Industry leaders have built substantial IP portfolios in their respective fields, granting them crucial patent rights to protect innovation and create market value. To compete with industry leaders effectively and capture a share of the global marketplace, new entrants must carefully consider their IP strategies.
AI technology is rapidly changing established IP concepts such as patents, designs, and copyright protections. Laws must be supportive of innovation, not replace it, to ensure that they continue to serve their intended purpose – supporting creative thinking rather than replacing it. Three artists sued generative art platforms in court recently (Andersen V Stability AI, et al) for using their works without their permission as training data for their new works that may or may not qualify for copyright protection.
Many companies that use AI tools to create software face new intellectual property questions. One such question involves the allocation of ownership. Companies should consider a variety of options, including demanding terms of service from generative AI vendors that confirm proper licensing of content used as training data. Additionally, they should include provisions in contracts that support copyright registration and ownership tracking of resulting works. Here is how AI can be used in Intellectual Property Licensing.
License Agreement Optimization
AI can play a vital role in optimizing licensing agreements through the use of historical data and best industry practices. Here are some ways AI can help in this process.
a. Data Driven Insights
AI Systems can analyze huge amounts of historical data including licensing agreements, outcomes and performance of licensed assets. AI systems can identify patterns and correlations within this data to provide valuable insight into the terms and conditions that have historically resulted in successful agreements. This data-driven method allows parties to make better decisions when structuring licensing deals.
AI is able to assess the risks that may be associated with licensing agreements. It can analyze past data to identify clauses and provisions that led to legal disputes or issues. AI can help parties to mitigate risk and create agreements that are less likely than ever to lead into disputes by identifying potential pitfalls.
b. Market Benchmarking
AI compares the terms of a license agreement with industry standards and benchmarks. It can use a database of licensing agreements from specific industries to suggest terms that align with current practices. It ensures the agreement is fair, competitive and prevents one party from gaining unfair advantage.
Customization AI allows customization of licensing agreements based on their unique circumstances. AI draws from historical data, industry standards and AI. AI systems can take into account specific factors, such as the type of intellectual property, market conditions, financial health, and the goals of each party. AI can tailor terms to fit these factors and ensure that parties have their own interests in mind.
AI simulates different scenarios to evaluate the possible outcomes of various terms and conditions. The parties can then see how their decisions may affect the overall success of the agreement and its profitability. AI can, for example, model the impact of different royalty rates and exclusivity clauses on revenue generation. This helps parties make better strategic decisions.
c. Continuous improvement
AI provides feedback and recommendations to optimize the agreement during its entire lifecycle. AI can adjust its suggestions as new data become available and the market conditions change. This will ensure that an agreement is competitive and mutually beneficial. This process of continuous improvement helps parties to adapt to changing circumstances.
AI is a powerful tool to optimize licensing agreements. It can analyze historical data, assess risk, benchmark against industry standards, customize terms, create scenarios, and give ongoing feedback. AI-driven insights can be used to negotiate and structure agreements in a way that maximizes the benefits and minimizes potential risks for both parties.
d. Identifying and Licensing Intellectual Property Assets
The intersection of AI and intellectual property law raises several issues, including how businesses can identify and track their digital content and determine whether it is subject to licensing agreements. AI can also be used to streamline content licensing negotiations and improve the accuracy of such agreements, reducing costs and speeding up the process.
In addition, AI may help identify intellectual property rights violations and prevent them from occurring in the first place. For example, AI can help businesses detect trademark infringement, copyright infringement, and patent infringement in data and information that is gathered from the web. AI can also be used to identify and protect important intellectual property assets, such as software.
One of the most critical legal issues related to AI and intellectual property involves ownership issues. If a business cannot clearly define ownership of its AI-generated software, it may not be able to monetize the product or sell it to third parties. This can create substantial risks for the company and affect its valuation. A software licensing attorney can help businesses resolve these issues.
IP disputes in the era of AI often center on copyright and patent issues. For example, recent lawsuits between Getty Images and Stability AI over the use of images in their respective training processes raise questions about how generative AI art might be protected by copyright law in the future. In addition, a case currently before the U.S. Supreme Court could help refine the definition of “derivative work” under copyright law, and thus affect how generative AI works are treated in the future.
Some stakeholders believe that a new right for computer-generated works would be beneficial, as it could better align with existing copyright law and promote innovation in this area. However, they believe that the scope and term of protection should be limited to encourage investment in AI technology and not unreasonably restrict wider competition or innovation by third parties.
In a discussion on the topic, some stakeholders also highlighted the importance of transparency in the patent system, and how this should be achieved by identifying the inventor in cases where an AI has devised an invention itself. However, they cautioned that a change in the rule for inventorship could have unforeseen consequences for the system.
Copyright is a legal right that gives an author the exclusive legal right to produce, reproduce, publicly display and perform an original literary, artistic, dramatic, or musical work. This includes computer programme code.
