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. 12, 2018

Filed:

May. 27, 2016
Applicant:

Accenture Global Solutions Limited, Dublin, IE;

Inventors:

Cyrille Bataller, Mougins, FR;

Anders Astrom, villa marina, SG;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06F 17/30 (2006.01); H04N 7/18 (2006.01); G06T 15/20 (2011.01); G06T 19/00 (2011.01); G06K 9/62 (2006.01); G06T 7/00 (2017.01); G06T 7/80 (2017.01); G06T 7/73 (2017.01); G06T 7/13 (2017.01); G06T 7/292 (2017.01); G08B 13/196 (2006.01);
U.S. Cl.
CPC ...
G06K 9/00765 (2013.01); G06F 17/3028 (2013.01); G06K 9/00255 (2013.01); G06K 9/00295 (2013.01); G06K 9/00718 (2013.01); G06K 9/00744 (2013.01); G06K 9/00771 (2013.01); G06K 9/00778 (2013.01); G06K 9/6202 (2013.01); G06K 9/6263 (2013.01); G06T 7/0002 (2013.01); G06T 7/13 (2017.01); G06T 7/292 (2017.01); G06T 7/75 (2017.01); G06T 7/80 (2017.01); G06T 15/205 (2013.01); G06T 19/006 (2013.01); H04N 7/18 (2013.01); H04N 7/181 (2013.01); G06K 2009/00738 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/20061 (2013.01); G06T 2207/30168 (2013.01); G06T 2207/30201 (2013.01); G06T 2207/30232 (2013.01); G06T 2207/30242 (2013.01); G08B 13/19602 (2013.01); G08B 13/19608 (2013.01);
Abstract

Computerized methods and systems, including computer programs encoded on a computer storage medium, may detect events shown within digital video content captured by one or more video cameras during a prior time period, and predict an occurrence of an additional event during a future time period based on time-varying patterns among the detected events. For example, a computing system may detect events shown within digital video content captured by one or more video cameras, and may establish a predictive model that identifies one or more time-varying patterns in event parameter values that characterize the detected events within the prior time period. Based on an outcome of the predictive model, the computing system may determine an expected value of one of the event parameters during the second time period.


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