Dynamic AI Hazard Prioritization for Geofenced Safety Reports
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Solution Overview
Problem
Existing safety reporting processes in industrial and organizational settings are inefficient, lacking systematic prioritization and oversight, leading to delays in hazard identification and resolution, and exposing personnel to potential risks due to subjective prioritization methods.
Innovation Solution
A dynamic hazard prioritization system that uses AI models to objectively assess and prioritize safety concerns based on specific reports, integrating geofenced locations and predefined priority levels, ensuring critical issues are addressed promptly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review and prioritization of safety reports is performed, then safety personnel can assess individual submissions, but the process is inefficient and lacks systematic prioritization leading to delays
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated AI-based system that uses machine learning models to prioritize safety hazards. The system automatically analyzes report data, assigns priority levels, and generates notifications, eliminating the need for manual review while improving processing efficiency and reducing delays in hazard resolution.
Solution Approach 2:
The system enables self-service automation where the AI model independently assesses safety reports, determines priority levels, and triggers notifications without requiring safety personnel intervention for initial triage. This allows the system to handle routine prioritization tasks autonomously, freeing safety personnel to focus on critical decision-making and resolution activities.
2Reliability
If subjective prioritization methods are used, then safety personnel can make decisions based on experience, but biases and inconsistencies arise leading to unreliable hazard prioritization
Solution Approach 1:
The patent transforms subjective prioritization into objective parameter-based assessment by defining specific criteria (severity, urgency, impact, probability) that the AI model evaluates. Each safety hazard is scored against these standardized parameters, ensuring consistent and unbiased prioritization decisions that eliminate subjective influences while maintaining reliability through systematic evaluation.
Solution Approach 2:
The system implements feedback mechanisms where safety personnel can review and adjust AI-generated priority assignments, and the model learns from these corrections to improve future prioritization accuracy. This feedback loop ensures that the system maintains reliability while continuously reducing biases through data-driven learning from actual safety outcomes and expert judgments.
3Adaptability or versatility
If additional communication devices are provided to workers, then communication capabilities are enhanced, but the devices are fragile, difficult to use in field conditions, or overly bulky
Solution Approach 1:
The patent integrates multiple communication capabilities into a single robust two-way radio device that functions as a universal communication tool for field workers. The device combines traditional radio communication with smartphone-like applications for safety reporting, hazard notification, and team collaboration, eliminating the need for multiple separate devices while maintaining ease of use in field conditions through ruggedized design and simplified interfaces.
Data Source
AI summary
The technology discloses a method for generating a prioritized queue containing reported safety hazards in a site. The system receives a message with at least one issue within the site, and the system generates a command set containing the message and other instructive parameters. The system inputs the command set in an AI model, which identifies issue(s) within the message and integrates the issue(s) in a prioritized queue. Determining where each issue is integrated into the prioritized queue is directed by the instructive parameters in the command set. The system receives the generated prioritized queue from the AI model, in which the system presents to a safety user device.