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AIOMEGA expands U.S. patent portfolio for responsive oral-airway tech

14 hours ago
By AI, Created 09:06 UTC, Oct 19, 2026, AGP -

AIOMEGA founder Raghavendra V. Ghuge says the company has expanded a U.S. patent portfolio covering dynamic oral-airway systems, biometric tracking and physical-activity applications. The work is aimed at technologies that can sense changing conditions and respond to anatomy, motion and ventilatory demand.

Why it matters: - AIOMEGA is positioning oral-airway technology as a dynamic system, not a fixed mechanical device. - The portfolio targets airway function during sleep, movement and physical activity. - The company’s longer-term goal is to link sensing, measurement and response into a platform that could adapt to changing physiology.

What happened: - Founder and inventor Raghavendra V. Ghuge highlighted an expanding U.S. intellectual-property portfolio for oral-airway technologies. - The announcement covers inventions developed from work started in 2019. - The portfolio includes issued U.S. patents US 11,484,434; US 11,484,435; US 11,666,477; US 11,666,478; US 11,806,273; US 11,819,449; US 12,029,679; US 12,318,325; US 12,369,687; US 12,478,500; US 12,485,036; US 12,636,127; and US 12,685,665. - The company also has continuing published applications across the broader technology platform.

The details: - The inventions cover dynamic mandibular and lingual repositioning. - The portfolio also includes maxillary and mandibular devices. - The work includes controller-associated systems and technologies intended to prevent device disengagement. - The patent set also includes health and fitness biometric tracking. - Ghuge described the portfolio as an evolving technological architecture centered on increasingly dynamic oral-airway function. - The company’s framework is SENSE, LEARN, RESPOND and MEASURE. - SENSE refers to capturing physiologic, anatomic, positional, movement or device-related information. - LEARN refers to using accumulated and longitudinal information to characterize an individual’s response. - RESPOND is the core concept behind moving beyond a predetermined mechanical position. - MEASURE is intended to close the loop by evaluating results for later analysis. - AAVOAT®, or Adaptive Antero-Vertical Optimization of Airflow Technology, is the broader technology and treatment philosophy. - CAOM®, or Connected Arc of Motion, describes a connected-motion approach to mandibular movement. - PLL™, or Posterior Leaning Linear, and PLC™, or Posterior Leaning Curvilinear, are additional geometry concepts. - AIOMEGA summarizes the progression as AAVOAT® → CAOM® → PLL™ / PLC™ → Intelligent Responsive Systems. - The company says multiple patents address maxillary and mandibular devices designed to increase the smallest concentric airway cross-sectional area during physical activity. - The broader portfolio also includes published intellectual property tied to tracking and improving health and fitness biometrics.

Between the lines: - The work reflects a shift from asking how far forward the mandible should be positioned to asking what should happen when conditions change. - The company is framing airway technology as a platform that could combine geometry, sensing and data analysis. - That approach suggests AIOMEGA is trying to create a broader research and product moat around responsive oral-device systems. - The LA28 Olympic Games give the company a timely setting to argue for research on airway physiology in elite athletics.

What's next: - AIOMEGA says it will continue investigating oral-airway technologies for changing ventilatory and performance demands. - The company plans to keep developing responsive systems that can measure outcomes and adapt over time. - AIOMEGA says its next phase will focus on what happens when RESPOND becomes a core design objective. - The company’s LA28-focused research interest points toward future work in high-performance athletics and physiologic measurement.

The bottom line: - AIOMEGA is building a patent-backed pitch that oral-airway devices should evolve from static appliances into adaptive, data-driven systems.

Disclaimer: This article was produced by AGP Wire with the assistance of artificial intelligence based on original source content and has been refined to improve clarity, structure, and readability. This content is provided on an “as is” basis. While care has been taken in its preparation, it may contain inaccuracies or omissions, and readers should consult the original source and independently verify key information where appropriate. This content is for informational purposes only and does not constitute legal, financial, investment, or other professional advice.

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