BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VEVENT DTSTAMP:20260124T044340Z UID:B8D9BDD0-EDAF-4B7E-99DF-86E328B20153 DTSTART;TZID=Etc/UTC:20251120T120000 DTEND;TZID=Etc/UTC:20251120T130000 DESCRIPTION:[ALLSTaR: Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN]\n\nSpecial Presentation by Dr. Alex G. Lee (TechIPm \, USA)\n\nHosted by the Future Networks Artificial Intelligence & Machine Learning (AIML) Working Group\n\nDate/Time: Thursday\, 20 November 2025 @ 12:00 UTC (12 PM GMT)\n\nPDH 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 regi stering\; actual\, verified real-time attendance required for PDH\; additi onal terms and conditions apply.\n\nTopic:\n\nAI-Native 6G IP Moats: Rethi nking Global Policy for SEP/FRAND\n\nAbstract:\n\nAI-native 6G treats inte lligence as a built-in network function: models live in the stack (PHY/MAC /RAN/Core)\, steer behavior in real time\, and enable semantic communicati ons\, 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 enforceabl e IP (including SEPs)\, data rights\, and standards participation — I as k: what global policy makes those moats defensible for true contributors w hile keeping consumer devices affordable?\n\nI propose a practical baselin e with two pillars. Pillar 1: Mandatory essentiality evaluation at ETSI de claration (and at major spec revisions)\, performed by independent evaluat ors under a common\, auditable protocol with rebuttal rights — recognizi ng that in 6G\, essentiality often requires behavioral evidence (simulatio ns/benchmarks)\, not text alone. Pillar 2: Transparent\, method-driven FRA ND (fair\, reasonable and non-discriminatory) that reflects multi-layer AI value (edge silicon\, radio/PHY\, RAN control\, edge orchestration\, clou d inference) while guarding against stacking and protecting consumer prici ng.\n\nTo operationalize this\, I introduce two agentic\, provenance-first co-pilots:\n\n- Agentic AI-Powered Essentiality Evaluation Framework — aligns claim elements to standards text and versions\, ingests simulation/ benchmark artifacts\, produces source-pinned evidence packs with confidenc e scores\, flags family overlap/over-declaration\, and supports human-in-t he-loop review.\n- Agentic AI-Powered FRAND Evaluation Framework — build s auditable rate models (top-down\, incremental value\, usage-based\, or h ybrid) from shared inputs: portfolio size and essentiality-confidence dist ribution\, stack-layer contribution mapping\, device/IoT usage metrics\, A SP tiers\, geography mix\, pool comparables\, and anti-stacking constraint s. Outputs include rate corridors\, sensitivity bands\, tiered pricing and safe-harbor pool options\, plus triggers for de-declaration as specs evol ve.\n\nSpeaker:\n\n[]\nAlex G. Lee is a NY State attorney\, USPTO-register ed patent attorney\, and Certified Licensing Professional (CLP) with a Ph. D. in Physics (Johns Hopkins) and J.D. (Suffolk Law). He bridges 3GPP stan dards and IP strategy\, having led hundreds of 3G/4G/5G essentiality evalu ations for global programs. As Principal Consultant at TechIPm\, he has su pported SEP licensing\, portfolio sales\, and enforcement for global compa nies. He has built agentic AI-powered IP intelligence for SEP development\ , licensing\, and litigation. Earlier\, he held roles at Hsuanyeh Law Grou p\, Liquidax Capital\, Korea’s National Radio Research Agency\, and Kore a Telecom (ITU-R/early 3GPP representation). His work focuses on agentic A I-powered frameworks for 5G/6G innovation and SEP portfolio development an d monetization.