Special Presentation by Dr. Alex G. Lee (TechIPm, USA) Hosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Date/Time: Thursday, 20 November 2025 @ 12:00 UTC (12 PM GMT) PDH Certificate: while basic attendance is free, this course also offers one (1) Professional Development Hour (PDH) for a nominal fee; please choose the appropriate "Registration Fee" when registering; actual, verified real-time attendance required for PDH; additional terms and conditions apply. Topic: AI-Native 6G IP Moats: Rethinking Global Policy for SEP/FRAND Abstract: AI-native 6G treats intelligence as a built-in network function: models live in the stack (PHY/MAC/RAN/Core), steer behavior in real time, and enable semantic communications, digital-twin control, and federated learning across device, edge, and cloud. That shift changes how we innovate and how we govern IP. Using “IP moats” in the Buffett sense — durable advantage from enforceable IP (including SEPs), data rights, and standards participation — I ask: what global policy makes those moats defensible for true contributors while keeping consumer devices affordable? I propose a practical baseline with two pillars. Pillar 1: Mandatory essentiality evaluation at ETSI declaration (and at major spec revisions), performed by independent evaluators under a common, auditable protocol with rebuttal rights — recognizing that in 6G, essentiality often requires behavioral evidence (simulations/benchmarks), not text alone. Pillar 2: Transparent, method-driven FRAND (fair, reasonable and non-discriminatory) that reflects multi-layer AI value (edge silicon, radio/PHY, RAN control, edge orchestration, cloud inference) while guarding against stacking and protecting consumer pricing. To operationalize this, I introduce two agentic, provenance-first co-pilots: - Agentic AI-Powered Essentiality Evaluation Framework — aligns claim elements to standards text and versions, ingests simulation/benchmark artifacts, produces source-pinned evidence packs with confidence scores, flags family overlap/over-declaration, and supports human-in-the-loop review. - Agentic AI-Powered FRAND Evaluation Framework — builds auditable rate models (top-down, incremental value, usage-based, or hybrid) from shared inputs: portfolio size and essentiality-confidence distribution, stack-layer contribution mapping, device/IoT usage metrics, ASP tiers, geography mix, pool comparables, and anti-stacking constraints. Outputs include rate corridors, sensitivity bands, tiered pricing and safe-harbor pool options, plus triggers for de-declaration as specs evolve. Speaker: [] Alex G. Lee is a NY State attorney, USPTO-registered patent attorney, and Certified Licensing Professional (CLP) with a Ph.D. in Physics (Johns Hopkins) and J.D. (Suffolk Law). He bridges 3GPP standards and IP strategy, having led hundreds of 3G/4G/5G essentiality evaluations for global programs. As Principal Consultant at TechIPm, he has supported SEP licensing, portfolio sales, and enforcement for global companies. He has built agentic AI-powered IP intelligence for SEP development, licensing, and litigation. Earlier, he held roles at Hsuanyeh Law Group, Liquidax Capital, Korea’s National Radio Research Agency, and Korea Telecom (ITU-R/early 3GPP representation). His work focuses on agentic AI-powered frameworks for 5G/6G innovation and SEP portfolio development and monetization. Brochure (PDF): (https://drive.google.com/file/d/1iKy0C_zNIuI3EhB0qOlGQmV82UDxAsXk/view?usp=share_link) Co-sponsored by: Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group Virtual: https://events.vtools.ieee.org/m/500656
