Foursquare Labs, Inc.

Yellowpages.com LLC

Sense Networks, Inc.

Columbia University

Blake Shaw

Noah Weiss

Justin Moore

Tony Jebara

Benjamin N Lee

Markus Loecher

Jason Uechi

Joseph Mattiello

Greg Skibiski

Christine Lemke

Girish Rao

Max Elliot Sklar

Alex (Sandy) Pentland

Jason Liszka

Tristan J Walker

David Rosenberg

Tim Julien

Cagatay Berk Kapicioglu

Bert Huang

Siobhan Quinn

Alex Sandy Pentland

Tianhui Li

Tony Jabara

Jackson Davis

Patrick Hayes

Dennis Crowley

Siddhartha Sinha

Jon Shea

Anoop Ranganath

Timothy Julien

Geoffroy Bablon

Mark Wyszomierski

Daniel Salinas

Rongjing Xiang

Andrew Hogue

A mobile application is provided that provides intelligent recommendations based on the knowledge of where the user has been, and what venues the user would like to visit. Further, such an application may be capable of determining where people in a user's social network have been and what venue locations these related users would like to visit. Also, in another implementation, the application may be capable of determining where people with similar taste have been, and where they would like to go. Some or all of this information may be used by a mobile application that provides recommendations to a user. For instance, in one implementation, a user having a mobile device such as a cell phone wishes to locate a venue based on one or more parameters, and some or all of this information may be used to order to rank recommendations with the interface.

System and method for providing recommendations with a location-based service

In non-limiting examples, wireless scan data collected from a processing device is directly mapped to venues. Wireless scan data is received from a processing device where the wireless scan data comprises at least a first identifier of a wireless connection. Venues associated with the first identifier are identified from a mapping of wireless identifiers to venues based on active validation data associated with particular venues including historical wireless scan data of the particular venues. The plurality of venues are ranked according to a likelihood that the processing device is located at a particular venue based on evaluating the active validation data including the historical wireless scan data from the mapping. A predicted venue that the processing device is located at is determined based on the ranking of the plurality of venues. Other examples are also described.

Venue identification from wireless scan data

A system and method are provided for associating location data from one or more unique sources. The place and time of a unique location enabled device are associated with stored demographic information relating to the particular place and particular time. The place and time of the unique location enabled device are associated with a historical record of past locations and time of locations that the device has been. Based on the association of demographical information and historical information, the unique location enable device is assigned to one or more groups or tribes. The location of all members of the group or tribe can be aggregated and exported for further analysis or display, thereby showing all group or tribe members at a particular time and place.

System and method of performing location analytics

A mobile application is provided that provides intelligent recommendations based on the knowledge of where the user has been, and what venues the user would like to visit. Further, such an application may be capable of determining where people in a user's social network have been and what venue locations these related users would like to visit. Also, in another implementation, the application may be capable of determining where people with similar tastes have been, and where they would like to go. Some or all of this information may be used by a mobile application that provides recommendations to a user. For instance, in one implementation, a user having a mobile device such as a cell phone wishes to locate a venue based on one or more parameters, and some or all of this information may be used to order or rank recommendations within the interface.

Methods and systems for predicting links in a network, such as a social network, are disclosed. The existing network structure can be used to optimize link prediction. The methods and systems can learn a distance metric and/or a degree preference function that are structure preserving to predict links for new/existing nodes based on node properties.

Network information methods devices and systems

A system and method are provided for associating location data from one or more unique sources. The place and time of a unique location enabled device are associated with stored demographic information relating to the particular place and particular time. The place and time of the unique location enabled device are associated with a historical record of past locations and time of locations that the device has been. Based on the association of demographical information and historical information, the unique location enabled device is assigned to one or more groups or tribes. The location of all members of the group or tribe can be aggregated and exported for further analysis or display, thereby showing all group or tribe members at a particular time and place.

A method of detecting an event anomaly includes receiving one or more data points, in which each data point represents a spatial or temporal event; associating a unique identifier with each of the one or more data points to obtain one or more individualized data points; distributing the one or more individualized data points across a grid, in which the grid includes one or more cells; determining an event likelihood ratio for one or more of the grid cells; identifying one or more event clusters, in which each event cluster includes one or more of the grid cells; and storing in a data repository an event cluster having a significance level above a threshold significance level.

Event identification in sensor analytics

Systems and computer-implemented methods are provided for comparing, associating and deriving associations between two or more spatial temporal data trails. One or more spatial-temporal data trails comprising one or more places are received at a processor. Each place is identified by a spatial temporal data point. And each spatial-temporal data trail is associated with an individual. The similarity between pairs of places is determined to establish one or more groups of places or one or more groups of individuals. Similarity and/groups can be determined based on demographics associated with the place or individual.

Comparing spatial-temporal trails in location analytics

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New York, NY, United States of America

Blake Shaw