Semantic Space Vector Search for ESI Precision
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Solution Overview
Problem
The challenge lies in efficiently managing and analyzing large volumes of electronically stored information, particularly in complying with disclosure requirements and electronic discovery, due to diverse file formats and the need to identify relevant documents amidst vast data sets, which current technologies struggle with in terms of precision and recall.
Innovation Solution
A semantic space is created using term and document vectors, where each document is represented as a vector based on its terms, allowing for concept searches and relevancy rankings, facilitating information filtering and retrieval by weighting terms by frequency and global entropy, and enabling efficient storage and retrieval of relevant documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword search methods are used to search through large volumes of electronically stored information, then the search process is simple to implement, but the precision and recall of finding relevant documents is insufficient
Solution Approach 1:
The patent transforms the search problem from keyword matching to vector space comparison. Documents and queries are converted into numerical vectors where each dimension represents a term's weight (calculated using TF-IDF or similar methods). The search then becomes a mathematical operation (computing cosine similarity or other distance metrics) between vectors, fundamentally changing the parameters from discrete keywords to continuous numerical representations. This enables more nuanced similarity detection while maintaining computational efficiency through established linear algebra operations.
Solution Approach 2:
The patent replaces the mechanical keyword-matching system with a mathematical vector space model. Instead of mechanically comparing individual keywords between documents and queries, the system uses mathematical operations (vector addition, multiplication, normalization) to compute semantic similarity. This substitution enables the system to capture contextual relationships and partial matches that keyword-based approaches miss, significantly improving precision and recall.
2Productivity
If employees manually review emails to respond to regulatory audits and investigations, then comprehensive analysis can be performed, but the process is time-consuming and costly
Solution Approach 1:
The patent implements automated self-service capabilities where the system independently performs document retrieval, vector generation, similarity computation, and ranking without human intervention. The automated semantic search system processes large volumes of electronically stored information, generates relevance scores, and presents results to users, eliminating the need for manual review of every document while maintaining high accuracy through mathematical rigor.
Solution Approach 2:
The patent introduces vector representations as an intermediary between the raw document corpus and the user's search query. Instead of directly comparing documents to queries or relying on manual review, the system transforms both into vector form and uses mathematical operations to determine relevance. This intermediary layer enables automated processing at scale while preserving semantic meaning, dramatically increasing productivity compared to manual methods.
3Measurement precision
If semantic space with term vectors is generated for all documents, then concept searches and relevancy rankings improve, but the computational resources and storage requirements increase
Solution Approach 1:
The patent projects high-dimensional document-term matrices into lower-dimensional vector spaces using techniques like Singular Value Decomposition (SVD) or Truncated SVD. Instead of storing and comparing full term-by-document matrices (which are extremely large and sparse), the system transforms them into compact vector representations with fewer dimensions that preserve the essential semantic relationships. This dimensionality reduction dramatically decreases storage requirements while maintaining search precision.
Solution Approach 2:
The patent creates simplified vector copies of documents that capture their essential semantic content without storing the full original documents or complete term matrices. These vector representations serve as efficient proxies that can be stored, retrieved, and compared with minimal computational overhead. The copying approach enables the system to work with compressed representations that retain the necessary information for accurate concept search while reducing storage and processing demands.
Data Source
AI summary
In various embodiments, a semantic space associated with a corpus of electronically stored information (ESI) may be created and used for concept searches. Documents (and any other objects in the ESI, in general) may be represented as vectors in the semantic space. Vectors may correspond to identifiers, such as, for example, indexed terms. The semantic space for a corpus of ESI can be used in information filtering, information retrieval, indexing, and relevancy rankings.