AI Crime Linking Network for Cross-Organization Incident Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for linking crime incidents across organizations are time-intensive and inefficient, making it impractical for companies to identify serial offenders and crime organizations due to the sheer volume of incident reports.
Innovation Solution
A computing system accesses and preprocesses crime incident data from multiple organization systems using a trained crime linking model to identify and generate links between incidents, allowing for the sharing of sensitive information without revealing sensitive details, thereby enabling better identification of linked criminal entities across organizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human personnel manually review incident reports to link crimes, then accuracy of linking can be maintained, but time consumption and operational cost increase significantly
Solution Approach 1:
The patent introduces an intermediary system comprising a trained machine learning model that acts as a mediator between incident reports and human analysts. The model pre-processes and scores potential crime links, presenting only high-probability candidates to human reviewers. This intermediary layer maintains linking accuracy by filtering out obvious false positives while dramatically reducing the time humans spend on manual review of all incident pairs.
2Productivity
If companies share incident data across organization systems, then ability to identify serial offenders improves, but data security and privacy protection become more difficult
Solution Approach 1:
The patent extracts and shares only the essential linking features and anonymized incident characteristics needed for crime linkage, while leaving sensitive organizational details and proprietary information within each company's private system. The shared data model includes only the minimum necessary elements (crime descriptions, timestamps, locations) required for the AI model to identify serial offenders, thereby improving collaborative detection capability while minimizing data security exposure.
3Adaptability or versatility
If multiple organization systems are integrated to analyze crime data, then comprehensive crime linking capability is achieved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent implements a universal data interface and standardized incident schema that enables multiple different organization systems to connect to the crime linking platform through a common protocol. The system accepts incident data from various sources (retail stores, law enforcement, security companies) using a unified data model, allowing comprehensive crime linking across diverse systems without requiring complex custom integrations for each organization type.
Data Source
AI summary
A computing system accesses crime incident data from two or more organization systems. The crime incident data includes a first set of inputs associated with a plurality of crime incident groupings and a second set of inputs associated with incident data for incidents associated with each of the two or more organization systems. The computing system preprocesses the crime incident data to remove incidents that include suspect identifiers not present in at least two or more organization system. The computing system analyzes the preprocessed crime incident data to identify links between incidents across two or more organization systems using a trained crime linking model. The computing system generates a link between a first incident at a first organization system of the two or more organization systems and a second incident at a second organization system based on the analyzing.