Mental Digital Twin Modeling for Continuous State Prediction
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Solution Overview
Problem
Current digital twin technologies do not effectively simulate and predict the mental state of a human, lacking consideration for mental health and stress levels.
Innovation Solution
A computer-implemented system and method for simulating the mental state of a human using a digital twin, incorporating mental data storage, neural networks, and sensors to generate a dynamic representation of the mind, enabling prediction and visualization of mental states and behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current digital twin technologies are used to represent physical health states, then physiological status can be simulated, but mental state simulation capability is lacking
Solution Approach 1:
The digital twin system is segmented into separate modules: a physical digital twin for physiological parameters and a mental digital twin for psychological states. This segmentation allows independent development and optimization of each twin type, enabling accurate mental state simulation without compromising the physical health representation capabilities.
Solution Approach 2:
The patent merges physical and mental digital twins into an integrated system where both physical and mental states are represented simultaneously. This combination enables comprehensive health monitoring and allows the system to capture the interconnectedness between physical and mental health, improving overall reliability of the digital twin representation.
2Measurement precision
If digital twins are updated continuously with new data, then accuracy of mental state representation improves, but data processing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical mental state data in a database before new data arrives. When updates are needed, the system queries relevant historical data rather than processing all data from scratch, significantly reducing real-time processing complexity while maintaining high accuracy through comprehensive data consideration.
Solution Approach 2:
The system implements feedback mechanisms where new mental state data is continuously compared with historical data and model predictions. This feedback loop allows the digital twin to self-correct and refine its representation, improving accuracy over time while the feedback algorithm manages processing complexity by focusing computational resources on meaningful updates rather than redundant calculations.
3Adaptability or versatility
If mental state data is collected from multiple sources, then comprehensiveness of mental health assessment improves, but system complexity increases
Solution Approach 1:
The system employs a universal data collection framework that can accommodate multiple data sources (self-reporting, physiological sensors, environmental data, historical records) through a common processing architecture. This multi-functional design allows the system to integrate diverse data types without requiring separate processing pipelines for each source, thereby managing complexity while maintaining comprehensive assessment capability.
Data Source
AI summary
This document teaches a computer-implemented system for simulating a mental state of a mind of a human. The system comprises a memory including a plurality of instructions, virtual data storage and human mental data storage. The mental data storage stores mental data relating to the mental state of the mind of the human. At least one processor can execute ones of the plurality of instructions to generate at least a digital twin of the mind of the human. The digital twin includes one or more models of the mind of the human based on human mental data. The one or more models of the mind are stored in a virtual data storage and enable analysis of the mind of the human by the at least one processor within the virtual data storage. The digital twin includes a link between the mental data storage and the virtual data storage.