Patents List

Improves AI-enabled web delivery by reducing unnecessary language model token consumption through intelligent page handling and selective content presentation. The invention is directed to lowering inference cost while preserving user experience. It is applicable to AI search, enterprise knowledge systems, and web-scale AI applications. The technology reduces operational expense while improving scalability for large language model deployments.

Introduces an operating-system-level security layer that governs interactions between AI agents and protected computing resources. Rather than trusting every AI-generated request, the firewall validates intent, context, and policy before execution. The architecture provides enterprise-grade governance for autonomous AI agents and establishes a foundational security layer for future AI operating systems.

Optimizes cooling infrastructure by understanding the computational characteristics of AI workloads rather than treating all processing equally. The system dynamically allocates cooling resources based on predicted task demand, reducing energy consumption while maintaining performance. The invention addresses one of the largest operational costs associated with modern AI infrastructure.

Extends teleoperation beyond simple remote control by enabling intelligent collaboration between humans and autonomous physical AI systems. Human operators can intervene, supervise, or share control with AI while maintaining safe execution. The technology supports industrial automation, service robotics, and hazardous-environment operations.

Provides continuous identity verification for AI sessions using biometric validation rather than one-time authentication. The system continuously verifies authorized users throughout an AI interaction, reducing the risk of session hijacking and unauthorized access. It is applicable to enterprise AI, secure assistants, and regulated industries.

Introduces mechanisms for securely terminating, purging, or isolating AI inference state stored within transformer key-value caches. The technology enhances privacy, security, and regulatory compliance by preventing residual inference state from persisting after session termination. It is relevant to cloud AI providers and enterprise AI deployments.

Reduces computational cost in embodied AI by minimizing unnecessary visual processing while preserving environmental awareness. The system intelligently prioritizes visual information needed for robotic decision-making, enabling more efficient physical AI operation on constrained hardware.

Protects sensitive information during edge AI inference through operating-system-level privacy controls. The invention enables AI workloads to execute near users while enforcing policy-driven data protection. It supports privacy-preserving AI deployment across distributed edge environments.

Distributes AI inference intelligently across multiple compute tiers, balancing latency, cost, and resource utilization. Different portions of an AI workload execute where they are most efficient while appearing as a unified system. This architecture supports scalable enterprise AI infrastructure.

Establishes the TrustBoundary by requiring predictive validation before AI-generated physical actions reach robot actuators. Candidate actions are evaluated against safety and operational constraints before execution. The architecture is designed to improve safety, insurability, and governance of physical AI systems.

Enables robots within a fleet to validate actions cooperatively using shared knowledge and distributed reasoning. The invention strengthens safety and reliability by allowing robots to learn from each other's observations and operational context.

Coordinates charging and energy allocation among multiple autonomous robots. The system validates charging actions against fleet-wide priorities and resource availability, improving utilization of shared infrastructure.

Allows autonomous robots to safely coordinate movement through shared environments by validating occupancy, access, and movement decisions. The technology supports warehouses, hospitals, factories, and public spaces.

Creates a shared semantic hazard layer allowing robots to exchange contextual hazard knowledge rather than raw sensor data alone. This improves fleet safety and operational awareness.

Coordinates multiple robots performing cooperative lifting and object manipulation. The system validates shared actions to improve stability, safety, and synchronized execution.

Improves AI execution efficiency by dynamically reorganizing neural memory resources during runtime. The architecture supports more efficient utilization of modern AI hardware.

Allows long-running generative video creation to be paused, resumed, and modified without restarting the entire generation process. The technology enables practical interactive AI media workflows.

Introduces persistent conversational branches using transformer inference state, allowing users to explore multiple continuations while preserving prior computation. This improves AI productivity and interactive workflows.

Automates and improves Information Disclosure Statement preparation for patent prosecution. The invention reduces administrative effort while improving filing accuracy and compliance.

Adds localized predictive tactile validation directly within a robotic hand before finger actuation occurs. The system predicts grasp outcomes and selectively authorizes execution, improving dexterous manipulation and reducing damage risk.

Introduces ownership, custody, and authorization controls governing whether autonomous robots may perform requested missions. The architecture supports trusted commercial deployment of mobile physical AI.

Provides a secure policy layer between conversational AI systems and media editing applications. The firewall validates requests before allowing AI-driven media modification, improving safety and governance.

Allows conversational AI systems to create and manage multiple branches of creative media generation while preserving shared work. Users can explore alternative outputs without restarting generation.

Enables intelligent transfer of AI tasks between models or systems based on compatibility, capability, and execution requirements. The architecture improves reliability and efficiency across heterogeneous AI environments.

Defines an operating system architecture specifically designed for embodied AI. The platform coordinates planning, validation, execution, governance, and hardware interaction while providing a deterministic layer between AI reasoning and physical action. It serves as the architectural foundation for the broader TrustRobotics portfolio.

Uses predictive world models to simulate and evaluate candidate physical actions before actuator execution. By forecasting likely outcomes, the system authorizes only actions that satisfy safety and operational constraints, strengthening the TrustBoundary architecture.