In this part 2 of the series, we will examine some key considerations and hurdles in patenting machine learning-based biotech or synthetic biology inventions. In this series, we are focusing on artificial intelligence inventions, but as Alan Turing aptly pointed out, that neologism is a "suitcase" term because you can stuff a lot of intelligence classifications and different types of technologies into it. Many of the ground-breaking AI developments in biotech are in the AI subfield of Machine Learning. First, we will briefly discuss what is meant by Machine Learning and discuss some relevant terms. Second, we will review some real world challenges in patenting AI inventions.
On August 22, 2019, the United States Patent and Trademark Office (USPTO) published a request for comments on patenting artificial intelligence inventions. Written comments must be received on or before October 11, 2019. The AI inventorship issue came to a head earlier this year when the inventor of an algorithm named DABUS (device for the autonomous bootstrapping of unified sentience) filed beverage container and flashing light patent applications in DABUS' name in the United Kingdom, Europe, and the United States. Additionally, many today see patent eligibility as a significant hurdle to obtaining patent protection in AI technologies. China and the United States lead in patent filings in all AI techniques and functional applications, as well as AI application fields.
Artificial Intelligence (AI) inventions have aided development in nearly every industry, but perhaps none more so than synthetic biology. For synthetic biology researchers, AI has developed into a vital tool to create cutting edge applications. Growth is expected to accelerate with the AI healthcare market set to reach $6.6 billion by 2021, a 40 percent growth from its current size. Biotech and synthetic biology companies that use AI and investors in these companies should be aware of various legal aspects related to patenting. This blog is part 1 of a multi-part series that explores various patenting considerations for AI in biotech and synthetic biology.
Posted in America, Deception, Patents at 5:13 am by Dr. Roy Schestowitz Summary: With resurgence of rather meaningless terms like so-called'clouds' (servers/hosting) and'AI' (typically anything in code which does something clever, including management of patents) the debate is being shifted away from 35 U.S.C. § 101 (Section 101); but courts would still see past such façade THE EPO and USPTO both have a bad new habit that they spread to other patent offices, such as KIPO in Korea. They use or misuse buzzwords. They try to make things outside patent scope seem so innovative that somehow this supposed innovation defies the rules (scope). Sometimes that manages to impress or at least confuse examiners and judges. "So let's start with this assumption that patent maximalists have come to accept Section 101/Alice renders software patents worthless and even overzealous, very large law firms (Finnegan is one of the biggest) insist that patenting has gone too far for practical purposes. Where do they go from here? So it's not hard to see why patent maximalists would pursue such tricks. As recently as Sunday Watchtroll published this rant about Section 101/Alice -- the basis (or legal framework) upon which most software patents become void. "This has prompted many to cast a grim prospect for the software patent industry," Babak Nouri (at Watchtroll) wrote less than a couple of days ago, as if the patents themselves are the industry… "A Realistic Perspective on post-Alice Software Patent Eligibility" is the headline and here's a snide remark directed at the law itself: "Much of the havoc wrought in the software patent system by the landmark decision Alice v. CLS Bank International, 134 S. Ct. 2347 (2014) stems from the unworkable two-part patent eligibility test based on vaguely defined and nebulous Abstract idea and significantly more constructs.
The use of artificial intelligence (AI) in life sciences, or "Life Tech", has increased at a rapid pace. According to World Intellectual Property Organization (WIPO), there has been "a shift from theoretical research to the use of AI technologies in commercial products and services," as reflected in the change in ratio of scientific papers to patent applications over the past decade.1 Indeed, while research into AI began in earnest in the 1950s, more than 1.6 million scientific papers have been published on AI, with more than half of identified AI inventions in the last six years alone.2,3 A review article in Nature Medicine reported last year that despite few peer-reviewed publications on use of machine learning technologies in medical devices, FDA approvals of AI as medical devices have been accelerating.4 Many of these FDA approvals relate to image analysis for diagnostic purposes, such as QuantX, the first AI platform to evaluate breast abnormalities; Aidoc, which detects acute intracranial hemorrhages in head CT scans, assisting radiologists to prioritize patient injuries; and IDx-DR, which analyzes retinal images to detect diabetic retinopathy.