Segmented AI Topology Influence Balancing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cloud-based AI services face challenges such as high processing and storage demands, unpredictable performance due to simultaneous requests or denial of service attacks, and unavailability when user devices are offline. Additionally, these services are designed as single-node AI elements, limiting their functionality to meet users' diverse objectives.
Innovation Solution
The development of adaptable multi-node AI topologies that operate in both remote and local segments, allowing for segmented and sub-segmented processing. This approach enables influence balancing across segments, supports diverse AI generation objectives, and allows for user feedback and reruns of generated segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud-based AI services are used to handle high processing and storage demands, then AI model functionality and user service capability are improved, but service reliability deteriorates due to unpredictable performance under simultaneous requests or denial of service attacks
Solution Approach 1:
The patent divides the AI service into multiple independent nodes (first AI node, second AI node, third AI node) that can operate autonomously. Each node handles specific processing tasks, allowing the system to segment workload and maintain functionality even when individual nodes fail or are overwhelmed during high-demand periods.
Solution Approach 2:
The patent introduces a temporal dimension by processing AI tasks in sequential segments rather than simultaneous batch processing. The first AI node generates initial content, the second AI node refines it, and the third AI node finalizes it, creating a staged processing pipeline that improves reliability by avoiding simultaneous resource exhaustion.
2Device complexity
If cloud-based AI services are designed as single-node elements, then system simplicity is maintained, but functionality to meet diverse user objectives is limited
Solution Approach 1:
The patent creates a multi-functional AI system where each node can perform multiple operations. The first AI node handles initial generation, the second AI node performs refinement and editing, and the third AI node completes final processing. This universal design allows a single distributed system to meet diverse user objectives that would require multiple specialized systems.
Solution Approach 2:
The patent merges the functionality of multiple AI nodes into a coordinated sequence where each node builds upon the previous one's output. The first AI node's output becomes the second AI node's input, and so on, combining their individual capabilities into a unified system that achieves complex processing objectives.
3Power
If cloud-based AI services are used, then processing power and storage capacity are improved, but cost to users increases due to advertising or periodic charges
Solution Approach 1:
The patent implements a self-service model where the AI nodes process tasks autonomously without requiring continuous cloud service subscriptions. The distributed nodes can operate independently, reducing dependency on paid cloud infrastructure and eliminating advertising or periodic charges while maintaining processing capabilities.
4Adaptability or versatility
If cloud-based AI services are used, then AI model capabilities are improved, but availability deteriorates when user devices are offline
Solution Approach 1:
The patent extracts AI processing capabilities from centralized cloud services and distributes them to local nodes that can operate independently. The first AI node, second AI node, and third AI node can function without continuous cloud connectivity, allowing users to access AI capabilities even when offline.
Data Source
AI summary
Optional segmented generative AI (Artificial Intelligence) topologies with multiple AI type outputs participate to serve overall AI generation objectives. Multiple remote and local topology nodes within overall topology segments utilize pattern based influence along with both inner and inter segment influence to adapt as new generations occur, to maintain context and to address changing circumstances. Outside influence delivers such change notifications. AI output and input interface elements provide some such change notifications and provide alternatives to conventional driver based circuit architectures. Support processing nodes, discriminative AI elements, generative AI elements along with outside influence from input, output and communication circuity along with other outside interactions are arranged in sub-segments that are carried out to deliver generated pieces of a multimedia based objective. Segment breaks allow for user review interaction along a segmented generation flow for editing and regeneration interactions. Personalized and random elements of influence, e.g., via objective, episodic and content patterns constrain AI generation via influence to hold to expected and desired output flow across segments. Inner and inter segment influence is delivered in a feed forward, feedback and cyclical manner, wherein correlation evaluations play part. Too much or too little correlation also drives regeneration cycling. Random influence via inherent and random tags requiring population before application within patterns. Random tags may comprise tree structures of randomness generated in lists from private and public data.