System and method for detection, classification, and management of collusion in online activity

Abstract

The present invention relates to the detection, classification, and management of collusion in online gaming. Specifically, it relates to detecting collusion in online poker and other games of chance. The present invention may also be used to detect collusion in other types of strategy based online gaming such as first person shooter games. The present invention can then be used to classify and visualize any detected collusion. Finally, the present invention can be used to make recommendations regarding what possible courses of action could be taken, if any, to prevent and/or control collusion.

1 . A method comprising the steps of:
 gathering data regarding users of an online service participating in a online activity;   comparing said data to control data using a mathematical algorithm;   generating a current score based on said comparison; and   deciding whether said data indicates collusion between said users based on said current score.   
   
   
       2 . The method of  claim 1 , wherein said deciding step comprises:
 compiling a list of users participating in an online activity on an online service;   comparing combinations of said users to previously compiled lists of users that have participated in activities on said service; and   identifying combinations of users with an affinity for participating with one another.   
   
   
       3 . The method of  claim 1 , wherein the deciding step comprises:
 comparing said current score to a pre-determined score;   returning a value indicating collusion if said current score exceeds said pre-determined score; and   returning a value indicating non-collusion if the current score does not exceed the pre-determined score.   
   
   
       4 . The method of  claim 1 , wherein the mathematical algorithm is one or more selected from the group consisting of Bayesian networks, statistical correlation, symbolic rule induction, clustering, rules based expert systems, similarity classification, genetic programming, decision trees, fuzzy rules, neural networks, Bayesian classifiers, and game theory. 
   
   
       5 . The method of  claim 1 , wherein said data is one or more types of data selected from the group comprising demographic data, user action, and financial instruments. 
   
   
       6 . The method of  claim 4 , wherein said user demographics are one or more selected from the group comprising of age, gender, geographic location, username, service provider, and IP address. 
   
   
       7 . The method of  claim 5 , wherein the action is one or more actions selected from a group comprising bet, call, raise, fold, cheek, bid, purchase, or play. 
   
   
       8 . A system comprising:
 A memory device for storing a program;   a processor in communication with said storage device;   said processor operable with said program to:   gather data regarding users of an online service participating in a online activity;   compare said data to control data using a mathematical algorithm;   generate a current score based on said comparison; and   decide whether said data indicates collusion between said users based on said current score.   
   
   
       9 . The system of  claim 8 , wherein said processor is further operable to:
 compile a list of users participating in an online activity on an online service;   compare combinations of said users to previously compiled lists of users that have participate in activities on said service; and   identify combinations of users with an affinity for participating with one another.   
   
   
       10 . The system of  claim 8 , wherein said processor is further operable to:
 compare said current score to a pre-determined score;   return a value indicating collusion if said current score exceeds said pre-determine score; and   return a value indicating non-collusion if the current score does not exceed the pre-determined score.   
   
   
       11 . The system of  claim 8 , wherein the mathematical algorithm is one or more selected from the group consisting of Bayesian networks, statistical correlation, symbolic rule induction, clustering, rules based expert systems, similarity classification, genetic programming, decision trees, fuzzy rules, neural networks, Bayesian classifiers, and game theory. 
   
   
       12 . The system of  claim 8 , wherein said data is one or more types of data selected from the group comprising user hand rank, action, and demographics. 
   
   
       13 . The system of  claim 12 , wherein said user demographics are one or more selected from the group consisting of age, gender, geographic location, username, service provider, and IP address. 
   
   
       14 . The system of  claim 12 , wherein the action is selected from a group of actions comprising bet, call, raise, fold, check, bid, purchase, or play. 
   
   
       15 . An article of manufacture comprising:
 a computer readable medium comprising instructions, said instructions comprising instructions for:   gathering data regarding users of an online service participating in a online activity;   comparing said data to control data using a mathematical algorithm;   generating a current score based on said comparison; and   deciding whether said data indicates collusion between said users based on said current score.   
   
   
       16 . The article of manufacture of  claim 15 , wherein said article of manufacture instructions for said deciding step comprise:
 compiling a list of users participating in an online activity on an online service;   comparing combinations of said users to previously compiled lists of users that have participated in activities on said service; and   identifying combinations of users with an affinity for participating with one another.   
   
   
       17 . The article of manufacture of  claim 15 , wherein said article of manufacture instructions for said deciding step comprise:
 comparing said current score to a pre-determined score;   returning a value indicating collusion if said current score exceeds said pre-determined score; and   returning a value indicating non-collusion if the current score does not exceed the pre-determined score.   
   
   
       18 . The article of manufacture of  claim 15 , wherein the mathematical algorithm is one or more selected from the group consisting of Bayesian networks, statistical correlation, symbolic rule induction, clustering, rules based expert systems, similarity classification, genetic programming, decision trees, fuzzy rules, neural networks, Bayesian classifiers, and game theory. 
   
   
       19 . The article of manufacture of  claim 15 , wherein said data is one or more types of data selected from the group comprising user hand rank, action, and demographics. 
   
