Introduction
In the rapidly evolving landscape of artificial intelligence (AI), intangible assets [2] [3], particularly intellectual property (IP) [1] [2] [3] [4], play a crucial role in determining the value and success of AI companies. An effective IP strategy is essential for protecting these assets [2] [3], enhancing company valuations [2] [3], and navigating the complex legal landscape associated with AI technologies.
Description
Intangible assets constitute over 90% of the value of AI companies [2] [3], making an effective intellectual property (IP) strategy essential for protecting these assets and enhancing company valuations [2] [3]. A successful IP strategy should focus on the revenue drivers of the business [2], recognizing the unique aspects of AI compared to traditional software companies [2] [3]. Developers [1], procurers [1], and deployers of AI tools face significant IP uncertainties and risks that can hinder the realization of their investments [1], particularly concerning unclear IP rights related to AI-generated content.
AI companies are heavily data-driven [2] [3], and the legal protection of data assets is complex and varies by jurisdiction [2] [3]. For instance [2] [3], in Europe [2] [3], AI tools that utilize third-party information must track the origin of that data [2]. In the UK [1], existing IP and data protection laws apply to AI outputs but were not designed with AI in mind [1], leading to ambiguity regarding the protectability and ownership of such outputs [1]. Companies must ensure they have the legal rights to exploit their products [2], which involves addressing IP protection for specific data types and establishing a legal chain-of-title for necessary data rights [2]. A comprehensive data governance approach is necessary to safeguard these rights effectively [3], especially in light of compliance with data protection laws like GDPR.
The drafting and negotiation of contracts related to intangible assets and their commercialization are crucial [2]. Contracts must clearly define ownership of derivative data and technology improvements [2] [3], as well as the rights to use third-party software or data [2] [3]. In the UK [1], while contractual agreements can clarify ownership [1], they do not guarantee IP protection under existing laws [1], potentially leading to disputes [1]. The negotiation of these agreements significantly impacts company value [3], and any oversights can lead to costly corrections [3].
Innovations in AI can be difficult to protect through traditional IP filings like patents [2]. A focused patent strategy that emphasizes quality over quantity is essential [3], particularly in a competitive market. Companies must carefully determine which IP tools to employ for their AI innovations [2], ensuring adherence to current interpretations by US courts regarding the patentability of software patents [4], as established in the Alice v [4]. CLS Bank case [4]. The UK courts have yet to address critical questions surrounding AI [1], including the patentability of inventions developed by AI and the copyright protection of AI outputs [1]. Identifying relevant patents for freedom-to-operate and specific AI technology aspects is critical [3].
Trade secrets play a significant role in the IP strategy of AI companies [2]. Effective protection of trade secrets requires intentional management through established business processes to identify and safeguard valuable information [2]. Many companies default to using trade secrets without a comprehensive strategy [3] [4], leading to potential vulnerabilities that can be exposed during due diligence, especially in financing or enforcement scenarios [4]. Using trade secrets as input prompts can risk their disclosure [1], leading to a loss of protection [1]. Establishing processes to identify and manage valuable trade secrets [3] [4], including applied know-how [4], can enhance efficiency and effectiveness [4]. A trade secrets register can help categorize and log protections [4], balancing risk mitigation with development and commercialization needs [4]. Companies that implement robust trade secrets management programs find that it enhances risk management and can justify higher valuations [4].
The use of open source and open data is prevalent in AI [2] [3] [4], but not all open resources are legally unrestricted [2] [3] [4]. Open-source software can present challenges depending on its licensing terms [4], which may impact core innovations [4]. While remediation is often possible [4], it can be costly and time-consuming [4], particularly if issues arise during legal due diligence [4], potentially jeopardizing deals [4]. Implementing basic controls over the types of open-source or open data utilized can help mitigate these risks [2] [4], and there are often viable alternatives to problematic open-source libraries that offer more favorable terms [4].
Data protection risks also arise when using AI tools [1], particularly regarding personal data [1]. Compliance with data protection laws is essential, and regulators may impose restrictions on certain AI tools until compliance is assured [1]. Businesses can mitigate risks by employing ‘closed’ AI tools that do not connect to external systems [1], safeguarding confidential and personal data [1]. A disciplined commitment to a value-driven IP strategy can help AI companies navigate these challenges while mitigating risks [2] [3]. With expert guidance [2] [4], companies can prioritize their efforts and resources on building significant IP assets that align with their business development stages as they evolve [2]. Ensuring human involvement in the creative and inventive processes alongside AI is vital [1], as this engagement will facilitate valuable IP protection and address regulatory concerns, allowing for continued development of AI technologies [1].
Conclusion
The strategic management of intellectual property is paramount for AI companies to protect their intangible assets and maximize their market value. By addressing the unique challenges posed by AI, such as data protection [1], patentability [1] [4], and trade secret management [4], companies can mitigate risks and enhance their competitive edge. A well-structured IP strategy not only safeguards innovations but also supports sustainable growth and development in the dynamic AI industry.
References
[1] https://www.pinsentmasons.com/en-gb/out-law/analysis/ip-risks-uncertainties-hinder-ai-innovation-businesses
[2] https://www.lexology.com/library/detail.aspx?g=fc36ce8c-9d0b-40cf-989a-c0f2bcd8252b
[3] https://www.jdsupra.com/legalnews/best-practices-in-developing-winning-ip-9022901/
[4] https://www.mintz.com/insights-center/viewpoints/2231/2024-11-18-best-practices-developing-winning-ip-strategies-ai
