Artificial intelligence is one of the fastest growing technologies in terms of sheer volume of patent filings at the United States Trademark and Patent Office. Between 2002 and 2018, annual AI patent applications increased by more than 100 percent. This increase evidences the importance for applicants to develop a strategy for building a patent portfolio around their artificial intelligence technology. In this article, we take a look at a few considerations when crafting claims for a patent filing which may help ensure that an AI patent application stands out. In the wake of the Supreme Court's 2014 decision in Alice, software-based innovations are subject to higher scrutiny during examination.

If you've ever had an MRI scan before, you'll know how unsettling the experience can be. You're placed in a claustrophobia-inducing tube and asked to stay completely still for up to an hour while unseen hardware whirs, creaks, and thumps around you like a medical poltergeist. New research, though, suggests AI can help with this predicament by making MRI scans four times faster, getting patients in and out of the tube quicker. The work is a collaborative project called fastMRI between Facebook's AI research team (FAIR) and radiologists at NYU Langone Health. Together, the scientists trained a machine learning model on pairs of low-resolution and high-resolution MRI scans, using this model to "predict" what final MRI scans look like from just a quarter of the usual input data.

Claims 1 and 8 as recited are not practically performed in the human mind. As discussed above, the claims recite monitoring operation of machines using neural networks, logic decision trees, confidence assessments, fuzzy logic, smart agent profiling, and case-based reasoning. . . .

Claims 1 and 8 as recited are not practically performed in the human mind. As discussed above, the claims recite monitoring operation of machines using neural networks, logic decision trees, confidence assessments, fuzzy logic, smart agent profiling, and case-based reasoning. . . .

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.

There is currently keen interest amongst tech companies, investors, and research institutes in both artificial intelligence and blockchain technologies. The buzz has reached corporations and financial institutions looking to tailor these technologies for their own businesses in order to keep pace in our increasingly digitized economy.-- This excitement has implications for securing patent protection -- Canada and the United States (as well as many other countries) operate on a "first to file" basis.-- That means you will want to file a patent application before other inventors monopolize protection for the technology in your area, potentially curtailing your competitive business advantage. The United States Patent and Trademark Office (USPTO) has issued revised guidelines on what constitutes subject-matter that is eligible for patent protection (under 35 U.S.C. -- 101), effective January 7, 2019.

There is currently keen interest amongst tech companies, investors, and research institutes in both artificial intelligence and blockchain technologies. The buzz has reached corporations and financial institutions looking to tailor these technologies for their own businesses in order to keep pace in our increasingly digitized economy. This excitement has implications for securing patent protection – Canada and the United States (as well as many other countries) operate on a "first to file" basis. That means you will want to file a patent application before other inventors monopolize protection for the technology in your area, potentially curtailing your competitive business advantage. The United States Patent and Trademark Office (USPTO) has issued revised guidelines on what constitutes subject-matter that is eligible for patent protection (under 35 U.S.C. § 101), effective January 7, 2019. These guidelines will likely have the largest impact in the area of computer-implemented inventions, including artificial intelligence and blockchain technologies, and may make it easier to patent some of these inventions.

Artificial Intelligence (AI) is an increasingly valuable tool for diagnostics and drug development and is being used to make medical devices more adaptive. However, the U.S. Supreme Court's 2014 decision in Alice has made it difficult to patent inventions involving AI, seeing as the court's derived test for Section 101 of the Patent Act prevents abstract ideas from being patented. Interestingly enough, the court's decision failed to define what an abstract idea is, resulting in Section 101 often being applied unevenly. Nimalka Wickramasekera, an Intellectual Property litigator and Los Angeles partner, discusses in BioWorld's article "Supreme Court's Alice Decision Leaves Drug, Device Firms in AI Wonderland" that Section 101 proves to be a big hurdle for evolving technology, and AI specifically, "since people are still wrapping their heads around what they're claiming as the invention." She added that she "wouldn't be surprised if every patent application based on AI has had at least one rejection at the USPTO…To get around the court's prohibition on patents for abstract ideas, AI inventors must go beyond the algorithm and its application in computing devices."

Match says its lawsuit is anything but baseless -- detailing, in hundreds of pages of court documents, numerous similarities between the two apps. In the process, Match has accused Bumble of "almost every type of [intellectual property] infringement you could think of," says Sarah Burstein, a professor at the University of Oklahoma College of Law whose research focuses on design patents. One of the central questions revolves around Tinder's patented system for connecting people over the Internet. The matching is based on mutual interest, as expressed through a swiping motion.

Can we train a machine to detect if another machine has understood a concept? In principle, this is possible by conducting tests on the subject of that concept. However we want this procedure to be done by avoiding direct questions. In other words, we would like to isolate the absolute meaning of an abstract idea by putting it into a class of equivalence, hence without adopting straight definitions or showing how this idea "works" in practice. We discuss the metaphysical implications hidden in the above question, with the aim of providing a plausible reference framework.