How can this be legal? Facebook did not invent machine learning. How can they patent it?
The whole point is to avoid conflict. Even if it's not enforceable it might cost a few hundred thousand to million dollars to fight it.
It's a way of allowing R&D externalities to be captured.
The system doesn't work all that well, but that is the intent.
We very explicitly discussed that we would either aquire them or use the patents portfolio to shut them down.
Is there any kind of public comment period where the patent's claims to novelty could be disputed? I feel like a lot of the worst patents may be granted because no one else was watching.
You might even be able to find prior art someplace you don't expect, like some sociology academic paper.
Inventive steps are hard to come by if math isn't patentable and the fundamental principles of software creation aren't really changing.
I recently did not renew the patent and have allowed it go into the public domain.
If the claims pointed to specific features and specific processes that gave you more reliable determinations than prior-art methods, that'd probably be patentable. (Leaving aside whether it should be, it probably would be). But machine learning presents an odd situation. With machine learning, you don't iterate on an algorithm, improving it until it generates reliable results. You feed data to a machine learning algorithm, which then infers e.g. which features are particularly probative of the desired classifications. I would wonder whether even specific applications of machine learning would be patentable under Alice, since the computer rather than the patent applicant makes the inventive inferences.
This is analogous to patenting image classification, in the abstract. It's completely ridiculous.
Patent a very specific model for determining socioeconomic status? Sure. Patent the process in general? No fuckin way.
That's my point. If you're using machine learning, isn't the "specific model" just the output of the machine learning algorithm? And if so, even that may not be patentable, because the applicant didn't create the inventive parts, the computer did.
All the big credit agencies (Experian, Equifax, Transunion, etc) have products that cluster people into socioeconomic groups, with ridiculous names.
Examples from that brochure:
"Small Town Shallow Pockets"
"Full Pockets, Empty Nests"
This entire surveillance industry is an escalating crime against humanity.
1. Patents starts out as patent applications (what this document is). A patent application by itself doesn't give you any legal rights.
2. After waiting a year or more, your patent application gets reviewed by a USPTO examiner. The examiner either approves it and grants you a patent contingent on you paying a fee, or (more likely) tells you what's wrong with your patent application or cites prior art that already does what you claim and asks you to make changes.
3. All parts of the patent application except the claims section (which is the very last part) are for background and explanation only. They don't determine what you're patenting or whether someone is infringing your patent. These sections are supposed to be clear enough that someone with ordinary skill in whatever field your invention comes from could read these parts of your patent and reproduce your invention.
4. The claims section (which starts with "What is claimed is:") is very carefully worded to be as unambiguous as possible and carefully lay out exactly what the invention is and what its scope is. Any good patent agent or attorney will make these claims as broad as possible while still being patentable so that you get the broadest monopoly possible and have the greatest chance of successfully prosecuting an infringer.
5. The claims section is the only part that matters for determining infringement. The text in the rest of the patent and the figures show what the invention is so that someone can reproduce it, but they aren't used to determine the scope of your invention.
Relevant Twitter thread: https://twitter.com/WolfieChristl/status/960630738256367617
Copy-and-paste friendly version of the patent application, but without images: http://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=H...
Limited news coverage, e.g., http://expressnewsline.com/2018/02/05/facebook-to-develop-sy...
Full disclosure: repost of https://news.ycombinator.com/item?id=16315606 (from 70 days ago). Reposted by explicit request of the mods. May now be exceptionally topical in light of the Facebook revelations that have occurred since.
TV stations need to broadcast a certain number of hours of public good programming. Online ads should show a percentage of ads for public good.
An algorithm like this could do good since it may have better sensitivity and specificity than self reported data but would need to be used by non-FB people to make sure that it’s really for public good rather than has the appearance of public good.
Of course, you can pad it in weasel words and boring facts. Nobody will remember those parts of the message anyway.
a machinist can just say "all the steps to produce that device can be done in a lathe. should we all start to patent any part we turn in a lathe now?"
and while I agree that all patents are bad, the answer still is Yes.
On one side I'm not against using/collecting data for the purpose of better content (=better engagement) or more targeted ads (still better than looking at ads that have nothing to do with me). Though I am for it to be anonymized (ie separating the personal data from the generic things like names from items bought) however that is pretty much useless from the moment you enter your name anywhere as they can basically fingerprint the data and match it up to you anyways.
On the flipside of this, I don't like the direction this all is going - sure I like tailored content, but at what point do we reach a threshold where people are just shoved content down their throat since they cant find stuff outside these mechanisms to broaden their view and interests.
More related to this topic, as long as the data is there, someone will analyze it, and socioeconomic targeting has been done for a while already (you don't market 8K curved suround sound TVs to 90 year olds) this is just a step up. However I'm a bit scared of when we reach the tipping point on content in terms of targeting: what we might want to see vs what it might want us to see.
