Our central claim is that user interactions with productivity applications (e.g., word processors, Web
browsers, etc.) provide rich contextual information that can be leveraged to support just-in-time access to task-relevant information. As evidence for our claim, we present Watson, a system which gathers
contextual information in the form of the text of the document the user is manipulating, in order to
proactively retrieve documents from distributed information repositories related to task at hand, as well as process explicit requests in the context of this task. We close by describing the results of several
experiments with Watson, which show it consistently provides useful information to its users. The
experiments also suggest that, contrary to the assumptions of many system designers, similar documents are not necessarily useful documents in the context of a particular task.

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