I mean, you could just look at the papers the Google researchers themselves published, and just consider the sections on prior work and the bibliography.
Claims, even about a specific software system embodying an RNN device for a specific audio sample generation program, would obviously include research effort by Google staff to understand prior IP, much of which their neural network software mechanisms are inherently and directly based on in a way that most experts would view as clearly dependent on prior art.
What frustrates me is seeing so many people in the thread trying to defend Google and rationalize these applications as defensive patents, assuming the best of Google, and gainsaying people who are worried about the broad effects of the patents by obsessing over the mostly irrelevant fact that titles & abstracts don’t determine the content of an award.
There are a few other aspects to the problem that are also very serious.
- the popular belief that Google has some patent war chest in neural network devices can create a hugely chilling effect on other research, especially by other corporate research teams, borne purely out of beauracratic risk-aversion. Compliance and legal teams can and often do put the kibash on whole research programs just out of unnecessary risk aversion. In computer vision, just consider the effects of the patents granted for SIFT and SURF, which are many orders of magnitude smaller in scope than even the most graciously assumed narrowing of these Google patent applications. And SIFT and SURF had big chilling effects on various lines of research back when manual scale-invariant feature construction was state of the art. I even worked for a company that required my team to abandon and remove all use of OpenCV period once SIFT was added, just because we could, in theory, be sued into proving that we weren’t using SIFT through OpenCV. Companies will take these bonkers extreme stances. It’s bad for innovation overall. The corresponding effects for these Google applications would be huge: no TensorFlow, no Keras, unless it’s used via vendor lock-in with cloud providers that either are Google or pay Google — to be clear even if the ultimately awarded patents are much narrower in scope.
- The other huge thing is that Google puts a burden on civic infrastructure here. They are requiring the PTO to analyze the applications, and the public is trusting that the PTO has adequate expertise, time and budget to get the answer right. But Google knows that by making the claims overly broad, when they are inevitably walked back to narrower claims, the likelihood for PTO to get this completely right is super low, and with lobbying any of the round of error is sure to go in Google’s favor.
So even if you don’t see it as a bad faith action from the specific patent applications, it’s clearly bad faith in terms of inducing secondary effects of extreme legal risk aversion and basically wasting taxpayer resources to play this stupid cat and mouse game of how to narrow down the claims.
When I ask myself what is the long term self-interest here, it seems clear and in lock-step with other Google strategies: to try to own all the pipes by which modern machine learning can be commoditized, and to try to create a future where machine learning is only consumed as a service from the hybrid cloud-and-consulting platforms of a few entrenched incumbent corporations.
Defensive patents made sense until it turned out that most offensive action was from patent trolls who are not trying to create a product so they don't need to defend anything.