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:
Dec. 01, 2020

Filed:

May. 30, 2017
Applicant:

Facebook, Inc., Menlo Park, CA (US);

Inventors:

Yang Mu, Fremont, CA (US);

Emanuel Alexandre Strauss, San Mateo, CA (US);

Daniel Olmedilla de la Calle, Mountain View, CA (US);

Assignee:

Facebook, Inc., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/00 (2012.01); G06Q 30/02 (2012.01); G06K 9/00 (2006.01); G06K 9/62 (2006.01); G06Q 50/00 (2012.01);
U.S. Cl.
CPC ...
G06Q 30/0242 (2013.01); G06K 9/00442 (2013.01); G06K 9/6267 (2013.01); G06K 9/00456 (2013.01); G06Q 50/01 (2013.01);
Abstract

For various content campaigns (or content), an online system predicts a likelihood score of context violations (e.g., account term violations) of a content campaign. The online system derives a plurality of feature vectors of the content campaign. The online system predicts a likelihood score of context violation of the content campaign using a memorization model based on the plurality of feature vectors. The memorization model comprises a plurality of categories and a plurality of items of each category. Each of the plurality of categories has a category weight, and each of the plurality of items of each category has an item weight. The predicted likelihood score is based on a combination of a plurality of category weights and a plurality of item weights associated with the plurality of feature vectors. The online system performs an action affecting the content campaign based in part on the predicted likelihood score.


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