Sensory Perception Prediction Using Semantic Distance

The patent segments the sensory descriptor space into multiple hierarchical levels (e.g., primary descriptors like 'sweet' branching into secondary descriptors like 'honeyed', 'fruity'). This segmentation allows the system to provide detailed sensory discrimination when needed while maintaining a simple interface for basic descriptions, resolving the contradiction between measurement precision and ease of operation.

The patent introduces semantic similarity as an additional dimension to organize sensory descriptors. By calculating semantic distances between descriptors and organizing them in a multi-dimensional semantic space, the system can automatically select the most appropriate descriptors for any given sensory input, achieving high measurement precision without requiring users to manually navigate complex descriptor lists.

The patent implements a self-service system where the computational engine automatically performs descriptor selection, semantic distance calculation, and sensory characterization without requiring complex user interaction with rating forms. The system serves itself by using algorithms to navigate the descriptor space and generate accurate sensory descriptions, thereby maintaining high measurement precision while reducing device complexity and user burden.

The patent replaces the mechanical system of manual rating form completion with an automated computational system. Instead of users manually selecting and rating multiple descriptors, the system uses semantic vector calculations and algorithms to automatically characterize sensory inputs, significantly reducing device complexity while maintaining or improving measurement precision.

The patent performs preliminary action by pre-calculating and storing semantic vectors for all sensory descriptors in advance. When a sensory input needs to be characterized, the system only needs to calculate distances between the input and pre-computed vectors, rather than performing full semantic analysis in real-time. This preliminary preparation enables rapid, accurate sensory discrimination without time-consuming testing procedures.

The patent uses semantic vectors as computational copies or representations of actual sensory descriptors. Instead of requiring physical or manual testing against multiple descriptors, the system creates mathematical copies (vectors) that capture the essential semantic properties of each descriptor. These copies can be rapidly compared and processed computationally, achieving high measurement precision with minimal time investment.