EFFICIENT SERVICE RULES CREATION THROUGH SUBJECTIVE LOGIC AND TEMPORAL PATTERN RECOGNITION

• US 20120191638A1
• Filed: 01/26/2011
• Published: 07/26/2012
• Est. Priority Date: 01/26/2011
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Li, Zhiguo, Minhas, Rajinderjeet Singh
• Granted Patent: US 8,538,908 B2
• Time in Patent Office: Days
• US Class Current: 706/48
• CPC Class Codes: G06N 5/025 Extracting rules from data

Abstract

A method for generating service rules corresponding to business data is disclosed. A plurality of business related data is gathered from various sources. The data is combined using a subjective logic technique. The data is then evaluated for temporal patterns. Finally a set of service rules corresponding to the combined business data are developed.

Claims

1. A method for developing service rules, said method comprising:

gathering a plurality of business data sets from a plurality of sources;

combining said business data sets among said plurality of business data sets into combined business data to produce a level of confidence in said combined business data;

evaluating said combined business data for temporal patterns therein; and

generating a service rule corresponding to said combined business data and said temporal error patterns.

2. The method of claim 1 wherein combining said business data sets among said plurality of business data sets into combined business data to produce said level of confidence in said combined business data, further comprises:

determining a belief vector and an uncertainty mass for each business data set among said plurality of business data sets; and

evaluating each belief vector associated with each business data set among said plurality of business data sets and uncertainty mass with a subjective logic technique

3. The method of claim 1 wherein said combined business data comprises machine service data.

4. The method of claim 1 wherein said plurality of business data sets comprises at least one of the following types of data:

data provided by a service technician;

data provided to a support center; and

data collected by a machine.

5. The method of claim 1 wherein evaluating said combined business data for temporal patterns therein, further comprises:

determining an event sequence;

defining a window associated with said event sequence;

evaluating said event sequence for a failure signature; and

determining the frequency said window covers said failure signature.

6. The method of claim 5 wherein said failure signature comprises at least one of the following types of failure signatures:

a serial failure signature;

a parallel failure signature;

or

a composite failure signature.

7. The method of claim 5 further comprising utilizing said failure signature to generate said service rule to predict an occurrence of a failure event.

8. A method for developing service rules comprising:

gathering a plurality of business data sets from a plurality of sources;

combining said business data sources to produce a level of confidence in said combined business data by;

determining a belief vector and an uncertainty mass for each of said business data sets; and

evaluating each of said business data set'"'"'s belief vector and uncertainty mass with a subjective logic technique;

evaluating said combined business data for temporal patterns by;

determining an event sequence;

defining a window associated with said event sequence;

evaluating said event sequence for a failure signature; and

determining the frequency said window covers said failure signature; and

generating a service rule corresponding to said combined business data and said temporal error patterns.

9. The method of claim 8 wherein said combined business data comprises machine service data.

10. The method of claim 8 wherein said plurality of business data sets comprises at least one of the following types of data:

data provided by a service technician;

data provided to a support center; and

data collected by a machine.

11. The method of claim 8 wherein said failure signature comprises at least one of the following types of failure signatures:

a serial failure signature;

a parallel failure signature;

or

a composite failure signature.

12. The method of claim 8 further comprising utilizing said failure signature to gene rat said plurality of service rules to predict the occurrence of a failure event.

13. The method of claim 8 wherein said subjective logic technique provides an estimate of a system and problem code.

14. A system for developing service rules, comprising:

a processor;

a data bus coupled to said processor; and

a computer-usable medium embodying computer code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for;

gathering a plurality of business data sets from a plurality of sources;

combining said business data sets among said plurality of business data sets into combined business data to produce a level of confidence in said combined business data;

evaluating said combined business data for temporal patterns therein; and

generating a service rule corresponding to said combined business data and said temporal error patterns.

15. The system of claim 14 wherein said instructions are further configured for:

determining a belief vector and an uncertainty mass with respect to each of said business data sets among said plurality of business data sets; and

evaluating said belief factor an said uncertainty mass with respect to each of business data set among said plurality of business data sets with a subjective logic technique.

16. The system of claim 14 wherein said business data comprises machine service data.

17. The system of claim 14 wherein said plurality of business data sets comprise at least one of the following types of data:

data provided by a service technician;

data provided to a support center; and

data collected by a machine.

18. The system of claim 14 wherein said instructions are further configured for:

determining an event sequence;

defining a window associated with said event sequence;

evaluating said event sequence for a failure signature; and

determining the frequency said window covers said failure signal.

19. The system of claim 18 wherein said failure signature comprises at least one of the following types of failure signatures:

a serial failure signature;

a parallel failure signature;

or

a composite failure signature.

20. The system of claim 18 wherein said failure signature is utilized to generate a plurality of service rules to predict the occurrence of a failure event.