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:
Jun. 30, 2020

Filed:

Mar. 27, 2015
Applicant:

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

Inventors:

Stuart Michael Bowers, Menlo Park, CA (US);

Hussein Mohamed Hassan Mehanna, Cupertino, CA (US);

Andrey Malevich, Redwood City, CA (US);

Sai Nishanth Parepally, Sunnyvale, CA (US);

David Paul Capel, Belmont, CA (US);

Alisson Gusatti Azzolini, San Francisco, CA (US);

Assignee:

FACEBOOK, INC., Menlo Park, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06Q 30/00 (2012.01); G06Q 50/00 (2012.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06Q 30/00 (2013.01); G06Q 50/01 (2013.01);
Abstract

An online system identifies an additional feature to evaluate for inclusion in a machine learned model. The additional feature is based on characteristics of one or more dimensions of information maintained by the online system. To generate data for evaluating the additional feature, the online system generates various partitions of stored data, where each partition includes characteristics associated with one or more dimensions on which the additional feature is based. Using values of characteristics in a partition, the online system generates values for the additional feature and includes the values of the additional feature in the partition. Values for the additional feature are generated for various partitions based on the values of characteristics in each partition. The online system combines multiple partitions that include values for the additional feature to generate a training set for evaluating a machine learned model including the additional feature.


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