Medical Digital Twin Mesh Control With Trusted Device Governance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital twin computing systems face challenges in efficiently managing and securing data transmission and control across distributed environments, particularly in real-world premises where objects interact dynamically, leading to inefficiencies in energy consumption, production waste, and operational complexity.
Innovation Solution
A self-forming communication and control system utilizing blockchain-encoded records to securely manage data assets, where only trusted devices can modify or transfer control, and computing entities form a mesh network to facilitate seamless data exchange and control, integrating AI processing for real-time monitoring and prescriptive actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blockchain-encoded records are used to secure data transmission and control, then data security and trust management are improved, but device complexity and operational overhead increase
Solution Approach 1:
The patent introduces blockchain technology as an intermediary layer that mediates data transmission and control between devices. The blockchain network acts as a trusted mediator that verifies and records data exchanges, ensuring security without requiring direct trust between individual devices. This resolves the contradiction by providing robust security (improving reliability) while distributing the complexity across the network infrastructure rather than concentrating it in individual devices.
Solution Approach 2:
The system implements self-service mechanisms where devices automatically perform blockchain operations such as generating cryptographic keys, creating transactions, and validating blocks without manual intervention. The mesh network nodes autonomously maintain the blockchain ledger and enforce consensus protocols, reducing the need for centralized management. This automation improves security through consistent enforcement while managing complexity through decentralized self-organization.
2Productivity
If mesh network is formed for seamless data exchange, then communication efficiency and adaptability are improved, but network management complexity and energy consumption increase
Solution Approach 1:
The patent implements a dynamic mesh network where device connections, routing paths, and data flow patterns continuously adapt based on network conditions, device availability, and data priorities. The network topology is not static but evolves in real-time to optimize performance. This dynamic behavior improves data exchange efficiency by automatically finding optimal paths while distributing management complexity across all participating nodes through standardized protocols.
Solution Approach 2:
The mesh network is segmented into logical domains or communities of devices with related functions or locations. Each segment can operate semi-independently with its own routing and management protocols, reducing the complexity burden on any single node. Data exchange between segments follows standardized interface protocols, maintaining overall efficiency while isolating management complexity within manageable boundaries.
3Productivity
If AI processing is integrated for real-time monitoring and prescriptive actions, then operational efficiency and adaptability are improved, but computational requirements and energy consumption increase
Solution Approach 1:
The AI processing functionality is segmented and distributed across the mesh network nodes rather than centralized in a single powerful device. Each node runs localized AI models appropriate to its function and data context, performing real-time monitoring and decision-making for its specific domain. This segmentation reduces the computational burden and energy consumption at any single node while maintaining overall operational efficiency through coordinated distributed intelligence.
Solution Approach 2:
The system implements partial AI processing where only the necessary subset of AI capabilities is deployed at each node based on its specific requirements. Not all devices need full AI functionality - boundary devices perform edge detection and classification, while core devices handle more complex analysis. This partial action approach provides sufficient operational efficiency for each device's role while minimizing energy consumption by avoiding excessive computational overhead.
Data Source
AI summary
A method for execution by a computer includes detecting an object of a medical treatment environment based on environment signaling of the medical treatment environment to produce identified medical treatment devices and object profile information. The method further includes facilitating object tracking of the identified medical treatment devices within the medical treatment environment. The method further includes storing patient care tracking information for the identified medical treatment devices within a digital twin memory to facilitate subsequent management of patient care provided by the identified medical treatment devices that includes causing a clinical workflow de-assignment.