The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 08, 2015

Filed:

Mar. 13, 2013
Applicants:

Antoine Raux, Cupertino, CA (US);

Rakesh Gupta, Mountain View, CA (US);

Deepak Ramachandran, Mountain View, CA (US);

Yi MA, Columbus, OH (US);

Inventors:

Antoine Raux, Cupertino, CA (US);

Rakesh Gupta, Mountain View, CA (US);

Deepak Ramachandran, Mountain View, CA (US);

Yi Ma, Columbus, OH (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01C 21/26 (2006.01); G01C 21/36 (2006.01);
U.S. Cl.
CPC ...
G01C 21/26 (2013.01); G01C 21/3608 (2013.01); G01C 21/3644 (2013.01);
Abstract

An utterance is received from a user specifying a location attribute and a landmark. A set of candidate locations is identified based on the specified location attribute, and a confidence score can be determined for each candidate location. A set of landmarks is identified based on the specified landmark, and confidence scores can be determined for the landmarks. An associated kernel model is generated for each landmark. Each kernel model is centered at the location of the associated landmark on a map, and the amplitude of the kernel model can be based on landmark attributes, landmark confidence scores, characteristics of the user, and the like. The candidate locations are ranked based on the amplitudes of overlapping kernel models at the candidate locations, and can also be ranked based on confidence scores associated with the candidate locations. A candidate location is selected and presented to the user based on the candidate location ranking.


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