Google Inc.

Ebay, Incorporated

Paypal, Inc.

Palash Nandy

Benjamin Liebald

Christina Ilvento

Jamie Davidson

Dasarathi Sampath

Ye Niu

Patrick Hung

Philo Juang

Tim Dasilva

Stanley Charles Ross Wolf

Yu-To Chen

Murali Krishna Viswanathan

Murali Krishna Visawanathan

Michael Sho Ogawa

Junning Liu

Online fraud prevention including receiving a rules set to detect fraud, mapping the rules set to a data set, mapping success data to members of the rules set, filtering the members of the rules set, and ordering members of the data set by giving priority to those members of the data set with a greater probability for being fraudulent based upon the success data of each member of the rule set in detecting fraud. Further, a receiver coupled to an application server to receive a rules set to detect fraud, and a server coupled to the application server, to map the rules set to a data set, and to map the success data to each members of the rules set. The server is used to order the various members of the data set by giving priority to those members of the data set with a greatest probability for being fraudulent.

Online fraud prevention using genetic algorithm solution

A system and method for externally verifying content popularity served via a content sharing service, including an access analyzer to create a list based on a number of accesses associated with content items served via the content sharing service, an external source interface module to monitor external sources for content items being served via the external sources, a list verifier to determine if the content items from the list match any of the content items from the external sources, and a list generator to create an externally verified list based on the determination from the list verifier.

External verification of content popularity

A system and method for developing a user's profile based on the user's interaction with content items. A module on the client rendering the content items or the service including the content items tracks the user's interactions with the content items and transmits the tracked data to a user analysis module. The user analysis module determines the topics associated with the interacted upon content items. The user analysis module then selects the topics for the user's profiles based on the received tracked data and the associated topics. The selected topics are stored in association with the user profile.

Topic based user profiles

A personal content stream comprising a plurality of videos is generated for a user. The user selects topics used in the generation of a personal content stream. The plurality of user selected topics is expanded to include topics related to one or more of the user selected topics. Each of the topics in the expanded plurality of topics includes a topic weight. Videos are selected that are related to one or more of the expanded plurality of topics to generate a plurality of stream videos. Additional videos are selected and added to the plurality of stream videos as the user watches videos. The topic weights are adjusted during video playback based on feedback from the user.

Personal content streams based on user-topic profiles

Updating personal content streams based on feedback

This disclosure relates to systems and methods for providing a map-like interface to navigate topics and content.

Visual display of topics and content in a map-like interface

A system and method for developing a user's profile based on the user's interaction with content items. A module on the client rendering the content items or the service including the content items tracks the user's interactions with the content items and transmits the tracked data to a user analysis module. The user analysis module determines the topics associated with the interacted upon content items. The user analysis module then selects the topics for the user's profiles based on the received tracked data and the associated topics. The selected topics are mapped to topic clusters and the topic clusters are stored in association with the user profile. Recommendations for a user are made based on the topic clusters associated with the user'profile.

Recommendations based on topic clusters

Chain-letters within user-generated comments on a website are detected based on an inserted watermark. Comments identified as likely chain-letters are rendered for display with the inserted watermark. When a user propagates a chain-letter by copying and pasting a comment already rendered on the website with the inserted watermark, the inserted watermark is recognized, and a spam score associated with the comment is adjusted. Those comments with spam scores above a pre-defined threshold are displayed on the website in an altered format.

Growing community of inventors

San Francisco, CA, United States of America

Palash Nandy