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. 29, 2015

Filed:

Mar. 13, 2013
Applicant:

Google Inc., Mountain View, CA (US);

Inventors:

Qingzhou Wang, Santa Clara, CA (US);

Yu Liang, Santa Clara, CA (US);

Ke Yang, Cupertino, CA (US);

Kai Chen, Brisbane, CA (US);

Assignee:

Google Inc., Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 15/18 (2006.01); G06E 1/00 (2006.01); G06E 3/00 (2006.01); G06G 7/00 (2006.01); G06N 3/02 (2006.01); G06F 17/30 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06K 9/62 (2006.01);
U.S. Cl.
CPC ...
G06N 3/02 (2013.01); G06F 17/30707 (2013.01); G06F 17/30864 (2013.01); G06K 9/627 (2013.01); G06N 3/0427 (2013.01); G06N 3/084 (2013.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scoring concept terms using a deep network. One of the methods includes receiving an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource; processing each of the features using a respective embedding function to generate one or more numeric values; processing the numeric values using one or more neural network layers to generate an alternative representation of the features, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values; and processing the alternative representation of the input using a classifier to generate a respective category score for each category in a pre-determined set of categories, wherein each of the respective category scores measure a predicted likelihood that the resource belongs to the corresponding category.


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