System and method for electronic communication management

• US 7,644,057 B2
• Filed: 05/05/2004
• Issued: 01/05/2010
• Est. Priority Date: 01/03/2001
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Hajaj, Nissan, Cohen, Dani, Magdalen, Josemina, Nelken, Yoram
• Primary Examiner(s): Vincent; David R
• Assistant Examiner(s): Fernandez Rivas; Omar F
• Application Number:
  US10/839,829
• Publication Number:
  US 20040254904A1
• Time in Patent Office: 2,071 Days
• Field of Search: 706/10, 706 12- 21, 706 45- 48, 706/50, 706/61, 706/909, 709/202, 709/206, 707/1, 707/3, 707/10, 704/9, 704/236, 704/255
• US Class Current: 706/55
• CPC Class Codes: G06F 16/355 Class or cluster creation or modification
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G10L 15/26 Speech to text systems G10L15/08 takes precedence

Abstract

A system and method for classifying text includes a pre-processor, a knowledge base, and a statistical engine. The pre-processor identifies concepts in the text and creates a structured text object that contains the concepts. The structured text object is then passed to a statistical engine, which applies statistical information provided in nodes of a knowledge base to the structured text object in order to calculate a set of match scores, each match score representing the relevance of the text to an associated one of a plurality of predefined categories. The pre-processor may be implemented in the form of an interpreter which selects and executes a script that includes language- and scenario-specific instructions for performing linguistic and semantic analysis of the text.

Claims

1. A computerized text classifier system for classifying text on a computer for electronic communication management in a contact center, comprising:

a pre-processor, performed by the computer, configured to analyze text from an electronic communication received from a customer to determine the customer'"'"'s intent by identifying concepts in the text and building a concept model containing the identified concepts;

a knowledge base, stored in the computer, having a plurality of nodes including a set of learning nodes, each of the learning nodes being provided with statistical information for determining a relevance of the text to a category associated with the node;

a statistical engine, performed by the computer, for calculating a set of match scores for the concept model using the knowledge base, each match score of the set of match scores indicating the relevance of the text to a category associated with a node of the knowledge base, the category including at least one suggested action to be performed in response to the electronic communication, wherein the suggested action is representative of the relevance of the text to the category, and the suggested action includes generating an automatic response to the customer or routing the electronic communication to an agent to generate an assisted response to the customer; and

performing, in the computer, the suggested action in response to the electronic communication based on the calculated set of match scores, in order to improve the response of the contact center to the electronic communications received from customers by the contact center.

2. The computerized text classifier system of claim 1, wherein the text includes a plurality of fields, a first subset of the plurality of fields consisting of unstructured data and a second subset of the plurality of fields consisting of structured data.

3. The computerized text classifier system of claim 1, wherein the plurality of nodes further includes a set of rule-based nodes.

4. The computerized text classifier system of claim 1, wherein the plurality of nodes are organized into a tree structure.

5. The computerized text classifier system of claim 1, wherein the match scores are calibrated to values of an operational parameter.

6. The computerized text classifier system of claim 5, wherein the operational parameter is selected from a group consisting of precision and recall.

7. The computerized text classifier system of claim 1, wherein the pre-processor selects a script from a plurality of scripts and executes the selected script to identify concepts.

8. The computerized text classifier system of claim 7, wherein at least two of the plurality of scripts correspond to different languages.

9. The computerized text classifier system of claim 7, wherein the statistical engine is further configured to receive real-time feedback to adapt the statistical information provided to one or more learning nodes of the set of learning nodes.

10. The computerized text classifier system of claim 9, wherein the real-time feedback comprises a response of a human agent to the relevance of the text to associated categories based upon the set of match scores.

11. The computerized text classifier system of claim 9, wherein the real-time feedback comprises a reply to the suggested action, the suggested action comprising the response or a link to a web-resource.

12. The computerized text classifier system of claim 9, wherein the statistical engine is further configured to modify weights associated with the statistical information, in accordance with the received real-time feedback.

13. A system for classifying text on a computer for electronic communication management in a contact center, comprising:

means for analyzing, in the computer, text from an electronic communication received from a customer to determine the customer'"'"'s intent by identifying concepts in the text and building a concept model containing the identified concepts;

means for providing, in the computer, a knowledge base having a plurality of nodes including a set of learning nodes, each of the learning nodes being provided with statistical information for determining a relevance of the text to a category associated with the node;

means for calculating, in the computer, a set of match scores for the concept model using the knowledge base, each match score of the set of match scores indicating the relevance of the text to a category associated with a node of the knowledge base, the category including at least one suggested action to be performed in response to the electronic communication, wherein the suggested action is representative of the relevance of the text to the category, and the suggested action includes generating an automatic response to the customer or routing the electronic communication to an agent to generate an assisted response to the customer; and

means for performing, in the computer, the suggested action in response to the electronic communication based on the calculated set of match scores, in order to improve the response of the contact center to the electronic communication received from customers by the contact center.

14. The system for classifying text of claim 13, wherein the text includes a plurality of fields, a first subset of the plurality of fields consisting of unstructured data and a second subset of the plurality of fields consisting of structured data.

15. The system for classifying text of claim 13, wherein the plurality of nodes further includes a set of rule-based nodes.

16. The system for classifying text of claim 13, wherein the plurality of nodes are organized into a tree structure.

17. The system for classifying text of claim 13, wherein the match scores are calibrated to values of an operational parameter.

18. The system for classifying text of claim 17, wherein the operational parameter is selected from a group consisting of precision and recall.

19. The system for classifying text of claim 13, wherein the pre-processor selects a script from a plurality of scripts and executes the selected script to identify concepts.

20. The system for classifying text of claim 19, wherein at least two of the plurality of scripts correspond to different languages.

21. The system for classifying text of claim 19, wherein the statistical engine is further configured to receive real-time feedback to adapt the statistical information provided to one or more learning nodes of the set of learning nodes.

22. The system for classifying text of claim 21, wherein the real-time feedback comprises a response of a human agent to the relevance of the text to associated categories based upon the set of match scores.

23. The system for classifying text of claim 21, wherein the real-time feedback comprises a reply to the suggested action, the suggested action comprising the response or a link to a web-resource.

24. The system for classifying text of claim 21, wherein the statistical engine is further configured to modify weights associated with the statistical information, in accordance with the received real-time feedback.