The problem they want to solve is predicting whether an email in your inbox needs to be replied to or not. This is very relevant research as it would allow users to quickly scan their email for items they actually need to act on. The result could just be a simple "needs reply" indicator in the list of incoming items.
How do they do this? The authors used a number of attributes to classify emails, such as whether the user frequently replies to the author, whether the email contains any question marks, and combinations of words appearing in the text.
The results aren't quite there yet. For the best test corpus, they achieve are 77% recall - which means that they find 77% of the emails that need replying. However, they come in at 76% precision, which means that 24% of emails they mark as "needs reply" don’t actually need replying.
Thus, reply prediction remains exciting. I'm hoping that they come up with a better classifier, and that someone then turns this into an industrial-grade email application.
 Mark Dredze, Tova Brooks, Josh Carroll, Joshua Magarick, John Blitzer, Fernando Pereira: Intelligent Email: Reply and Attachment Prediction, Intelligent User Interfaces 2008, Spain. [PDF]