Sensor Data Drift Detection for Dynamic Insurance Premium Adjustment
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Solution Overview
Problem
Existing security and access control systems in commercial and residential premises lack effective data mining and analysis capabilities to predict risk levels and adjust insurance premiums dynamically based on real-time sensor data, leading to inefficient risk assessment and premium adjustments.
Innovation Solution
A computer program product that collects sensor information from multiple sensors, applies unsupervised learning models to analyze data, detects drift sequences, generates alerts, and adjusts insurance premiums based on continuous risk assessment, using a cloud-based server to correlate sensor data with policy exclusions and pricing tables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is collected and analyzed continuously to improve risk assessment accuracy, then measurement precision and reliability improve, but device complexity and energy consumption increase
Solution Approach 1:
The system segments the complex risk assessment task into multiple components: sensor data collection module, drift sequence detection module using unsupervised learning, state transition analysis module, and premium adjustment module. Each component handles a specific aspect of the analysis, making the overall system more manageable and efficient despite the continuous analysis requirement
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes sensor data for drift sequences and state transitions before generating risk assessments. This intermediary layer filters and processes raw sensor data, reducing the computational burden on the final risk assessment engine while maintaining high measurement precision
2Reliability
If continuous sensor data analysis is performed to detect drift sequences, then reliability of risk detection improves, but loss of time for processing increases
Solution Approach 1:
The system performs preliminary action by continuously analyzing sensor data in the background to detect drift sequences and state transitions before critical risk events occur. The unsupervised learning models are trained beforehand to recognize patterns, enabling rapid detection when anomalies occur without requiring intensive real-time computation during critical moments
Solution Approach 2:
The patent implements continuous monitoring and analysis of sensor data streams, maintaining constant vigilance for drift sequences and state transitions. This continuous useful action ensures reliable risk detection while the system processes data efficiently through optimized algorithms that balance thoroughness with processing speed
3Adaptability or versatility
If dynamic premium adjustments are made based on real-time risk assessment, then adaptability of insurance pricing improves, but device complexity for automated decision-making increases
Solution Approach 1:
The system implements dynamic premium adjustments that automatically adapt to changing risk conditions detected through sensor data analysis. The pricing model transitions from static to dynamic, with premiums adjusting in real-time based on detected drift sequences and state transitions, providing high adaptability through automated decision-making
Solution Approach 2:
The patent establishes a feedback loop where sensor data continuously informs risk assessment, which in turn triggers premium adjustments, and these adjustments are communicated back to the insured. This feedback mechanism enables adaptive pricing while the automated nature of the feedback loop reduces the need for complex manual decision-making processes
Data Source
AI summary
Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.