Artificial intelligence (AI) has become a digital frontier that will have a profound impact on the world. It will have immense technological, economic, and social consequences and will transform the way humans work, live, and produce and distribute goods and services. Although it is too early to say, it is clear that AI will affect traditional intellectual property (IP) concepts.
Commercial AI-generated music and AI-created inventions are not so far, and it is expected that it will define the concepts of the ‘composer’, ‘author’, and ‘inventor’. But how that will happen is not clear yet.
AI & IP
The fundamental goals of the intellectual property (IP) system have always encouraged new technologies and creative works, and to create a sustainable economic basis for invention and creation. From a purely financial perspective, if other aims of the IP system such as ‘just reward’ and moral rights are set aside, there is no reason not to use IP to reward AI-generated inventions or creations.
The wide use of AI technologies will define established IP concepts such as patents, designs, literary and artistic works. For instance, the life sciences generate vast quantities of data that have significant value but do not constitute an invention in the classical sense. But before that, working out on the rights and obligations that attach to them is required. Many argue that as data is a foundation of AI, it should be freely available to enable the development of AI and other applications.
Data and algorithms raise several fundamental IP-related questions like how one creates property rights in an algorithm that is continuously changing.
Demand for IP rights continues to surpass economic growth rates across the globe. The IP system is known is not certainly going out of trend. It is being used more than ever. But new challenges are emerging and the result may be an additional layer of IP instead of replacing the existing system.
Infringement of IP
The flip side is whether AI can own IP or it can infringe IP. If an AI machine can generate subject matter, who will be held responsible if that subject matter violates third party IP? The issue that arises here is copyright infringement requires actual copying. The author of the infringement work must have had access to the work protected by copyright. In the case of an AI machine that is expected to have access to everything on the internet, the trouble of showing that the infringer had access to the protected work might be much easier to overcome.
How Can AI Improve the Administration of IP?
AI systems will increasingly play an essential role in IP administration in the future. Given the costs involved with collecting and cleaning large corpora of data to feed AI-systems, one needs to encourage the sharing of resources. “The international IP community can work together to achieve high levels of interoperability in a cost-effective manner deploying the AI-powered systems in the future,” says Francis Gurry, Director General of World Intellectual Property Organization (WIPO).
WIPO explore ways to develop AI applications using training data provided by member states and other institutional partners. In return, it shares new AI applications developed using those data with the partners.
For example, WIPO has developed an AI-powered state-of-the-art neural machine translation tool, known as WIPO Translate,” cites Gurry. “We are sharing this tool with 14 intergovernmental organisations and various patent offices across the globe.”
As the system depends on access to and availability of data, developing such tools can benefit this sector using and supplying data to improve it.
The broader question is how AI will change the categories and concepts of IP itself. However, it is taking place at a time when the world is putting less energy into multilateral rule-making over the past 70 years. It is a serious problem that goes beyond IP and needs to be resolved in this area because IP is essentially an international phenomenon. Technology is global, so as are the patent data involved with it. Patents are rarely linked to a single jurisdiction. Thus, it requires global solutions that ensure functional interoperability.
Barriers of Deploying AI-enabled systems
Building AI capacity across IP offices is a significant challenge. Although AI is around for a while, it has recently become an obvious technological solution. In this sector, the number of professionals with the required training and knowledge is limited, which makes developing in-house AI capacity difficult, specifically in the face of competition from better-resourced, higher-paying private enterprises.
Smaller IP offices face some inevitable challenges. AI systems depend on data and algorithms, and smaller offices naturally have access to fewer data, that means the imperative of volume, which is forcing the development and deployment of AI applications in larger offices. It is less effective in smaller offices where the importance of applications remains manageable. The IP world has a generally accepted policy of open access to data associated with IP registrations for patents, trademarks and designs. It will help the smaller IP offices, which, in principle, can access these data. Overcoming these challenges will require a greater emphasis on integration and coordination.
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