Paul Horn, the director of IBM Research: It continues to be a big thing for IBM and for IBM Research, but it's not just WebFountain. The basic issues are, really, natural language understanding in general. What WebFountain was able to do, which made it powerful, was it would go in and would scan text documents on the Web and it would understand enough about what people were saying that you could query it about what people were saying. You could imagine that there's a lot of countries, including our own, that would care a lot about scanning documents and even open documents and crawling through them to see what people were saying. A lot of the early work on WebFountain was done in three languages--English, Arabic and Chinese--and you can guess who might sponsor that work.
WebFountain is an example of a natural language technology that allows you to essentially analyze from an intelligence point of view what people are saying, but the important point is that this is just a small piece of many, many problems that companies have and where you want to take advantage of natural language understanding, such as translating spoken English to Russian and back again.
We talked about call centers. Natural language understanding can be incredibly powerful, even if you've got a call center operator, just by monitoring the calls and trying to understand what the issues are. There's enormous amounts of natural language and analytic issues in how companies interact with their customers. WebFountain was a specific application of natural language and search technology, but it's just one.