Artificial intelligence ("AI") is rapidly transforming the world of medicine, as the recent decades have marked a surge in the development of medical AI.1 These thinking machines are now used in diagnosis, treatment, and drug development. As the technology advances, so too must our understanding of patent law and patent protection. The use of AI in these fields raises several issues, all hinging on the question of personhood and human contributions, affecting both inventorship (and ownership) and patentability (including subject matter eligibility and predictability). This article addresses these questions in turn after addressing the recent advances in medical AI.
Artificial Intelligence in Medicine
AI techniques utilized in medicine include artificial neural networks, fuzzy expert systems, evolutionary computation, and hybrid intelligent systems.2
Artificial neural networks are used extensively in clinical diagnosis and image analysis because of the parallel processing power that allows the networks to learn from historical examples and known patterns.3 Artificial neural networks have been used for diagnosing prostates as benign or malignant, cervical screening, and imaging analysis (including radiographs, ultrasounds, CTs, and MRIs), as well as for analyzing heart waveforms to diagnose conditions such as atrial fibrillation and ventricular arrhythmias.4
For example, researchers at Stanford University trained a deep convolutional neural network to classify skin lesions into either benign or malignant groupings based on known images, using only pixels and disease labels as inputs.5 The researchers started with an algorithm developed by Google to perform image recognition6 and then trained their neural network to recognize skin cancer using 129,450 clinical images of 2,032 different diseases.7 The neural network was then tested against board-certified dermatologists on clinical images that had been confirmed through biopsy.8 The AI performed on par with the certified dermatologists, demonstrating that the AI was capable of classifying skin cancer with the same level of competence as the trained dermatologists.9
As yet another example, medical chatbots utilize neural networks to learn from medical textbooks, scientific research, patient records, and messages between actual patients and doctors.10 The AI chatbot is constantly learning and can be kept up to date on the latest medical research.11 Baidu, a Chinese search engine, utilizes a chatbot named Melody within its Baidu Doctor app.12 When a patient asks a question to the doctor, the chatbot asks appropriate follow-up questions to help learn more about the patient's symptoms so the doctor can make a more informed decision on treatment.13 Interventional radiologists at the University of California at Los Angeles have developed a chatbot to assist physicians in providing real-time evidence-based answers to the patient about the next phase of treatment, or information about their interventional radiology treatment.14
Fuzzy logic AI is applicable in medicine because diseases, symptoms, and diagnoses are described in imprecise and terms.15 Because fuzzy logic rests on the premise that everything is a matter of degree, it can recognize "partial truth logics," beyond just the true and false values applied in traditional programming.16 Fuzzy logic AI has been applied to cancer diagnosis for lung cancer, acute leukemia, breast cancer, and pancreatic cancer.17 Fuzzy logic has been applied to diagnosis of other conditions, including tuberculosis, aphasia, arthritis, and hypothyroidism.18
"Evolutionary computation is the general term for several computational techniques based on natural evolution process that imitates the mechanism of natural selection and survival of the fittest in solving real-world problems."19 Genetic algorithms are the most widely used form of evolutionary computation in medicine, creating numerous solutions to a single problem, and then evolving those solutions from one generation to the next to arrive at the best solution.20 Evolutionary computation has been used in diagnosis, prognosis, imaging, and signal processing.21
Combining these AI techniques generates hybrid intelligent systems that incorporate the advantages of each technology.22 For example, the combination of neural networks and fuzzy logic or "neuro-fuzzy" systems have become popular because they can absorb some of the "noise" generally present in the neural network.23
Uncertainties in Patenting AI—Subject Matter Eligibility
As the use of AI in medicine becomes ever more prevalent, the patent system must answer increasingly difficult questions regarding the protection afforded these technologies. Perhaps the most significant question is that of subject matter eligibility. With the Supreme Court decisions in Alice and Mayo, the hurdle to meet subject matter eligibility has grown ever higher.24
Subject matter eligibility is one of the core criteria for receiving a patent, in addition to novelty and nonobviousness. An invention must contain patent-eligible subject matter in order to receive patent protection. 35 U.S.C. § 101 states that "[w]hoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor." Abstract ideas, laws of nature, and natural phenomena are excluded from patentable subject matter.25 The U.S. Supreme Court has further enunciated the requirement for subject matter eligibility, ultimately laying out a two-part test that must be met by any claimed invention.
In Mayo, the Supreme Court invalidated issued patent claims directed to the relationship between the concentrations of certain metabolites in the blood and the likelihood that a drug dosage would prove ineffective or cause harm for failing to meet this requirement.26 The Supreme Court held that the claims were not subject matter eligible under 35 U.S.C. § 101 because the claims provided "instructions [that] add nothing specific to the laws of nature other than what is well-understood, routine, conventional activity, previously engaged in by those in the field."27 According to the Court, the dosage limits at which a drug would prove ineffective or cause harm was a law of nature that was unpatentable, and the claims merely instructed doctors to apply this law of nature using techniques that were already known.
