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Host J.D. Houvener runs solo on a special Christmas Eve episode, fielding community IP questions before welcoming Ram Bulusu, an AI innovator and healthcare technology leader with 36 years of experience across providers, payers, hospital systems, medical devices, pharma, and gene therapy. The conversation dives into what’s actually working with GenAI today—and where it still breaks down.
✨ Episode Highlights
🧠 Public funding + patents: J.D. tackles a policy question on whether patent transfers tied to publicly funded R&D should mandate profit-sharing or price-linking—flagging the antitrust and price-fixing risks that can arise in patent licensing structures.
🛋️ Trademark confusion in the marketplace: A furniture naming question leads to a clear takeaway: trademark rights generally favor first use in commerce, and using a similar name in the same category can create a likely confusion problem (even if you attempt registration).
📜 Inventor vs. owner: J.D. breaks down the difference between being named on a patent (inventorship) and owning a patent (assignment/transfer)—and how to verify ownership using USPTO assignment records.
🤖 Ram Bulusu on GenAI in healthcare:
✅ Where GenAI wins: Ram shares real examples of GenAI driving major gains in structured, repeatable workflows—especially in data-heavy environments like spreadsheets.
📉 Error reduction: One initiative reduced analysis errors by ~93% while automating complex shelf-life estimation workflows.
⏱️ Time savings: Financial and operational reporting workflows saw significant cycle-time reduction by automating repetitive analysis.
⚠️ Where GenAI struggles:
🗣️ Unstructured human conversations (like virtual nursing calls) remain risky—because patients often respond unpredictably and require judgment and escalation logic that current tools don’t handle reliably.
🔒 Security & governance are the real bottleneck:
Ram explains why enterprise adoption isn’t just about tools—it’s about containment, authorization, and training, especially in healthcare. Key risks include:
• Shadow AI usage (employees using unauthorized tools)
• Vendor data handling mistakes (including off-device/offshore exposure)
• “Deepfake-enabled” identity and fraud threats
• Cybersecurity frameworks that weren’t designed for GenAI
🚀 Inventing opportunities:
Ram’s big thesis: GenAI platforms are broad—innovation happens in the verticals. The biggest inventions will come from applying GenAI to specific industries and workflows, not merely building another general model.
⚡ Energy and scale:
Ram discusses the growing energy demands of AI compute, how hyperscalers are preparing, and why data center scaling may become a limiting factor as adoption accelerates.
🏁 Final takeaway:
GenAI is already delivering results in structured workflows, but the future belongs to the inventors who can apply it safely inside real-world constraints—security, governance, reliability, and economics.
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DISCLAIMER
Everything discussed on this podcast is for informational purposes only and not for the purpose of providing legal advice.