Multi-User AI Content Offloading in Mobile Computing Environments

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Solution Overview

Problem

Existing systems struggle to efficiently manage and deliver dynamically generated content in mobile computing environments due to the high computational demands, particularly in scenarios where multiple users have varying content requests and dynamic conditions.

Innovation Solution

A method and system that utilize generative AI computing systems, both local and remote, to orchestrate content generation by generating metrics based on user demands, context, and sensor data, and distribute workload efficiently through an offloading strategy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If dynamically generated content is provided to users in mobile computing environments, then content personalization and interactivity are improved, but computational resource requirements and system complexity increase significantly

Engineering Contradiction:

Improvecontent personalizationVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the content generation system into multiple independent generative AI computing systems (first GAICS, second GAICS, third GAICS) with specialized functions. The first GAICS handles local content generation, the second handles additional content generation, and the third handles demand redistribution, allowing each component to be optimized independently while reducing overall system complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces a demand redistribution mechanism that acts as an intermediary between user devices and generative AI computing systems. This intermediary receives content demands, determines optimal distribution strategies, and routes demands to appropriate systems, thereby managing system complexity centrally rather than distributing it across all components.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Power

If multiple generative AI computing systems are used to handle content demands, then computational capacity is improved, but coordination complexity and response time increase

Engineering Contradiction:

Improvecomputational capacityVSAvoidresponse time

Core Design Contradiction:

PowerVSLoss of time

Solution Approach 1:

The patent implements preliminary action by having the demand redistribution system pre-establish communication channels and readiness status with multiple generative AI computing systems. When content demands arrive, the system can immediately route them to pre-vetted, ready systems rather than establishing connections in real-time, thus maintaining fast response times while utilizing multiple systems.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces dynamic load balancing where the demand redistribution system continuously monitors the operating states and capacity of multiple generative AI computing systems, dynamically adjusting demand allocation in real-time. This dynamic adaptation allows the system to optimize for both computational capacity utilization and response time based on current system conditions.

Inventive Principle:

Principle #15Dynamics

3Productivity

If content demands are distributed across multiple generative AI computing systems, then computational efficiency is improved, but demand routing complexity increases

Engineering Contradiction:

Improvecomputational efficiencyVSAvoiddemand routing complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements self-service through autonomous demand routing where the demand redistribution system automatically evaluates incoming content demands, determines optimal system allocation based on current operating states, and routes demands without manual intervention. This self-service automation improves computational efficiency while the centralized autonomous decision-making actually reduces routing complexity compared to distributed manual routing.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent incorporates feedback mechanisms where the demand redistribution system continuously monitors the performance, load, and operating states of multiple generative AI computing systems. This feedback information is used to dynamically adjust demand routing decisions, improving computational efficiency through learned optimization while simplifying the routing logic through data-driven decision-making rather than complex predefined rules.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260030075A1Fluid orchestration of multi-user content generation in a mobile computing environment

Publication Date: 2026.01.29 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Orchestrating multi-user content generation includes generating metrics based, at least in part, on a plurality of demands for content, a context of a mobile computing environment, sensor data for the mobile computing environment and a plurality of users within the mobile computing environment, and operating states of a plurality of generative artificial intelligence (AI) computing systems. An offloading strategy that defines which demands of the plurality of demands are to be performed by different ones of the plurality of generative AI computing systems is generated based on the metrics. The plurality of demands are distributed to one or more selected generative AI computing systems selected from the plurality of generative AI computing systems based on the offloading strategy. Content generated by the one or more selected generative AI computing systems is provided to devices corresponding to the plurality of users.