Risk Score Determination Using Sensor Data and Machine Learning
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Solution Overview
Problem
Current risk assessment methods rely heavily on demographic data, such as age and occupation, which may not accurately capture an individual's behavior patterns, leading to incomplete risk profiling and ineffective risk mitigation strategies.
Innovation Solution
A system utilizing sensors like accelerometers, gyroscopes, and heart rate monitors to collect data, combined with machine learning algorithms, to analyze user behavior patterns and determine risk scores, thereby selecting appropriate test groups for personalized risk assessment and mitigation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demographic data (age, occupation, etc.) is used for risk assessment, then the assessment process is simple and quick, but the accuracy and completeness of risk profiling is insufficient
Solution Approach 1:
The patent segments the risk assessment system into multiple data sources: traditional demographic data, sensor data from wearable devices (accelerometers, gyroscopes, heart rate monitors), and environmental data. Each segment contributes specific information that, when combined, creates a comprehensive risk profile that is more accurate than any single data source alone.
Solution Approach 2:
The patent merges multiple types of data (demographic, sensor, environmental) and multiple measurement methods into a unified risk assessment model. This combination allows the system to capture both traditional risk factors and behavioral patterns, resulting in more complete and accurate risk profiling.
2Loss of information
If sensor data collection is implemented to capture behavior patterns, then risk profiling completeness improves, but data processing complexity increases
Solution Approach 1:
The patent introduces machine learning algorithms as an intermediary between raw sensor data and risk assessment results. These algorithms automatically process, analyze, and interpret sensor data to extract meaningful behavior patterns, reducing the manual processing complexity while maintaining information completeness.
Solution Approach 2:
The patent replaces manual data analysis methods with automated machine learning systems. The machine learning models automatically identify behavior patterns from sensor data without requiring manual intervention, significantly reducing processing complexity while capturing comprehensive behavioral information.
3Measurement precision
If machine learning algorithms are used to analyze behavior patterns, then risk score accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models on extensive datasets before deployment. This pre-training allows the models to quickly process new sensor data and generate risk assessments without requiring extensive computation during actual risk evaluation, reducing processing time while maintaining accuracy.
Data Source
AI summary
A computer-implemented method for operating a computing device including: receiving, by one or more processors and from one or more sensors, sensor data corresponding to a user; receiving, by the one or more processors, demographic data corresponding to the user; determining a pattern of behavior of the user based on the sensor data and the demographic data; and determining, using a trained risk analyzer machine learning model, a risk score of the user based at least in part upon a test group associated with the pattern of behavior of the user. Other descriptions are enclosed.