to user search queries. In an aspect, quality online content is identified by analyzing posts by users of a social network over specific time periods. For each item of quality online content identified, a virality score is calculated, and a social signature is constructed. The social signature can be constructed from the content itself, as well as from posts referencing the content. Based on this processing, relevant quality online content having the highest virality scores may be retrieved and served in response to user queries. Further techniques are provided for designing a user interface for the awareness engine.

Awareness engine

Various techniques of content virality determination and visualization are disclosed herein. For example, a method of determining network content virality metric includes constructing a diffusion cascade for a computer network content based on a plurality of time points at which individual users adopt the network content and connection information of the users. The method also includes calculating, with a processor, a virality metric of the network content based on a structural characteristic of the constructed diffusion cascade. Based on the calculated virality metric, one may determine if the network content is viral.

Content virality determination and visualization

Methods, system and computer readable medium for discovering social groups include extracting activity related data associated with a user's social interactions from a source. The activity related data identifies information related to social contacts used during the social interactions at the source. The activity related data of the user is analyzed to identify co-occurrences of the social contacts. The co-occurrences determine a set of related contacts. The set of related contacts identified from the activity related data are clustered into distinct social groups. The social groups are used by a user to facilitate subsequent interactions.

System and method to enable communication group identification

Collaborative-filtering software creates a neighborhood using a map-reduce architecture by pair-wise application of a similarity measure to a sparse matrix of users and items of media designated by the users. The collaborative-filtering software then generates recommendations for a particular user by rating items of media designated by other users in the neighborhood (but not the particular user). The collaborative-filtering software rates the item of media, using a weighted vote of the users in the neighborhood. The weighted vote depends at least in part on the mean similarity of the users in the neighborhood who have designated the item of media. Then the collaborative-filtering software records the item of media as a recommendation for subsequent presentation to the other user, if the rating of the item of media is among the highest in comparison to the ratings of other items of media designated by users in the neighborhood.

Growing community of inventors

New York, NY, United States of America

Jake M Hofman