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:
Sep. 01, 2020

Filed:

Sep. 05, 2019
Applicant:

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

Inventors:

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

Rajiv Maheswaran, San Marino, 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); G06F 3/01 (2006.01); H04N 21/8549 (2011.01); H04N 21/2187 (2011.01); G11B 27/28 (2006.01); G11B 27/031 (2006.01); H04N 13/204 (2018.01); H04N 5/222 (2006.01); G06N 20/00 (2019.01); H04N 21/45 (2011.01); H04N 21/4223 (2011.01); H04N 21/25 (2011.01); H04N 21/234 (2011.01); H04N 21/434 (2011.01); H04N 21/44 (2011.01); H04N 21/466 (2011.01); H04N 13/243 (2018.01); H04N 13/117 (2018.01);
U.S. Cl.
CPC ...
G06K 9/00724 (2013.01); 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

Presenting event-specific video content that conforms to a user selection of an event type includes processing at least one video feed through at least one spatiotemporal pattern recognition algorithm that uses machine learning to develop an understanding of at least one event within the at least one video feed to determine at least one event type, wherein the at least one event type includes an entry in a relationship library at least detailing a relationship between two visible features of the at least one video feed, extracting the video content displaying the at least one event and associating the understanding with the video content in a video content data structure. A user interface is configured to permit a user to indicate a preference for at least one event type that is used to retrieve and provide corresponding extracted video content with the data structure in a new video feed.


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