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. 23, 2023

Filed:

Feb. 26, 2019
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Seyedmohsen Jamali, Sunnyvale, CA (US);

Samaneh Abbasi Moghaddam, Sunnyvale, CA (US);

Ali Abbasi, Mountain View, CA (US);

Revant Kumar, Mountain View, CA (US);

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 16/74 (2019.01); G06N 5/02 (2023.01); G06F 16/78 (2019.01); G06F 16/735 (2019.01); H04N 21/81 (2011.01); G06Q 30/0251 (2023.01); H04N 21/25 (2011.01);
U.S. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 16/735 (2019.01); G06F 16/74 (2019.01); G06F 16/7867 (2019.01); G06N 5/02 (2013.01); G06Q 30/0255 (2013.01); H04N 21/251 (2013.01); H04N 21/812 (2013.01);
Abstract

Techniques for using online engagement footprints for video engagement prediction are provided. In one technique, events are received from multiple client devices, each event indicating a type of engagement of a video item from among multiple types of engagement. One or more machine learning techniques are used to train a prediction model that is based on the events and multiple features that includes the multiple types of engagement. In response to receiving a content request, multiple entity feature values are identified for a particular entity that is associated with the content request. Two or more of the entity feature values correspond to two or more of the types of engagement. A prediction is generated based on the entity feature values and the prediction model. The prediction is used to determine whether to select, from candidate content items, a particular content item that includes particular video.


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