Immunological Peptide Sequence Embeddings for Immunity Status Prediction

The patent introduces language models as intermediary computational tools that process immunological peptide sequences through latent space embeddings. These language models act as mediators between raw sequence data and clinical predictions, enabling accurate classification of immunity status, disease status, and vaccination status without requiring complex manual analysis protocols

The patent replaces traditional mechanical and manual immunological evaluation methods with computational language model systems. Instead of physical laboratory procedures and manual sequence analysis, the system uses neural network-based language models to automatically extract features and predict immunological states, significantly improving accuracy while reducing operational complexity

The patent performs preliminary action by pre-training language models on large corpora of immunological sequences before actual assessment. The models learn latent representations and patterns in advance, enabling rapid inference during actual clinical or research evaluation without requiring computationally intensive processing at the time of assessment

The patent uses latent space embeddings as computational copies of the original peptide sequences. Instead of processing the full complexity of raw B cell and T cell receptor sequences during evaluation, the system works with compressed vector representations that capture essential immunological information, significantly reducing processing time while maintaining assessment reliability