Fraudulence monitoring system of table game and fraudulence monitoring program of table game
A detection system of the present disclosure stores positions and the amount of game tokens that a game participant places on a game table based on a measurement result by a bet chip measuring device in the same persons for each game participant or player positions of the game table. A management control device compares an actual winning rate and a total return amount with figures obtained by a probability statistic calculation at the time of an end of the number of games to determine whether there is a significant difference therebetween and specifies any one of the game participant or the player position, the game table, or a room having the game table where the significant difference is occurring.
1. A fraudulence monitoring system comprising:
a first sensor configured to generate first sensor data based on detected distribution of one or more cards from a card shoe onto a game table;
a second sensor configured to, for each game of one or more games played via the game table, generate second senor data that indicates an amount of objects and a position of the objects for a wager source, the wager source is a game participant or a player position;
one or more processors coupled to the first sensor and the second sensor, the one or more processors configured to:
for each game of the one or more games, determine a return amount obtained by the wager source in the game based on the amount of the objects for the game identified based on the second sensor data, a position of the objects for the game identified based on the second sensor data, and a win-lose result for the game determined based on the first sensor data;
based on one or more return amounts associated with a predetermined number of the one or more games, determine a winning rate and a total return amount for the wager source over the predetermined number of the one or more games; and
based on a comparison between a first numerical property of the determined winning rate and the determined total return amount, and a second numerical property of a winning rate and a total return amount obtained by a probability statistic calculation, determine whether there is a significant difference between the first numerical property and the second numerical property.
2. The fraudulence monitoring system according to claim 1 , wherein the second sensor includes a camera or a radio frequency identification (RFID) sensor.
3. The fraudulence monitoring system according to claim 1 , wherein the one or more processors is further configured to track, based on the first sensor data, the one or more games.
4. The fraudulence monitoring system according to claim 1 , wherein the one or more processors is further configured to, for each game of the one or more games:
obtain a win-lose result determined based on the first sensor data; and
identify, based on the second sensor data, the amount of the objects and the position of the objects.
5. The fraudulence monitoring system according to claim 1 , wherein the one or more processors is further configured to:
receive a win-lose result indicator determined based on the first sensor data; and
for each game of the one or more games, store token data associated with the amount of the objects for the game and the position of the objects for the game.
6. The fraudulence monitoring system according to claim 1 , further comprising the card shoe, wherein the card shoe includes the first sensor.
7. The fraudulence monitoring system according to claim 1 , wherein the one or more processors is further configured to perform the comparison based on the first numerical property of the determined winning rate and the determined total return amount, and the second numerical property of the winning rate and the total return amount obtained by the probability statistic calculation.
8. The fraudulence monitoring system according to claim 1 , wherein the one or more processors include:
a first processor configured to determine the win-loss result; and
a second processor configured to determine the amount of the objects and the position of the objects.
9. The fraudulence monitoring system according to claim 1 , wherein the one or more processors is configured to identify and output a cause of the significant difference.
10. The fraudulence monitoring system according to claim 1 , wherein the one or more processors are further configured to:
perform the comparison to determine whether there is the significant difference for each of at least two sequentially played ones of the one or more games or in response to the predetermined number of the one or more games having been played; and issue a warning based on the determined significant difference.
11. The fraudulence monitoring system according to claim 1 , wherein the one or more processors are further configured to:
perform the comparison to determine whether there is the significant difference according to each of a plurality of predefined criteria; and
identify according to which of the plurality of predefined criteria the significant difference was identified.
12. The fraudulence monitoring system according to claim 11 , wherein the one or more processors are configured to select one or more of a plurality of predefined warnings for output in response to the determined significant difference depending on which of the plurality of predefined criteria the significant difference was determined.
13. The fraudulence monitoring system according to claim 11 , wherein the one or more processors are configured to determine respective scores for each of the plurality of predefined criteria.
14. The fraudulence monitoring system according to claim 1 , wherein the one or more processors are configured to determine whether the significant difference is present based on a conditional probability calculation.
15. The fraudulence monitoring system according to claim 1 , wherein the one or more processors is configured to:
determine whether the significant difference is present by executing an artificial intelligence program developed with data of a past fraudulent game as training data; and
identify the game table from a plurality of game tables or a room having the game table where the significant difference occurred.
16. The fraudulence monitoring system according to claim 15 , wherein the one or more processors are further configured to:
identify respective dealers managing respective ones of a plurality of games; and
determine which of a plurality of game participants, which of the dealers, or which of a plurality of player positions, is associated with occurrence of the significant difference.
17. The fraudulence monitoring system according to claim 1 , wherein the objects include game tokens or currency.
18. The fraudulence monitoring system according to claim 1 , wherein the one or more processors are further configured to:
correlate movement of the objects on the game table to one of the one or more games; and
based on the correlated movement, determine a casino income and a casino expenditure at the game table for each of the one or more games;
for the predetermined number of the one or more games, perform a comparison based on the determined casino income and the determined casino expenditure at the game table for each of the one or more games, and figures obtained by the probability statistic calculation and determine, based on the comparison, whether there is another significant difference therebetween according to each of a plurality of predefined criteria; and
when the other significant difference is determined to be present:
identify according to which of the plurality of predefined criteria the significant difference was identified; and
identify the game table from a plurality of game tables or a room having the game table where the other significant difference occurred.
19. A fraudulence monitoring method comprising:
receiving, at one or more processors from a first sensor, first sensor data based on a detected distribution of one or more cards from a card shoe onto a game table;
receiving, at the one or more processors from a second sensor and for each game of one or more games played via the game table, second senor data that indicates an amount of objects and a position of the objects for a wager source, the wager source is a game participant or a player position;
for each game of the one or more games, determining a return amount obtained by the wager source in the game based on the amount of the objects for the game identified based on the second sensor data, a position of the objects for the game identified based on the second sensor data, and a win-lose result for the game determined based on the first sensor data;
based on one or more return amounts associated with a predetermined number of the one or more games, determining a winning rate and a total return amount for the wager source over the predetermined number of the one or more games; and
based on a comparison between a first numerical property of the determined winning rate and the determined total return amount, and a second numerical property of a winning rate and a total return amount obtained by a probability statistic calculation, determining whether there is a significant difference between the first numerical property and the second numerical property.
20. The fraudulence monitoring method according to claim 19 , further comprising issuing a warning based on the determined significant difference.