State navigation, causal prediction, and intelligent sensing in autonomous systems require genuine knowledge of the environment. The AIT framework covers the mathematical foundations of how machines acquire that knowledge โ and act on it.
The sequential form of COP models causal and temporal dependencies in any sequence of states โ sensor readings, navigation decisions, action-outcome pairs. Covers the prediction mechanisms in autonomous decision systems.
PM_kl applied to spatial and sensory data. Point clouds, sensor arrays, and multi-modal sensing systems that derive structured knowledge of the physical environment from participation structure.
The full COP matrix as the knowledge representation of the environment. Covers scene understanding, object relationship modelling, and contextual prediction in dynamic physical environments.
The AIT autonomous systems patents cover a broad range of industries deploying intelligent machines that must navigate, predict, and act in physical or simulated environments.
SciPhAI โ the Physical AI venture of Science Counter Inc โ is the commercial deployment of the autonomous systems portfolio. The ISS architecture at its core is the working implementation of the patented methods.
Tier-1 automotive decisions are expected 2027โ2029. The addressable market is substantial. Licensing conversations for the autonomous systems portfolio are particularly relevant to companies making platform commitments in that window.
Visit physical-ai.com โTier-1 decisions are made years before deployment. If you are evaluating autonomous systems architecture today, this is the right time to understand the patent landscape.