The impact of generative AI on established intellectual property concepts is becoming increasingly apparent. In particular, the ownership and protection of creative outputs generated by generative AI has not yet been fully defined.
For example, it is unclear whether a creator of an AI tool can retain copyright in works created with the tool, even if those works are based on data inputs and queries that the AI generates itself. One possible solution is to use an AI tool that understands and respects this issue, such as Content at Scale, which automatically passes any copies of content created with the tool to its customers, regardless of who drove the creation process.
In the US, it is well-established that an AI cannot be the author of a creative work. However, a court case in late 2017 is likely to refine this understanding of the law and open up new paths for AI-generated works to qualify for copyright protection.
It is also unclear how AI-generated works will be treated under existing copyright law. For instance, if an AI creates music that is a derivative of another artist’s work, can that piece be considered a fair use under copyright law? The answer to this question may depend on the result of a forthcoming court case against the Andy Warhol Foundation.
While the emergence of generative AI is transformative for the music industry, it is important to remember that intellectual property laws are not static and must be updated as our world continues to evolve. As a result, it is critical to incorporate IP into your company’s business strategy early and develop an ongoing IP portfolio management plan.
This approach to IP management democratizes access to IP protection resources, empowering independent artists. With the right tools, artists can navigate the complexities of the music industry independently and uphold their own rights. However, it is also crucial to ensure that ethical considerations and transparency are integrated into all AI implementations, particularly those related to copyright.
Intellectual property (IP) encompasses intangible assets such as inventions, brands, new technologies, source code and artistic works. IP rights are enforceable in court and can be licensed, transferred or sold to others. Some of the most common forms of IP are patents, trademarks and copyrights.
AI is being used in the patent process to help search for and identify prior art that could potentially invalidate a newly filed patent application. This use of AI is being driven by the need to manage patent applications at scale and the desire for more accurate search results. It is also being driven by the fact that many patent offices are experiencing increased volumes of applications and shorter patent terms.
However, this use of AI has the potential to change established concepts of patents and IP. The use of AI may lead to IP being applied in different ways, for example, where the invention or creative work is created by an AI rather than a human. In this context, the patent system needs to adapt and ensure that it delivers the value of the innovation to society.
This may involve changing the way that patents are granted and managed to make it easier for patent applicants to describe how their inventions or creative works are enabled by AI. It might also mean introducing new types of rights for AI-generated creations or extending existing IP rights to incorporate AI support. The latter approach would need to be carefully thought through, as it could encourage AI users to not acknowledge AI inventorship in order to gain patent protection or might reward creators who do not use AI to create their work but instead rely solely on AI tools for the creative work.
While some laws are starting to recognize the existence of AI-generated creations, there are still a number of legal issues with respect to ownership, infringement, and attribution. For instance, generative AI tools are trained using vast amounts of data scraped from the internet, including potentially copyrighted content. Consequently, there are a number of intellectual property disputes related to the training of these AI systems, including lawsuits by Getty Images and Stable Diffusion against an AI art generator.
While the broad use of AI is transforming established IP concepts – patents, designs, literary and artistic works, and trademarks – it also poses new legal questions that must be addressed. These include copyright protection for works created by AI and how to protect AI itself.
In addition, companies that make and sell AI tools face issues arising from their use of existing intellectual property rights. A software licensing attorney can help businesses determine whether AI-generated work is protectable and how to go about protecting it. They can also assist with identifying and assessing third-party ownership and licensing risks.
One way to balance the competing interests of AI and IP is through patents. By incentivizing the invention of new AI-devised innovations, patents can promote innovation without unnecessarily restricting follow-on innovation by others. However, if AI-devised inventions cannot be patented, they may not attract investment or encourage further research.
As such, a patent system should be flexible enough to adapt to new technological developments. This could be achieved by clarifying the meaning of “inventor,” or by allowing an AI system to qualify as an inventor, provided that it is not the actual deviser of the invention. In this case, the human inventor would still need to be named in the application and provide full disclosure.
For example, generative AI (using data lakes and question snippets to recover patterns and relationships) is increasingly used in creative industries. While this technology is useful, it can also infringe on other people’s intellectual property rights by creating unauthorized derivative works. As a result, many artists are filing class action lawsuits against generative AI platforms that do not have license agreements with them to access their original works.
A global solution to these challenges is important, given that AI is a worldwide phenomenon and IP is a globally-used asset. To this end, WIPO is deploying new, innovative AI-enabled technologies to streamline and speed up processes, including our new AI-empowered trademark image search tool.
To further accelerate this work, Global Partnership on Artificial Intelligence (GPAI) is building a network of international stakeholders to support project-oriented collaboration on responsible AI through working groups focused on issues such as the future of work, the future of IP, and data governance and responsibility. We hope that this will ultimately contribute to a more balanced and sustainable approach to AI and IP.