SYSTEM AND METHOD OF SENTIMENT ACCURACY INDEXING FOR CUSTOMER SERVICE

• US 20160335252A1
• Filed: 05/11/2016
• Published: 11/17/2016
• Est. Priority Date: 05/12/2015
• Status: Abandoned Application

• Current Assignee: CrowdCare Corporation (Calabrio, Inc.)
• Original Assignee: CrowdCare Corporation (Calabrio, Inc.)
• Inventors: Sanderson, David Harold, Brunet, Jeffrey, Chowdhary, Yousuf, Collins, Ian, Chan, Karen
• Application Number:
  US15/152,394
• Publication Number:
  US 20160335252A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06Q 30/0278 Product appraisal
• CPC Class Codes: G10L 15/1822 Parsing for meaning understanding

Abstract

A method is provided for evaluating a customer generated communication about a customer device. Terms of a customer generated communication are received with respect to the customer'"'"'s device. Through a sentiment analysis engine, a sentiment expressed through the customer generated communication is determined. The sentiment has a sentiment strength, positive or negative. Through a parsing engine, an issue is extracted with respect to the device as expressed through the terms of the customer generated communication. A device profile of the device is retrieved, which has device parameters. Relevant device parameters to the extracted issue are determined, and these are forwarded to a rules engine. Through the rules engine, the extent to which the extracted issue is factually justified is verified. The extent of factual justification is correlated with the sentiment strength to arrive at a sentiment accuracy index.

Claims

1. A method for evaluating a customer generated communication about a customer device, comprising:

receiving terms of a customer generated communication with respect to the customer'"'"'s device;

through a sentiment analysis engine, determining a sentiment expressed through the customer generated communication, the sentiment having a sentiment strength, positive or negative;

through a parsing engine, extracting an issue with respect to the device as expressed through the terms of the customer generated communication;

retrieving a device profile of the device, the profile having device parameters;

determining relevant device parameters to the extracted issue, and forwarding these to a rules engine;

through the rules engine, verifying the extent to which the extracted issue is factually justified; and

correlating the extent of factual justification with the sentiment strength to arrive at a sentiment accuracy index.

2. The method of claim 1, further comprising queuing the issue for resolution.

3. The method of claim 1, further comprising resolving the issue.

4. The method of claim 2, wherein the sentiment accuracy index is used for prioritization of resolution.

5. The method of claim 2, wherein the issue is only queued for resolution if the sentiment accuracy index is above a preset threshold.

6. The method of claim 1, wherein the parsing uses a method to standardize and normalize terms.

7. The method of claim 6, wherein the parsing uses natural language processing.

8. The method of claim 6, wherein the parsing identifies terms related to device or application functions or services.

9. The method of claim 1, further comprising:

retrieving operator information with respect to the customer'"'"'s account for a service provided on the device.

10. The method of claim 9, wherein relevant operator information is forwarded with the device parameters to the rules engine for verification of the extent to which the extracted issue is factually justified.

11. The method of claim 10, wherein the operator information includes at least one of subscription levels or limits, billing, usage patterns, Call Detail Records (CDRs), address, language, other services.

12. The method of claim 10, wherein the operator information includes current and historical information, and wherein the rules engine is programmed to compare current and historical information to determine any changes.

13. The method of claim 10, wherein the operator information includes billing or usage information, which is evaluated by the rules engine where the extracted issue is related to billing or charges on the account.

14. The method of claim 1, wherein the device parameter includes at least one of make, model, OS, firmware version, apps running or installed, customer country, location, language, service provider, subscription, time zone, connected devices, device crash logs, error logs, or activity logs.

15. The method of claim 10, where the extracted issue relates to a service level, further comprising comparing the extracted issue with data or reports from other customers or other devices in the network.

16. The method of claim 1, wherein the terms of the customer generated communication are received as typed text.

17. The method of claim 1, wherein the terms of the customer generated communication are received as voice input.

18. The method of claim 1, wherein the device profile is freshly extracted at the time of the customer generated communication.

19. The method of claim 1, wherein the device profile is from a cache of a previous device profile.

20. The method of claim 1, further comprising packaging at least the extracted issue of the customer generated communication and the relevant device parameters for resolution by a CSR.

21. The method of claim 20, wherein the packaging is done only if the sentiment accuracy index is above a preset threshold.