Threat Level Determination Using Aggregated Communication and Transaction Data
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Solution Overview
Problem
Law enforcement officials and correctional facility staff face challenges in determining an individual's threat level due to reliance on limited data, often overlooking statistically relevant communication and transaction history that can indicate potential threatening behavior.
Innovation Solution
A computer-implemented method and system that access aggregated communication and transaction history data, applying predetermined metrics to determine threat level information, which is then displayed for decision-making purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods relying on limited correctional staff reports and crime nature data are used, then the assessment process remains simple and quick, but the accuracy and comprehensiveness of threat level determination deteriorates
Solution Approach 1:
The patent segments the threat assessment process into distinct functional modules: data collection module (gathering communication and transaction data), data processing module (applying predetermined metrics), and output module (generating threat level reports). This segmentation allows the system to handle complex multi-source data while maintaining operational simplicity for end users, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The patent introduces an intermediary computer system that acts as a mediator between raw data sources (communication records, transaction histories) and decision-makers. This intermediary automatically collects, processes, and analyzes data from multiple sources using predetermined metrics, transforming disparate data into actionable threat level assessments without requiring officials to manually analyze complex datasets, thus improving accuracy while maintaining ease of use.
2Measurement precision
If comprehensive communication and transaction history data are collected and analyzed, then the threat level assessment becomes more accurate and comprehensive, but the data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-establishing predetermined metrics and analysis frameworks before data collection begins. The system is configured with predefined rules, weights, and thresholds for analyzing communication and transaction data patterns. This preliminary setup allows the system to rapidly process incoming data without requiring complex real-time decision-making about which metrics to apply, significantly reducing processing time while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The patent utilizes parameter changes by transforming raw communication and transaction data into standardized analytical parameters that can be quickly processed. The system converts diverse data sources into uniform metrics (e.g., communication frequency, transaction patterns, network relationships) that can be efficiently compared and analyzed against predetermined thresholds, enabling fast processing of comprehensive datasets without sacrificing accuracy.
3Ease of operation
If traditional limited data sources are used, then the system remains easy to operate and implement, but the ability to identify potential threats early deteriorates
Solution Approach 1:
The patent implements self-service by enabling the system to automatically collect, process, and analyze data without requiring manual intervention for each assessment. The computer system autonomously gathers communication and transaction history data, applies predetermined metrics, and generates threat level determinations. This automation maintains ease of operation for users while significantly improving reliability through consistent application of comprehensive analytical criteria across all assessments.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors and analyzes patterns in communication and transaction data over time. By comparing current data against historical patterns and predetermined metrics, the system provides ongoing feedback about threat level changes. This automated feedback loop enables early identification of potential threats while maintaining simple operation, as the system self-adjusts its analysis based on accumulated data without requiring manual reconfiguration.
Data Source
AI summary
A system and computer-implemented method for determining a threat level for one or more individuals includes accessing a data structure to obtain aggregated data stored therein, wherein the aggregated data comprises at least one of communication history data or transaction history data for one or more individuals. One or more predetermined metrics are applied to the obtained aggregated data, to determine threat level information for the one or more individuals. The determined threat level information is provided for display.