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Day-9: Certificate Course On The Interface Between Artificial Intelligence And Intellectual Property Rights

Session on AI Inventions and Inventorship in Patent law

Event Date: 23rd December 2025

Event Brief Description:

The School of Law, Galgotias University, in collaboration with the Centre for Artificial Intelligence & Technology and the Centre for IP & Innovation, hosted a specialized session of its Certificate Course on the Interface of AI and Intellectual Property Law. The lecture featured Mr. Rodney D. Ryder, Founding Partner of Scriboard and a distinguished expert in Cyber Law.

The session focused on the critical legal challenges posed by AI-generated inventions and the strategic role of Trade Secrets in protecting AI-centric enterprises. Participants explored the shift from traditional patenting to trade secret protection, particularly in jurisdictions like India and the EU where algorithmic patentability remains restricted. The discussion bridged the gap between theoretical IP frameworks and the practical realities of "Artificial Inventors," addressing whether non-human entities can hold authorship and how "The Bundle of Rights" applies to machine-generated outputs. By blending legal doctrine with governance strategies, the event provided a comprehensive roadmap for navigating the complexities of data-driven innovation and the risks of proprietary leakage in the era of Generative AI.

Event Detailed Description:

The lecture provided a deep dive into the dual pillars of AI protection: Patent Law and Trade Secret Management. Mr. Ryder navigated the evolving landscape where technology often outpaces legislation, specifically addressing the "Artificial Inventor" controversy.

AI Inventions and the Patentability Hurdle:

The session opened with an analysis of Inventorship. A central question was whether current laws contemplate a non-human creator. Referencing the Alice Corporation v. CLS Bank (2014) precedent, the speaker highlighted the "Inventive Step" test, explaining that patent claims are often invalidated if they cover subject matter achievable through ordinary mental processes. This poses a significant challenge for AI-generated works, raising questions about who owns the "Bundle of Rights"—including moral rights and royalties—when a machine is the primary creator.

The Strategic Shift to Trade Secrets:

Given that algorithms are frequently unpatentable in many jurisdictions, the lecture emphasized Trade Secrets as the "forever" protection mechanism. Unlike patents, trade secrets offer no protection against independent development or reverse engineering but are "naturally conducive" to AI models. The speaker defined the core components of AI trade secrets:

  • Proprietary datasets and training methodologies.
  • Model architecture and feature engineering techniques.
  • Source codes and the specific "mix and match" of public data into protectable secrets.

Risks and Governance in the AI Era:

A significant portion of the discussion was dedicated to the risks of misappropriation. Using a practical illustration, the speaker showed how employees unknowingly leaking trade secrets into public AI tools (like ChatGPT) can dilute a company’s competitive advantage.

To mitigate these risks, a Four-Pillar Governance Framework was proposed:

  1. Policy: Establishing clear rules for AI usage.
  2. Technology: Architecting secure systems to prevent data leakage.
  3. People: Focused training and role-based accountability.
  4. Process: Continuous auditing and incident response.

The session concluded with a masterclass on Information Classification, urging organizations to tag and label data at the point of creation to ensure that high-sensitivity assets, like model parameters, receive maximum technical protection.

Department Name: School of Law, Galgotias University


Event Outcome:

  • Conceptual Clarity: Participants gained an advanced understanding of the legal distinction between patentable inventions and trade secrets within the AI domain.
  • Risk Identification: Attendees learned to identify "improper means" of misappropriation and the specific vulnerabilities associated with using third-party AI tools.
  • Strategic Frameworks: The session equipped students and professionals with a practical four-pillar framework (Policy, Technology, People, Process) for organizational AI governance.
  • Classification Skills: Participants were provided with a roadmap for implementing formal information classification schemes to safeguard proprietary algorithms and datasets.
  • Policy Insights: The lecture fostered an interdisciplinary dialogue on the need for legislative evolution regarding non-human creators and the future of "Moral Rights" in AI-generated IP.