Mobile context-aware personal messaging assistant

Abstract. A previous study shows that busy professionals receive in excess of 50 emails per day of which approximately 23 % require immediate attention, 13 % require attention later and 64 % are unimportant and typically ignored. The flood of emails impact mobile users even more heavily. Flooded inboxes cause busy professionals to spend considerable amounts of time searching for important messages, and there has been much research into automating the process using email content for classification; but we find email priority depends also on user context. In this paper we describe the Personal Messaging Assistant (PMA), an advanced rule-based email management system which considers user context and email content. Context information is gathered from various sources including mobile phones, indoor and outdoor locationing systems, and calendars. PMA uses separate scales of importance and urgency to prioritize emails and to decide on an appropriate action, such as SMS to user, defer to later, file or forward. Initial results yield 96 % recall and 88 % precision in importance classification of emails; 95 % recall and 92 % precision in urgency classification of emails. PMA shows a 30X reduction in false-negative rates over existing systems. A key contribution of our work has been to leverage an extensible set of context information, gathered in a mobile environment, for the classification of emails and customizable decision making