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:
Feb. 01, 2011

Filed:

Mar. 30, 2007
Applicants:

Raj K. Kizhakkekalathil, Sammamish, WA (US);

Karen R. Haberkorn, Seattle, WA (US);

Llewllyn J. Mason, Seattle, WA (US);

Brian J. Schuster, Seattle, WA (US);

Richard J. Cole, Seattle, WA (US);

Mike Perkowitz, Seattle, WA (US);

Mark Jacobson, Seattle, WA (US);

Andrew B. Dickinson, Seattle, WA (US);

Inventors:

Raj K. Kizhakkekalathil, Sammamish, WA (US);

Karen R. Haberkorn, Seattle, WA (US);

Llewllyn J. Mason, Seattle, WA (US);

Brian J. Schuster, Seattle, WA (US);

Richard J. Cole, Seattle, WA (US);

Mike Perkowitz, Seattle, WA (US);

Mark Jacobson, Seattle, WA (US);

Andrew B. Dickinson, Seattle, WA (US);

Assignee:

Amazon Technologies, Inc., Reno, NV (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 15/18 (2006.01);
U.S. Cl.
CPC ...
Abstract

An ad system is described for providing ad selections in response to an ad presentation opportunity. The ad system can use multiple content providers to generate multiple sets of ad candidates. The content providers can apply different ad selection paradigms in generating their sets of ad candidates. The paradigms may act on different aspects of a context pertaining to the ad presentation opportunity. A chooser module and filtering module can cull the set of ad selections from among the plural sets of ad candidates. At least one content provider uses machine learning functionality in generating ad candidates, such as a Naïve Bayesian approach, an affiliate similarity approach, etc. Various content providers also find application in a stand-alone mode.


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