Last week, the Federal Communications Commission (FCC) released a Notice of Proposed Rulemaking (NPRM) and Notice of Inquiry (NOI) regarding its Emergency Alert Service (“EAS”) rules. These rules govern how emergency alerts are transmitted by federal, state, local, Tribal, and territorial officials to the public over mobile phones, radios, and televisions.
On February 19, 2021, the European Commission published two draft decisions finding that UK law provides an adequate level of protection for personal data. The first would allow private companies in the EU to continue to transfer personal data to the UK without the need for any additional safeguards (e.g., the Commission’s standard contractual clauses), while the second would allow EU law enforcement agencies to transfers personal data subject to Directive 2016/680 — the Data Protection and Law Enforcement Directive (LED) — to their UK counterparts.
A foundation of intellectual property rights (IPR) is that authors and inventors are entitled to some level of exclusivity over their works in the form of copyrights and patents to incentivize innovation; that’s written into the Constitution. However, various voluntary open innovation practices have emerged, highlighting that developers also can benefit by choosing to widely share certain intellectual property in ways that also can help foster innovation.
While there is no “one size fits all approach,” with the growth of artificial intelligence (AI), there has been a trend to similarly facilitate more voluntary data sharing. Especially considering how AI is being used to address the COVID-19 pandemic and other important needs, voluntary open access to data could have a significant impact in the immediate future. However, practices for voluntarily sharing or providing open access to data are still developing and vary widely (in part because of the state of IPR protection for data). These evolving practices create some challenges for data contributors and users alike. However, the challenges often can be overcome by carefully selecting contract terms to govern the data sharing arrangement that factor in the goals and needs of the participants and relevant legal principles.
On February 11, 2021, the European Commission launched a public consultation on its initiative to fight child sexual abuse online (the “Initiative”), which aims to impose obligations on online service providers to detect child sexual abuse online and to report it to public authorities. The consultation is part of the data collection activities announced in the Initiative’s inception impact assessment issued in December last year. The consultation runs until April 15, 2021, and the Commission intends to propose the necessary legislation by the end of the second quarter of 2021.
The USPTO issued a Report in October 2020 titled Inventing AI: Tracing the diffusion of artificial intelligence with U.S. patents, along with supplementary material that describes the methodology and scope of patent related data used in the Report. Following a first report also issued in October 2020 that pertains to AI and IP policy, Inventing AI chronicles the USPTO’s increase in the number of filed AI-related patent applications and issued patents in recent years, and the attendant proliferation of AI technologies.
As new technologies are developed, it takes time for them to be understood, adopted, and effectively utilized. Technologies that do become broadly adopted may have potentially large effects on innovation, productivity, economic growth, and the volume and variety of goods and services provided. The economic impact of AI-related technology is said to be larger when a growing number of inventors, companies, and other organizations use AI in their invention and production processes.
The Inventing AI Report utilizes U.S. patents as a proxy to gauge the extent to which AI technologies are growing in volume and diffusing across a broad spectrum of technical areas, inventors, companies, and geographies. To gauge the potential impact of AI, the USPTO developed a machine learning patent landscape algorithm that was used to determine the volume, nature, and evolution of AI technologies contained in U.S. patents from 1976 through 2018.
AI Component Technologies
The Report defines an AI patent application or issued patent to include one or more of eight component technologies that span software, hardware, and applications, as listed below:
- Planning/control — contains processes to identify, create, and execute activities to achieve specified goals
- Knowledge processing — involves representing and deriving facts about the world and using this information in automated systems
- AI hardware — includes physical computer components designed to meet performance requirements through increased processing efficiency and/or speed
- Computer vision — extracts and understands information from images and videos
- Machine learning — contains a broad class of computational models that learn from data
- Natural language processing — relates to understanding and using data encoded in written language
- Speech recognition — relates to techniques to understand a sequence of words given an acoustic signal, including answering articulated questions and responding to spoken commands
- Evolutionary computation — contains a set of computational routines using aspects of nature and, specifically, evolution
(1) AI Patent Applications More Than Doubled From 2012 to 2018
At the end of 1999, the American Inventors Protection Act (AIPA) provided for publication of patent applications 18 months after filing, which subsequently increased the volume of publicly available patent applications. Because of the implementation period associated with publishing patent applications after the expiration of 18 months from the earliest claimed filing date, the trends that pertain to the volume and share of public AI patent applications are most informative after 2002.
In 2000, there were approximately 10,000 published AI patent applications, which constituted approximately 6% of all published applications. From 2002 to 2018, AI patent applications increased by more than 100%, from 30,000 to more than 60,000 annually. Over the same period, the share of all patent applications that included some form of AI grew from 9% to almost 16%.
Relatedly, on a global scale, a report by the World Intellectual Property Organization (“WIPO Report”) found that nearly 340,000 AI-related patent families were published from 1960 until early 2018. The number of AI patent applications filed annually grew by a factor of 6.5 between 2011 and 2017, with over half of the identified inventions being published since 2013.
