Latent Token Representation for Answer Scoring Accuracy

The patent introduces latent tokens as an intermediary layer between traditional keyword matching and answer scoring. These latent tokens capture semantic relationships and contextual information that go beyond exact keyword matches, enabling the system to identify relevant documents even when specific keywords are missing. The latent token representation acts as a mediator that bridges the gap between simple keyword matching and complex semantic understanding.

The patent extends the scoring mechanism from a single-dimensional keyword match approach to a multi-dimensional approach by incorporating latent token representations. This adds new dimensions of analysis including semantic similarity, contextual relevance, and latent feature matching, allowing the system to evaluate candidate answers from multiple perspectives simultaneously rather than relying solely on exact keyword presence.

The patent performs preliminary action by pre-computing and storing latent token representations for documents in advance. This allows the system to have semantic features ready and available before the actual question-answering process begins, significantly reducing the computational burden and processing time during runtime when answers need to be scored and selected.

The patent segments the document processing into distinct components: traditional keyword matching, latent token generation, and scoring integration. This segmentation allows each component to be optimized independently and processed in an efficient pipeline, reducing overall processing time while maintaining the benefits of latent token representation for improved precision.