\n\nBrochure (PDF): [Webinar-AIML-2025-11-20-Lee-AI-6G-IP-M oats-Brochure.pdf](https://drive.google.com/file/d/1iKy0C_zNIuI3EhB0qOlGQm V82UDxAsXk/view?usp=share_link)\n\nCo-sponsored by: Future Networks Artifi cial Intelligence & Machine Learning (AIML) Working Group\n\nVirtual: http s://events.vtools.ieee.org/m/500656 LOCATION:Virtual: https://events.vtools.ieee.org/m/500656 ORGANIZER:baw@ieee.org SEQUENCE:33 SUMMARY:AI-Native 6G IP Moats: Rethinking Global Policy for SEP/FRAND URL;VALUE=URI:https://events.vtools.ieee.org/m/500656 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="margin-top: .25in \;"><img src="https://events.vtools.ieee.org/vtools_ui/media/display/62472 21f-c153-46e1-8ec0-55d69e864d7c" alt="ALLSTaR: Automated LLM-Driven Schedu ler Generation and Testing for Intent-Based RAN" width="750" height="197"> </p>\n<p class="MsoNormal" style="margin-top: 12.0pt\;">Special Presentati on by<strong> Dr. Alex G. Lee (TechIPm\, USA)</strong></p>\n<p class="MsoN ormal" style="margin-top: 12.0pt\;">Hosted by the Future Networks<strong> Artificial Intelligence &amp\; Machine Learning (AIML) Working Group</stro ng></p>\n<p class="MsoNormal" style="margin-top: 12.0pt\;"><strong><span s tyle="font-size: 14.0pt\; font-family: Copperplate\; mso-fareast-font-fami ly: PMingLiU\; mso-fareast-theme-font: minor-fareast\; mso-bidi-font-famil y: Arial\; mso-bidi-theme-font: minor-bidi\; mso-ansi-language: EN-US\; ms o-fareast-language: ZH-TW\; mso-bidi-language: AR-SA\;">Date/Time</span></ strong><span style="font-size: 12.0pt\; font-family: 'Calibri'\,sans-serif \; mso-ascii-theme-font: minor-latin\; mso-fareast-font-family: PMingLiU\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme-font: minor-latin \; mso-bidi-font-family: Arial\; mso-bidi-theme-font: minor-bidi\; mso-ans i-language: EN-US\; mso-fareast-language: ZH-TW\; mso-bidi-language: AR-SA \;">: <strong>Thursday\, 20 November 2025</strong><strong> @ 12:00 UTC (12 PM GMT)</strong></span></p>\n<p class="MsoNormal" style="margin-top: 12.0 pt\;"><span style="font-size: 12.0pt\; font-family: 'Calibri'\,sans-serif\ ; mso-ascii-theme-font: minor-latin\; mso-fareast-font-family: PMingLiU\; mso-fareast-theme-font: minor-fareast\; mso-hansi-theme-font: minor-latin\ ; mso-bidi-font-family: Arial\; mso-bidi-theme-font: minor-bidi\; mso-ansi -language: EN-US\; mso-fareast-language: ZH-TW\; mso-bidi-language: AR-SA\ ;"><strong><em><span style="font-size: 14.0pt\; font-family: Copperplate\; mso-fareast-font-family: PMingLiU\; mso-fareast-theme-font: minor-fareast \; mso-bidi-font-family: Arial\; mso-bidi-theme-font: minor-bidi\; mso-ans i-language: EN-US\; mso-fareast-language: ZH-TW\; mso-bidi-language: AR-SA \;">PDH Certificate</span>: </em></strong><em>while basic attendance is fr ee\, this course also offers one (1) Professional Development Hour (PDH) f or a nominal fee\; please choose the appropriate "Registration Fee" when r egistering\; actual\, verified real-time attendance required for PDH\; add itional terms and conditions apply.</em></span></p>\n<p class="MsoNormal" style="margin-top: .25in\;"><strong><u><span style="font-size: 16.0pt\; fo nt-family: Copperplate\;">Topic</span></u></strong><strong><span style="fo nt-size: 16.0pt\; font-family: Copperplate\;">:</span></strong></p>\n<p cl ass="MsoNormal"><strong><span style="font-size: 16pt\;">AI-Native 6G IP Mo ats: Rethinking Global Policy for SEP/FRAND&nbsp\;</span></strong></p>\n<p class="MsoNormal" style="margin-top: .25in\;"><strong><u><span style="fon t-size: 16.0pt\; font-family: Copperplate\;">Abstract</span></u></strong>< strong><span style="font-size: 16.0pt\; font-family: Copperplate\;">:</spa n></strong></p>\n<p>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\, a nd federated learning across device\, edge\, and cloud. That shift changes how we innovate and how we govern IP. Using &ldquo\;IP moats&rdquo\; in t he Buffett sense &mdash\; durable advantage from enforceable IP (including SEPs)\, data rights\, and standards participation &mdash\; I ask: what gl obal policy makes those moats defensible for true contributors while keepi ng consumer devices affordable?