The accelerating shift towards digital health and personalized medicine is driving demand for advanced simulation tools. Regulatory pressures for reduced animal testing and ethical considerations for cadaver use are also pushing for synthetic, high-fidelity anatomical models. This technology directly addresses these trends by offering a cost-effective, reproducible solution for creating transparent, biorealistic models, enabling faster R&D cycles and superior medical training globally.
Enables complete internal structure visualization, achieving high transparency previously difficult with conventional 3D printing materials, significantly enhancing precision in medical simulations and education.
Combines flexibility with shape retention, allowing for precise 3D printed forms that mimic biological tissue, thereby improving the realism and quality of surgical training.
Establishes robust technical superiority, with patentability confirmed against 7 prior art documents, demonstrating clear differentiation from existing technologies and providing a stable foundation for business development.
This patent protects a novel gel material composition for 3D printers, characterized by its specific polymer and photoinitiator components, enabling high transparency and biorealistic properties. With 8 claims, the patent offers broad technical protection, having successfully navigated examination against 7 prior art documents, indicating a robust and well-defined scope.
This patent primarily covers the novel gel material composition. White space exists in developing advanced 3D printing hardware optimized for this material, integrating haptic feedback systems for enhanced simulation, or creating AI-driven model generation software.
Companies could reduce external procurement of expensive bio-tissue models by creating precise models in-house. For example, an 80% reduction on ~$67K/year (AI est.) in external procurement costs could save ~$53.5K/year (AI est.). Additionally, shortening prototyping and development periods could reduce labor and material costs, e.g., a 20% reduction on ~$47K/year (AI est.) could save ~$9.5K/year (AI est.). This combined effect could lead to estimated operational cost savings of ~$63K/year (AI est.), directly supporting expanded educational content and accelerated R&D.
X: High Definition & Multifunctionality
Y: Cost Performance