Binary Entity Fingerprints for Scalable Unstructured Data Search
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Solution Overview
Problem
Conventional systems face challenges in efficiently managing, retrieving, and analyzing large-scale unstructured data due to scalability, accuracy, and computational inefficiencies, with language models often producing inaccurate results and hindering real-time decision-making.
Innovation Solution
A knowledge management system that generates compressed vector embeddings into binary fingerprints for efficient storage and comparison, using statistical measures to aggregate entity embeddings and apply Hamming distance for similarity searches, supported by a query engine for rapid and accurate information retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If language models are used for retrieving and summarizing information from unstructured data, then semantic understanding and contextualization improve, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent segments the retrieval process into two distinct stages: (1) compression-based fingerprint generation and initial filtering, and (2) language model-based semantic analysis. This segmentation allows the computationally expensive language model to process only a small subset of pre-filtered candidates rather than the entire corpus, thereby maintaining high semantic understanding while dramatically improving retrieval speed and reducing computational costs.
Solution Approach 2:
The patent applies preliminary compression-based filtering using binary fingerprints and Hamming distance calculations before invoking language models. This preliminary action reduces the search space from millions of documents to a manageable subset of highly relevant candidates, enabling the language model to operate efficiently on pre-selected data while preserving semantic accuracy.
2Productivity
If conventional keyword-based search methods are used, then computational efficiency is maintained, but retrieval accuracy and semantic understanding deteriorate
Solution Approach 1:
The patent merges two previously separate approaches into a unified hybrid system: compression-based keyword search (using binary fingerprints and Hamming distance) is combined with language model-based semantic search. The compression component ensures computational efficiency and fast filtering, while the language model component provides semantic understanding and retrieval accuracy, thereby achieving both speed and precision simultaneously.
Solution Approach 2:
The patent introduces compression-based binary fingerprints as an intermediary layer between the query and the language model. This intermediary enables efficient pre-filtering of the document corpus using simple bitwise operations, then passes only the most relevant candidates to the language model for final semantic ranking, thus bridging the gap between fast keyword search and accurate semantic search.
3Quantity of substance
If language models process large-scale datasets, then comprehensive analysis is achieved, but scalability and real-time performance are hindered
Solution Approach 1:
The patent segments the large-scale dataset processing into two phases: (1) offline compression-based indexing of the entire corpus into binary fingerprints, and (2) online query processing that uses this index for rapid filtering. This segmentation enables the system to handle large data volumes by performing heavy preprocessing offline, then achieving real-time performance during actual queries through efficient fingerprint matching and selective language model invocation.
Solution Approach 2:
The patent performs preliminary compression and indexing of the entire dataset before query processing. By pre-computing binary fingerprints for all documents and organizing them in an efficient data structure, the system enables real-time queries to rapidly filter candidates using simple Hamming distance calculations, thereby achieving both comprehensive data coverage and real-time performance.
Data Source
AI summary
A knowledge management system may receive a set of data instances. The system may extract a plurality of entities from the set of data instances. The system may convert the plurality of entities into a plurality of entity embeddings, each entity embedding representing an entity in a latent space. The system may generate a reference embedding that has the same length as the plurality of entity embeddings. The system may compare, for each value in each entity embedding, the value to a corresponding value of the reference embedding. The system may generate a plurality of entity fingerprints, each entity fingerprint corresponding to an entity embedding, each entity fingerprint comprising Boolean values that are generated based on comparing values in each entity embedding to corresponding values of the reference embedding. The system may store the plurality of entity fingerprints to represent the plurality of entities.