Automatic agent training system

• US 8,068,598 B1
• Filed: 04/02/2008
• Issued: 11/29/2011
• Est. Priority Date: 04/02/2008
• Status: Active Grant

• Current Assignee: Serenova LLC (Lifesize Communications Incorporated)
• Original Assignee: Liveops Incorporated
• Inventors: Russi, Dario, Chen, Qian
• Primary Examiner(s): Smith; Creighton
• Application Number:
  US12/080,289
• Time in Patent Office: 1,336 Days
• Field of Search: 379/265.07, 379/265.03, 379/265.06, 379/265.02
• US Class Current: 379/265.07
• CPC Class Codes: H04M 2201/39 using speech synthesis
• CPC Class Codes: H04M 2201/40 using speech recognition
• CPC Class Codes: H04M 2203/403 Agent or workforce training
• CPC Class Codes: H04M 3/5175 Call or contact centers supervision arrangements
• CPC Class Codes: H04M 3/5233 Operator skill based call distribution

Abstract

An exemplary method for training call center agents over a communications network using automatically selected training scenarios comprises the steps of obtaining confirmations of availability of a plurality of call center agents, determining a proctor based on proctor attributes stored in a database, selecting an agent from the plurality of agents, based on agent attributes stored in the database, to be trained by the proctor, automatically determining a training scenario based on the selected agent'"'"'s attributes, and enabling the proctor and the agent to engage in the training scenario.

Claims

1. A method for training call center agents over a communications network using automatically selected training scenarios, comprising:

(a) obtaining confirmations of availability of a plurality of call center agents;

(b) determining a proctor based on proctor attributes stored in a database;

(c) selecting an agent from the plurality of agents, based on agent attributes stored in the database, to be trained by the proctor;

(d) automatically determining a training scenario based on the selected agent'"'"'s attributes; and

(e) enabling the proctor and the agent to engage in the training scenario.

2. The method of claim 1, wherein said proctor is selected based on said proctor'"'"'s past performance.

3. The method of claim 1, wherein said proctor is selected from said plurality of call center agents.

4. The method of claim 1, wherein said proctor is an interactive voice response unit.

5. The method of claim 1, wherein said agent attributes include past call handling performance.

6. The method of claim 1, wherein metrics for selecting an agent based on agent attributes are customizable.

7. The method of claim 1, wherein said automatically determining includes dynamically generating a new training scenario.

8. The method of claim 1, wherein said automatically determining includes dynamically selecting a pre-defined training scenario.

9. The method of claim 1, wherein said enabling includes instructing an automatic call director to send appropriate software scripts to the proctor and the agent.

10. A system for training call center agents over a communications network using automatically selected training scenarios, comprising:

a web server interface configured to obtain confirmations of availability of a plurality of call center agents;

a proctor selector configured to determine a proctor based on proctor attributes stored in a database;

an agent selector configured to select an agent from the plurality of agents, based on agent attributes stored in the database, to be trained by the proctor;

a training scenario selector configured to automatically determine a training scenario based on the selected agent'"'"'s attributes; and

a automatic call director interface configured to enable the proctor and the agent to engage in the training scenario.

11. The system of claim 10, wherein said proctor is selected based on said proctor'"'"'s past performance.

12. The system of claim 10, wherein said proctor is selected from said plurality of call center agents.

13. The system of claim 10, wherein said proctor is an interactive voice response unit.

14. The system of claim 10, wherein said agent attributes include past call handling performance.

15. The system of claim 10, wherein metrics for selecting an agent based on agent attributes are customizable.

16. The system of claim 10, wherein said training scenario selector is further configured to dynamically generate a new training scenario.

17. The system of claim 10, wherein said training scenario selector is further configured to dynamically select a pre-defined training scenario.

18. The system of claim 10, wherein said automatic call director interface is further configured to instruct an automatic call director to send appropriate software scripts to the proctor and the agent.

19. A non-transitory computer-readable medium for training call center agents over a communications network using automatically selected training scenarios, comprising logic instructions that, if executed:

(a) obtain confirmations of availability of a plurality of call center agents;

(b) determine a proctor based on proctor attributes stored in a database;

(c) select an agent from the plurality of agents, based on agent attributes stored in the database, to be trained by the proctor;

(d) automatically determine a training scenario based on the selected agent'"'"'s attributes; and

(e) enable the proctor and the agent to engage in the training scenario.

20. The computer-readable medium of claim 19, wherein said proctor is selected based on said proctor'"'"'s past performance.

21. The computer-readable medium of claim 19, wherein said proctor is selected from said plurality of call center agents.

22. The computer-readable medium of claim 19, wherein said proctor is an interactive voice response unit.

23. The computer-readable medium of claim 19, wherein said agent attributes include past call handling performance.

24. The computer-readable medium of claim 19, wherein metrics for selecting an agent based on agent attributes are customizable.

25. The computer-readable medium of claim 19, wherein said logic instructions for automatically determine include logic instructions that, if executed, dynamically generate a new training scenario.

26. The computer-readable medium of claim 19, wherein said logic instructions for automatically determining include logic instructions that, if executed, dynamically select a pre-defined training scenario.

27. The computer-readable medium of claim 19, wherein said logic instructions for enable includes logic instructions that, if executed, instruct an automatic call director to send appropriate software scripts to the proctor and the agent.