When I read a printed tech magazine, I get the ad targeting that may be interesting for me, just because of the content I'm reading. There is no other information flowing back to online advertisers, or, to be more realistic, person tracking companies, which is what they really are. The content should be enough to determine the ad category, which would suite your desire for more engaging ads.
There's no reason why online publications can't do that as well. If I read a blog post about food, show me an ad about the coolest frying pan on earth and also one about the one with the best price to performance to ratio.
Show me ads for a Porsche even if I can't afford it, I may in a couple of years, and in the meantime the ads may have told my subconsciousness that this Porsche should be my goal.
If I'm researching about food, ads about where to buy ingredients or kitchen tools will be integrated into my thinking lines without problems, and that's fine. I might even click them if they seem legit and honest and fit to what I'm trying to do.
If you start showing me, say, ads about the newest movie or holiday destinations, if I allow them, they will derail my thoughts into different areas. I hate that, and that is a large part of why I block ads too.
edit: There is even a place where I like advertisements. In the work area, please do inform me what new developments your company has made that can help me with my developments, and do inform me what your company sells, roughly, so I can get back to you when I do need that UV CCD.
If I am in the mood of car prOn - show me porsche or stuff. If I ready about python - show me current books on the topic. Or Udemy courses. Or what not.
If I currently read about how to get rid of pimples - show me beauty products.
But don't try to influence me into buying the latest camera gear, when I am interaction with my animal protection buddies over facebook.
Open street map is a great example, along with wikipedia and numerous open source projects which are either backed by some foundations or collect donations and membership fees. The broader scope here is that while this is a better approach for the world, as long as there is the possibility to earn a buck on some service, there will be a comercial version of it. Guess capitalism just works that way.
2. The title barely matters at all. The claims define what they’re trying to patent.
-Why do they feel the need to patent this? The algorithm look trivial.
-How is this even acceptable? Making decisions based on such "predictors" should be (and often is) illegal.
>Oh, you're physically disabled? I predict this will cause you emotional distress and you will perform badly in my college. Sorry, you're rejected.
>Oh, you're black? Sorry, my predictors predict you're a criminal! Don't worry, the police will arrive soon. You are free to use other banking institutions.
>Oh, you're poor? Sorry sir, we don't sell cars to poor people, because our predictors say they typically use our free repair services! That costs us money, please buy a used car from craigslist or something.
I can't comprehend how they could publish and acknowledge using this kind of profiling. And that's without getting into politics.
It's like FB is trying to push us in the direction of China and they don't care who knows it.
This is a tool to assist in automated censorship: oh, this person claiming to be black isn't clicking like a black man, he must be a troll and we must stop fake news!
You do realize how easy that is to compromise, right?
Does your profile picture contain anime/cat/dog features on your face?
If the anwer to both is no, you're clear.
I would think they are classifying each individual into one of many groups, each group being an arbitrarily delineated social class. If that's true, than Mr. Buffet would indeed have the potential to be misclassified, because his behaviours ("features") are atypical of people as rich as him.
It is not a bad thing because in the end yes, will be discriminated at the counter by amount of money they have. Of course no one should get stuff for free.
It is bad in general, because people will be closed by their socioeconomic status and will have less possibilities to see better things. It can reduce economic mobility by some business not wanting to deal with people who are classified by automatic classifiers, even though they might have more money, be smarter, than what their FB history is telling.
Luxury cars aren't the concern. It's tumbrels we're worried about.
Certainly, you could classify these groups in a closed process and operate to exploit their individual wants and desires, which is something existing models of capitalist industry engage in already. However, what if Facebook were to do this all transparently? For example, being sold something with the explicit revelation that your economic class is the primary consumer?
There's nothing wrong or useless about advertising based on W-2 income level; but that won't have greater sociological effects on economic class or whatever.
Of course via the magic of widely shared credit reports, everyone already knows actual class breakdowns based on class differences in personal balance sheets and other measures.
The advice from the IP lawyers at every company I’ve worked for (Microsoft, Dropbox, Amazon, Google) has been to never read patents or patent applications.
Yes. You are subject to triple damages if you read the patent, so it's foolish to read patents unless you have to. It's impossible to read all patents, and patents in almost all cases are useless, so "I didn't read the patent" is the normal case for software developers.
Patents make no sense for software, and should be abolished for software.
When people have asked “how would they even know?”, there are usually two answers: 1) don’t underestimate what can come out in discovery, and 2) no lawyer is going to advise perjuring yourself
I shouldn't be surprised that a machine learning neural model is classified on the merit of users who are the nosiest classifiers :).