Lessons Learned from Clippit

1. Lessons Learned from Clippit

2. Lessons Learned from Clippit • Many good ideas underlie Clippit • Developed by Microsoft’s Lumière team • So why does Clippit have problems? • Are there flaws in the underlying UI model? • Were the problems introduced during productization? • Either way, we’ve learned from Clippit about what not to do • We’ve had better success with our interface agents

3. The Lumière Model • Observe the user’s actions • Infer the user’s goal from user’s actions • Recognize problems in the user’s actions, assuming the user’s goal • Consider intervention to help the user • Evaluate: • Utility of offering help • Utility of not offering help

4. Problems with Applying Lumière Model to Office Tasks • Lumière’s model is a useful way to frame the problem • But: • User goals are difficult to infer in office tasks • Benefits of intervention are small (for some users) • Costs are high (for some users) • Penalty of mistakes is especially high • Lumière's model is still insufficient • Doesn’t adapt sufficiently if it makes a mistake

5. These Problems are Not Hard to Remedy • Focus on applications where the user’s goals are better known • E.g., education, or specialized applications • There are clever tricks for eliciting user goals • Focus on interventions with high utility • E.g., be careful about proactive interventions • Find out if the intervention was useful • Give user option of rejecting advice • Adaptation isn’t that hard • E.g., let user specify preferences • There is plenty more user data to exploit

6. Productization Problems • Lumière inference mechanism was not transferred • Goal inference particularly deficient • Office developers did not understand Lumière design methodology • They used Clippit as a catch-all error reporting mechanism • Utility assessments were not done • Or maybe it is not obvious how to perform them