Based on a brief read, it looks like the "breakthrough" is discarding the two measurements in a point group that are the furthest outliers from the mean. That can't be it, can it? Because that's an afternoon's work for an intern, not a breakthrough.
There’s nothing in this “breakthrough” that isn’t already being done anyway, and the comparison stats are completely made up. 13.8cm resolution in current scanners? Have they even used a 3D scanner made after the millennium turned?
There's a bunch of problems that, together, make the whole greater (i.e. worse) than the sum of its parts:
- In many industries, but especially in everything involving computers, patent protection is too long - so long that the invention is likely to become useless before the patent expires. Technologies develop in lock step, riff of each other. If you lock down a crucial invention, you're also locking out a whole branch of R&D that may forever remain not explored[0].
- Patents are supposed to contain enough details in them that experts in a relevant field should be able to recreate the invention from the patent text. This is not what happens, we (the public) are being cheated here. The interesting patents are more like invention hashes - they contain just enough data to identify when someone is in the vicinity of invention, but not enough data to reproduce the invention itself.
- Patents are routinely awarded for things that do not have an inventive step (i.e. they aren't non-obvious). I'm willing to give some benefit of the doubt here, because ideas tend to look more obvious in hindsight, but I think we've all seen or heard examples of ridiculously obvious patents.
- Patents take too long to grant. The process between inventing something and getting it patented can take 2+ years - in that time, the patented invention may be well on its way to becoming obsolete (see point 1). This encourages shotgun patenting.
- Patents do not come with an obligation to actually productize an invention. Companies can, and do, patent inventions they have no interest in productizing, with the sole purpose of preventing competitors from exploring some technology or product space. This is as pure slowdown of progress as you can get.
I could go on. The point is, the ideas behind a patent system aren't bad - but the implementation decayed to the point of being just another rent seeking system parasitizing society.
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[0] - It's something plainly visible in software, but perhaps software is a special case, due to existence of FLOSS. Maybe, if all software was proprietary, patents would only slow down progress. With FLOSS, nobody is waiting for patents to expire - eventually, someone will find a patent unencumbered alternative, release it for free, and the world ends up adopting that. And then ends up stuck with it, due to sheer momentum. So if that patent locked out the optimal solution to a problem, it's possible we'll never use it, because too much stuff uses the suboptimal, FLOSS version.
Could the case be made that certain FLOSS licenses (i.e GPL3) hold up progress by limiting how people can build on said software or deliver it to their customers / users? Or does the elasticsearch relicense in some way resemble a self-issued "patent" where they only enforce their rights against certain entities? (much like a patent holder has the task of enforcing their own IP and can pick and choose) And perhaps forcing you into choosing between buying their product or using a suboptimal free version?
ofcourse programmers will have none of this. They'll code up a fancy function called computeMean(x) in java or python or what have you, with proper types & unit tests & suchlike, and declare "the mean is 5.166", and call it a day. No amount of persuasion will convince them that the mean is unknown, because the population's distribution is unknown, so population mean can at-best be hypothesized to some constant c, and then you can run a hypothesis test to see if you can disprove said hypothesis at some alpha level. ofcourse with a sample size of n=6 your power will take a beating and type-2 errors will spike, but who cares about these esoterica when our famed intern has coded up the mean function itself in one afternoon!
Snark aside, every summary statistic has widely varying properties. Sample means will asymptotically converge to the population ("true") mean as sample size grows. Sample means are an unbiased estimator of the population mean, so they have the mvue i.e. minimum variance, the variance decreases linearly with sample size. But that doesn't mean one should ditch biased estimators. Choosing a clearly biased estimator such as a trimmed mean instead of the sample mean, which is what they've done here, can definitely result in a huge breakthrough (at the expense of higher variance but that's ok).
We have laser scanners and Faro arms at work, and in the end it’s a person manually entering the geometry into Solidworks as the point cloud is useless.