Entity Fingerprint Search for Scalable Semantic Retrieval

The patent replaces conventional keyword-based mechanical search with a neural network-based semantic embedding system. The system uses language models to generate vector embeddings that capture semantic meaning, enabling accurate retrieval of unstructured data based on meaning rather than exact keyword matches. This substitution resolves the contradiction by achieving high retrieval accuracy through semantic understanding while managing complexity through automated neural network processing.

The patent transforms the search parameter from discrete keywords to continuous vector embeddings in a high-dimensional space. By representing data and queries as vectors and using similarity metrics (e.g., cosine similarity) to measure semantic proximity, the system achieves accurate retrieval. The parameter change from categorical keywords to continuous semantic vectors enables nuanced matching while the automated embedding process manages the complexity of handling unstructured data.

The patent applies preliminary action by pre-processing unstructured data into semantic embeddings and organizing them in a searchable vector space before queries are submitted. Data is transformed into vector representations and stored with metadata, enabling efficient retrieval without requiring heavy language model computation during query processing. This pre-computation approach reduces real-time computational costs while maintaining high semantic understanding accuracy.

The patent segments the data processing pipeline into distinct stages: data ingestion, embedding generation, vector storage, and query processing. By dividing the workflow, the system can optimize each stage independently—using language models only for embedding generation during data preparation, then relying on efficient vector similarity searches during querying. This segmentation reduces overall computational cost by avoiding repeated heavy model inference while preserving semantic understanding capabilities.

The patent applies partial action by retrieving a limited number of top-k most relevant results based on similarity scoring rather than processing and returning all matching data. The system ranks embeddings by similarity to the query vector and returns only the highest-ranked results that meet a relevance threshold. This approach ensures retrieval completeness for the most important results while significantly reducing processing time by avoiding exhaustive analysis of entire datasets.

The patent performs preliminary indexing and organization of data embeddings with associated metadata and similarity metrics before queries are executed. The vector database pre-computes and stores embedding representations, enabling rapid similarity searches during querying. This preliminary preparation ensures that comprehensive retrieval can be performed efficiently by leveraging pre-processed data structures rather than computing similarities from raw data during query processing.

The patent extracts only the essential semantic features into compact vector embeddings rather than storing complete high-dimensional representations for all data. The system generates embeddings that capture the most important semantic information and stores these compressed representations in a vector database. This extraction approach maintains retrieval accuracy by preserving key semantic relationships while significantly reducing memory consumption compared to storing full-text or complete feature sets.

The patent changes the parameter representation from high-dimensional continuous vectors to compressed binary fingerprints for storage and comparison. The system transforms vector embeddings into compact binary representations that retain essential similarity information while occupying minimal storage space. This parameter transformation enables efficient storage and rapid comparison of large numbers of data instances while maintaining the ability to perform accurate semantic retrieval through binary similarity metrics.