Challenges in IP Ownership, Protection, and Regulation

The large-scale integration of artificial intelligence (AI) in accelerating innovation cycles across sectors has prompted important questions about ownership, valuation, and protection of intellectual assets. In this AI-driven economy, intellectual property (IP) creation, protection, and regulation has become an important consideration for businesses. MSMEs, for example, while benefiting from increased access to AI, struggle with IP protection due to ambiguous ownership, technical complexity, and evolving legislation. 

Trust Risks in AI Adoption

The widespread adoption of AI has led to significant risks, notably in establishing its trustworthiness and in intellectual property (IP) management. Legal frameworks originally designed for human inventors are under strain amid questions over inventorship, ownership, eligibility, and infringement. Today’s routine AI applications are not easily patentable and AI-generated content often lacks copyright protection. This leads to new forms of risk- lack of transparency, bias, misinformation, deepfakes, hallucinations while using LLMs, and trademark misuse, all which call for stronger governance and ethical standards.

While the risks associated with AI systems are genuine and expanding with technological advances, the benefits are substantial if development and deployment adhere to compliance requirements. Addressing risks and trust considerations through the AI system lifecycle rather than post-development ensures trustworthy AI, fosters public confidence, and helps mitigate adverse outcomes.

Key Challenges in IP Management and Protection 

AI has transformed innovation but made IP protection more complex. Traditional frameworks are challenged by AI’s co-creative and autonomous capabilities. 

• Patents: Currently, only humans are recognized as inventors, even when AI significantly assists in the inventive process. Patent offices and courts continue to require a natural person to be named as the inventor. This creates legal uncertainty in situations where AI systems contribute substantially or autonomously to the inventive step.
• Copyright: Purely autonomous AI-generated outputs generally do not receive copyright protection in most major jurisdictions because they lack human authorship. However, AI-assisted works may qualify for protection where there is meaningful human creative involvement, making the degree of human contribution a critical legal consideration.

• AI-related patents are increasingly vulnerable where inventions rely on applying general ML techniques to specific use cases without demonstrating a non-obvious technological advance, weakening enforceability and portfolio strength. 
• Copyright infringement claims in AI training are also difficult to establish due to jurisdictional issues, the role of user inputs, and questions over ownership of training data, resulting in fragmented and unpredictable legal outcomes. 
• AI-driven patents are subject to infringement and invalidation challenges, increasing legal costs and undermining investor confidence. This leads to uncertainty around the commercialization and monetization of these patents. 

IP Strategy and policy Recommendations 

It is important to align IP management with responsible AI principles and emerging technology trends to address the key challenges faced in IP management and protection. According to the report by CII, patent laws must evolve in tandem with evolution of AI and safeguard inventions produced by artificial intelligence. This will help increase investment and research in the area to encourage inventions in AI. It also suggests harmonizing IP laws among international patent offices for protection of AI-driven IP and introducing a sui generis IP framework for machine learning outputs that do not meet current patent criteria.

The government is also encouraged to invest in specialized trainings and continuous capacity-building programs for patent examiners on advancements in AI technologies for faster and higher quality examination of patented AI-related patent applications. Inventors should be encouraged to clearly establish non-obvious technical contributions in their patent applications, as a mere application of AI does not satisfy patentability requirements. The use of industry recognition, commercial success, and long-felt unmet needs should be used to counter claims of obviousness during invalidation proceedings.

Ultimately, the coming decade will determine whether AI serves as a catalyst or exacerbates inequality among India’s small businesses. The path forward requires the working together of all stakeholders, be it businesses, government, academic institution or independent inventors, to combine technological innovation with regulatory foresight and continuous reskilling.

Balancing Use and Challenges 

This evolution of AI has fundamentally reshaped the landscape of IP management, presenting complex legal and operational challenges across patents, copyrights, and trademarks. While legal precedents and regulatory frameworks worldwide emphasize the need for clear human authorship, ethical standards, and robust compliance, ambiguity and risks persist around inventorship, ownership, and fair use. With changing global regulations, organizations must remain vigilant, adapting to jurisdictional nuances, evolving case laws, and heightened compliance requirements to safeguard innovation, IP value, and business reputation in the AI era.