Artificial Brain Neural Synchronization for Real-Time AI Adaptation

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Solution Overview

Problem

Current artificial intelligence systems face network bottlenecks and inability to adapt in real-time due to inefficient data processing and lack of scalability, limiting their ability to mimic human-level intelligence.

Innovation Solution

A neural synchronization architecture that translates sensory data into thalamic motion, allowing for the conversion of general intelligence to symbolic intelligence and automation of motor controls, enabling faster-than-real-time processing and adaptation in unpredictable environments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If current AI systems process data using traditional methods, then they can handle basic tasks, but they cannot adapt in real-time and suffer from network bottlenecks

Engineering Contradiction:

Improvereal-time adaptationVSAvoidprocessing speed

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent replaces traditional mechanical data processing systems with a neural synchronization architecture that mimics human brain function. This substitution enables real-time adaptation by using neural oscillations and synchronization mechanisms instead of conventional sequential processing, allowing the system to dynamically respond to changing conditions without network bottlenecks

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the fundamental parameters of data processing by transitioning from static, pre-programmed algorithms to dynamic neural oscillation frequencies and synchronization patterns. This allows the AI system to adapt its processing characteristics in real-time based on environmental conditions, resolving the contradiction between adaptability and processing speed

Inventive Principle:

Principle #35Parameter changes

2Quantity of substance

If network capacity is upgraded to handle more data, then bandwidth increases, but costs increase and bottleneck problems persist

Engineering Contradiction:

Improvedata capacityVSAvoidsystem cost

Core Design Contradiction:

Quantity of substanceVSEase of manufacture

Solution Approach 1:

The patent extracts the core intelligence-generating mechanism from massive data transmission and isolates it in the neural synchronization architecture. By taking out only the essential processing function and implementing it through efficient neural oscillation synchronization, the system achieves high data capacity utilization without requiring proportionally increased network infrastructure, thereby reducing costs while maintaining capacity

Inventive Principle:

Principle #2Taking out (Extraction)

3Productivity

If more hardware capacity is added to AI systems, then processing power increases, but bottleneck problems worsen and scalability is limited

Engineering Contradiction:

Improveprocessing powerVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The neural synchronization architecture serves multiple functions simultaneously: it processes data, adapts to new conditions, generates intelligence, and coordinates distributed components through universal neural oscillation patterns. This multi-functionality increases processing power without proportionally increasing system complexity, as the same core mechanism handles diverse computational tasks

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Ease of operation

If traditional AI methods are used, then implementation is straightforward, but they cannot duplicate human-level intelligence

Engineering Contradiction:

Improveimplementation easeVSAvoidhuman-level intelligence capability

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The patent introduces neural oscillation patterns and synchronization mechanisms as intermediary elements between traditional computing and human-level intelligence. These intermediaries bridge the gap by providing a natural mechanism for information integration and adaptive processing that mimics biological intelligence, enabling human-level capabilities while maintaining implementability through defined synchronization protocols

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20230206081A1System, method, and computer program product for the production and consumption of natural intelligence using an artificial brain

Publication Date: 2023.06.29 EDGAR DAVID ALLAN

AI summary

Systems and methods are described herein which may be implemented using computer programs comprising instructions that replicate the neural synchronization based natural intelligence algorithm of the human brain. These implementations result in the production and consumption of naturally forming intelligence in a computer system or network. An embodiment of the invention comprises an artificial brain, further comprising a thalamic controller, motion reactor, motion translator, motion actuator, time-dilation memory, and cognitive object interface. Data is translated from original format into thalamic motion and further encoded with motion signal protocol, then reproduced for the purpose of sensory perception and aggregated through a process of thought production, thereby replicating the process of natural intelligence in a manner designed to dramatically improve current artificial intelligence standards. By duplicating the human brain's natural intelligence process, overall computer intelligence should approach or surpass human level intelligence in automation and adaptation ability.