Linkedin Corporation

Microsoft Technology Licensing, LLC

Jiong Wang

Abhishek Gupta

Anmol Bhasin

Christian Posse

Ramesh Dommeti

Alexis Pribula

Shakti Dhirendraji Sinha

Timothy Colin Larson

Daniel E Castro

Andrew Ryan Pickering

Xufei Wang

A gamified challenge is presented to a user communicating with a website when the user is suspected of being a bot. The gamified challenge consists of a dynamic sequence of animated geometric objects displayed in an order that the user has to interact with within an allotted amount of time. The complexity of the gamified challenge increases when the challenge is failed and when features extracted from the user session indicate a high level of suspicion that the gamified challenge is performed by a non-human user.

Gamified challenge to detect a non-human user

A statistically overrepresented token in the descriptions of users associated with a target entity may be descriptive of the target entity. This may be true regardless of whether a primary description of the entity includes the overrepresented token. Accordingly, the entity description machine may access multiple descriptions of multiple users associated with the target entity. A portion of the multiple descriptions may each include a token descriptive of the target entity and of a subset of the multiple users. The entity description machine may determine that the token is overrepresented among the tokens within the multiple descriptions and generate a supplemental description of the target entity, where the supplemental description includes the overrepresented token. Once the supplemental description is generated, the entity description machine may use the supplemental description in referencing the target entity.

Generating a supplemental description of an entity

Techniques for estimating, in real time, the likelihood that any particular member of a social network service is a third degree connection of another member are described. Consistent with some embodiments, various member profile attributes of a member are used as a sort of proxy for determining the likelihood or probability that the member is a third degree connection of another member. For example, in some instances, the number of first-degree connections a member has is used to derive a probability score indicating the likelihood that the member is a third-degree connection of another member, such as a person performing a people-search. Once derived, the probability score for each member may be used in various applications, such as a people-search engine, to boost or increase a ranking score assigned to each search result and used to order the search results when presented to the user who has performed the search.

Techniques for estimating distance between members of a social network service

Growing community of inventors

San Ramon, CA, United States of America

Jiong Wang