Apple's traditional way to solve a problem started with the brass: Steve Jobs wanted his music library in his pocket, and he bet that others did, too. Following that hunch, we got the iPod.

Google's way to solve a problem needn't rely on hunches, as the company has a massive database of what people are actually Googling. They know precisely what people are searching for in their lives. So when a Google engineer in Boston noticed that a lot of local queries were related to solar panels, he began analyzing the nature of the questions.

Turns out that the process of getting solar panels onto the roof of your house is a royal pain in the ass, beginning with the research. People want to know the basic things, like how much money they'll actually save and what the up-front cost of the panels is. But these answers are different for everyone depending on where your house is located, which way it's facing, what your local utility costs are, and how friendly your local government is to alternative energy.

Thus Google formed Project Sunroof, which cleverly harnesses the data they've already amassed for Google Maps, Google Earth and plain ol' Google. Residents of the three pilot areas for Project Sunroof—Boston, San Francisco and Fresno, California—can type in their address, and Google's data reveals exactly where your house is and how much sun coverage it gets. Local obstructions to light are seen and calculated. The project can ballpark how many panels your specific roof will need, and generate estimates to send to local installers. Local solar incentives are added up, and end users can thus get the magic numbers of "How much will it cost" and "how much will it save."

While it's too early to tell if the project will be successful, it does point the way towards a potential future approach to product development. Remember the woman who searched online—undoubtedly using Google—to find a device that could help her blind dog? Unable to find an existing product, she invented her own. Imagine if Google had a team of industrial designers waiting around to see what people were searching for, and once the queries hit critical mass—X-thousand number of people—they then designed a physical product that fit the description.