Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Artificial Intelligence in Intellectual Property & Global Health Justice is a three-day, mentor-guided course that unites legal scholars, AI/ML practitioners, public-health experts, and policy advocates. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Artificial Intelligence in Intellectual Property & Global Health Justice is a three-day, mentor-guided course that unites legal scholars, AI/ML practitioners, public-health experts, and policy advocates.
📋 Course Objectives
1. Translate AI Enablement theory into practical, reproducible analysis.
2. Assemble a documented case study that evidences your applied capability.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Patents, Data and Software
• Patentability requirements and their application to AI-assisted invention
• Inventorship questions raised by AI-generated output
• Trade secrets, copyright and database rights over training data and models
Medicines, Monopoly and Access
• Patent thickets, evergreening and secondary patents on medicines
• Data exclusivity and regulatory protection distinct from patents
• AI-assisted drug discovery and the IP status of its outputs
TRIPS and Its Flexibilities
• TRIPS obligations, transition periods and the Doha Declaration
• Compulsory licensing and government use in practice
• India's Section 3(d) and comparable public-interest provisions
Health Equity in Practice
• Technology transfer, voluntary licensing and patent pools
• Pricing, procurement and the evidence on access outcomes
• Pandemic preparedness and equitable distribution commitments
Building an Argument
• Patent landscaping with text analytics over patent databases
• Evaluating access claims critically from both industry and advocacy sources
• Drafting a policy brief that survives adversarial scrutiny
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
Frequently Asked Questions
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