Activity-Centric Email: A Machine Learning Approach

Our use of ordinary desktop applications (such as email, Web, calendars) is often a manifestation of the activities with which we are engaged (Moran, Cozzi, & Farrell 2005). Planning a conference trip involves sending travel expense forms, and visits to airline and hotel sites. Renovating a kitchen involves sketches, product specifications, emails with the architect and spreadsheets for tracking expenses. Every enterprise has (often implicit) processes for managing customer queries, requesting maintenance, hiring a new employee, purchasing equipment, and so on. Unfortunately, ordinary desktop applications do not know anything about these activities. Within an enterprise, many activities have been formalized into business workflows such as hiring or ordering equipment. However, the way people interact with these workflows is often through email and desktop applications. If these applications are not aware of the activity context, people bear the burden of organizing their information into activities, typically using crude techniques such as manual search, file directories, and email folders/threads. Email has emerged as the primary tool for people to communicate about their work and manage activities. Motivated by the importance of email in conducting activities, we have recently developed several machine learning algorithms for automatically discovering and tracking activities in email. We observe that activities come in many forms, from structured workflows to informal person-to-person communication. In this paper, we summarize our efforts to provide automated assistance with two types of activities: rigid structured activities, and unstructured conversational activities. We first discuss highly structured activities such as ecommerce transactions. A consumer purchasing an item may receive email messages confirming the order, warning of a delay and then a shipment notification. Existing email clients do not understand this structure, so users must manage their transactions by sifting through lists of messages. As a step to providing high-level support for structured activities, we consider the problem of automatically learning an activity’s structure. This structure could be used to support features such as notifying the user if an item failed to