Digital Twin Human Inclusion Using Secure Digital Identifiers
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Solution Overview
Problem
Computing devices face challenges in securing human health, safety, and productivity data in digital twin simulations due to risks of undesired exposure, data privacy issues, and compliance with regulations, while existing solutions are resource-intensive.
Innovation Solution
A framework utilizing unique digital identifiers, configurable and verifiable human data feed criteria, and blockchain technology to manage and secure data, with edge computing for tamper-proof data processing and scalable simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security measures are implemented to protect human health, safety, and productivity data in digital twin simulations, then data security is improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent segments data security by implementing role-based access control (RBAC) that divides data protection into hierarchical levels. Different user roles (data producers, data consumers, administrators) are assigned specific permission sets, allowing fine-grained control over data access without requiring a monolithic security system. This segmentation reduces overall system complexity while maintaining comprehensive security coverage.
Solution Approach 2:
The patent introduces an intermediary data access control mechanism that sits between data requests and data storage. This intermediary layer validates access permissions, enforces security policies, and manages data flow without requiring changes to the underlying data structures or application logic. The intermediary approach simplifies security implementation by centralizing control functions.
2Reliability
If comprehensive data protection measures are applied to ensure privacy and compliance, then data security is improved, but processing speed and scalability deteriorate
Solution Approach 1:
The patent implements preliminary action by pre-establishing role-based permission templates and data classification schemas before data processing begins. Access control policies are defined in advance based on user roles and data sensitivity levels, allowing the system to quickly evaluate access requests against pre-computed permission sets rather than performing complex security checks in real-time. This preliminary configuration significantly improves processing speed while maintaining comprehensive privacy protection.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting data access parameters based on user roles, data types, and simulation contexts. The system transforms static security configurations into dynamic parameter sets that can be efficiently evaluated during data processing. By changing security from a rigid structure to flexible parameters, the system achieves both strong privacy protection and high processing throughput.
3Measurement precision
If detailed and verifiable human data feed criteria are implemented, then measurement precision is improved, but data collection complexity increases
Solution Approach 1:
The patent implements universality by creating a multi-functional data validation framework that handles multiple verification tasks through a single unified mechanism. The same role-based access control structure that manages data security also validates data feed criteria, ensuring data quality, authenticity, and compliance simultaneously. This universal approach reduces collection system complexity by eliminating redundant validation layers while maintaining high measurement precision through comprehensive verification.
Data Source
AI summary
A method for managing a state of a user includes: receiving a first dataset from a client, in which the user performs an activity using the client; obtaining a second dataset from a database; analyzing the first dataset and the second dataset to generate a unique digital identifier (DI) for the user; analyzing, based on a predetermined threshold, the first dataset and the second dataset to extract relevant data; making a determination that the predetermined threshold is violated; providing, based on the determination, the relevant data and the unique DI to a first infrastructure node (IN); in response to the providing, receiving a recommendation from the first IN to mitigate an issue associated with the user, in which the recommendation is generated for the unique DI; and sending the recommendation to the client to manage the state of the user.