Alice addressed the holding in Mayo, further enunciating a two-step test for subject matter patent eligibility: (1) determine whether the claims are directed to a patent-ineligible concept (laws of nature, abstract ideas, and natural phenomena); and (2) determine whether the claim's elements, considered both individually and as an ordered combination, transform the nature of the claims into a patent-eligible application.28 If a claim is directed to a patent in eligible concept and the claim's elements do not transform the nature of the claim, then it will fail to meet § 101.
These two Supreme Court cases present a hurdle that medical AI inventions will have to overcome in order to receive patent protection. Current AI medical device/system patents can be directed to both the methods and apparatuses that perform the above described analyses. Many AI medical patents are directed to the AI algorithms and the machines used to generate those algorithms.29 As described above, AI has been found to be extremely successful in diagnosis and prognosis, relating known images to new cases and extrapolating based on the similarities or differences between the two. In some instances, this is the same process followed by a doctor or medical expert, just with greater efficiency or accuracy. The steps for diagnosis struck down in Mayo echo the steps taken in many medical AI algorithms. Practitioners and inventors alike will need to carefully consider the full scope of eligible subject matter in order to ensure that a patent can be obtained from the U.S. Patent and Trademark Office ("PTO") and maintained through any subsequent challenges.
Indeed, the Federal Circuit has already found revolutionary diagnostic technology to be patent-ineligible subject matter under the Mayo/Alice framework. In Ariosa Diagnostics, Inc. v. Sequenom, Inc., the court concluded that a novel method of prenatal diagnosis of fetal DNA was not directed to patent-eligible subject matter, despite agreeing that the claimed method "reflects a significant human contribution . . . that revolutionized prenatal care."30 The patent claims were generally directed to detecting the presence of cell-free fetal DNA in maternal plasma. Because the presence of cell-free fetal DNA was a natural phenomenon, the court turned to the second step in the Mayo/Alice framework—whether the claim contained an inventive concept sufficient to transform the naturally occurring phenomenon into patent-eligible subject matter.31 The court found that the second step was not met because the method steps "were well-understood, conventional, and routine," despite acknowledging their breakthrough nature.32
More recently, the Federal Circuit found methods for detecting myeloperoxidase ("MPO") in blood, and correlating the results to cardiovascular risk, to be directed to patent-ineligible subject matter in Cleveland Clinic Foundation v. True Health Diagnostics LLC. 33 Although Cleveland Clinic argued that the discovery of the correlation was groundbreaking, the Federal Circuit affirmed the district court's decision that the correlation between MPO levels in blood and cardiovascular disease was a law of nature.34 The court noted that Cleveland Clinic had not invented any new and useful laboratory technique to detect MPO levels.35 In the second step, the court found the claims applied well-known techniques to determine the level of MPO and applied established statistical methods to make the correlation.36
Although the Supreme Court cautioned against construing the exclusionary principle of § 101 overbroadly, "lest it swallow all of patent law,"37 many believe it has done just that in the life sciences and medical spaces.38 The concurring opinion in Ariosa echoed these concerns, stating that "[b]ut for the sweeping language in the Supreme Court's Mayo opinion, [there was] no reason, in policy or statute, why this breakthrough invention should be deemed patent ineligible."39 The same reasoning could well curtail the patent protections afforded medical AI absent a change in Supreme Court precedent or statute. Until such a change occurs, AI inventors and owners must draft their patents with an eye to this two-step test, including features related to the AI in the claims, such as detailing the computing or mathematical techniques applied by the system or describing how the computer interacts with other components to drive the AI processing.
Footnote
1. A.N. Ramesh et al., Artificial Intelligence in Medicine, 86 Annals Royal C. Surgeons Eng. 334 (2004).
2. Id. at 335.
3. Id.
4. Id. at 335–36.
5. Andre Esteva et al., Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks, 542 Nature 115 (Feb. 2, 2017).
6. Taylor Kubota, Deep Learning Algorithm Does as Well as Dermatologists in Identifying Skin Cancer, Stan. News (Jan. 25, 2017), http://news .stanford.edu/2017/01/25/artificial-intelligence-used-identify-skin-cancer/.
7. Esteva et al., supra note 5. 322 The Journal of Robotics, Artificial Intelligence & Law [1:313
8. Id.
9. Id.
10. Jelor Gallego, An AI-Powered Chatbot is Helping Doctors Diagnose Patients, Futurism (Oct. 13, 2016), https://futurism.com/an-aipowered-chatbot-is-helping-doctors-diagnose-patients/.
11. Artificial Intelligence ChatBots—Explore Intelligence in a Bot!, KatPro, (June 7, 2017) https://katprotech.com/blog/artificial-intelligencechatbots-explore-intelligence-in-bot/ ("AI Chatbots can read up to 25 million and more published medical papers in about a week and scan the web for references to latest research. They can be trained to read, interpret and analyze medical literature. A continuous input, learning, understanding and analyzing, which is never forgotten.").
