R.C.A.P. — Configurational Recognition through Actualization of Predefined Patterns

This presentation summarizes the R.C.A.P. principle (Configurational Recognition through Actualization of Predefined Patterns) and its structural invariance across heterogeneous systems. The analysis compares a discrete configurational system described in the 1984 patent (Italian Patent IT 1,183,052) with contemporary technological systems based on continuous data streams, such as sensor-driven autonomous systems. Despite differences in data acquisition and processing, both converge toward the same functional structure: discrete or discretized data, convergence into a predefined geometric form, and direct recognition. The presentation highlights that recognition does not emerge from data alone, but from the stabilization of a predefined geometric configuration. In contemporary systems, this requires the discretization of continuous data through classification, thresholding, and sensor fusion processes, leading to operational states functionally equivalent to discrete data. The invariant structural core of R.C.A.P. is thus identified as the condition enabling recognition: data must converge into a stable predefined geometric configuration. This establishes a principle of configurational equivalence across systems with different technological implementations, linking a non-digital 1984 device to modern computational architectures. The presentation provides a concise visual and conceptual synthesis of the R.C.A.P. framework and its implications for cognitive systems, information integration, and pattern recognition.