Automated discovery of gaming preferences

• US 10,042,748 B2
• Filed: 06/12/2015
• Issued: 08/07/2018
• Est. Priority Date: 01/13/2012
• Status: Active Grant

• Current Assignee: Igt Canada Solutions Ulc (GTECH SpA)
• Original Assignee: Igt Canada Solutions Ulc (GTECH SpA)
• Inventors: Gadher, Bharat Kumar, McIntyre, Andrew R., Idris, Fayez
• Primary Examiner(s): PIERCE, DAMON JOSEPH
• Application Number:
  US14/737,846
• Publication Number:
  US 20150278081A1
• Time in Patent Office: 1,152 Days
• Field of Search: None
• CPC Class Codes: G06F 12/00 Accessing, addressing or allocating within memory systems or architectures digital input from, or digital output to record carriers, e.g. to disk storage units, G06F3/06
• CPC Class Codes: G07F 17/3225 Data transfer within a gaming system, e.g. data sent between gaming machines and users
• CPC Class Codes: G07F 17/3227 Configuring a gaming machine, e.g. downloading personal settings, selecting working parameters
• CPC Class Codes: G07F 17/3237 about the players, e.g. profiling, responsible gaming, strategy/behavior of players, location of players
• CPC Class Codes: G07F 17/3239 Tracking of individual players

Abstract

Systems and methods for automated discovery of gaming preferences and delivery of gaming choices based gaming preferences are disclosed. The systems and methods may operate in real time and may detect and analyze data representing various game features and/or game player behavior and match the data with predetermined models, profiles or game player types. Game choices may then be presented to the game player based on the analysis of the data. Systems and methods to analyze and categorize the game player behavior are also disclosed, including mining data in a cluster model based analysis to identify and develop the models, profiles or game player types and to select the games to be provided for each of the identified models, profiles or game player types. A different collection of games may be provided for each of the identified models, profiles or game player types.

Claims

1. A computer implemented method for analyzing a set of data representing game player behavior, comprising:

partitioning, with a processor, a first set of data into a plurality of game play periods for a plurality of different players, the set of data being related to a plurality game factors associated with one or more wagering games;

analyzing, with a processor, the first set of data within each game play period,

creating, with a processor, a plurality of generic game player types based at least in part on the analysis of the first set of data from the plurality of game play periods and from the plurality of different players;

associating each of the generic game player types with a corresponding selection of games based on data related to games in the selection of games and the first set of data, wherein the selection of games for a first game player type of the plurality of generic game player types differs from the selection of games for a second game player type of the plurality of generic game player types;

collecting a second set of data related to the game factors for game play in an ongoing game by a current game player;

analyzing the second set of data;

associating at least one game player type of the plurality of generic game player types with the current game player based on the analysis of the second set of data; and

displaying to the current game player the corresponding selection of games identified for the at least one game player type associated with the current game player.

2. The method of claim 1, further comprising:

prior to the step of partitioning, collecting, with a processor, the first set of data related to one or more game factors.

3. The method of claim 1, wherein the step of analyzing the set of data comprises:

performing a cluster analysis of the set of data.

4. The method of claim 1, wherein analyzing the set of data comprises:

detecting indicators from within each game play period.

5. The method of claim 1, wherein analyzing the set of data comprises:

detecting trends within the first set of data.

6. The method of claim 1, wherein analyzing the first set of data comprises:

detecting trends within one or more of the plurality of game play periods.

7. The method of claim 1, wherein analyzing the second set of data comprises:

detecting indicators from within the second set of data.

8. The method of claim 1, wherein analyzing the second set of data comprises:

matching the second set of data to at least one game player type.

9. The method of claim 1, wherein analyzing the second set of data comprises:

detecting trends within the second set of data.

