Event Classification Workflow for Faster Hazard Response Planning
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Solution Overview
Problem
Incidents that pose risks to individuals, property, and reputation are often unexpected and difficult to identify and respond to efficiently, leading to delayed responses or misallocation of resources.
Innovation Solution
A system and method for identifying and managing anomalous or hazardous events by processing data from various sources, using classifiers to determine event likelihood and severity, sending alerts to users, and facilitating response planning based on user input and thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple sources is processed and analyzed using classifier modules, then event identification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex event identification process into multiple independent classifier modules, each responsible for specific event types or data sources. This modular architecture allows each classifier to focus on specific patterns, improving overall accuracy while maintaining manageable complexity through division of labor.
Solution Approach 2:
The patent introduces intermediary components including data normalization layers and feature extraction modules that preprocess raw data before classification. These intermediaries simplify the input to classifier modules, reducing their complexity while maintaining or improving identification accuracy through standardized processing.
2Loss of time
If real-time monitoring and alerting is implemented, then response time to hazardous events is improved, but computational resource consumption increases
Solution Approach 1:
The system implements periodic sampling and threshold-based alerting instead of continuous full-scale analysis. Classifier modules operate at scheduled intervals or trigger only when predefined thresholds are approached, reducing computational load while maintaining timely detection of hazardous events through rhythmic monitoring cycles.
Solution Approach 2:
The system employs self-adjusting thresholds and adaptive sampling rates that automatically reduce computational intensity during low-risk periods. The classifier modules self-regulate their operation based on current conditions, consuming fewer resources when events are unlikely while maintaining high alertness when risks increase.
3Measurement precision
If user feedback and classification input are integrated, then event classification accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary automated classification using classifier modules before presenting options to users. This pre-processing provides users with informed suggestions rather than requiring analysis from scratch, reducing the time users need to spend while incorporating their expertise to improve final classification accuracy.
Solution Approach 2:
User classifications are fed back into the system to continuously retrain and refine classifier modules. This feedback loop progressively improves automated classification accuracy over time, reducing the need for manual user input and thereby decreasing processing time while maintaining or enhancing precision.
4Loss of information
If comprehensive event data is collected and stored, then event analysis capability is improved, but data management complexity increases
Solution Approach 1:
The system extracts and stores only the most relevant event features and classification outcomes rather than retaining all raw data. By identifying and preserving only the critical attributes needed for analysis, the system maintains comprehensive analytical capability while significantly reducing data management complexity through selective data retention.
Solution Approach 2:
The system transforms raw event data into standardized parameters and normalized formats suitable for classifier modules. This parameter transformation organizes diverse data sources into consistent structures, improving analysis capability through uniformity while reducing management complexity through standardization.
Data Source
AI summary
An anomalous/hazardous event or incident identification and response coordination system is provided. Events or incidents that could pose threats to people, property, and/or reputation are identified based on data received from a variety of sources that is filtered based on parameters associated with each user of the system and run through appropriate classifier models. When a potential event is identified, an alert is sent to an appropriate user of the system and the user may confirm, reject, or otherwise modify the event's classification. If the user indicates that a response may be necessary, a pre-established response plan is selected based on the nature and/or severity of the event. Certain aspects of the selected plan may be pre-determined based on the event type and the information received that generated the event alert. The response plan may then be distributed to appropriate personnel and executed, and updated data is provided as warranted.