Sensor Data Prediction and Automated Claim Generation
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Solution Overview
Problem
Existing security and surveillance systems require manual review of sensor data for incident analysis, which is time-consuming and inefficient, especially for generating insurance claims, and lack automated predictive monitoring capabilities to enhance security and risk assessment.
Innovation Solution
A computer program product that analyzes sensor data using unsupervised learning models to predict impending insurable events, modifies sensor operations, and automatically generates insurance claim forms by integrating data from various sources, including weather and sensor data, to enhance monitoring and claim processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review of sensor data is used for incident analysis, then comprehensive analysis can be performed, but it is time-consuming and inefficient
Solution Approach 1:
The system enables self-service through automated claim generation and validation. Sensor data is automatically collected, processed, and used to generate insurance claims without manual intervention. The system validates claims by comparing sensor data against policy conditions and automatically notifies relevant parties, eliminating the need for manual claim processing while maintaining comprehensive analysis capabilities.
2Reliability
If video recording is performed continually or based on trigger events, then incident data is captured, but manual review is required which reduces efficiency
Solution Approach 1:
The system implements feedback loops where sensor data from multiple sources (video cameras, motion detectors, environmental sensors) is continuously monitored and fed back to the claim generation system. When sensor data indicates an insured event has occurred, the system automatically triggers claim generation and validation processes, enabling both reliable incident detection and efficient processing through automated feedback-driven actions.
3Adaptability or versatility
If multiple sensor types are deployed for comprehensive monitoring, then better risk assessment is achieved, but system complexity increases
Solution Approach 1:
The system achieves universality by creating a multi-functional sensor network where different sensor types (video cameras, motion detectors, environmental sensors) work together under a unified claim generation and validation platform. The system processes data from various sensor sources through common algorithms that evaluate multiple policy conditions, enabling comprehensive risk assessment across diverse monitoring needs while managing complexity through standardized multi-functional processing.
4Measurement precision
If sensor data is collected and stored for insurance claims, then claim validation is improved, but data storage requirements increase
Solution Approach 1:
The system extracts only the essential sensor data needed for claim validation by comparing sensor readings against specific policy conditions. Rather than storing and analyzing all sensor data continuously, the system extracts relevant data points that directly relate to insured events and policy terms, maintaining high claim validation accuracy while minimizing data storage requirements through selective extraction of critical information.
Data Source
AI summary
Techniques for controlling operation of sensors at a physical premises are described. The techniques process received messages corresponding to a prediction of an impending event and produce commands that modify operation of one or more specific sensors at the physical premises, send the commands that modify the operation of the one or more sensor devices at the physical premises at a period of time prior to a likely occurrence of the predicted insurable event, collect sensor information from the plurality of sensor devices deployed at the premises, and store the sensor information in a remote persistent storage system.