somewhat active on Ask Patents. In fact, I've submitted an answer that is pretty sure to kill at least one Google patent application, and possibly another from Uniloc. A few comments on this article: 1) This is a very unusual case; most answers (and almost all questions) from "lay engineers" completely misunderstand the scope of the patent, since they don't even know what claims are. And even if they do, they are ver…
Re (2): In 1924, legendary federal judge Learned Hand [0] remarked that "the antlike persistency [sic] of [patent] solicitors has overcome, and I suppose will continue to overcome, the patience of examiners, and there is apparently always but one outcome." [0] http://en.wikipedia.org/wiki/Learned_Hand [1] Lyon v. Boh, 1 F.2d 48, 50 (S.D.N.Y.1924), copy available at http://scholar.google.com/scholar_case?case=96965975…
I think this is one reason that most examiners (at least IME) have their default mindset to "Reject! Reject! Reject!" Also, this is why something like Ask Patents is invaluable to even the odds.
I don't agree with the quantitative approach, but I can't help think that technology can help. Google has already (in my opinion) helped the PTO greatly narrow claims the past decade; similar technology can help even more.
I have some background in NLP. And I know it's surprisingly effective when it comes to domains with specific jargon (cf. Watson and medical language). I've lurked long enough to know some here (such as VanL) have already experimented in this area. Personally, I have toyed with the idea of constructing parse trees out of multiple technical texts and claims, "normalizing" them using ontologies, and trying to find matches (i.e. prior art) using various tree-matching algorithms. I have a feeling it would be very effective. (Maybe Google already does this!)
But that does not address the problem of identifying patents that are quantitatively invalid but qualitatively valuable. To me, that is the more important long-term problem.