Faults and Performance Issue Prediction

• US 20130190095A1
• Filed: 01/31/2013
• Published: 07/25/2013
• Est. Priority Date: 11/18/2008
• Status: Active Grant

• Current Assignee: Igt Canada Solutions Ulc (GTECH SpA)
• Original Assignee: Spielo International ULC
• Inventors: Gadher, Bharat Kumar, Idris, Fayez
• Granted Patent: US 9,084,937 B2
• Time in Patent Office: Days
• US Class Current: 463/42
• CPC Class Codes: A63F 13/77 involving data related to game devices or game servers, e.g. configuration data, software version or amount of memory
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: G06F 11/3447 Performance evaluation by modeling
• CPC Class Codes: G06F 11/3466 Performance evaluation by tracing or monitoring
• CPC Class Codes: G06F 11/3636 by tracing the execution of the program
• CPC Class Codes: G06F 2201/86 Event-based monitoring
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance

Abstract

Systems and methods for an online predictive diagnostic and prognostic maintenance system are disclosed. The systems and methods may be configured for use with networked gaming machines. The systems and methods may operate in real time and may detect and analyze data representing various indicators of machine performance or a current or future decrease in machine performance. The data may represent or be used to predict machine performance and risk of failure and to identify necessary or recommended repair, maintenance or other performance issues. In another embodiment systems and methods are disclosed for automated analysis of data regarding machine operation and generation of rules related to predicting the future performance, repair and maintenance needs of machines.

Claims

1. A computer implemented method for generating a set of fault prediction rules comprising the steps of:

operating one or more gaming devices;

collecting event data from the one or more gaming devices or from sensors associated with the one or more gaming devices;

analyzing event data from the gaming devices with a computer processor;

generating a set of one or more fault prediction rules from the event data analysis with a computer processor based on patterns of gaming device operation and faults.

2. The method of claim 1, further comprising the step of modifying a set of one or more preexisting fault prediction rules based on the recently generated fault prediction rules to form a new fault prediction rule set.

3. The method of claim 1, further comprising the step of analyzing device event data over a time window against the fault prediction rule set and predicting the likelihood of device malfunction.

4. The method of claim 1, wherein the event data comprises device malfunctions.

5. The method of claim 1, wherein the event data comprises device repair or service.

6. The method of claim 1, further comprising the step of triggering diagnostic tests on a gaming device responsive to the event data analysis.

7. The method of claim 3, further comprising the step of determining a time window for the device malfunction.

8. The method of claim 1, wherein the event data is specific to a particular gaming site.

9. The method of claim 1, wherein the event data is specific to a particular gaming device.

10. The method of claim 1, wherein the event data is specific to a particular model of gaming device.

11. The method of claim 1, wherein the event data represents at least one malfunction selected from the group consisting of mechanical, electrical and software malfunctions.

12. The method of claim 1, further comprising the step of, responsive to a prediction of a likelihood of device malfunction, providing an alert to a gaming server.

13. The method of claim 1, further comprising:

continuing to operate the one or more gaming devices during a period of continued operation;

analyzing event data from the gaming devices during the period of continued operation;

generating a second set of fault prediction rules event data analysis with a computer processor based on patterns of gaming device operation and faults during the period of continued operation; and

modifying any preexisting fault prediction rule set based on the second set of prediction rules and forming a modified fault prediction rule set.

14. The method of claim 1, wherein the event data is data corresponding to machine status, problematic issues, or machine errors.

25. The method of claim 3, wherein the time window is predetermined.

26. The method of claim 3, further comprising the step of receiving an indicator of a time window for the device malfunction.

15. A method of predicting a performance issue with a gaming machine, the method comprising:

monitoring a plurality of data elements representing operational characteristics of a gaming machine;

analyzing the plurality of data elements to determine whether the plurality of data elements are indicative of a risk of a performance issue with the gaming machine based on a set of rules for determining the risk of a performance issue;

responsive to a determination that one or more of the data elements are indicative of a risk of a performance issue with the gaming machine, transmitting a signal to a server indicating the risk of a performance issue.

16. The method of claim 15, wherein the risk of a performance issue is determined for a particular time period.

17. The method of claim 15, further comprising the steps of:

receiving an indication that the gaming machine has been serviced;

recording servicing data related to the servicing of the gaming machine; and

including the servicing data in the plurality of data elements to determine whether the plurality of data elements are indicative of a risk of a performance issue with the gaming machine based on a set of rules for determining the risk of a performance issue.

18. The method of claim 15, further comprising the steps of:

determining whether the gaming machine has been serviced; and

suppressing the signal indicating the risk of a performance issue.

19. The method of claim 15, wherein the step of analyzing the plurality of data elements incudes filtering the data elements.

20. The method of claim 15, further comprising the server outputting a communication for receipt by a field technician, a game machine operator or a malfunction ticket handling system, the communication indicating the risk of a performance issue.

21. The method of claim 15, further comprising the server assembling and communicating data elements representing risks of performance issues.

27. The method of claim 15, further comprising at least one of the steps of:

filtering the determined performance issues to remove any false positives;

prioritizing the determined performance issues based on a severity of the performance issue or an assessed risk of game machine malfunction; and

escalating the reporting of the determined performance issues to game operators, service technicians or malfunction ticketing systems.

28. The method of claim 15, further comprising the step of:

providing a report of the determined performance issues via a graphical user interface.

22. A system that monitors and facilitates an analysis of data collected from a gaming machine that is used to determine a level of risk of a performance issue with the machine comprising:

a processor configured to collect data representing operational aspects of the gaming machine and transmit the collected data;

a database configured to receive and store the collected data, the database being operatively linked to the gaming machine;

a server operatively linked to the database, the server being configured to process the collected data and to generate a risk factor for a performance issue with the gaming machine, the risk factor being based on a set of rules for determining the risk of a performance issue with the gaming machine.

23. The system of claim 22, wherein the server is further configured to revise the set of rules for determining the risk of a performance issue with the gaming machine based on analysis of gaming machine performance data.

24. The system of claim 22, wherein the collected data comprises data corresponding to machine status, problematic issues, or machine errors.