Context-Based Driving Output Models for Personalized Risk Scoring
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Solution Overview
Problem
Enterprise organizations using generic machine learning models to correlate driving trip risks often fail to provide accurate, customized insights due to a lack of context-specific analysis, leading to limited insights and potential inaccuracies.
Innovation Solution
A computing platform that trains machine learning models using historical data classified into trip, device interaction, driver physical condition, and personality contexts, generating customized driving outputs by integrating new data and providing contextual insights for improved risk assessment and insurance rate modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic machine learning models are used for driving risk assessment, then the system complexity is reduced and ease of operation is improved, but measurement precision and reliability of risk assessment deteriorate
Solution Approach 1:
The patent segments the driving risk assessment system into multiple specialized machine learning models, each trained on specific contextual data types (trip context, device interaction context, physical condition context, personality context). This segmentation allows each model to specialize in particular aspects of driver behavior and risk factors, thereby improving measurement precision without requiring a single overly complex generic model.
Solution Approach 2:
The patent introduces additional dimensions of analysis by incorporating multiple contextual layers (trip context, device interaction context, physical condition context, personality context) beyond traditional driving behavior data. This multi-dimensional approach enhances measurement precision by considering a broader range of factors that influence driving risk.
2Measurement precision
If context-specific machine learning models are trained using historical data, then measurement precision and reliability of risk assessment are improved, but device complexity and loss of time for data processing increase
Solution Approach 1:
The patent applies preliminary action by pre-training multiple specialized machine learning models using historical data across various contextual dimensions before deployment. These models are trained in advance on trip context, device interaction context, physical condition context, and personality context data, so that when new driving data arrives, the system can quickly apply pre-trained models rather than training from scratch, thereby reducing real-time processing complexity.
Solution Approach 2:
The patent utilizes parameter changes by adjusting and optimizing the parameters of multiple specialized machine learning models based on different contextual data types. Each model's parameters are tuned for its specific context (e.g., trip context parameters, device interaction parameters), allowing the system to achieve high measurement precision through parameter optimization rather than increasing overall device complexity.
3Reliability
If multiple contextual factors are integrated into risk assessment, then reliability and measurement precision are improved, but loss of time for data collection and processing increases
Solution Approach 1:
The patent collects and processes contextual data (trip context, device interaction context, physical condition context, personality context) in advance and uses it to pre-train machine learning models. This preliminary data preparation allows the system to quickly generate reliable risk assessments when new driving data is available, reducing the time loss associated with collecting and processing multiple contextual factors in real-time.
4Ease of operation
If generic one-size-fits-all models are used, then device complexity is reduced and ease of operation is improved, but adaptability to individual drivers and situations deteriorates
Solution Approach 1:
The patent segments the risk assessment system into multiple specialized models that can be selectively applied based on the specific driver and situation. Instead of a single generic model, the system divides functionality into trip context analysis, device interaction analysis, physical condition analysis, and personality analysis, allowing adaptive assessment tailored to individual drivers and specific driving situations.
Solution Approach 2:
The patent implements dynamics by making the risk assessment system adaptable and flexible through multiple specialized models that can be dynamically selected and combined based on available data and specific driving contexts. The system dynamically adjusts which contextual factors are most relevant for each assessment, improving adaptability while maintaining ease of operation through automated model selection.
Data Source
AI summary
Aspects of the disclosure relate to using machine learning methods for customized output generation. A computing platform may train a model (using historical data) by classifying the historical data by a trip context, a device interaction context, and physical condition context, or a personality context, and training models using the classified historical data. The computing platform may monitor a data source system to collect new data, which may include information about multiple drivers. The computing platform may generate, by inputting the new data into the model, a customized driving output for a first driver, where the customized driving output is based at least in part on information about a second driver. The computing platform may send, to a computing device, the customized driving output and commands directing the computing device to display the customized driving output, which may cause the computing device to display the customized driving output.