12. Gallego, supra note 10; Dyllan Furness, Baidu releases Melody, a medical assistant chatbot to keep physicians humming, Digital Trends (Oct. 11, 2016), https://www.digitaltrends.com/health-fitness/ baidu-melody-medical-chatbot/.
13. Gallego, supra note 10; Furness, supra note 12.
14. K. Seals, et al., Utilization of Deep Learning Techniques to Assist Clinicians in Diagnostic and Interventional Radiology: Development of a Virtual Radiology Assistant, 28 J. Vascular and Interventional Radiology S153; Future is now: Artificial intelligence virtual consultant helps deliver better patient care, Soc. Interventional Radiology (Mar. 8, 2017), https:// www.sirweb.org/advocacy-and-outreach/media/news-release-archive/ news-release-artificial_intelligence/.
15. Angela Torres & Juan J. Nieto, Fuzzy Logic in Medicine and Bioinformatics, J. Biomedicine & Biotechnology (2006).
16. Id.
17. Id.
18. V. Prasath et al., A Survey on the Applications of Fuzzy Logic in Medical Diagnosis, 4 Int'l J. Sci. & Eng'g Res. 1199 (2013).
19. Ramesh et al., supra note 1, at 336.
20. Id.
21. Id.
22. Id. at 337.
23. Georgios Dounias, Hybrid Computational Intelligence in Medicine, ResearchGate (2018), https://www.researchgate.net/profile/Georgios_ Dounias/publication/228596764_Hybrid_computational_intelligence_ in_medicine/links/09e4150b72b8ded221000000/Hybrid-computationalintelligence-in-medicine.pdf.
24. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347 (2014); Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66 (2012).
25. Alice, 134 S. Ct. at 2354.
26. Mayo, 566 U.S. at 69. 2018] Patenting Artificial Intelligence 323
27. Id.; see also id. at 82 (noting that "[b]eyond picking out the relevant audience, namely those who administer doses of thiopurine drugs, the claim simply tells doctors to: (1) measure (somehow) the current level of the relevant metabolite, (2) use particular (unpatentable) laws of nature (which the claim sets forth) to calculate the current toxicity/inefficacy limits, and (3) reconsider the drug dosage in light of the law").
28. Alice, 134 S. Ct. at 2355.
29. See, e.g., Artificial Intelligence Sys. for Genetic Analysis, U.S. Patent No. 8,693,751 (filed Jan. 12, 2012); Clinical Decision-Making Artificial Intelligence Object Oriented Sys. & Method, U.S. Patent App. No. 14/324,396 (filed July 7, 2014); Integrated Med. Platform, U.S. Patent App. No. 15/039,419 (filed July 7, 2015); Local Diagnostic & Remote Learning Neural Networks for Med. Diagnosis, U.S. Patent App. No. 00/027,220 (filed Oct. 3, 2000).
30. 788 F.3d 1371, 1376, 1379 (Fed. Cir. 2015).
31. Id.
32. Id. at 1377.
33. Cleveland Clinic Found. v. True Health Diagnostics LLC, 859 F.3d 1352, 1363 (Fed. Cir. 2017).
34. Id. at 1361.
35. Id.
36. Id. at 1362.
37. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2354 (2014) (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71–72 (2012)).
38. Alexa Johnson, A Crisis of Patent Law and Medical Innovation: The Category of Diagnostic Claims in the Wake of Ariosa v. Sequenom, 27 Health Matrix 435 (2017); Patent Publius, Federal Circuit Threatens Innovation: Dissecting the Ariosa v. Sequenom Opinion,Ctr. for Protection Intell. Prop. (June 23, 2015), https://cpip.gmu.edu/2015/06/23/federal-circuit-threatensinnovation-dissecting-the-sequenom-v-ariosa-opinion/; Gene Quinn, Supreme Court Denies Cert. in Sequenom v. Ariosa Diagnostics,IPWatchdog (June 27, 2016), 38. Alexa Johnson, A Crisis of Patent Law and Medical Innovation: The Category of Diagnostic Claims in the Wake of Ariosa v. Sequenom, 27 Health Matrix 435 (2017); Patent Publius, Federal Circuit Threatens Innovation: Dissecting the Ariosa v. Sequenom Opinion,Ctr. for Protection Intell. Prop. (June 23, 2015), https://cpip.gmu.edu/2015/06/23/federal-circuit-threatensinnovation-dissecting-the-sequenom-v-ariosa-opinion/; Gene Quinn, Supreme Court Denies Cert. in Sequenom v. Ariosa Diagnostics,IPWatchdog (June 27, 2016), http://www.ipwatchdog.com/2016/06/27/70409/id=70409/.
39. Ariosa, 788 F.3d at 1381 (Linn, J., concurring).
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