Fuzzy Cache Query Clustering for Higher Content Cache Hit Rates

The patent transforms the cache matching parameter from exact equality to approximate similarity using vector embeddings. Queries and cached items are represented as vectors in a multidimensional space, and matching is performed based on similarity metrics (e.g., cosine similarity) rather than exact matches. This parameter change enables the fuzzy cache to retrieve relevant content even when queries don't exactly match cached keys, significantly improving cache hit rates while maintaining implementation feasibility through established vector similarity techniques.

The patent applies preliminary action by pre-computing and storing vector embeddings for cached content items during the caching process. When a query arrives, the system only needs to compute the query's vector embedding and perform similarity comparisons, rather than computing embeddings on-the-fly for every comparison. This pre-processing approach significantly reduces the computational complexity at query time while maintaining the benefits of fuzzy matching and high cache hit rates.

The patent substitutes the traditional mechanical key-value matching mechanism with a vector similarity-based retrieval system. Instead of using exact string or identifier matching, the system uses vector embeddings and similarity metrics to determine cache hits. This substitution enables the cache to scale more effectively because vector similarity searches can be optimized using techniques like approximate nearest neighbor search, dimensionality reduction, and hierarchical clustering, which reduce computational complexity and improve scalability while maintaining memory efficiency.