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. 31, 2019

Filed:

May. 04, 2017
Applicant:

Second Spectrum, Inc., Los Angeles, CA (US);

Inventors:

Yu-Han Chang, South Pasadena, CA (US);

Rajiv Maheswaran, Los Angeles, CA (US);

Jeffrey Wayne Su, South Pasadena, CA (US);

Noel Hollingsworth, Sunnyvale, CA (US);

Assignee:

Second Spectrum, Inc., Los Angeles, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); H04N 21/466 (2011.01); H04N 21/44 (2011.01); H04N 21/434 (2011.01); H04N 21/234 (2011.01); H04N 21/25 (2011.01); H04N 21/4223 (2011.01); H04N 21/45 (2011.01); G06N 20/00 (2019.01); H04N 13/204 (2018.01); G11B 27/031 (2006.01); G11B 27/28 (2006.01); H04N 21/2187 (2011.01); H04N 21/8549 (2011.01); H04N 5/222 (2006.01); G06F 3/01 (2006.01); A63F 13/60 (2014.01); H04N 13/243 (2018.01); H04N 13/117 (2018.01);
U.S. Cl.
CPC ...
G06K 9/00724 (2013.01); A63F 13/60 (2014.09); G06F 3/012 (2013.01); G06F 3/013 (2013.01); G06K 9/00744 (2013.01); G06N 20/00 (2019.01); G11B 27/031 (2013.01); G11B 27/28 (2013.01); H04N 5/2224 (2013.01); H04N 13/204 (2018.05); H04N 21/2187 (2013.01); H04N 21/23418 (2013.01); H04N 21/251 (2013.01); H04N 21/4223 (2013.01); H04N 21/4345 (2013.01); H04N 21/44008 (2013.01); H04N 21/4532 (2013.01); H04N 21/4662 (2013.01); H04N 21/8549 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30221 (2013.01); H04N 13/117 (2018.05); H04N 13/243 (2018.05);
Abstract

Providing enhanced video content includes processing at least one video feed through at least one spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of a plurality of events and to determine at least one event type for each of the plurality of events. The event type includes an entry in a relationship library detailing a relationship between two visible features. Extracting and indexing a plurality of video cuts from the video feed is performed based on the at least one event type determined by the understanding that corresponds to an event in the plurality of events detectable in the video cuts. Lastly, automatically and under computer control, an enhanced video content data structure is generated using the extracted plurality of video cuts based on the indexing of the extracted plurality of video cuts.


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