This is the Spherical Modeling Engine (US Patent No. 7,408,544). It solves the “Ontological Gap” by structuring human context not as text, but as geometry.
The diagram illustrates how we map complex human attributes (like trust, tension, or integrity) onto a 3-dimensional coordinate system. By calculating the “Vector Distance” between these points, we generate a mathematical value for “Human Ground Truth” that AI can process, reason with, and align to.
This map illustrates the evolution of scientific paradigms—from the linear, reductionist models of the past to the Complex Adaptive Systems of the 21st century.
While physics and biology moved into “Complexity Science” decades ago, current AI architecture is still largely relying on linear, probabilistic models. This creates an “Ontological Gap.” We are trying to use linear tools to solve non-linear human problems. This diagram shows why simply scaling compute won’t bridge that divide.
