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:
May. 05, 2020

Filed:

Oct. 18, 2016
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Ye Tian, Santa Clara, CA (US);

Seyed Reza Mir Ghaderi, San Jose, CA (US);

Xuejun Tao, San Jose, CA (US);

Matthew Courtney, Sunnyvale, CA (US);

Pei-Chun Chen, Mountain View, CA (US);

Christian Posse, Belmont, CA (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 10/10 (2012.01); G06Q 10/06 (2012.01); G06Q 30/08 (2012.01); G06Q 30/02 (2012.01); G06F 16/9535 (2019.01); G06F 16/2457 (2019.01); G06N 20/00 (2019.01); G06N 5/02 (2006.01);
U.S. Cl.
CPC ...
G06Q 10/1053 (2013.01); G06F 16/24578 (2019.01); G06F 16/9535 (2019.01); G06Q 10/105 (2013.01); G06N 5/022 (2013.01); G06N 20/00 (2019.01);
Abstract

Methods, systems, and apparatus, including computer programs encoded on storage devices, for performing a job opportunity search. In one aspect, a system includes a data processing apparatus, and a computer-readable storage device having stored thereon instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations. The operations include defining a vector vocabulary, defining an occupation taxonomy that includes multiple different occupations, obtaining multiple labeled training data items, wherein each labeled training data item is associated with at least (i) a job title, and (ii) an occupation, generating, for each of the respective labeled training data items, an occupation vector that includes a feature weight for each respective term in the vector vocabulary, and associating each respective occupation vector with an occupation in the occupation taxonomy based on the occupation of the labeled training data item used to generate the occupation vector.


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