Automated Incident Linking via Machine Learning and NLP
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Solution Overview
Problem
The sheer volume of incident reports related to criminal activity makes it impractical for law enforcement and retail companies to manually link related crimes, leading to missed opportunities for preventing future incidents and identifying serial offenders or coordinated crime groups.
Innovation Solution
A method that uses machine-learning algorithms to automatically link incident reports by extracting unique IDs and applying probabilistic determinations based on text analysis, location, and time indicators, with human verification and AI-assisted filtering to identify related incidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of incident reports is performed, then accuracy of linking related crimes is improved, but productivity and time efficiency deteriorate due to the sheer volume of reports
Solution Approach 1:
The patent introduces an intermediary system consisting of natural language processing algorithms and machine learning models that act as a mediator between the large volume of incident reports and human analysts. The system automatically extracts entities, normalizes data, generates feature vectors, and ranks potential links, thereby filtering and prioritizing reports for human review while maintaining high accuracy in identifying related crimes.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computational system that uses natural language processing, machine learning algorithms, and probabilistic models to analyze incident reports, extract features, and identify linked crimes, thereby dramatically increasing productivity while maintaining or improving accuracy through consistent application of analytical criteria.
2Productivity
If automated processing of incident reports is implemented, then productivity is improved, but measurement precision may deteriorate due to challenges in text normalization and entity recognition
Solution Approach 1:
The patent applies preliminary action by implementing a preprocessing stage that performs text normalization, entity extraction, and feature generation before the main analysis. This includes converting text to lowercase, removing special characters, extracting entities like names and locations, and generating feature vectors that capture semantic relationships, thereby preparing the data in advance to improve the accuracy of subsequent automated processing.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system generates probabilistic scores for potential crime links and allows for human verification and correction. The system can learn from human feedback to improve its entity extraction and link prediction accuracy over time, creating a closed-loop system that continuously refines its precision while maintaining high productivity.
3Reliability
If comprehensive analysis of all incident reports is performed, then reliability of identifying serial offenders is improved, but loss of time increases due to the time-intensive nature of manual review
Solution Approach 1:
The patent applies partial action by focusing computational resources on analyzing only the most promising links identified through automated feature extraction and probabilistic scoring. Rather than requiring comprehensive manual review of all possible crime pairings, the system identifies and prioritizes a subset of high-probability links for detailed analysis, thereby achieving reliable identification of serial offenders with significantly reduced time investment.
Solution Approach 2:
The system performs preliminary filtering and scoring of all incident reports using automated algorithms to identify potential links before human analysts invest time in detailed review. This preliminary action ranks potential connections by probability, allowing analysts to focus on the most promising cases and ensuring that comprehensive analysis is directed only where it will have the greatest impact on identifying serial offenders.
4Measurement precision
If human personnel are assigned to review incident reports, then measurement precision is improved, but loss of energy and resources increases due to lack of return-on-investment
Solution Approach 1:
The patent introduces an automated processing intermediary that handles the bulk of data extraction, normalization, and initial analysis, reserving human personnel for high-value tasks such as verifying top-ranked links and making final determinations. This intermediary system reduces the volume of work requiring human energy while maintaining or improving accuracy through consistent application of analytical criteria across all reports.
Solution Approach 2:
The system applies partial action by having human personnel review only a fraction of total incident reports—specifically, the high-probability links identified by the automated system. This selective human involvement maintains measurement precision for critical decisions while dramatically reducing the loss of human energy and resources compared to comprehensive manual review of all reports.
Data Source
AI summary
A method for automatically linking associated incidents related to criminal activity is disclosed. A system for processing the method is also disclosed. The method operates by scraping text from a number of incident reports. The scraped text is then analyzed to determine the present of one or more unique IDs, which are used to calculate similarity between each incident report. The system employs machine-learning to better identify these pairs in the future, and optionally with the assistance of a human user providing a feedback loop to enhance the machine-learning.