Facebook, Inc.

Sean Michael Bruich

Justin Alexander Shaffer

David Harry Garcia

Brian Krausz

Robert Franklin Daniel

Robert Taaffe Lindsay

Erick Tseng

Yoon Kean Wong

Frederick Ross Leach

Bradley Hopkins Smallwood

Michael John McKenzie Toksvig

Gregory Joseph Badros

Cameron Marlow

Colin Cameron Brennan Campbell

Ime Archibong

Yong Kean Wong

Erich Tseng

Users of a social networking system are assigned to households using prediction models that rely, in part, on user profile information and social graph data. Information about users may be received by a social networking system through various channels (e.g., declared/profile information, user history, IP addresses, Global Positioning System (GPS) data from check-in events and/or continuously provided by mobile devices, external household information, and/or social information). Scoring models may use statistical analysis of the received user information to predict household membership for users. User attributes, such as previous names, date of birth, social graph data, locations, life events, and check-ins, may be factors in generating confidence scores of predicted household memberships. Weighted scoring models may use machine learning methods for measuring the accuracy of the household membership prediction. The social networking system may use a machine learning algorithm to analyze user information to determine confidence scores for matching potential households.

Assigning social networking system users to households

A social networking system may provide user metrics for unknown dimensions to an external system using received hash codes from user devices performing actions on the external system. Embodiments of the invention receive hash codes generated by external systems that are embedded in redirect Uniform Resource Locators (URLs) that are executed by user devices. The social networking system may match the received hash codes to identifying information about users, e.g., user identifiers. The user profile information may be reported to external systems based on hash codes in an aggregated format without disclosing identifying information about users. In one embodiment, a hash code may include identifying information of an administrator of an external system to enable the administrator to merge information about users of the social networking system with other unknown dimensions of information about the users that is known by the external system.

Providing user metrics for an unknown dimension to an external system

A social networking system allows a user to insert media information into content posted by the user, where the media information identifies a media item that the user is consuming while composing the posted content. When a user of a social networking system composes content via a composer interface, the user may select an option on the composer interface to record audio using a microphone on the user's device. A media item is identified from the recorded audio and information about the identified media item is added to the user's posted content. The system may also update information about the identified media item and the composing user.

Tagging posted content in a social networking system with media information

In particular embodiments, a social networking system queries a social graph of the social-networking system for social content associated with video content provided to one or more users of the social-networking system and accesses privacy settings associated with each of the one or more users. The privacy settings indicate whether a particular user has authorized the social-networking system to share social content associated with the particular user with one or more third parties. The social networking system provides the social content associated with video content provided to the one or more users to a particular third party if the privacy settings of the one or more users indicate that the one or more users have authorized the social-networking system to share the social content with one or more third parties.

Sharing television and video programming through social networking

A social networking system captures performance metrics of an offer by selecting a whisper code to include in an offer when presenting the offer to users of the social networking system. The social networking system receives information for providing its users with an offer for redemption at a merchant establishment. The social networking system also receives a set of rules for selecting a whisper code to go with the offer. The rules may be based on any context for which the advertiser or merchant may want to track performance of the offer. When presenting the offer, the social networking system selects a whisper code using those rules. This tracking using different whisper codes enables the merchant to understand the performance of the offer campaign based on the context in which the social networking system presents the offers to its users.

Selecting whisper codes to present with offers

In particular embodiments a social networking system captures data associated with video content provided to a first user of a social-networking system, identifies, using the captured data, the video content provided to the first user, and updates a graph of the social-networking system to associate the first user with the identified video content. The graph of the social-networking system has a plurality of nodes and edges connecting the nodes. The nodes of the graph include user nodes that are each associated with a particular user of the social-networking system.

A social networking system or other user registration site builds a log of exposures by users to advertisements outside of the user registration site to determine their effectiveness. For each user exposed to an advertisement, a log entry is created indicating that the user has been exposed to the advertisement. Tracking pixels are embedded into advertisements that, when accessed, enable the social networking system or user registration site to log access to the advertisement by the user. From the log files, the user registration site identifies exposed users and selects unexposed users with similar demographics and/or behavior information to generate a control group. The two groups can be surveyed about the advertisement to determine its effectiveness. The user exposure information also can be used to retarget advertisements, to measure advertising effectiveness on connections of exposed users, and to measure actions of exposed users.

Growing community of inventors

Palo Alto, CA, United States of America

Sean Michael Bruich