Quality management in a data-processing environment

• US 7,925,638 B2
• Filed: 09/08/2010
• Issued: 04/12/2011
• Est. Priority Date: 03/18/2005
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: BeyondCore, Inc. (Salesforce.com, Inc.)
• Inventors: Stronger, Brad A., Sengupta, Arijit
• Primary Examiner(s): Abel-Jalil; Neveen
• Assistant Examiner(s): Chbouki; Tarek
• Application Number:
  US12/877,995
• Publication Number:
  US 20100332899A1
• Time in Patent Office: 216 Days
• Field of Search: None
• US Class Current: 707/690
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Abstract

A method, system and computer program for quality management of a plurality of documents associated with a data-processing operation of an organization is provided. The documents include several fields, the organization has employees, and the method comprises determination of error rates. Errors are reduced in the plurality of documents by focusing on critical fields. The occurrence of errors is predicted by determining a correlation between the errors and a set of attributes, which is updated. Reducing and predicting errors control the error rate. A method for measuring the quality of a plurality of documents is also provided. The measurement is based on the relative operational impact of the errors and the frequency of errors associated with each field.

Claims

1. A computer-implemented 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:

a computer system executing software to effect the steps of;

for each document in a selected set of documents;

accessing at least three versions of the document, each version processed by a different data processing operator;

comparing the at least three versions of the document with each other;

determining a plurality vote answer for at least one field in the document, the plurality vote answer being the most common answer for the field in the at least three versions of the document, the plurality vote answer being determined on the basis of the comparison; and

identifying potential errors in the data processing operation based on using the plurality vote answer as the correct answer for the field;

recognizing a pattern of errors for a field;

identifying a root cause for the recognized pattern of errors;

generating a database on the basis of the recognized pattern of errors and the identified root causes;

predicting a root causes of error based on analysis of the error pattern of a field, the analysis based on comparison to the plurality vote answer for the field; and

performing a corrective action based on the prediction of root causes.

2. The method of claim 1, wherein the computer system executing software further effects the steps of:

combining the plurality vote answer for each field to determine a combined plurality vote answer for each document;

comparing each document from amongst the sets of documents with the combined plurality vote answer for that set of documents to check for errors; and

recording a frequency of errors in each field based on the comparison.

3. The method of claim 2, further comprising replacing an identified erroneous answer with the plurality vote answer as the correct answer.

4. The method of claim 2, wherein an algorithm based on the plurality vote answer is used to improve the error rate associated with Optical Character Recognition (OCR) systems.

5. The method of claim 1, further comprising the step of determining a correlation between the errors in the plurality of documents and a set of attributes.

6. The method of claim 5, wherein a learning algorithm provides the correlation.

7. The method of claim 5, wherein the correlation is used to predict errors and such predictions are used to perform at least one of preventive and corrective action to avoid the error.

8. The method of claim 5, wherein the plurality of documents are sampled for determining an error rate.

9. The method of claim 5, wherein the set of attributes is updated based on the error rate.

10. The method of claim 1, wherein the pattern of errors and corresponding root causes are further recognized for each data processing operator.

11. The method of claim 1 further comprising comparing a current error pattern with the recognized pattern of errors in the database.

12. The method of claim 11, wherein the current error pattern is correlated with a root cause in the database, based on the comparison.

13. The method of claim 12, wherein correlating the current error pattern and the root causes is implemented using one of a correlation engine, advanced clustering, pattern recognition, and learning algorithm.

14. The method of claim 10, further comprising identifying specific training needs of each data processing operator on the basis of the specific error patterns and critical fields of the data processing operator.

15. The method of claim 10, further comprising the step of determining a rule based correction, based on the correlation of the pattern of errors, the root cause, the specific error patterns, and critical fields of the data processing operator.

16. The method of claim 1, wherein the data processing operators are human data processing operators.

17. The method of claim 1, wherein the data processing operators are automated data processing systems.

18. The method of claim 1, wherein the data processing operation comprises at least one of a data entry operation, a data transfer operation, a data reporting operation, and a transformation of data operation.