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:
Oct. 27, 2020

Filed:

Feb. 20, 2019
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Shilpa Arora, Sunnyvale, CA (US);

Colin McCulloch, Half Moon Bay, CA (US);

Niyati Yagnik, Mountain View, CA (US);

Creighton Thomas, Mountain View, CA (US);

Manohar Prabhu, Palo Alto, CA (US);

Timothy Lipus, Sunnyvale, CA (US);

Michael Eugene Aiello, Hoboken, NJ (US);

Yi Zhang, Sunnyvale, CA (US);

Ajay Kumar Bangla, San Jose, CA (US);

Bahman Rabii, San Francisco, CA (US);

Gaofeng Zhao, Cupertino, CA (US);

Yingwei Cui, Palo Alto, CA (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/00 (2012.01); G06Q 30/08 (2012.01); G06N 20/00 (2019.01); G06F 16/951 (2019.01); G06F 16/958 (2019.01); G06F 16/2457 (2019.01);
U.S. Cl.
CPC ...
G06Q 30/08 (2013.01); G06F 16/24578 (2019.01); G06F 16/951 (2019.01); G06F 16/958 (2019.01); G06N 20/00 (2019.01);
Abstract

The present disclosure selects third party content based on feedback. A selector identifies several content items including first and second content items (or more) responsive to a request. A machine learning engine determines a first feature of the first content item, a second feature of the second content item, and a third feature of the web page or a device associated with the request. The machine learning engine determines, responsive to the first feature and the third feature, a first score for the first content item based on a machine learning model generated using historical signals received from devices via a metadata channel formed from an electronic feedback interface. The machine learning engine determines a second score for the second content item responsive to the second feature and the third feature. A bidding module determines a price for the first content item based on the first and second scores.


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