!20180712  Economist letter: AI threat to drug development

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Is university innovation hurt by giving university most rights in patents?   An interesting article in the lastest issue of the American Economic Review:

University Innovation and the Professor's Privilege
Hans Hvide and Benjamin Jones
American Economic Review, July 2018, 1860-98

National policies take varied approaches to encouraging university-based innovation. This paper studies a natural experiment: the end of the "professor's privilege" in Norway, where university researchers previously enjoyed full rights to their innovations. Upon the reform, Norway moved toward the typical US model, where the university holds majority rights. Using comprehensive data on Norwegian workers, firms, and patents, we find a 50 percent decline in both entrepreneurship and patenting rates by university researchers after the reform. Quality measures for university start-ups and patents also decline. Applications to literature on university technology transfer, innovation incentives, and taxes and entrepreneurship are considered.

Isaac Thuret: watch inventor denied intellectual credit.   A nice historical article on inventorship. From the Abstract: "Despite Isaac Thuret's intellectual contributions [to the invention of the spring-driven watch, along with Christiaan Huygens in the mid-1600s], but in the absence of robust intellectual property rights framework, Hyugens insisted on claiming the invention's sole ownership." Nice article at: https://arxiv.org/ftp/arxiv/papers/1807/1807.03489.pdf

Mises Institute: the Supreme Court is much too powerful.   Mises Institute has issued an article, "The Supreme Court is Much Too Powerful" ... "The Court is just a group of nine politicians in fancy robes". Certainly the Supreme Court is disastrously powerful with regards to patent law: its refusal to define "abstract" and have 101 caselaw be constitutionally correct re Due Process; its refusal to admit that Markman hearings also violate Due Process in being an admission that claims that need construction fail to satisfy 35 USC 112 at issuance; its refusal to rule that 35 USC 103 is unconstitutionally vague due to the undefined terms "obvious", "skilled", "art", and more. Time to remove the federal courts from patent activities - they hate progress and inventors too much. Article at: mises.org/wire/supreme-court-much-too-powerful.

Can somebody please say what Gibbsian statistical mechanics says?   One reason why patent caselaw is so unconstitutional is that both the caselaw and statues are multiply-defective with regards to semantics - a manure-pile of undefined terms such as: abstract, obvious, enabled, skilled, duly, etc. And this attack on progress continues because of the general contempt for semantic clarity, meaning, understanding, standards in patent language. Whereas in the world of physics, there are few things more fun to debate that meaning in the fundamentals of physics, starting with "What is a wavefunction?", "Do we need / what is / ... time?". A new, entertainingly titled paper is out: "Can somebody please say what Gibbsian statistical mechanics says? ... Yet a closer look at GSM reveals that it is unclear what the theory actually says and how it bears on experimental practice." God, that describes most of 101, 103 and 112 caselaw - it is unclear what guidance the courts are trying to say and how it bears on inventing and progress. The paper provides what it argues is a coherent interpretation of GSM. How many fckin centuries are we going to have to wait for a coherent interpretation of 101/103/112? Paper at: https://arxiv.org/pdf/1807.04218.pdf. Ironically, one solution they propose is to interpret GSM using 'qualified probabilism" under the right thermodynamic conditions. Qualified probabilism is a powerful technique in the hands of physicists ... but in the hands of blacked-robed clowns, the phrase is completely abused as the basis for 103 caselaw (according to the intellectual nonsense otherwise known as KSR, an invention is obvious if it is a qualified probable outcome).

---    Economist letter: AI threat to drug development

      Off and on for many years now, I have been talking (mostly to myself) of how the growing power of AI drug discovery tools combined with the semantic nonsense and judicial malpractice of KSR is a growing threat to the patentability of drugs. But few are talking about it, because few in the courts, PTO and patent bar read scientific literature to realize this growing threat (similar to how few in the courts read software engineering journals to learn anything about 'abstract' software).

So it was a bit reassuring to my self-discussion of this issue to see the following letter to the editor of the Economist:

Artificial intelligence could help to identify more-effective candidate drugs. However, this dream held by patients, clinicians, physicians and the public-health system could become a nightmare for the pharmaceutical industry.

The development of a clinically active ingredient generally costs hundreds of millions of euros, so the compound needs to be protected by a worldwide patent for the process to be economically feasible. A patent is generated only when a compound's application can be classified as both 'new' and 'invented'. A highly effective compound thrown up by an AI algorithm could indeed be new.

Whether it is 'invented' [Note: the author is from Germany, and is referring to obviousness - 'inventive step' is their undefined phrase ] is debatable. This is because the inventor might be considered as either the algorithm (so not a person) or its programmer. It could be argued that if there is a connection between the program and the compound's structure, then it is predictable by experts and so no longer inventive. Or, if the programmer can't explain how the AI algorithm found the structure, then he or she didn't invent anything.

Assigning and agreeing on intellectual property rights will be even more complex when different parts of the process behind important discoveries involve multiple contributors.

Patent law would presumably have to be adapted, for example by acknowledging the development work through a temporary ban on imitation.

Lutz Heuer
Dormagen, Germany
Lutz Heuer is a chemist with 20 patents.

Earlier in the year, the mainstream media, in this case Newsweek, had an article on AI breakthroughs in drug discovery. Excerpts below. This issue is not going away. Yet one more issue for which the patent bar has to stop failing to serve their clients.

AI promises life-changing Alzheimer's drug breakthrough
Kevin Maney, Newsweek, 29 January 2018

If Verge Genomics co-founder Alice Zhang is right, the kind of technology that allows you to search the web for "Japanese baseball jerseys" and find a $49.99 Hokkaido Nippon-Ham Fighters shirt will help discover a cure for Alzheimer's. And that’s just the beginning. Her company could make traditional drug research in a lab seem more outmoded than an Amish wagon on an eight-lane highway. "Verge moves drug discovery from the lab to the computer.", Zhang says.

For her Ph.D. program, Zhang wrote software to find such networks of problematic genes—an approach inspired by the algorithm behind Google's search engine, which looks for billions of connections between keywords, websites and user activity to find just the right results.

In 2015, Verge took in $4 million in venture capital. The company hired a team of neuroscientists and computer scientists, threw them into an office and had them build sophisticated AI to map interactions among genes involved in a neurological disease. Once the networks are mapped, Verge's software looks for known drugs that can affect all the genes in the network at once to essentially turn off the disease. The technology can look for millions of possible answers in the time it would take a human to set up a single lab experiment.

As Verge and other companies hone AI-based approaches to drug discovery, new cures for neurological conditions -- including Alzheimer's -- could come quickly. "Our software platform will be able to discover not one drug but dozens of billion-dollar drugs.", Zhang adds.

Verge isn't the only company to see this trend toward AI drug discovery. Atomwise, founded by AI researchers from the University of Toronto, has raised $6 million and is collaborating with Stanford University, IBM, Merck and two dozen other companies and research labs. Atomwise's technology uses machine learning to study how potential drug molecules interact and bind with target molecules in the body, and promises to make predictions about drug compounds before physically testing them in labs. That could greatly speed discovery of effective drugs. IBM researchers have been developing AI tools that can look for patterns in the side effects of existing drugs and make predictions about what other conditions a drug might treat. A company called ID Genomics is using machine learning to predict which antibiotics will be most effective on different bugs, helping patients get better faster, with fewer pills.
The AI is going to get more and more powerful. 103 KSR is continuing to get worse and worse (along with 101 caselaw) - worse and worse. At some point, the two curves will intersect. And every case of pharmaceutical patent infringement will have the following two discovery questions:

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