Human-like Emulation Enterprise System for Life Extension
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Solution Overview
Problem
Current technologies lack a comprehensive system for building, maintaining, and transferring perceptions between human biological and human-like bio-mechatronic and mechanical systems, failing to incorporate recent advancements in enterprise architecture, brain activity sensing, artificial neural networks, and other relevant technologies for human-like life extension and emulation.
Innovation Solution
A human-like emulation enterprise system that transitions humans into adaptable sentient entities by configuring biological, bio-mechatronic, and mechatronic subsystems to communicate and interact, utilizing neural networks for maintaining and sustaining these entities, and addressing system engineering, societal, and environmental issues through a relational database of brain activity data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a comprehensive system for building, maintaining, and transferring perceptions between human biological and human-like bio-mechatronic and mechanical systems is implemented, then human-like life extension and emulation capabilities are achieved, but system complexity and integration challenges increase significantly
Solution Approach 1:
The system is divided into distinct modular components: biological entity subsystem, bio-mechatronic entity subsystem, mechatronic entity subsystem, neural network subsystem, and relational database subsystem. Each module can be independently developed, tested, and maintained, reducing overall system integration complexity while enabling comprehensive perception transfer capabilities across different entity types.
Solution Approach 2:
The enterprise system is designed with universal interfaces and communication protocols that enable perception transfer across multiple entity types (biological, bio-mechatronic, mechatronic). The neural network subsystem serves multiple functions including perception processing, memory storage, and entity control, reducing the need for separate specialized systems for each function.
2Reliability
If neural networks are used to maintain and sustain biological, bio-mechatronic, and mechatronic entities, then entity sustainability and adaptability improve, but computational resource requirements and energy consumption increase
Solution Approach 1:
The neural network subsystem dynamically adjusts its processing intensity and resource allocation based on the operational state and requirements of the entities it sustains. During stable periods, the network operates at lower computational intensity, while increasing processing power during critical transitions or when entities require adaptation, thereby reducing average energy consumption while maintaining reliability.
Solution Approach 2:
The neural network subsystem incorporates self-optimization capabilities that allow it to automatically adjust its architecture and processing strategies to minimize energy consumption while maintaining entity sustainability. The system monitors its own performance and reconfigures neural pathways and computational resources to achieve efficient operation without external intervention.
3Adaptability or versatility
If recent advancements in enterprise architecture, brain activity sensing, artificial neural networks, and other technologies are integrated into a unified system, then system capability and functionality are enhanced, but system design and maintenance difficulty increase
Solution Approach 1:
The system incorporates distinct modular components for each technology domain: enterprise architecture framework, brain activity sensing subsystem, neural network processing subsystem, and relational database subsystem. This segmentation allows each component to be designed, implemented, and maintained by specialized teams using domain-specific best practices, reducing overall design difficulty despite the system's comprehensive functionality.
Solution Approach 2:
Standardized interface layers and communication protocols are introduced as intermediaries between different technology subsystems. These intermediary components provide uniform data exchange mechanisms and abstraction layers that simplify integration between diverse technologies, reducing design complexity while preserving the enhanced functionality provided by each advanced technology.
Data Source
AI summary
An enterprise system and method for maintaining and transitioning humans to a human-like self-reliant entity is presented. Said system including at least one a biological, biomechatronic, and mechatronic entity with a biological or artificial neural network to at least one transform or maintain. Embodiments are provided to assist in the transition of human between a biological state to a bio-mechatronic and mechatronic entity. Said entity's biological, biomechatronic, and mechatronic subsystems are configured to communicate and interact with one another in order for said enterprise system to manage, configure, maintain, and sustain said entity throughout the entity's life-cycle. Subsystem embodiments and components supported by the enterprise system are presented.