Database Record Projection for Force-Based Causality Measurement

The patent replaces traditional binary-based mechanical processor architectures with a field-based information physics system. Instead of using discrete binary operations to analyze data correlations, the system projects data elements into a continuous field space where causal relationships emerge naturally through field interactions. This substitution enables the system to handle complex multivariant models without requiring increasingly complex processor hardware, as the field-based approach inherently captures complex relationships through spatial and temporal field dynamics.

The patent introduces field-based causal measurements as an intermediary between data observation and causal conclusion. Instead of directly inferring causality from data patterns through black-box AI models, the system uses field projections and field interactions as mediators. Data elements are projected into field space where their causal relationships are revealed through field dynamics, providing both precise measurement and interpretable results. The field acts as a transparent intermediary that makes causal mechanisms visible and measurable rather than hidden.

The patent creates a universal field-based framework that can process diverse data types from disparate databases through a common mechanism. Instead of requiring separate processing methods for different data sources, the system projects all data elements into the same field space where they interact according to universal field laws. This multi-functional approach allows the same field-based processing machinery to efficiently handle tabular data, text, images, and other formats from various database sources, integrating them seamlessly while maintaining processing efficiency.