Machine Learning Risk Profiling From Mobile Sensor Data
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Solution Overview
Problem
Existing systems require human interaction and evaluation to process vast amounts of user data from mobile devices, making it difficult for users to access and utilize valuable insights, and conventional processes often lead to inaccurate risk profiles.
Innovation Solution
A system that utilizes machine learning to analyze sensor data from mobile devices to generate accurate risk profiles by evaluating device usage, travel patterns, and driving behaviors, reducing the need for user input and enhancing data retrieval from third-party systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human interaction and evaluation are used to process user data, then accuracy of risk profiles can be maintained, but processing time and complexity increase significantly
Solution Approach 1:
The system enables automated self-service processing where the machine learning model independently evaluates user data from mobile devices, sensors, and third-party sources to generate risk profiles without requiring human interaction. The model autonomously processes sensor data, travel patterns, and driving behaviors to produce accurate risk assessments.
Solution Approach 2:
The patent replaces the mechanical system of human evaluation with an automated machine learning system. The ML model processes and analyzes data systematically, substituting human cognitive evaluation with algorithmic processing that maintains accuracy while eliminating the complexity and time associated with human interaction.
2Productivity
If machine learning automation is implemented to reduce human interaction, then processing speed increases, but system complexity increases
Solution Approach 1:
The machine learning system is designed as a universal platform that handles multiple functions: processing sensor data from mobile devices, analyzing travel patterns, evaluating driving behaviors, retrieving data from third-party sources, and generating risk profiles. This multi-functional approach consolidates complexity into a single automated system rather than requiring separate processes for each function.
3Measurement precision
If comprehensive sensor data from mobile devices is collected, then precision of risk assessment improves, but data management complexity increases
Solution Approach 1:
The machine learning model serves as an intermediary that automatically manages and processes the comprehensive sensor data. Rather than requiring manual data management, the ML system intermediates between the diverse data sources (sensors, third-party systems) and the risk assessment output, automatically integrating and analyzing the data to improve precision while managing complexity.
4Ease of operation
If automated machine learning processes are used, then user interaction requirements decrease, but automation extent increases
Solution Approach 1:
The system implements self-service automation where the machine learning model independently performs all data collection, processing, and risk profile generation without requiring user interaction. The automated system serves itself by autonomously retrieving data from third-party sources, processing sensor information, and generating outputs, thereby minimizing user involvement while achieving high automation levels.
Data Source
AI summary
Methods, computer-readable media, systems, and/or apparatuses are provided for providing offer and insight generation functions. User input requesting an offer or insight may be received and an image of a photographic identification of a user may be requested. The image of the photographic identification may be captured and stored. A self-captured image of the user may be captured (e.g., via an image capture device of the computing device) and compared to an image of a user from the photographic identification. Responsive to determining that the images match, displaying an instruction to capture a vehicle identification number. The vehicle identification number may be captured. Data, including location data, may be extracted and an archive including the extracted data may be generated and the data may be transmitted to an entity computing system for processing. The entity computing system may evaluate the data and generate one or more insights and/or outputs.