ML Model Ensemble for Vehicle Repair Decision Data Processing
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Solution Overview
Problem
Existing systems generate extensive amounts of data, leading to memory capacity issues and difficulty in identifying key data, which affects efficient decision-based processing and compromises system performance and security.
Innovation Solution
A dynamic and iterative process using a combination of weighted outputs from multiple trained machine learning models to identify key data and facilitate efficient decision-based computational processing, specifically for determining repair or total loss of a motor vehicle by integrating image, video, telematics, and triage data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If extensive data is generated and stored, then more information is available for analysis, but memory capacity is quickly filled and key data becomes difficult to identify
Solution Approach 1:
The system extracts only the most relevant features and key data elements from extensive insurance claim data using machine learning models. Instead of storing and processing all raw data, the system identifies and extracts critical features (e.g., damage patterns, vehicle identifiers, claim amounts) that are essential for decision-making, thereby reducing data volume while maintaining information availability.
Solution Approach 2:
The patent segments data processing into multiple specialized machine learning models, each handling specific aspects of claim analysis (e.g., image processing models for damage assessment, NLP models for claim description analysis). This segmentation allows the system to process different data types efficiently and store only the processed results rather than raw data, reducing overall data requirements.
2Reliability
If extensive data is processed, then more comprehensive analysis is possible, but processing efficiency decreases and system performance is compromised
Solution Approach 1:
The system performs preliminary data processing and feature extraction before main analysis. Machine learning models pre-process raw data (images, text, telematics) to extract meaningful features and store them in structured formats. This preliminary action enables faster querying and analysis later, as the system works with pre-processed features rather than raw data during decision-making.
Solution Approach 2:
The patent replaces traditional rule-based and manual analysis systems with machine learning models that automatically process and analyze claim data. These models learn optimal processing methods from training data, enabling comprehensive analysis of complex patterns while maintaining high processing speeds that manual or rule-based systems cannot achieve.
3Loss of information
If extensive data is stored, then more complete records are maintained, but memory capacity is quickly filled
Solution Approach 1:
The system creates compressed representations and feature vectors that capture the essential information from extensive raw data. Instead of storing complete raw datasets, the machine learning models generate condensed feature copies (e.g., extracted damage features from images, sentiment scores from text) that preserve critical information while occupying minimal storage space.
4Measurement precision
If more data is analyzed, then decision accuracy may improve, but noise and subjectivity increase compromising system performance
Solution Approach 1:
The patent introduces machine learning models as intermediaries between raw data and decision-making processes. These models act as mediators that filter, clean, and standardize data from multiple sources (images, text, telematics), converting heterogeneous data into consistent numerical features. This intermediary processing reduces noise and subjectivity while preserving accurate information for final decisions.
Data Source
AI summary
Systems and methods are provided for a dynamic and iterative process for determining a weighted decision using a combination of weighted output from multiple, trained machine learning (ML) models. Key data can be identified and efficient decision-based processing can be achieved. In some examples, the system calculates a weighted decision of a repair or total loss determination for a motor vehicle, yet any industry or data set may be implemented with the use of the dynamic and iterative decision process.