Google Inc.

David Andrew Huffaker

Abhijit Bose

Makoto Uchida

Andrew Stephen Tomkins

Jessica Nicola Staddon

David Andrew Gibson

Alexander Fabrikant

Zach Yeskel

Lujun Fang

Rachel Ida Rosenthal Schutt

A method including obtaining search results for a search query, determining a type of the search query, calculating a degree of focus score for the search query when the type of the search query is a non-navigation type, determining one or more social annotations to provide for display along with the search results based on the degree of focus score, and providing the one or more social annotations for display with the search results.

Applying social annotations to search results

Systems and methods for providing real-time analysis of feature relationships are provided. In some aspects, a method includes receiving user activity data and user status data for users in the interactive network, the interactive network comprising at least two user features; generating a user dataset by associating, for each user, the user's activity data with the user's status data using a unique identification of the user and a timestamp; analyzing the user dataset using a statistical model; and providing, for display, an output of the analysis by the statistical model, the output including an indicator of a relationship between a use of one of the two user features with a use of the other of the two user features.

Real-time analysis of feature relationships for interactive networks

A system and method for facilitating automatic objective-based feature improvement, the method including receiving a request to identify an optimal alternative for each of one or more features of a computer-implemented entity, determining an alternative feature range for each of the one or more features, the alternative feature range defining a range of possible alternative features available with respect to the feature, selecting one or more alternative features for each feature from the alternative feature range of the feature, generating a plurality of alternative sets, each including an alternative feature for at least one of the one or more features, selecting a plurality of user groups from a pool of users and assigning each user group of the plurality of users groups to one of the plurality of alternative sets based on user characteristics of the users and historical information regarding the interaction of the user with the computer-implemented entity.

Automated objective-based feature improvement

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Palo Alto, CA, United States of America

David Andrew Huffaker