!20200224 Is first fully-AI-predicted drug KSR-predictably obvious?
But first, ...
Apologies, folks, still busy repositioning myself. More to follow in the coming months. For now, some tidbits.
Amster Rothstein sued for $10 million by patent client for not informing client about aspects of prior art laws
Tech Transfer eNews blog has an article on how Amster Rothstein is being sued for $10 million by Albert Einstein College and two of its inventors. They allege that Amster Rothstein "failed to advise these inventors (neither of whom are lawyers, let alone patent lawyers) that any earlier on-line publication or disclosure date should have been viewed as the critical publication date in determining the deadline for filing the Application". Not surprisingly, the USPTO rejected their patent application, using their online publications as prior art. Article at: techtransfercentral.com/2020/02/12/law-firm-sued-over-missed-publication-date-potentially-costing-a-school-millions/.
Trump Administration backs Oracle in its API copyright battle with Oracle
Newswires are reporting that the Trump Administration is backing Oracle (led by Trump supporter and fundraiser Larry Ellison) in its API copyright battle with Google. This lawsuit shows the complete corruption of the world of software copyright. Decades ago, the courts rightly ruled that Application Programming Interfaces were too functional, too much 17 USC 102b ideas and processes, to be granted copyright protection. That the CAFC, which long ago abandoned competence with software concepts, decided to reverse this prcedent based on software engineering, relying in part of arguments by lawyers who decades ago argued the exact opposite - is pure corruption. A corruption that traces bank to IBM's corruption of software IP through CONTU.
Anyway, here's hoping that the "We Love To Reverse The CAFC" Supreme Court reverses the CAFC and rule that APIs are what they were decades ago - too much ideas and processes to be granted copyright protection. Years ago, Peter Menell, right before the Lotus case, wrote an excellent article that nothing was copyrightable about software, except maybe for user interfaces. When Lotus ruled that user interfaces were not copyrightable, he should have BUT DIDN'T write an article that nothing about software is copyrightable, an article written along with Michael Jacobs who won the Apple case that ruled that APIs are not copyrightable and wrote an article on why APIs are not copyrightable.
Article on the case at: www.bloomberg.com/news/articles/2020-02-20/trump-backer-ellison-gets-president-s-support-in-google-fight
Exercise bike company Flywheel shuts down after losing patent case to Peloton
Newswires are reporting that exercise bike company Flywheel is going out of business after losing a patent infringement lawsuit to Peloton. As part of the lawsuit's settlement, Flywheel admitted that it "copied elements of the Peloton bike in developing its Fly Anywhere bike". Vice Motherboard has an excellent article on how it came out during the trial, that Flywheel engaged in corporate espionage and intellectual property theft. And theft apparently was "pardonable":Excellent article at: www.vice.com/en_us/article/qjdz7v/project-magnum-flywheel-peloton-patent-lawsuitPeloton began the patent lawsuit process by claiming Flywheel had specifically sent one of its major investors, twice-pardoned "junk bond king" Michael Milken, to obtain proprietary information from Foley under false pretenses. ... "At no time before, during or after the meeting did Milken disclose that he had any financial interest whatsoever in Flywheel."
Anti-software patent groups in Europe challenge Unitary Patent ratification after Brexit
Article at: blog.ffii.org/germany-can-no-longer-ratify-the-unitary-patent-due-to-brexit-and-the-established-aetr-case-law-says-ffii/
Coming soon - seriously - the IPISC AI Drug Patent Defense policy. The patent insurance policy will cover all litigation costs and damages (minus deductibles) for any patent that claims drugs that were completely invented by a AI computer program tool with no human intervention that sifts through hundreds of millions to billions of drugs. The risk-basis for the insurance policy? AI programs designed to make optimal predictions of drug candidates are tools that fall within the range of Ordinary Skills of a person in the drug design arts. As it is predictable that a PHOSITA using a predicting-optimized tool will find a useful drug if it is to be found in a huge database of potential candidates, then thanks to the unconstitutional (it is a Restatement of Cuno) and unscientific (judges know nothing about what goes in laboratories) KSR and its "predictable", all such drugs are OBVIOUS.
And it seems we have the first such drug. A group at MIT used their AI drug prediction tool on a database of 6,000 compounds, and out popped halicin. They then used their program on a database of 1.5 billion drugs, the program returned a shortlist of 23 potential antibiotics, and after standard robotic lab tests, two potentially potent drugs were found.
Grant a patent on a new robotic system for testing drug candidates? Sure, always room for improvement. Grant a patent on new software to generate a clinical protocol for testing drug candidates? Sure. Grant a patent on the predictive AI program? Definitely. Writing such software is not obvious, and successful such programs are so rare to all be novel. And these software tools are increasingly very sophisticated. Consider one pending patent:But all three are tools easily stitched together. 21st century tools, but tools nonetheless. And the outcome of the use of these tools is quite predictable - if there are drugs to be found, the AI program/tools will find them. Raising a legitimate 103 obviousness argument for any patent sought - thanks to the unconstitutional (it is a Restatement of Cuno) and unscientific (judges no nothing about what goes in laboratories) "predictable" nonsense of KSR International.Systems and methods for machine learning for drug design and discovery
U.S. Patent Application 2019/304568
From the Abstract: "... These machine learning models may be trained to predict characteristics such as protein-protein or protein-ligand/protein/nucleic acid binding affinity, toxicity endpoints, B-factor, chemical shift, atomic spectroscopy, free energy changes upon mutation, protein flexibility/rigidity/allosterism, membrane/globular protein mutation impacts, plasma protein binding, partition coefficient, permeability, clearance, and/or aqueous solubility, among others."
