AI-based business models (hereinafter BM)
once again received a lot of attention as the AI language model ChatGPT3 was
released. In particular, the whole world was seething beyond the industry when
Open AI introduced ChatGPT4 not long after the ChatGPT3. How can the
intellectual property rights of AI-based BM be protected? In this issue, we
will introduce research on IP protection of AI-based BM.
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| clipartkorea.co.kr |
Intellectual property can be protected in
both formal and informal ways. Formally it’s
protected by patents, design, trademarks, and copyrights. Informally, trade secrets,
strengthening product and complexity of manufacturing process to increase the
difficulty of imitation, and lead-time advantage to secure competitive
advantage through faster innovation than competitors. This study examines how
formal and informal protection strategies can be applied to AI-based BM, with
an emphasis on balancing open innovation with existing practices. Depending on
the BM, optimization and supplementation of formal and informal IP protection
strategies may be necessary to maximize value creation.
Challenges in applying a formal IP strategy
One of the major challenges for patent
protection of AI-based inventions is that algorithms play an important role in
designing AI concepts. Algorithms are considered mathematical methods “in their
own right” under patent law and are therefore excluded from patentability. AI
concepts are often aimed at automating or performing tasks or activities
currently performed by the human mind, which fall under patent ineligibility
because they are mere theoretical concepts or lack novelty. However, many
AI-based inventions are currently based on and implemented in software, and
over the past few decades, patent law and practice have been building around
how software-based inventions are dealt with.
Europe
In Europe, AI as a mathematical method is
exempt from patent, but if the method involves a technological means (e.g. a
computer) or a device, it may be of a technical nature as a whole (computer-implemented
inventions, CII). The European Patent Office (EPO) applies a 'two-hurdle
approach' to 'Mixed-type inventions' when evaluating patentability to see if AI
methods contribute to the technical characteristics of the invention. In this
context, the EPO recently updated its screening guidelines with a specific
section on ‘Artificial Intelligence and Machine Learning’. In this context, the
EPO recently updated its screening guidelines with a specific section on
‘Artificial Intelligence and Machine Learning’. These Guidelines provide
guidance on how to assess whether inventions relating to artificial
intelligence and machine learning are based on the 'technical characteristics'
required to be patentable, and provide detailed information about how to evaluate
relevant cases and the CII's technological prowess as determined by the EPO
Appeals Board.
US
In the United States, abstract ideas cannot
be patented. Also, just using a computer to implement an abstract idea to
inventions is not enough to qualify for a patent. Law firm Baker McKenzie
explains that perhaps the biggest legal hurdle to patenting AI inventions in
the US is §101 US Federal Regulations (35 U.S.C.). This is because patentable
subjects are limited to ‘process, machine, manufacturing, or composition of
matter’, and abstract ideas, natural laws, and natural phenomena are excluded
from patentability. The criteria for eligibility for these patents were further
strengthened in the 2014 U.S. Supreme Court decision for Alice Corporation vs
CLS Bank, which applied a more stringent two-step test for software and
computer-implemented inventions.
Current status of AI-related innovation
patents
AI-related innovations are often based on
software and computer-implemented inventions. These inventions may be directed
towards one or more specific AI applications. Based on previous and current
laws, various companies and research institutes have begun to apply for patents
in the field of AI as well. As shown in the
figure below, approx.. 340,000 family patents have been filed and published
since the 1960s. Additionally, by mid-2018, a total of more than 1.5 million scientific
publications had been published, indicating that AI has become a major field of
science. By the early 2000s, scientific publications had grown significantly
(nearly doubling at an average annual growth rate of 18% from 2002 to 2007),
but it took another decade for patent applications to skyrocket (CAGR of 28%
from 2012-2017). It is reasonable to interpret that this is because basic
research is usually published as scientific publications first, whereas R&D
related to industrial use takes a considerable amount of time and, leads to
patent applications by its nature.
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Trend in the number of AI family patent/ scientific publications
by year of first publication (WIPO 2019) |
As seen in the figure below, patent applications
for specific application field have emerged since the mid-1990s. Those are mainly
transportation and communication field, and artificial intelligence-related
inventions are constantly being filed across multiple application fields.
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Trend in the number of family patents with the earliest priority year
by application field (WIPO 2019) |
IP protection strategy for AI-based BM
AI research and innovation requires
significant investment. The European Union (EU) aims to invest at least EUR20
billion per year in AI after 2020 (Euro Commission 2018). According to the World Intellectual Property
Organization (WIPO), more than 3,000 AI-related companies have received funding
worth $46 billion, and M&A has become a
means of securing AI technologies, data access, and related patent portfolios.
Nearly 500 companies have been acquired, more than half of them have been happened
since 2016.
Given the high level of investment in AI
technologies and applications, companies and investors use a variety of
strategies for protecting AI-related intellectual property to protect these
investments and generate returns. These strategies include formal IP measures,
such as patents and copyrights for AI algorithms and codes, and informal IP
measures, such as trade secrets for AI data.
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| Formal/ Informal protection |
In addition, ③ there is a method based on
standardization through disclosure (Public Domain). There are two main
challenges when developing AI technologies. (a) developing AI systems and
algorithms from a technical perspective; and (b) accessing suitable data sets
to train optimized AI algorithms or AI systems. Access to data sets is an
important issue not only for competitive advantage across legal systems, but
also for investors investing in startups. Issues include available datasets,
costs associated with data quality, and more. In public institutions, access to
data is more difficult due to budget constraints or data protection
regulations. Especially in the life sciences, where regulations are more
stringent, accessing data can be more difficult. In other words, companies can
collect/expand datasets while accelerating development by utilizing
standardized open sources in AI R&D, and gain a competitive edge by
simultaneously using official/unofficial IP protection strategies.
Implication
There are various studies that suggest that
formal/informal protection strategies are complementary to each other in
AI-based BM. Since the current system mainly regards AI as software and clearly
stipulates the criteria for patent registration applying AI algorithms, legal,
practical, and ethical challenges remain regarding the patent protection of
AI-based methods and systems.
As the development of AI technology
accelerates, fast response is required than ever before. Especially when it
comes to financing or direct investment, how to deal with public contributions
(e.g. access to specific data sets or specific AI algorithms) and value
creation (e.g. monetization of ownership/access to specific data sets) are an
important premise to establish strategies. Andreessen Horowit, an American
private venture capital firm headquartered in Silicon Valley, notes that large
companies are investing heavily in AI, so they suggest that startups need to 1)
have smart and ambitious teams, 2) access unique data sets that larger
companies do not have, and 3) differentiate by not relying too much on AI.
AI technologies are already beginning to
impact urban life, and according to the Stanford 100 Year Study on Artificial
Intelligence, AI could challenge human cognitive jobs while strengthening
ownership of intellectual capital. Therefore, while the combination of AI-based
BM innovation and intellectual property protection strategies remains
important, it is important to strike a balance between public perception of the
fairness of AI technologies and the monopoly demands of innovators.
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< Source >
The above is excerpted from;
Patent Management: Protecting Intellectual Property and Innovation 2021, Oliver Gassmann, Martin A. Bader, Mark James Thompson, Springer.
