US 11,727,750 B2
Fraud detection system in a casino
Yasushi Shigeta, Shiga (JP)
Assigned to ANGEL GROUP CO., LTD., Shiga (JP)
Filed by ANGEL GROUP CO., LTD., Shiga (JP)
Filed on Dec. 9, 2021, as Appl. No. 17/546,590.
Application 17/546,590 is a continuation of application No. 16/933,548, filed on Jul. 20, 2020, granted, now 11,393,284.
Application 16/933,548 is a continuation of application No. 16/202,290, filed on Nov. 28, 2018, granted, now 10,748,378, issued on Aug. 18, 2020.
Application 16/202,290 is a continuation of application No. 16/016,128, filed on Jun. 22, 2018, granted, now 10,593,154, issued on Mar. 17, 2020.
Application 16/016,128 is a continuation of application No. 15/226,200, filed on Aug. 2, 2016, granted, now 10,032,335, issued on Jul. 24, 2018.
Claims priority of application No. 2015-163213 (JP), filed on Aug. 3, 2015; and application No. 2015-206735 (JP), filed on Oct. 1, 2015.
Prior Publication US 2022/0101687 A1, Mar. 31, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. A63F 9/24 (2006.01); A63F 11/00 (2006.01); G06F 13/00 (2006.01); G06F 17/00 (2019.01); G07F 17/32 (2006.01); A63F 1/18 (2006.01); A63F 3/00 (2006.01); G06Q 50/34 (2012.01); A63F 1/14 (2006.01); A63F 1/00 (2006.01)
CPC G07F 17/3241 (2013.01) [A63F 1/18 (2013.01); A63F 3/00157 (2013.01); G06Q 50/34 (2013.01); G07F 17/322 (2013.01); G07F 17/3206 (2013.01); G07F 17/3223 (2013.01); G07F 17/3234 (2013.01); G07F 17/3248 (2013.01); G07F 17/3251 (2013.01); G07F 17/3293 (2013.01); A63F 1/14 (2013.01); A63F 2001/001 (2013.01); A63F 2250/58 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A system comprising:
a camera; and
a control device, wherein:
the camera is configured to generate an image of a predetermined area of a gaming table that includes a chip placement area;
the control device is configured to:
use a deep learning convolutional neural network to perform image recognition, that includes extracting features in the image based on color information or a pattern, to identify as processing targets representations in the image of chips stacked in the chip placement area and captured by the camera from a horizontal direction or obliquely from above the chips; and
identify, even when the chips stacked in the chip placement area include one or more chips that are at least partially concealed from view by using image recognition processing of the deep learning convolutional neural network based on the color information or pattern in the image, one or more types and one or more numbers of the chips including the one or more chips that are at least partially concealed from view, the identification of at least one of (a) the one or more types, and (b) the one or more numbers being of the identified processing targets in the image; and
the deep learning convolutional neural network used by the control device is a neural network that performed learning on learning images with labels at targets corresponding to types of chips represented in the learning images.