Score Calculation Unit for Sensor Data Catalog Matching

The patent introduces a scoring mechanism that acts as an intermediary between arbitrary metadata terms. Instead of direct binary matching, the system calculates coincidence scores based on multiple factors including word frequency, co-occurrence relationships, and semantic associations. This intermediary scoring system bridges the gap between freely chosen terms and accurate matching requirements.

The patent transforms the matching problem from a binary coincidence judgment to a multi-parameter scoring evaluation. It changes the assessment parameters to include word frequency in catalogs, co-occurrence frequencies with other terms, and similarity scores with reference terminology. This parameter transformation allows flexible matching while maintaining accuracy.

The patent implements a dynamic matching system where the coincidence score is not fixed but adapts based on the specific terms involved. The scoring mechanism dynamically adjusts weights and considerations based on word frequency, catalog-specific usage patterns, and contextual co-occurrences. This dynamic approach maintains precision while adapting to various terminology choices.

The patent creates a universal scoring framework that can handle multiple terminology styles and domains. The same matching mechanism works across different sensor types, application domains, and metadata vocabularies by using relative frequency and co-occurrence statistics rather than domain-specific rules, making it universally applicable while maintaining precision.