</p>\n<p>I propose a practical baseline wit h two pillars. Pillar 1: Mandatory essentiality evaluation at ETSI declara tion (and at major spec revisions)\, performed by independent evaluators u nder a common\, auditable protocol with rebuttal rights &mdash\; recognizi ng that in 6G\, essentiality often requires behavioral evidence (simulatio ns/benchmarks)\, not text alone. Pillar 2: Transparent\, method-driven FRA ND (fair\, reasonable and non-discriminatory) that reflects multi-layer AI value (edge silicon\, radio/PHY\, RAN control\, edge orchestration\, clou d inference) while guarding against stacking and protecting consumer prici ng.</p>\n<p>To operationalize this\, I introduce two agentic\, provenance- first co-pilots:</p>\n<ol style="list-style-type: lower-alpha\;">\n<li>Age ntic AI-Powered Essentiality Evaluation Framework &mdash\; aligns claim el ements to standards text and versions\, ingests simulation/benchmark artif acts\, produces source-pinned evidence packs with confidence scores\, flag s family overlap/over-declaration\, and supports human-in-the-loop review. </li>\n<li>Agentic AI-Powered FRAND Evaluation Framework &mdash\; builds a uditable rate models (top-down\, incremental value\, usage-based\, or hybr id) from shared inputs: portfolio size and essentiality-confidence distrib ution\, 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 sa fe-harbor pool options\, plus triggers for de-declaration as specs evolve. </li>\n</ol>\n<p><strong><span style="font-size: 16.0pt\; font-family: Cop perplate\;"><u>Speaker</u>:</span></strong></p>\n<table style="border-coll apse: collapse\; width: 100%\;" border="1"><colgroup><col style="width: 14 .779271%\;"><col style="width: 85.12476%\;"></colgroup>\n<tbody>\n<tr>\n<t d><img src="https://events.vtools.ieee.org/vtools_ui/media/display/1f94dd9 f-1756-4d5b-9b81-21088596d894" alt="" width="134" height="150"></td>\n<td> \n<p class="MsoNormal" style="margin-top: 6.0pt\;"><strong>Alex G. Lee </s trong>is a NY State attorney\, USPTO-registered patent attorney\, and Cert ified Licensing Professional (CLP) with a Ph.D. in Physics (Johns Hopkins) and J.D. (Suffolk Law). He bridges 3GPP standards and IP strategy\, havin g led hundreds of 3G/4G/5G essentiality evaluations for global programs. A s Principal Consultant at TechIPm\, he has supported SEP licensing\, portf olio sales\, and enforcement for global companies. He has built agentic AI -powered IP intelligence for SEP development\, licensing\, and litigation. &nbsp\;Earlier\, he held roles at Hsuanyeh Law Group\, Liquidax Capital\, Korea&rsquo\;s National Radio Research Agency\, and Korea Telecom (ITU-R/ early 3GPP representation). His work focuses on agentic AI-powered framewo rks for 5G/6G innovation and SEP portfolio development and monetization.</ p>\n</td>\n</tr>\n</tbody>\n</table>\n<p>&nbsp\;</p>\n<p><strong>Brochure (PDF)</strong>: <strong><a id="ow261" class="pastedDriveLink-1" href="http s://drive.google.com/file/d/1iKy0C_zNIuI3EhB0qOlGQmV82UDxAsXk/view?usp=sha re_link">Webinar-AIML-2025-11-20-Lee-AI-6G-IP-Moats-Brochure.pdf</a>&nbsp\ ;</strong></p> END:VEVENT END:VCALENDAR