Simone Stumpf, Xinlong Bao, Anton Dragunov, Thomas G. Dietterich, Jon Herlocker, Kevin Johnsrude, Lida Li, and JianQiang Shen
Knowledge workers spend the majority of their working hours processing and manipulating information. These users face continual costs as they switch between tasks to retrieve and create information. The TaskTracer project at Oregon State University is investigating the possibilities of a desktop software system that will record in detail how knowledge workers complete tasks, and intelligently leverage that information to increase efficiency and productivity. Our approach combines human-computer interaction and machine learning to assign each observed action (opening a file, saving a file, sending an email, cutting and pasting information, etc.) to a task for which it is likely being performed. In this paper we report on ways we have applied machine learning in this environment and lessons learned so far.