Forensic System Keyword Highlighting for Document Relevance
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Solution Overview
Problem
Current forensic systems require significant labor and time for manual visual confirmation to determine the relevance of large amounts of digital document information to a lawsuit, leading to inefficiencies and potential errors.
Innovation Solution
A forensic system that acquires and analyzes digital information from multiple computers or servers, utilizing a database to register keywords, retrieving and extracting sentences with these keywords, calculating relevance scores based on feature values, and highlighting sentences according to their relevance, thereby automating the classification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual confirmation is used to determine relevance of digital document information, then accuracy of classification can be maintained, but labor and time expenditure increases significantly
Solution Approach 1:
The patent introduces an automated analysis system that acts as an intermediary between the large volume of digital document information and the reviewer. The system extracts text from documents, identifies keywords, calculates relevance scores, and presents prioritized results to reviewers, thereby reducing manual review time while maintaining classification accuracy through automated preprocessing and scoring mechanisms
Solution Approach 2:
The patent replaces the mechanical manual visual confirmation process with an automated computer-based analysis system. The system uses text extraction, keyword matching, and score calculation algorithms to automatically determine document relevance, substituting human visual inspection with computational processes that are both faster and equally accurate
2Measurement precision
If manual visual confirmation is used to determine relevance of digital document information, then classification accuracy can be maintained, but labor expenditure increases significantly
Solution Approach 1:
The automated analysis system serves as an intermediary that handles the labor-intensive tasks of text extraction, keyword identification, and relevance scoring. This allows reviewers to focus only on evaluating the prioritized results rather than manually reviewing every document, thereby maintaining accuracy while dramatically improving review efficiency
Solution Approach 2:
The system performs self-service by automatically extracting text from documents, identifying relevant keywords, calculating relevance scores, and organizing results without requiring manual intervention for each document. This automation eliminates repetitive manual labor while preserving classification quality
3Quantity of substance
If large amounts of digital document information are collected from multiple computers and servers, then completeness of evidence is improved, but processing complexity increases
Solution Approach 1:
The patent segments the processing task into distinct modular components: text extraction from documents, keyword identification from extracted text, score calculation based on keywords and document metadata, and result organization. This segmentation allows each component to be handled independently, reducing overall processing complexity while managing large volumes of information from multiple sources
4Productivity
If keyword-based automated analysis is implemented, then processing efficiency is improved, but measurement precision may deteriorate due to automated scoring
Solution Approach 1:
The system incorporates feedback mechanisms where reviewer evaluations of automated results are used to refine and update the keyword database and scoring algorithms. This continuous feedback loop allows the system to learn from actual classification outcomes, improving the precision of automated relevance determination over time while maintaining high processing efficiency
Solution Approach 2:
The system performs partial automated analysis by focusing on identifying and scoring only the most relevant keywords and sentences rather than attempting to analyze every aspect of each document. This selective approach maintains high processing efficiency while achieving sufficient precision for effective document prioritization
Data Source
AI summary
Disclosed is a forensic system capable of enhancing the accuracy and efficiency of classification work of whether to submit document information as evidence in a lawsuit by highlighting a portion including a specific keyword in a unit of a sentence. The forensic system includes: a database that registers a keyword for determining by a user whether a plurality of pieces of document information included in the digital information is related to a lawsuit; a retrieving unit that retrieves the keyword registered in the database from the document information; a sentence extracting unit that extracts a sentence including the retrieved keyword from the document information; a score calculating unit that calculates a score indicating a degree of relevance to the lawsuit using a feature value extracted from the sentence extracted by the sentence extracting unit; and a highlighting unit that changes a degree of highlighting of the sentence according to the score.