Interface and method of designing an interface

• US 7,139,369 B2
• Filed: 08/29/2002
• Issued: 11/21/2006
• Est. Priority Date: 03/21/2000
• Status: Expired due to Fees

• Current Assignee: Sbc Tri
• Original Assignee: SBC Properties, L.P. (AT&T, Inc.)
• Inventors: Pasquale, Theodore B., Martin, John M., Bushey, Robert R.
• Primary Examiner(s): TSANG, FAN S
• Application Number:
  US10/230,728
• Publication Number:
  US 20030156133A1
• Time in Patent Office: 1,545 Days
• Field of Search: 379/67.1, 379/88.16, 379/88.18, 379/88.22, 379/88.23, 379/88.24, 379/201.01, 379/93.17, 379/265.01, 379/266.01, 705/1, 705/8, 705/9, 705/261
• US Class Current: 379/88.16
• CPC Class Codes: G06F 8/20 Software design
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: H04M 15/00 Arrangements for metering, time-control or time indication ; Metering, charging or billing arrangements for voice wireline or wireless communications, e.g. VoIP

Abstract

A method of designing an interface system that allows users to map the representation of their task directly to the interface. There are three major phases to the Customer-Centric Approach to Interface Design (C-CAID). End-users'"'"' tasks are categorized to determine the frequency of reasons or tasks of why users interact with a particular system. These reasons and their relative frequencies are used to design interface options that emphasize the user'"'"'s task categories. Finally, the customer-centric interface designs are evaluated and compared with existing system interfaces using usability tests with actual users performing the tasks. The results from usability tests are used to pinpoint the task-option combinations that do not work well and which should be revised. Benefits of this customer-centric design are improved systems performance and increased user satisfaction.

Claims

1. A method for evaluating performance of a user interface design, comprising:

developing a prediction model for the user interface design based upon expected user task volume and task frequency by determining an estimated percentage of correctly routed calls and multiplying the estimated percentage by a task frequency adjusted for call volume across call centers to obtain a predicted routing percentage score, the task frequency adjusted for call volume comprising a sum of a relative call volume for each call center multiplied by a task frequency at each call center; and

evaluating potential performance of the user interface design by applying the prediction model.

2. The method of claim 1, further comprising comparing a prediction model of a newly designed interface with a prediction model of a current interface to estimate performance gains resulting from the newly designed interface.

3. The method of claim 1, in which the developing the prediction model further comprises summing predicted routing percentage scores across all task categories to obtain an overall predicted routing percentage for the interface design.

4. The method of claim 1, in which the determining the estimated percentage further comprises attempting to route a call based upon historical data, the attempt including routing while using the user interface design.

5. A computer readable medium storing a computer program for evaluating performance of a user interface design, comprising:

a prediction model code segment that develops a prediction model for the user interface design based upon expected user task volume and task frequency by determining an estimated percentage of correctly routed calls and multiplying the estimated percentage by a task frequency adjusted for call volume across call centers to obtain a predicted routing percentage score, and summing predicted routing percentage scores across all task categories to obtain an overall predicted routing percentage for the user interface design, and

an evaluation code segment that evaluates potential performance of the user interface design by applying the prediction model.

6. The medium of claim 5, further comprising a performance analysis code segment that compares a prediction model of a newly designed interface with a prediction model of a current interface to estimate performance gains resulting from the newly designed interface.

7. The medium of claim 5, in which the prediction model code segment estimates the percentage by attempting to route a call based upon historical data, the attempt including routing while using the user interface design.

8. The medium of claim 5, in which the task frequency adjusted for call volume comprises a sum of a relative call volume for each call center multiplied by a task frequency at each call center.

9. A method for modeling performance of a user interface design, comprising:

developing a prediction model for the user interface design based upon expected user task volume and task frequency by determining an estimated percentage of correctly routed calls and multiplying the percentage by the task frequency adjusted for call volume across call centers to obtain a predicted routing percentage score, and summing predicted routing percentage scores across all task categories to obtain an overall predicted routing percentage for the interface design.

10. The method of claim 9, further comprising comparing a prediction model of a newly designed interface with a prediction model of a current interface to estimate performance gains resulting from the newly designed interface.

11. The method of claim 9, in which the determining the estimated percentage further comprises attempting to route a call based upon historical data, the attempt including routing while using the user interface design.

12. The method of claim 9, in which the task frequency adjusted for call volume comprises a sum of a relative call volume for each call center multiplied by a task frequency at each call center.

13. A computer readable medium storing a computer program for modeling performance of a user interface design, comprising:

a prediction model code segment that develops a prediction model for the user interface design based upon expected user task volume and task frequency by determining an estimated percentage of correctly routed calls and multiplying the percentage by a task frequency adjusted for call volume across call centers to obtain a predicted routing percentage score,

wherein the task frequency adjusted for call volume comprises a sum of a relative call volume for each call center multiplied by a task frequency at each call center.

14. The medium of claim 13, further comprising a performance analysis code segment that compares a prediction model of a newly designed interface with a prediction model of a current interface to estimate performance gains resulting from the newly designed interface.

15. The medium of claim 13, in which the prediction model code segment further comprises summing predicted routing percentage scores across all task categories to obtain an overall predicted routing percentage for the user interface design.

16. The medium of claim 13, in which the prediction model code segment estimates the percentage by attempting to route a call based upon historical data, the attempt including routing while using the user interface design.