Digital Twin Property Evaluation Engine for Feature-Rich Risk Modeling
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Solution Overview
Problem
Existing digital twin models and machine learning systems face limitations in processing power and resource constraints, often requiring feature engineering to limit the number of features considered, which can result in less accurate outputs and neglect important factors in predictions.
Innovation Solution
A computing platform with a digital twin property evaluation engine, utilizing a knowledge graph with machine learning models to simulate physical properties, processes historical data, and generates event processing information, allowing for the analysis of all relevant features without resource limitations, by training models to output risk scores and loan information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If feature engineering is used to limit the number of features considered, then computing resources are conserved, but prediction accuracy deteriorates
Solution Approach 1:
The patent segments the feature analysis into multiple specialized machine learning models, each handling specific feature types (climate, credit, stock market, etc.). This allows the system to process all features comprehensively while maintaining computational efficiency through modular architecture, resolving the contradiction between feature quantity and prediction accuracy.
Solution Approach 2:
The patent introduces a knowledge graph dimension to organize and relate features systematically. By representing features as nodes and relationships as edges, the system can manage and process a comprehensive set of features without overwhelming computational resources, enabling accurate predictions while conserving computing power.
2Measurement precision
If all features are considered by the model, then prediction accuracy improves, but computing resources are exhausted
Solution Approach 1:
The system divides the comprehensive feature analysis into multiple specialized models, each processing specific feature subsets. This segmentation enables the system to consider all features for accurate predictions while distributing computational load across multiple manageable units rather than overwhelming a single model.
Solution Approach 2:
The knowledge graph acts as an intermediary that mediates between the comprehensive feature set and the machine learning models. It organizes and pre-processes feature relationships, allowing models to access processed feature information efficiently without directly handling the full complexity of all features simultaneously.
3Adaptability or versatility
If multiple machine learning models are used to analyze all features, then comprehensive analysis is achieved, but system complexity increases
Solution Approach 1:
The patent creates a universal digital twin property evaluation engine that integrates multiple machine learning models within a single cohesive system. This multi-functional platform can analyze diverse feature types (climate, credit, market, property-specific) using unified architecture and data processing pipelines, achieving comprehensive analysis while managing system complexity through standardization.
Solution Approach 2:
The knowledge graph serves as a central intermediary that manages the complexity of multiple models by providing a unified interface for feature organization and relationship mapping. It abstracts the complexity of coordinating multiple models, allowing the system to maintain high adaptability across different feature analyses while keeping the overall system architecture manageable.
Data Source
AI summary
Aspects of the disclosure relate to digital twin simulation. A computing platform may train, using historical property information, a digital twin property evaluation engine, configured to model a physical property based on characteristics of the physical property using a computer simulation. The computing platform may receive, from a client device, an event processing request identifying a first physical property. The computing platform may generate, using the digital twin property evaluation engine, a computer simulation of the first physical property. The computing platform may execute, over a simulated period of time, the computer simulation of the first physical property to output event processing information for the first physical property. The computing platform may send, to the client device, the event processing information and one or more commands directing the client device to display the event processing information, which may cause the client device to display the event processing information.