Sensor Data Analysis for Insurance Claim Accuracy
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Solution Overview
Problem
Existing security and access control systems in commercial and residential premises lack efficient data mining and analysis capabilities, limiting their ability to provide accurate risk assessments and insurance claim processing, as valuable operational and sensor data is not effectively utilized for predictive purposes.
Innovation Solution
A computer program product that collects and analyzes sensor data using unsupervised learning models to detect normal and drift states, generating predictions and reports to supplement insurance claims and enhance risk assessments by integrating operational data, service records, and sensor alerts into an insurance claim form.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor data and operational data are collected and stored in product memory or electronic logs, then data availability for analysis is improved, but data mining and analysis capabilities remain insufficient
Solution Approach 1:
The patent introduces an external server system as an intermediary between the sensor network and the user. This server performs data mining, analysis, and pattern recognition on the collected sensor data, replacing the need for complex local analysis capabilities. The server acts as a mediator that processes raw data and returns meaningful insights, risk assessments, and predictions to the user interface.
2Measurement precision
If more sensor data and operational data are collected, then risk assessment accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data processing and analysis by continuously collecting and storing sensor data and operational data in structured formats (databases, electronic logs). This preliminary action prepares the data in advance, so when risk assessment is needed, the pre-organized data can be quickly analyzed without requiring time-consuming data collection and formatting at the moment of assessment.
Solution Approach 2:
The patent replaces manual or mechanical data processing methods with automated computational systems. The server uses algorithms and automated processes to mine, analyze, and interpret large volumes of sensor data, substituting manual analysis with efficient computational methods that can process data rapidly and accurately.
3Reliability
If sensor-based state prediction system is integrated with insurance claim processing, then claim accuracy is improved, but system complexity increases
Solution Approach 1:
The server system is designed with multi-functionality, serving both sensor data analysis and insurance claim processing functions. The same infrastructure that collects and analyzes sensor data for risk assessment also provides the data foundation for insurance claim validation. This universal system handles multiple functions (data collection, analysis, risk assessment, and claim processing) through integrated modules, reducing the need for separate specialized systems.
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.