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. 03, 2019

Filed:

Jun. 08, 2018
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); G06F 16/51 (2019.01); 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 16/51 (2019.01); 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 event shown within digital video content captured by one or more video cameras, and correlate these detected events to real-world conditions that may not be captured within the digital video data. For example, a computing system may detect events shown within digital video content captured by one or more video cameras, and may obtain data that identifies at least one external event. The computer system may establish a predictive model that correlates values of event parameters that characterize the detected and external events during a first time period, and may apply the predictive model to an event parameter that characterizes an additional event detected during a second time period. Based on an outcome of the predictive model, the computing system may determine an expected value of the external event parameter during the second time period.


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