it sure is with our current USPTO... so long as you have the money to patent your flatulences!

...so I might be wrong. But even if they did have the foresight in 2003 to describe the exact technology of Podcasting (as executed in iTunes) before there was prior art for the idea, doesn't existing patented technology utilizing their structure (i.e. iTunes) nullify their claim? Or does the fact that they applied BEFORE there was prior art give them a "first-come-first-serve" access to the patent?

Either way, I assume we've got ourselves a cute little patent-troll baby developing here.

They should go after all those horrible, illegal grade school teachers who read books out loud to their classes! Don't they know that's an unlicensed derivative work! Just goes to show how crime-ridden our schools are these days.

Hmmm.. I've got 13 peer reviewed Neuroscience publications under my name, 1 book chapter in press, 2 articles in submission (one to Nature Neuroscience and one to Neuron), and several published and unpublished custom-written toolboxes for analyzing brain imaging data. I guess my Ph.D from UC Berkeley also ads to my geek-credit.

That said, Critchon was trained as a clinician through and through, not scientist. From what I can find, most of his non-fiction work in peer-reviewed journals is a review or meta-analysis. So he might have dabbled in programming and such (to give him some geek cred), I think he knew only the gist of the science he used in his popular works. He was by no means an expert.

Andromeda Strain wasn't written by a geek! It was written by a cheeky medical doctor who (like all clinicians) thinks he knows science, but really just knows how to read abstracts. No way a true geek would create Jeff Goldblum's horrifically bad mathematician character in Jurassic Park!

Both methods require an extensive training set. The only difference (that I can see) between the two is that the Japanese model assumes the representational structure of the underlying cortical areas being recorded (i.e., the size of the receptive fields), whereas the Berkeley group trains a simple linear classifier. Expect a few more of these classifier methods to come out in the next couple of years.

That's true, but advances in Optical Imaging might overcome the current imaging limitations. But sci-fi aside, at least getting the decoding algorithms perfected will answer a ton of basic science questions about network dynamics in primary sensory and motor areas.

A Berkeley group has already reported this in Nature using similar methods: http://www.nature.com/nature/journal/v452/n7185/abs/nature06713.html)