Digital Twin Model Selection System
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Solution Overview
Problem
The increasing complexity of digital twin models in various fields, such as healthcare and non-healthcare contexts, makes it difficult for both experts and non-experts to select and apply appropriate models due to the overwhelming volume and variety of available options, leading to challenges in determining which functions and models to use effectively.
Innovation Solution
A method and apparatus that utilize electronic medical records, sensors, and user-defined needs to select and apply digital twin models by identifying user and situational needs, allowing for the selection of appropriate models based on input parameters and providing visual or audible output, while maintaining control over privacy and ethical considerations through networked digital twins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple digital twin models are made available to users, then the versatility and applicability of the digital twin system is improved, but the device complexity and difficulty of model selection increases
Solution Approach 1:
The patent introduces an intermediary system that automatically selects appropriate digital twin models based on user profiles, situational context, and data analysis. This mediator layer shields users from the complexity of model selection while maintaining access to multiple specialized models, resolving the contradiction between versatility and selection complexity.
Solution Approach 2:
The system implements self-service model selection through automated algorithms that evaluate user needs, data characteristics, and situational factors to autonomously determine which digital twin models to apply. This eliminates the need for users to manually navigate complex model options while preserving access to diverse model capabilities.
2Loss of information
If comprehensive digital twin models are provided to patients, then the information completeness and diagnostic capability is improved, but the ease of operation and patient control decreases
Solution Approach 1:
The patent implements dynamic model selection that adapts to patient preferences and situational needs. The system can adjust which digital twin models are activated based on real-time user input, privacy settings, and clinical context, allowing comprehensive information processing while maintaining patient control through flexible, adaptable interfaces.
Solution Approach 2:
The system applies different levels of model complexity and information detail to different patient contexts and preferences. Rather than providing all possible information uniformly, the system tailors the depth and type of digital twin analysis to each patient's specific needs, improving ease of operation while maintaining information completeness where required.
3Productivity
If automated model selection is implemented, then the productivity and efficiency of model application is improved, but the measurement precision and model appropriateness may worsen
Solution Approach 1:
The patent incorporates feedback mechanisms where the automated model selection system continuously learns from outcomes and user responses. The system monitors the effectiveness of selected models and adjusts future selections based on performance data, ensuring that automated efficiency does not compromise selection accuracy and continuously improving model appropriateness over time.
Data Source
AI summary
Embodiments are described herein for selecting and applying models of digital twins for various purposes. In various embodiments, one or more user needs of a user seeking to utilize a digital twin may be identified. One or more situational needs may be identified of a subject simulated by the digital twin. The one or more situational needs may be identified based on data obtained from the subject. Based on one or more of the user needs and one or more of the situational needs of the subject, one or more models of the digital twin may be selected and applied to generate digital twin output that simulates one or more aspects of the subject.