The global push for sustainable infrastructure and renewable energy sources, alongside increasing climate change resilience efforts, is driving unprecedented demand for accurate and efficient subsurface analysis. Regulatory mandates for environmental impact assessments and safety in large-scale construction or resource projects necessitate advanced geotechnical insights. This technology provides a critical competitive edge by enabling faster, more cost-effective, and data-driven decision-making in these high-stakes global markets.
Generates high-precision AI learning data, accurately reproducing diverse subsurface structures.
Reduces ground survey costs by ~20% through improved AI prediction accuracy.
Accelerates development time by approximately 3 years compared to in-house solutions.
This patent protects an information processing apparatus and program for generating AI learning data with diverse subsurface resistivity structures. Its broad and meticulously designed claims, coupled with a swift grant after early examination without office actions, indicate strong novelty and inventiveness, providing a robust foundation for licensees.
This patent primarily covers the software and methods for generating AI learning data from subsurface resistivity. It leaves white space for developing novel hardware for physical resistivity data acquisition or advanced real-time AI inference engines for field deployment.
Assuming an adopting company conducts 100 ground survey projects annually, with an average cost of ~$50K (AI est.) per project, leveraging this technology's AI learning data could streamline the overall survey process by 20%. This is estimated to result in an annual cost reduction of 100 projects × ~$50K/project × 20% = ~$1M (AI est.).
X: Data Generation Accuracy & Diversity
Y: Survey Efficiency & Cost Performance