Search System Using Binary Hashing Signatures for Semantic Relevance

The patent transforms the search representation from keyword-based matching to vector-based semantic representation. Search queries and inventory listings are converted into vector embeddings that capture semantic meaning, allowing the system to measure similarity through vector distance rather than exact keyword matching. This parameter change enables the system to return relevant results even when queries do not precisely match stored information.

The patent introduces vector embeddings as an intermediary representation layer between the search query and the inventory database. Instead of directly comparing keywords, the system converts both queries and listings into vector space representations, where semantic relationships are preserved. This intermediary enables semantic search without requiring direct keyword matching, resolving the contradiction between simplicity and relevance.

The patent extracts only the essential semantic features needed for search by converting detailed inventory listings into compressed vector embeddings. These embeddings capture the core semantic meaning while discarding redundant information. During search, only these extracted vector representations are compared, significantly reducing the computational burden while maintaining search relevance.

The patent changes the computational metric from comparing full floating-point vectors to calculating Hamming distances between binary or reduced-dimensional vector representations. This parameter change in the distance calculation method reduces computational complexity from O(n) floating-point operations to simpler bitwise operations, dramatically decreasing processing time while preserving semantic search capability.

The patent employs simplified binary or reduced-dimensional vector representations instead of full-precision floating-point vectors. These simplified representations require less computational resources to process and can be regenerated or approximated efficiently. The use of cheaper, less computationally intensive data structures reduces energy consumption while maintaining adequate search relevance.

The patent changes the vector representation precision from high-precision floating-point to lower-precision binary or reduced-dimensional formats. This parameter change in data representation reduces the computational complexity of distance calculations and memory access patterns, leading to lower processing power requirements and reduced energy consumption during search operations.

The patent segments the inventory database into multiple partitions or clusters based on semantic similarity. Instead of searching all listings linearly, the system divides the search space into manageable segments and performs parallel searches across these partitions. This segmentation maintains search completeness by covering all segments while improving processing efficiency through parallelization and reduced per-segment computational load.

The patent performs preliminary organization of inventory listings into semantic clusters or partitions before the actual search operation. This preliminary action groups related items together, allowing the search system to efficiently navigate and query specific segments based on the search query's semantic vector. This pre-organization reduces the computational load during actual search operations while maintaining comprehensive coverage.