Google Inc.

Paul Eastlund

Ethan G Russell

Diego Ariel Gertzenstein

Alex Lorbeer

Andrea Lynn Frome

Nicholas Lee

Baris Yuksel

Douglas Richard Grundman

Elizabeth Hamon Reid

Howard Wellington Trickey

Melanie Clements

Diane Barram Westgate

Todd Gardner

Systems and methods are provided for determining whether given business listings are spam or are closed businesses. In one example, a system receives a business listing from a user. The business listing contains a geolocation and the name of a business. The system also receives one or more street level images related to a geographic object that is at or near the same geolocation as the business listing. The system determines if the business listing is spam or is closed based on the received street level images by determining if the geographic object is the business or by analyzing whether the business associated with the listing is shown in any of the street level images.

Systems and methods of correlating business information to determine spam, closed businesses, and ranking signals

A business listing verification server is operative to verify whether a business listing complies with a selected business configuration rule. The business listing verification server may include a business listing database comprising a plurality of business listings and a business information source pipeline for processing the various business listings. The business listing verification server may further include a set of business listing configuration rules, wherein each business listing configuration rule may be established for a separate business. A business listing configuration rule may establish whether a business listing is a potentially suspicious business listing. In addition, the business listing verification server may also include a moderation priority queue, where business listings to be moderated are prioritized based on whether the business listings have been identified as potentially suspicious business listings according to one or more business listing configuration rules.

Identifying potentially suspicious business listings for moderation

Systems and methods are provided that detect trends in clustered business listings. The last update time of a last attribute value of a business listing is determined. It is further determined whether a number of data providers supplying the last attribute value has decreased by at least a first predetermined percentage since the last date the last attribute value changed. Moreover, it is determined whether the number of data providers that have stopped supplying the last attribute value since the last date the attribute value changed is greater than the number of data providers that have begun supplying the last attribute value after the last date the last attribute value changed.

Systems and methods for detecting trends in clustered business listings

A system and method is provided that detect a business listing having erroneous geospatial data. In one aspect, it is determined whether a first location and a second location are at least partially situated within a cell, the cell representing a geographic region having a predetermined area. If the first location and the second location are at least partially situated within the cell, it is determined whether a geospatial coordinate of the first location is within a predetermined radius of the second location. If it is determined that the geospatial coordinate of the first location is within a predetermined radius of the second location, the first location is flagged for inspection.

System and method for identifying erroneous business listings

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Mamaroneck, NY, United States of America

Paul Eastlund