US 12,300,064 B2
Systems and methods for collusion detection
Martin S. Lyons, Henderson, NV (US); Ryan Yee, Las Vegas, NV (US); Michael Vizzo, Las Vegas, NV (US); Colin Helsen, Arundel (AU); and Robert McPeak, Mount Vernon, WA (US)
Assigned to LNW Gaming, Inc., Las Vegas, NV (US)
Filed by LNW Gaming, Inc., Las Vegas, NV (US)
Filed on May 7, 2024, as Appl. No. 18/657,257.
Application 18/657,257 is a continuation of application No. 17/752,192, filed on May 24, 2022, granted, now 11,990,000.
Claims priority of provisional application 63/192,658, filed on May 25, 2021.
Prior Publication US 2024/0339000 A1, Oct. 10, 2024
Int. Cl. G07F 17/34 (2006.01); G07F 17/32 (2006.01)
CPC G07F 17/3241 (2013.01) [G07F 17/3206 (2013.01); G07F 17/3239 (2013.01); G07F 17/3293 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
detecting, by a processor using sensors of one or more shufflers in a shuffler network, one or more anomalies on one or more cards used during play of one or more games at one or more gaming tables associated with the one or more shufflers, wherein the one or more anomalies vary from one or more previously taken images of the one or more cards;
in response to detecting the one or more anomalies, determining, via analysis by a machine learning model of image data captured by one or more image sensors at the one or more gaming tables, identifiers for participants that played at the one or more gaming tables for the one or more games when the one or more anomalies were detected; and
in response to determining the identifiers, relating, by the processor in a memory store associated with one or more collusion-confidence scores, the identifiers.