10. The method of claim 1, wherein data of the first set of data or the second set of data represents at least one feature selected from the group consisting of:

game session length, play behavior, game behavior, game language, game location, game selection, elapsed time with one game, wagering behavior, game type, game theme, wager amounts, wager denominations, play rates, typical bonus values, game brand, prize distributions, amounts of incremental wagers, frequency of wagering, for instance the presence or absence of multiple rounds of wagering in a game, the number of rounds of wagers permitted in a game, maximum wager amounts permitted, minimum wager amounts permitted, amount of wagering, elapsed time between selected events for instance starting a new game, reaction to bonus rounds, reaction to progressive outputs, pay table features, amount of incremental wagers, frequency of wagering, elapsed time for player reaction, amount of wagering, elapsed time between wagers, frequency of player action, game rules, game complexity, ability for a player to control or have an effect on a game outcome, whether an outcome is predetermined, whether parallel wagering is provided, average game speed, average wager amounts, average wager rate, presence or frequency of bonus rounds, presence and frequency of progressive outputs, payout percentages, win rates, win percentages, loss rates, loss percentages, use of special features, frequency of use of special features, number of lines played, total amount wagered, and type of payment received.

11. The method of claim 1, wherein collecting the second set of data related to the game factors comprises collecting data from a moment that the player begins to play a game or indicates a desire to play a game.

12. The method of claim 1, wherein the step of collecting the second set of data related to the game factors comprises collecting data for a predetermined length of time.

13. The method of claim 1, further comprising:

receiving a selection of a game to play from the game player.

14. The method of claim 1, wherein the game player is unregistered.

15. The method of claim 1, wherein the game player has no player account or other information identifying the player to a wagering game system.

16. The method of claim 1, where the second set of data is collected during a game play session and there is no historical data related to the game player'"'"'s game player type prior to the game play session.

17. The method of claim 1, further comprising providing a module to perform the step of collecting the second set of data related to the game factors.

18. The method of claim 17, wherein the module is provided on one or more of:

a gaming device, a controller in a gaming venue, a local system in the gaming venue, a system in a data center, a system in a social media network or in a private cloud, public cloud, hybrid cloud or community cloud.

19. The method of claim 18, wherein the gaming device is a video lottery terminal, electronic gaming machine, personal computer, laptop computer, tablet, mobile phone, or a functional equivalent of one of the foregoing.

20. The method of claim 1, further comprising:

receiving a selection of one of the displayed games from a player; and

presenting the selected game to the player on the video display.

21. The method of claim 1, where the second set of data is collected during a game play session and the game player type for the game player is previously identified.

22. The method of claim 21, wherein determining the at least one game player type for the current game player includes factoring in the previously identified game player type.

23. The method of claim 21, further comprising:

updating the previously identified game player type for the current game player.

24. The method of claim 21, wherein determining at least one game player type for the current game player based on the analysis of the second set of data comprises determining at least one updated game player type that is different from a previously identified game player type for the current game player;

and the method further comprises;

changing the previously identified game player type for the current game player to the updated game player type.

25. The method of claim 1, further comprising modifying at least one game player type based at least in part on the analysis of data from an additional set of one or more game play periods.

26. A wagering game system comprising:

an electronic gaming machine configured to collect a first set of data related to a plurality of game factors for a plurality of different players and associated with one or more wagering games, and

a modeling module to receive the first set of data and configured to;

partition the first set of data into a plurality of game play periods;

analyze the first set of data within each game play period;

create a plurality of generic game player types based at least in part on the analysis of the first set of data from the plurality of game play periods and from the plurality of different players;

associate each of the game player types with a corresponding selection of games based on data related to games in the selection of games and the first set of data, wherein the selection of games for a first game player type of the plurality of generic game player types differs from the selection of games for a second game player type of the plurality of generic game player types;

collect a second set of data related to the game factors for game play in an ongoing game by a current game player;

analyze the second set of data;

associate at least one game player type of the plurality of generic game player types with the current game player based on the analysis of the second set of data; and

display to the current game player the corresponding selection of games identified for the at least one game player type associated with the current game player.