   
       20 . The article of manufacture of  claim 19 , wherein said user demographics are one or more selected from the group consisting of age, gender, geographic location, username, service provider, and IP address. 
   
   
       21 . The article of manufacture of  claim 19 , wherein the action is selected from a group of actions comprising bet, call, raise, fold, check, bid, purchase, or play. 
   
   
       22 . A method comprising the steps of:
 gathering data regarding an online game;   comparing said data to control data using a mathematical algorithm;   generating a current score based on said comparison; and   deciding whether said online game indicates collusion based on said current score.   
   
   
       23 . The method of  claim 22 , wherein said deciding step comprises:
 compiling a list of users at a table;   comparing combinations of said list to previously compiled lists of users at previous tables; and   identifying combinations of users with an affinity for playing at the same table.   
   
   
       24 . The method of  claim 22 , wherein the deciding step comprises:
 comparing said current score to a pre-determined score;   returning a value indicating collusion if said current score exceeds said pre-determined score; and   returning a value indicating non-collusion if the current score does not exceed the pre-determined score.   
   
   
       25 . The method of  claim 22 , wherein the mathematical algorithm is one or more selected from the group consisting of Bayesian networks, statistical correlation, symbolic rule induction, clustering, rules based expert systems, similarity classification, genetic programming, decision trees, fuzzy rules, neural networks, Bayesian classifiers, and game theory. 
   
   
       26 . The method of  claim 22 , wherein said data is one or more types of data selected from the group comprising user hand rank, action, and demographics. 
   
   
       27 . The method of  claim 26 , wherein said user demographics are one or more selected from the group consisting of age, gender, geographic location, and username. 
   
   
       28 . The method of  claim 26 , wherein the action is selected from a group of actions comprising bet, call, raise, fold, and check. 
   
   
       29 . A system comprising:
 A memory device for storing a program;   a processor in communication with said storage device;   said processor operable with said program to:
 gather data regarding an online game; 
 compare said data to control data using a mathematical algorithm; 
 generate a current score based on said comparison; and 
 decide whether said online game indicates collusion based on said current score. 
   
   
   
       30 . The system of  claim 29 , wherein said processor is further operable to:
 compile a list of users at a table;   compare combinations of said list to previously compiled lists of users at previous tables; and   identify combinations of users with an affinity for playing at the same table.   
   
   
       31 . The system of  claim 29 , wherein said processor is further operable to:
 compare said current score to a pre-determined score;   return a value indicating collusion if said current score exceeds said pre-determined score; and   return a value indicating non-collusion if the current score does not exceed the pre-determined score.   
   
   
       32 . The system of  claim 29 , wherein the mathematical algorithm is one or more selected from the group consisting of Bayesian networks, statistical correlation, symbolic rule induction, clustering, rules based expert systems, similarity classification, genetic programming, decision trees, fuzzy rules, neural networks, Bayesian classifiers, and game theory. 
   
   
       33 . The system of  claim 29 , wherein said data is one or more types of data selected from the group comprising user hand rank action, and demographics. 
   
   
       34 . The system of  claim 33 , wherein said user demographics are one or more selected from the group consisting of age, gender, geographic location, and username. 
   
   
       35 . The system of  claim 33 , wherein the action is selected from a group of actions comprising bet, call, raise, fold, and check. 
   
   
       36 . An article of manufacture comprising:
 a computer readable medium comprising instructions, said instructions comprising instructions for:
 gathering data regarding an online game; 
 comparing said data to control data using a mathematical algorithm; 
 generating a current score based on said comparison; and 
 deciding whether said online game indicates collusion based on said current score. 
   
   
   
       37 . The article of manufacture of  claim 36 , wherein said article of manufacture instructions for said deciding step comprises:
 compiling a list of users at a table;   comparing combinations of said list to previously compiled lists of users at previous tables; and   identifying combinations of users with an affinity for playing at the same table.   
   
   
       38 . The article of manufacture of  claim 36 , wherein said article of manufacture instructions for said deciding step comprises:
 comparing said current score to a pre-determined score;   returning a value indicating collusion if said current score exceeds said pre-determined score; and   returning a value indicating non-collusion if the current score does not exceed the pre-determined score.   
   
   
       39 . The article of manufacture of  claim 36 , wherein the mathematical algorithm is one or more selected from the group consisting of Bayesian networks, statistical correlation, symbolic rule induction clustering, rules based expert systems, similarity classification, genetic programming, decision trees, fuzzy rules, neural networks, Bayesian classifiers, and game theory. 
   
   
       40 . The article of manufacture of  claim 36 , wherein said data is one or more types of data selected from the group comprising user hand rank, action, and demographics. 
   
   
       41 . The article of manufacture of  claim 40 , wherein said user demographics are one or more selected from the group consisting of age, gender, geographic location, and username. 
   
   
       42 . The article of manufacture of  claim 40 , wherein the action is selected from a group of actions comprising bet, call, raise, fold, and check.