ML Model Support Indication for Multivendor Inference Sharing

The patent segments the ML model information into distinct components: capability information (what the model can do) is shared separately from implementation details (how the model works). This segmentation allows nodes to exchange capability metadata and subscription information without exposing sensitive model weights, architectures, or training data, thus maintaining confidentiality while enabling reliable inference sharing across multivendor environments.

The patent introduces an intermediary mechanism in the form of standardized capability information structures and subscription protocols that mediate between ML model providers and consumers. These intermediaries enable indirect communication of model capabilities and inference requests without requiring direct exposure of implementation details, allowing reliable cross-vendor inference sharing while preserving model confidentiality through standardized interfaces.

The patent implements universal subscription mechanisms that work across different ML model types and vendors through standardized capability information structures. Nodes can subscribe to inference characteristics using a unified protocol that adapts to various model capabilities without requiring vendor-specific configuration management, thus achieving adaptability while reducing operational complexity through multi-functional standardized interfaces.

The patent enables nodes to autonomously manage their own subscriptions to ML inference characteristics based on their specific needs and capabilities. Each node can independently configure its subscription preferences, select desired inference characteristics, and manage its own model consumption without requiring complex centralized coordination, thus achieving adaptability while simplifying configuration management through self-service mechanisms.