Distributed Neural Network Injection Points for AI Customization

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Solution Overview

Problem

Current AI models operate as single nodes, requiring high costs and specialized skills for custom designs, limiting their applicability to large companies and failing to meet diverse user needs effectively.

Innovation Solution

A distributed neural network architecture with multiple injection points for influence data, including query, pattern, cross-node, and control data, allowing for tailored output generation through separate processing pathways and training subsets.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If single node AI models are used, then implementation is simple, but customization capability and adaptability are limited

Engineering Contradiction:

Improvecustomization capabilityVSAvoidsystem architecture complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The AI system is divided into multiple independent nodes, each capable of processing specific tasks. This segmentation allows the system to handle complex customization requirements by distributing functionality across specialized nodes rather than requiring a single complex model.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The distributed neural network architecture enables multiple nodes to serve different functions simultaneously. Each node can be configured for specific customization tasks while the overall system provides universal AI capabilities, allowing one system to handle diverse customization needs.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Manufacturing precision

If single node AI models are used, then cost is lower, but quality of AI output and discrimination are insufficient

Engineering Contradiction:

Improvequality of AI outputVSAvoidnumber of nodes

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

Multiple AI nodes are merged into a coordinated distributed system where each node contributes specialized processing capabilities. The combination of these nodes produces higher quality AI output with improved discrimination, as each node can focus on specific aspects of the task.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system incorporates feedback mechanisms that allow nodes to learn from each other's outputs. This feedback loop enables continuous improvement of AI output quality across the distributed network, as each node can refine its performance based on collective learning.

Inventive Principle:

Principle #23Feedback

3Ease of manufacture

If single node AI models are used, then deployment is straightforward, but skill level requirements and costs for custom designs are high

Engineering Contradiction:

Improveease of deploymentVSAvoidskill level requirement

Core Design Contradiction:

Ease of manufactureVSEase of operation

Solution Approach 1:

The distributed AI nodes are designed with standardized interfaces and automated coordination mechanisms that enable self-service deployment. The system can automatically configure and coordinate node operations without requiring specialized skills, making deployment as easy as configuring individual nodes.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system allows for flexible parameter configuration at each node, enabling customization without requiring complex operational skills. By changing parameters such as node capabilities, data sources, and output formats, the system adapts to different deployment scenarios through simple parameter adjustment rather than complex reconfiguration.

Inventive Principle:

Principle #35Parameter changes

4Adaptability or versatility

If single node AI models are used, then system simplicity is maintained, but ability to meet diverse user needs is limited

Engineering Contradiction:

Improveability to meet diverse needsVSAvoiddistribution architecture

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The distributed AI architecture provides dynamic scalability where nodes can be added, removed, or reconfigured based on diverse user needs. The system adapts its structure dynamically to match specific requirements, allowing meeting of diverse needs without requiring a completely different system for each scenario.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system adds the dimension of distribution to the AI architecture, transforming a single-node system into a multi-dimensional network. This dimensional change enables diverse needs to be met by routing tasks through different nodes and pathways, providing versatility while maintaining manageable complexity through standardized connection protocols.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS20250390760A1Distributed neural network having multiple injection points supporting automatic theater production

Publication Date: 2025.12.25 FANTAGIC HOLDINGS LLC

AI summary

An artificial intelligence infrastructure comprises a first circuitry configured to offer multiple injection points. The circuitry is configured to automatically distribute neural network processing across a local computing device and at least one remote computing device while receiving and processing influence data at the multiple injection points. Furthermore, the first circuitry is configured to inject influences at a plurality of injection points to serve an overall objective. The circuitry is also configured to automatically distribute neural network nodes across one local user's computing device and at least one remote host computing device to accomplish, using artificial intelligence, the overall objective.