Artificial intelligence (AI) is transforming many industries, and intellectual property (IP) is no exception. AI is changing the way businesses approach IP management, from patent search and analysis to IP protection.
Artificial Intelligence has become increasingly prevalent in everyday applications such as smartphone facial recognition, smart assistants for E-commerce services, autonomous driving systems, and user preference-based recommendations on search engines, entertainment platforms, and social media. With the emergence of AI chat bot programs like ChatGPT which intake user input and combine them with learning algorithms to tailor itself to generate more human-like responses for a variety of topics, AI has evidently advanced greatly since its early introduction and its influence reaching many industries. However, one industry that AI is a gray area in is Intellectual Property. In this article, we will explore the impact of AI on IP and opportunities and challenges it presents.
Benefits and Challenges
There are potential benefits of utilizing AI in IP management. One of the most significant benefits of AI in IP is improved efficiency and speed. AI-powered software can quickly search and analyze large volumes of IP data, including patents, trademarks, and copyrights. This enables IP professionals to identify relevant prior art, assess the patentability of inventions, and detect potential infringements much faster than traditional methods.
AI can also automate routine tasks such as drafting patent applications, freeing up IP professionals’ time to focus on more strategic tasks, such as developing new IP strategies and creating value from IP assets. Additionally, AI can be used to identify patterns and trends in IP data, providing insights that can inform strategic decision-making. For example, AI can be used to identify emerging technologies or market trends that may be relevant to a company’s IP strategy.
Challenges to Using AI in IP
While AI evidently presents many opportunities in IP it also poses some challenges. For example, AI-powered systems must be trained on large datasets to provide accurate results, and this can be time-consuming and costly. Additionally, there are concerns about the ethical use of AI such as issues related to quality, bias, and inventorship.
One of the biggest challenges to using AI in IP is the quality of data. AI systems require large amounts of high-quality data to be trained effectively. However, in the field of IP, the data can be complex, making it difficult to train an AI model that can reliably identify and classify information related to IP. Furthermore, AI models are only as good as the data they are trained on, and if the data is biased, the model’s output will also be biased. In the context of IP, bias can lead to unfair or inaccurate decisions, which can have significant consequences for individuals and businesses. As such, even though AI can help streamline and automate certain aspects of IP, it cannot replace human expertise entirely because IP decisions often require a deep understanding of legal and regulatory frameworks, as well as the ability to make nuanced judgments based on context and interpretation.
Redefining Investorship in IP
The main difficulties of assessing AI contribution and inventorship in the Intellectual Property industry stems from contributor rights. A significant case that set precedence and challenged traditional institutions was that of Thaler v. Vidal, in which Dr. Stephen Thaler filed patent applications for two inventions created by his Device for the Autonomous Bootstrapping of Unified Sciences (“DABUS”) and named DABUS as the sole inventor. These applications were rejected by the U.S. Patent and Trademark Office (USPTO) with the logic of the U.S. Patent Act that stated, “inventorship [is limited] to natural persons.” The decisions were appealed and eventually reached the Federal Circuit, which reaffirmed the lower courts’ decision to reject the applications on the basis that inventors must be “individuals.” The precedent definition used by the Federal Court was defined in Mohamad v. Palestinian Auth. which maintained that an “‘individual’ “ordinarily means a human being, a person.” Similarly, countries like the UK and various EPO member states rejected Thaler’s applications because they have laws in place that deem AI ineligible to hold patents. However, one country that challenged the status quo was Australia in the decision made by Justice Beach of the Federal Court stating that the Australian Patents Act did not explicitly define what an inventor was and thus, DABUS is eligible to be granted a patent. Despite Justice Beach’s arguments, the higher court ultimately overruled the decision.
Significance of Rulings
With AI becoming more commonplace, the decisions by the USPTO and Federal Circuit’s subsequent reaffirmation that AI programs are not eligible to become patent “inventors” set an important precedent in the intersection between the growing landscape of AI technology and intellectual property. Despite Dr. Thaler’s lack of success in convincing courts to grant a patent to DABUS, the involvement of AI in intellectual property matters presents several new questions regarding inventorship. One notable question that stands is whether people can take hold patents for inventions co-developed by AI, that is, can an invention be patented if AI is used majorly as a developmental tool? Ultimately, Thaler’s cases are likely the first of many to challenge courts to view traditional patent laws and property rights in a different angle amidst the growing AI technology.