Systems and methods for probabilistic semantic sensing in a sensory network

This invention describes a system that collects raw data from various sensors within a 'light sensory network' to identify basic events like detecting people or objects. It then uses a smart probabilistic engine to combine these basic events and infer more complex situations, assigning a confidence level to each inferred situation for use by other applications. The claims specifically narrow the application to a 'light sensory network' and events like detecting people, vehicles, or empty parking spaces.