This issue of KSR versus increasingly useful/powerful predictive AI programs has been ignored too long by the patent bar and the USPTO. The AI is getting more powerful everyday, and as well, 103 caselaw is getting more unconstitutional every day. Sometime, sometime soon, they intersect, and I have the risk-basis to design the IPISC AI Drug Patent Defense policy, which will effectively render such patents to be useless.
Which I have to believe is not what the Founding Fathers intended, nor what the 1952 Congress intended for 35 USC 103. But everyone has to admit that 35 USC 103 is unconstitutionally vague, for not defining "obvious" (or "skilled" or "in the art") in statute nor in the Congressional record (except for meaningless circular definition). Just like PTAB judges are unconstitutionally appointed. For the same reason. Congress just does not know how to write IP laws, and it gets lousy advice (Hi Bob!) when it does so.
And this unconstitutionality leads to tyranny. I wish everyone in Washington would stop whining about China stealing US technology. Every patent application wrongly rejected under 35 USC 103, every issued patent wrongly invalidated under 35 USC 103, because of Due Process violating "ad hoc" definitions of "obvious", every such rejection/invalidation is a theft of a company's technology (since the company could have retained it as a trade secret). Add to it the theft of US technology to due unconstitutional 35 USC 101 rejections/invalidations, and it is undeniable - the US patent system steals more technology than China, by forcing companies to give up their technology to the public - a horrible government taking.
Of course, none of this will be discussed in Naples nor in Texas. Nothing useful to ALL patent applications will be discussed.
Ian Sample, The Guardian, 20 Feb 2020
A powerful antibiotic that kills some of the most dangerous drug-resistant bacteria in the world has been discovered using artificial intelligence.
The drug works in a different way to existing antibacterials and is the first of its kind to be found by setting AI loose on vast digital libraries of pharmaceutical compounds.
Tests showed that the drug wiped out a range of antibiotic-resistant strains of bacteria, including Acinetobacter baumannii and Enterobacteriaceae, two of the three high-priority pathogens that the World Health Organization ranks as “critical” for new antibiotics to target.
"In terms of antibiotic discovery, this is absolutely a first.”, said Regina Barzilay, a senior researcher on the project and specialist in machine learning at Massachusetts Institute of Technology (MIT).
"I think this is one of the more powerful antibiotics that has been discovered to date.", added James Collins, a bioengineer on the team at MIT. "It has remarkable activity against a broad range of antibiotic-resistant pathogens."
Antibiotic resistance arises when bacteria mutate and evolve to sidestep the mechanisms that antimicrobial drugs use to kill them. Without new antibiotics to tackle resistance, 10 million lives around the world could be at risk each year from infections by 2050, the Cameron government's O'Neill report warned.
To find new antibiotics, the researchers first trained a "deep learning" algorithm to identify the sorts of molecules that kill bacteria. To do this, they fed the program information on the atomic and molecular features of nearly 2,500 drugs and natural compounds, and how well or not the substance blocked the growth of the bug E coli.
Once the algorithm had learned what molecular features made for good antibiotics, the scientists set it working on a library of more than 6,000 compounds under investigation for treating various human diseases. Rather than looking for any potential antimicrobials, the algorithm focused on compounds that looked effective but unlike existing antibiotics. This boosted the chances that the drugs would work in radical new ways that bugs had yet to develop resistance to.
Jonathan Stokes, the first author of the study, said it took a matter of hours for the algorithm to assess the compounds and come up with some promising antibiotics. One, which the researchers named "halicin" after Hal, the astronaut-bothering AI in the film 2001: A Space Odyssey, looked particularly potent.
Writing in the journal Cell, the researchers describe how they treated numerous drug-resistant infections with halicin, a compound that was originally developed to treat diabetes, but which fell by the wayside before it reached the clinic.
Tests on bacteria collected from patients showed that halicin killed Mycobacterium tuberculosis, the bug that causes TB, and strains of Enterobacteriaceae that are resistant to carbapenems, a group of antibiotics that are considered the last resort for such infections. Halicin also cleared C difficile and multidrug-resistant Acinetobacter baumannii infections in mice.
To hunt for more new drugs, the team next turned to a massive digital database of about 1.5 billion compounds. They set the algorithm working on 107 million of these. Three days later, the program returned a shortlist of 23 potential antibiotics, of which two appear to be particularly potent. The scientists now intend to search more of the database.
Stokes said it would have been impossible to screen all 107m compounds by the conventional route of obtaining or making the substances and then testing them in the lab. "Being able to perform these experiments in the computer dramatically reduces the time and cost to look at these compounds.", he said.
Barzilay now wants to use the algorithm to find antibiotics that are more selective in the bacteria they kill. This would mean that taking the antibiotic kills only the bugs causing an infection, and not all the healthy bacteria that live in the gut. More ambitiously, the scientists aim to use the algorithm to design potent new antibiotics from scratch.
"The work really is remarkable.", said Jacob Durrant, who works on computer-aided drug design at the University of Pittsburgh. "Their approach highlights the power of computer-aided drug discovery. It would be impossible to physically test over 100m compounds for antibiotic activity."
"Given typical drug-development costs, in terms of both time and money, any method that can speed early-stage drug discovery has the potential to make a big impact.", he added.
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