LNW Gaming, Inc. (20240339000). SYSTEMS AND METHODS FOR COLLUSION DETECTION simplified abstract
Contents
SYSTEMS AND METHODS FOR COLLUSION DETECTION
Organization Name
Inventor(s)
Martin S. Lyons of Henderson NV (US)
Michael Vizzo of Las Vegas NV (US)
Robert Mcpeak of Mount Vernon WA (US)
SYSTEMS AND METHODS FOR COLLUSION DETECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240339000 titled 'SYSTEMS AND METHODS FOR COLLUSION DETECTION
The system described in the patent application involves using sensors in card-handling devices to detect anomalies on cards used in games played at gaming tables. These anomalies are then analyzed using machine learning models to identify participants who played at the table when the anomalies were detected. The system also stores collusion-confidence scores related to the identified participants.
- Sensors in card-handling devices detect anomalies on cards during games.
- Machine learning models analyze the anomalies to identify participants at the table.
- Collusion-confidence scores are stored based on the identified participants.
Potential Applications: - Casino security systems - Fraud detection in card games - Player tracking in gaming establishments
Problems Solved: - Enhances security in card games - Improves fraud detection capabilities - Facilitates player identification in gaming environments
Benefits: - Increased game integrity - Enhanced player safety - Improved monitoring of gaming activities
Commercial Applications: Title: "Enhancing Casino Security with Anomaly Detection System" This technology can be utilized in casinos, card rooms, and other gaming establishments to improve security measures and ensure fair gameplay. The market implications include increased trust from players and regulatory bodies, leading to a more reputable gaming environment.
Prior Art: Readers can explore prior art related to anomaly detection systems in gaming environments, machine learning applications in fraud detection, and player identification technologies in the gaming industry.
Frequently Updated Research: Stay informed about advancements in sensor technology, machine learning algorithms, and gaming security protocols to enhance the effectiveness of anomaly detection systems in card games.
Questions about Anomaly Detection System in Gaming Environments: 1. How does the system differentiate between intentional cheating and accidental anomalies? 2. What measures are in place to prevent false positives in anomaly detection during gameplay?
Original Abstract Submitted
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.
