Vector-Based Content Discovery System
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Solution Overview
Problem
Organizations face challenges in effectively and efficiently managing and discovering relevant digital content across various systems, including searching for and surfacing relevant information at the right time to improve operational efficiency.
Innovation Solution
A content identification system that generates language and frequency models, creates a document database, and analyzes documents to identify relevant content by processing textual information, metadata, and user queries, using client-server architecture and specialized modules for document analysis and language training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If organizations store vast amounts of digital content across various systems, then the quantity of available information increases, but the difficulty of searching and discovering relevant content increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing document metadata, creating structured representations of content before search queries are submitted. This includes extracting entities, relationships, and attributes from documents and storing them in optimized data structures that enable rapid retrieval without analyzing full document contents during search operations.
Solution Approach 2:
The patent introduces an intermediary layer between the vast digital content repository and user queries. This intermediary system uses trained language models and vector representations to translate natural language queries into structured search operations, acting as a mediator that bridges the gap between unstructured content and structured retrieval mechanisms.
2Loss of time
If traditional search methods are used to manage digital content, then system complexity remains low, but the time required to locate relevant information increases
Solution Approach 1:
The system performs preliminary indexing and vector representation creation for all documents in the corpus before search operations. This pre-processing step transforms full document contents into condensed vector representations that capture semantic meaning, enabling rapid similarity searches without re-analyzing complete documents during query processing.
Solution Approach 2:
The patent creates simplified copies or representations of documents in vector space that preserve semantic information while occupying minimal storage and enabling fast computation. These vector copies serve as proxies for full documents, allowing the system to perform complex semantic searches by comparing vectors rather than analyzing complete text contents.
3Measurement precision
If comprehensive document analysis is performed to improve content discovery accuracy, then measurement precision increases, but processing speed decreases
Solution Approach 1:
The system segments the document analysis process into distinct phases: initial full-content analysis for training language models and creating vector representations, followed by rapid vector similarity comparisons for actual search operations. This segmentation allows comprehensive analysis to be performed once during indexing, while subsequent searches benefit from fast vector comparisons without re-processing full document contents.
Solution Approach 2:
Comprehensive document analysis is performed as a preliminary action during the indexing phase, where language models are trained on full document contents and vector representations are created. Once this preliminary analysis is complete, the system can perform rapid similarity searches by comparing query vectors against pre-computed document vectors, achieving both high accuracy and fast processing speed.
Data Source
AI summary
Described herein is a computer implemented method for identifying one or more documents of potential relevance to an input query. The method comprises receiving the input query; processing input text from the query to generate an input query vector; accessing document records from a record database, each document record including a document vector; generating a document similarity score in respect of each accessed document, the document similarity score for a given document record being generated using the document vector for the given document record and the input query vector, the document similarity score for a given document record indicating the similarity of the input text to a document that the given document record is in respect of; and identifying one or more potentially relevant document records based on their document similarity scores.