Meta Learner Autoencoder for New Signal Type Prediction

The system performs preliminary actions by training a meta-learner autoencoder on known signal types and their metadata representations before encountering new signal types. This pre-trained meta-learner encodes the relationships between signal characteristics and metadata, enabling it to generate predicted metadata representations for new signal types without requiring prior explicit training on those specific types.

The patent introduces a meta-learner autoencoder as an intermediary component that bridges the gap between known signal types in the knowledge graph and new signal types. This intermediary learns the underlying patterns and relationships in the metadata representations, allowing it to infer and generate metadata for new signal types that are not directly represented in the knowledge graph, thus enabling transfer of knowledge from known to new signal types.

The meta-learner autoencoder is designed as a universal model that can handle multiple signal types through a unified architecture. Rather than creating separate models for each signal type, the single meta-learner performs multiple functions: encoding signal sequences, processing metadata representations, generating predicted metadata, and enabling predictions for both known and new signal types. This multi-functionality reduces the need for multiple specialized components.

The system manages complexity by dynamically adjusting parameters rather than changing architectural structures. The meta-learner autoencoder modifies its internal parameters (weights and biases) during training on known signal types, allowing it to adapt to different signal characteristics. For new signal types, the same architecture with updated parameters generated from the knowledge graph enables predictions without requiring structural modifications.