(2) AI Patent Applications in Several Component Technologies More Than Doubled From 2012 to 2018
In 2018 there were slightly more than 40,000 planning/control applications and slightly less than 40,000 knowledge processing applications, more than doubling their respective numbers in 2002. Also in 2018, the number of public patent applications in computer vision (approximately 17,000) and machine learning (approximately 10,000) more than doubled since 2002.
The number of applications in AI hardware increased to about 19,000 in 2018 from approximately 10,000 in 2002. The close association between the increase in number of AI hardware and computer vision applications may reflect the interplay between advances in image recognition and the need for attendant increases in computational power and performance.
(3) The Percentage of Patent Groupings With AI Patents Quadrupled From 1976 to 2018
Patents containing AI appeared in about 10% of all technology groupings used by the USPTO in 1976 and about 35% of all technology groupings in 2002. In 2018, AI patents appear in more than 42% of groupings, in three distinct clusters with different diffusion rates. Planning/control and knowledge processing and are diffusing the fastest across patent technology groups, which reflects the general applicability of these AI components to a wide variety of technical areas.
The diffusion rate is slower, but still increasing, for a second cluster that includes computer vision, machine learning, and AI hardware, which individually appear in a range from approximately 17–20% of the technology groupings. Diffusion for the third cluster (evolutionary computation, speech recognition, and natural language processing) is the slowest, individually hovering around 5% in 2002 and between approximately 6–10% of technology groupings in 2018.
Relatedly, the WIPO Report found that patent literature focuses most extensively on machine learning, followed by logic programming (expert systems) and fuzzy logic. The most predominant AI functional applications are computer vision, natural language processing and speech processing.
(4) The Percentage of AI Inventors and Patent Owners Increased Substantially From 1976 to 2018
The percentage of inventor-patentees who are active in AI started at 1% in 1976 and increased to 25% by 2018. Growth in the percentage of organizations patenting in AI has been similar, from approximately 2% in 1976 to 22% in 2018.
From 1976 to 2000, AI inventor-patentees tended to be concentrated in larger cities and established technology hubs, such as Silicon Valley. Since 2001, AI inventor‑patentees have been increasing states such as Maine, South Carolina, Oregon, and Montana, as well as in several Midwest Region states.
Most of the top 30 AI companies are in the information and communications technology sector. These companies held 29% of all AI patents granted by the USPTO from 1976 to 2018.
Globally, companies from Japan, the U.S. and China dominate patenting activity. AI-related patent filings were made in the early 1980s in Japan, which was subsequently overtaken by the U.S. and China. Since 2014, China has led the world in the number of first patent filings in AI, followed by the U.S. Together, China, the U.S., and Japan account for 78 percent of total patent filings. See WIPO Report.
Key Takeaways and Considerations
- National and global AI-related patent filings have soared in recent years, and appear to be poised for continued growth.
- AI technology is being used beyond cutting-edge, high-tech fields and is diffusing broadly to sectors such as banking and airlines and technologies such as fitness equipment and agriculture. Organizations should ensure that IP policies and invention disclosure procedures facilitate disclosure and adequate protection of all AI-related technology and inventions.
- Global considerations such as patent eligibility requirements and the scope of patent protection available in key jurisdictions of interest, as well as other forms of possible IP protection should not be overlooked.
On 17 December 2020, the media authority of the German federal state of Schleswig-Holstein initiated legal proceedings against Google Ireland Ltd. under Germany’s new Interstate Media Treaty (Medienstaatsvertrag – “MStV”, downloadable here, German only). The authority (Landesmedienanstalt) is investigating whether Google’s treatment of information from a “National Health Portal” offered by the German Federal Ministry of Health (“Health Ministry”) constitutes a violation of the MStV.
On January 6, 2021, the UK’s AI Council (an independent government advisory body) published its AI Roadmap (“Roadmap”). In addition to calling for a Public Interest Data Bill to ‘protect against automation and collective harms’, the Roadmap acknowledges the need to counteract public suspicion of AI and makes 16 recommendations, based on three main pillars, to guide the UK Government’s AI strategy.
On December 23, 2020, the European Commission (the “Commission”) published its inception impact assessment (“Inception Impact Assessment”) of policy options for establishing a European Health Data Space (“EHDS”). The Inception Impact Assessment is open for consultation until February 3, 2021, encouraging “citizens and stakeholders” to “provide views on the Commission’s understanding of the current situation, problem and possible solutions”.
On 18 January 2021, the UK Parliamentary Office of Science and Technology (“POST”)* published its AI and Healthcare Research Briefing about the use of artificial intelligence (“AI”) in the UK healthcare system (the “Briefing”). The Briefing considers the potential impacts of AI on the cost and quality of healthcare, and the challenges posed by the wider adoption of AI, including safety, privacy and health inequalities.
The Briefing summarises the different possible applications of AI in healthcare settings, which raises unique considerations for healthcare providers. It notes that AI, developed through machine learning algorithms, is not yet widely used within the NHS, but some AI products are at various stages of trial and evaluation. The areas of healthcare identified by the Briefing as having the potential for AI to be incorporated include (among others): interpretation of medical imaging, planning patients’ treatment, and patient-facing applications such as voice assistants, smartphone apps and wearable devices.