State Machine Engine for High-Speed Pattern Recognition

The patent divides the pattern recognition system into multiple finite state machine lattices organized in a hierarchical structure. Each lattice handles specific pattern detection tasks, allowing parallel processing of data streams. This segmentation enables the system to process multiple patterns simultaneously without the sequential bottlenecks of conventional computers, directly addressing the speed and productivity contradiction.

The patent transitions from the sequential processing dimension of von Neumann architecture to a parallel processing dimension using multiple finite state machine lattices. By organizing lattices hierarchically and allowing them to operate simultaneously on different aspects of data analysis, the system adds a dimensional aspect of parallelism that resolves the contradiction between processing speed and data analysis efficiency.

The patent segments the pattern detection task across multiple finite state machine lattices, where each lattice can independently process and detect patterns. This allows the system to handle an increased number of patterns simultaneously without sequential delays, as each lattice operates in parallel to detect its assigned patterns, thereby improving adaptability without increasing search delay.

The hierarchical finite state machine lattices are designed to operate continuously and simultaneously, maintaining constant pattern detection capability. Unlike sequential processing where the system must complete one pattern search before starting the next, this parallel architecture ensures continuous useful action across all pattern detection tasks, eliminating delays even as the number of detectable patterns increases.

The patent implements a hierarchical structure where finite state machine lattices are nested within a larger system architecture. This nesting allows multiple levels of parallel processing, where inner lattices handle specific pattern aspects and outer lattices coordinate overall data flow. The nested architecture efficiently manages large volumes of data by organizing processing tasks at multiple hierarchical levels, maintaining high search speed while processing adequate data quantities.

The patent moves from a single-dimensional parallel processing approach to a multi-dimensional hierarchical parallel architecture. By organizing finite state machine lattices in hierarchical levels that process data simultaneously, the system expands the processing capacity across multiple dimensions, enabling it to handle both high search speeds and large data volumes effectively.