Operator-specific quality management and quality improvement

• US 7,933,934 B2
• Filed: 11/11/2010
• Issued: 04/26/2011
• Est. Priority Date: 03/18/2005
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: BeyondCore, Inc. (Salesforce.com, Inc.)
• Inventors: Sengupta, Arijit
• Primary Examiner(s): Abel-Jalil; Neveen
• Assistant Examiner(s): BUCKINGHAM, KELLYE DEE
• Application Number:
  US12/944,547
• Publication Number:
  US 20110060728A1
• Time in Patent Office: 166 Days
• Field of Search: None
• US Class Current: 707/806
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 99/00 Subject matter not provided for in other groups of this subclass

Abstract

Methods and systems for improving a data processing operation based on operator-specific quality management and/or monitoring. For example, operator-specific frequency of errors, error rates, error patterns and/or root causes may be identified. Operator-specific actions may then be taken based on these.

Claims

1. A method for improving quality of a data processing operation performed by data processing operators on a plurality of documents, each processed document comprising at least one field, the method comprising the steps of:

assigning a relative operational risk to each of at least two fields in the plurality of documents, the relative operational risk for a given field based on the operational impact of data processing errors in that field relative to the operational impact of data processing errors in other fields;

determining an operator-specific frequency of errors for each of the fields;

determining an operator-specific error rate for each of the fields, the operator-specific error rate for a field based on the relative operational risk for that field and the operator-specific frequency of errors for that field;

based on the operator-specific error rate, taking action for the operator to reduce the operator-specific error rate;

identifying an operator-specific pattern of errors, wherein the step of taking action for the operator to reduce the operator-specific error rate is further based on the identified operator-specific pattern of errors; and

identifying a root cause for the operator-specific pattern of errors, wherein the step of taking action for the operator to reduce the operator-specific error rate is further based on addressing the root cause.

2. The method of claim 1 wherein taking action comprises conducting training for the operator, the training selected based on the operator-specific error rate.

3. The method of claim 1 wherein taking action comprises changing a process followed by the operator for the data processing operation, the change in process determined based on the operator-specific error rate.

4. The method of claim 1 wherein taking action comprises changing a system used by the operator in the data processing operation, the change in system determined based on the operator-specific error rate.

5. The method of claim 1 wherein:

within a group of operators, certain operators have higher operator-specific error rates than others; and

taking action comprises taking action prioritized for said operators with higher operator-specific error rates.

6. The method of claim 5 wherein taking action comprises conducting training for said operators with higher operator-specific error rates.

7. The method of claim 1 wherein:

within a group of operators, many operators exhibit a similar pattern of errors for a specific field; and

taking action comprises taking action prioritized for said specific field.

8. The method of claim 7 wherein taking action comprises changing a process followed by the operators for the data processing operation, to reduce the frequency of errors for said specific field.

9. The method of claim 7 wherein taking action comprises changing a system used by the operators in the data processing operation, to reduce the frequency of errors for said specific field.

10. The method of claim 1 wherein:

within a group of operators, many operators exhibit a similar frequency of errors for a specific field; and

taking action comprises taking action prioritized for said specific field.

11. The method of claim 10 wherein taking action comprises changing a process followed by the operators for the data processing operation, to reduce the frequency of errors for said specific field.

12. The method of claim 10 wherein taking action comprises changing a system used by the operators in the data processing operation, to reduce the frequency of errors for said specific field.

13. The method of claim 1 wherein taking action comprises assigning operators to tasks within the data processing operation based on their operator-specific error rates.

14. The method of claim 1 wherein taking action comprises assigning operators to tasks within the data processing operation based on their operator-specific error patterns.

15. The method of claim 1 wherein the step of identifying a root cause comprises:

identifying the root cause by comparing the operator-specific identified pattern of errors against historical data that correlates different patterns of errors with their corresponding root causes.

16. The method of claim 1 wherein taking action is further based on the operator'"'"'s past patterns of error.

17. The method of claim 1 wherein the step of identifying an operator-specific pattern of errors is based on deterministic algorithms that are customized for the operator based on the operator'"'"'s historical performance.

18. The method of claim 1 wherein the step of identifying an operator-specific pattern of errors is based on a learning algorithm that is trained based on the operator'"'"'s historical performance.

19. The method of claim 1 wherein taking action is further based on the operator'"'"'s past error rates.

20. The method of claim 1 wherein the steps of determining an operator-specific frequency of errors and determining an operator-specific error rate are performed dynamically over time.

21. The method of claim 1 further comprising:

identifying an operator-specific set of critical fields based on which fields have higher operator-specific error rates, wherein the step of taking action for the operator to reduce the operator-specific error rate is further based on the operator-specific identified critical fields.

22. The method of claim 21 wherein taking action comprises conducting training for the operator, to reduce the operator-specific frequency of errors in the operator-specific critical fields.

23. The method of claim 21 wherein taking action comprises changing a process followed by the operator for the data processing operation, to reduce the operator-specific frequency of errors in the operator-specific critical fields.

24. The method of claim 21 wherein taking action comprises changing a system used by the operator in the data processing operation, to reduce the operator-specific frequency of errors in the operator-specific critical fields.