Lnw Gaming, Inc.

Colin Helsen

Martin S Lyons

Ryan Masao Yee

Michael Vizzo

Robert McPEAK

In one example, a system configured to detect a payout proximity trigger for a progressive jackpot game. The system analyzes, in response to the trigger, shuffle-state image data of a shuffler device and determines a card order of an undealt portion for a deck of cards shuffled by the shuffler device. The system further determines, based on the card order and based on card distribution rules, an anticipated timing for when a mystery card will be dealt from the shuffler device for a subsequent game play round during which the payout threshold value is reached. The system further determines a physical location of a closest gaming table relative to the shuffler device. The system further electronically provides, using the anticipated timing (e.g., transmits to an output device at the physical location), an anticipatory notification related to an upcoming appearance of the mystery card.

Controlling a network card game

According to one aspect of the present disclosure, a system comprises a processor configured to execute instructions that cause operations to detect, using sensors of one or more card-handling devices ('card-handling device(s)') in a network, one or more anomalies (“anomaly (ies)”) on one or more cards (“card(s)”) used during play of one or more games (“game(s)”). The game(s) are played at one or more gaming tables (“table(s)”) associated with the card-handling device(s). The anomaly (ies) vary from one or more previously taken images of the card(s). The operations further include, in response to detecting the anomaly (ies), determining, via analysis by a machine learning model of image data captured by one or more image sensors at the table(s), identifiers for participants that played at the table(s) for the game(s) when the anomaly (ies) was/were detected. The operations further include relating, in a memory store associated with one or more collusion-confidence scores, the identifiers.

Systems and methods for collusion detection

A method or system to, in response to automatic shuffling of a set of cards by a shuffler, detect a first anomaly of a first card of high value that was used during a round of play for a first card game in which a first player participated at a gaming table. The method or system is further to, in response to analysis of shuffler data, detect a relationship between the first anomaly and a second anomaly. The second anomaly is associated with a second card of high value that was used during a round of play for a second card game in which a second player participated. The system or method is further to, in response to detection of the relationship between the first anomaly and the second anomaly, relate via a collusion-confidence score a first identifier for the first player with a second identifier for the second player.

According to one aspect of the present disclosure, a system is provided for controlling a network game. In some instances, a processor of the system selects a winning card value of an undealt playing card for the network game. The network game spans a plurality of gaming tables having a deck of cards used for individual card games separate from the network game. A shuffler at each table shuffles the deck of cards for the individual card games. The system further detects, in response to analyzing image data at each of the gaming tables by a machine learning model, that a playing card, having the winning card value, is dealt. The system further determines, by a machine learning model, a participant to whom the playing card was dealt. The system further electronically validates a win for the network game with an electronic account for the participant.

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Arundel, Australia

Colin Helsen