Risk Relationship Sensor Data Analytics Platform
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Solution Overview
Problem
Existing methods for monitoring and processing risk relationship sensor data are costly, error-prone, and struggle with setting appropriate thresholds due to varying normal patterns, making it difficult to efficiently detect potential risks such as theft, vandalism, or slip and fall hazards in real-time.
Innovation Solution
A system comprising a plurality of risk relationship sensors, including image capturing sensors, with environment characteristic detection elements, power sources, and communication devices, connected to a risk relationship data store and an enterprise analytics platform. This platform automatically analyzes data using predictive analytics algorithms to detect potential risks in real-time and transmit alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual placement and interpretation of risk relationship sensors is used, then expert knowledge can be applied, but the process becomes expensive and error-prone
Solution Approach 1:
The system enables self-service through automated sensor deployment and intelligent interpretation algorithms that independently analyze risk data without requiring expert manual intervention. Sensors are automatically placed using deployment algorithms, and the interpretation system autonomously processes sensor data to identify risk relationships, eliminating the need for expensive expert analysts while maintaining high accuracy through machine learning models trained on historical risk data.
Solution Approach 2:
The patent replaces manual mechanical processes with automated electronic systems. Instead of experts manually placing and interpreting sensors, the system uses automated deployment algorithms to position sensors optimally and machine learning algorithms to interpret sensor data. This substitution of mechanical expert analysis with electronic automated systems reduces both cost and error rates while improving scalability.
2Ease of operation
If traditional sensor threshold methods are used, then simple alert triggering is achieved, but varying normal patterns make it difficult to set appropriate thresholds
Solution Approach 1:
The system implements dynamic threshold adjustment through adaptive algorithms that continuously learn from historical sensor data and automatically adjust thresholds based on time-of-day, day-of-week, and seasonal patterns. Instead of static thresholds, the system dynamically adapts to normal variations in environmental conditions and sensor readings, allowing simple operation while maintaining high measurement precision through continuous pattern recognition and automatic recalibration.
Solution Approach 2:
The patent changes the parameters used for threshold determination from fixed values to dynamic, context-aware parameters. The system incorporates temporal parameters (time of day, day of week), environmental parameters (temperature, humidity baselines), and behavioral parameters (normal foot traffic patterns) to adjust thresholds adaptively. This allows the system to maintain simplicity in operation while achieving high precision in risk detection by considering multiple varying parameters simultaneously.
3Speed
If real-time automated analysis is implemented, then rapid risk detection is achieved, but sophisticated predictive analytics algorithms are required
Solution Approach 1:
The patent segments the complex predictive analytics system into modular components: data collection modules, preprocessing modules, pattern recognition modules, and alert generation modules. Each module performs a specific function and can be independently optimized. This segmentation allows real-time processing by breaking down the complex analysis into smaller, manageable tasks that can be executed rapidly in sequence, maintaining high speed while managing algorithmic complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by pre-processing sensor data, pre-training machine learning models on historical data, and pre-establishing risk patterns before real-time analysis begins. This preliminary preparation reduces the computational burden during real-time operation, allowing rapid detection without requiring overly complex algorithms to run from scratch. The system pre-computes baseline patterns and pre-configures detection rules, enabling fast real-time response while managing complexity through advance preparation.
Data Source
AI summary
A plurality of risk relationship sensors, including at least one image capturing sensor (e.g., a camera), may each include an environment characteristic detection element, a power source, and a communication device to transmit data associated with risk relationship sensor data at a site. A risk relationship data store may contain electronic records associated with prior risk relationship events at other sites along with risk relationship sensor location data for those sites. An enterprise analytics platform may automatically analyze the electronic records in the risk relationship data store to create a predictive analytics algorithm. The data associated with potential risk relationship sensor data at the site may then be automatically analyzed, in substantially real-time, using the predictive analytics algorithm, and a result of the analysis may then be transmitted (e.g., to a party associated with the site).