Thin Client Graphics Rendering With AI-Based Real-Time Setting Prediction

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Solution Overview

Problem

Existing graphics rendering techniques for thin client applications lack a universal solution to optimize resource usage in real-time due to complex inter-dependencies between model size, graphics density, network resources, and client resources, leading to suboptimal performance and visual quality.

Innovation Solution

Implementing an AI/ML model to predict graphics rendering settings in real-time by training on data such as model size, graphics density, client resources, and network resources, and transmitting optimized settings to the client device via an API.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Use of energy by moving object

If rendering techniques optimize resource usage by adjusting LOD schedules, thread counts, and GPU memory usage, then CPU and GPU usage is reduced, but visual quality may deteriorate

Engineering Contradiction:

ImproveCPU and GPU usageVSAvoidvisual quality

Core Design Contradiction:

Use of energy by moving objectVSManufacturing precision

Solution Approach 1:

The system dynamically adjusts rendering parameters including LOD schedules, HTTP thread counts, worker thread counts, and GPU maximum memory usage based on real-time conditions. The AI/ML model predicts optimal parameter values that balance resource consumption with visual quality requirements, allowing the system to change parameters adaptively rather than using fixed settings.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system incorporates feedback mechanisms where client resources data and network resources data are continuously monitored and fed into the AI/ML model. This feedback loop enables the model to learn from actual system performance and refine its predictions, ensuring that rendering optimizations maintain visual quality while reducing resource usage.

Inventive Principle:

Principle #23Feedback

2Productivity

If manual adjustment of LOD schedule quality is allowed, then users can optimize settings for model size and density, but system complexity and user burden increase

Engineering Contradiction:

Improverendering optimization effectivenessVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The AI/ML model performs self-service by automatically analyzing client resources data, network resources data, model size, and graphics density to predict optimal rendering settings. This eliminates the need for manual user intervention while maintaining high optimization effectiveness, as the system serves itself by making intelligent decisions about resource allocation and rendering quality.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces manual mechanical adjustment mechanisms with an AI/ML-based automated prediction system. Instead of requiring users to manually tune LOD schedules and thread counts, the neural network model substitutes this mechanical process with intelligent pattern recognition and prediction, significantly reducing system complexity while maintaining or improving optimization effectiveness.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Quantity of substance

If graphics software renders with lower visual quality when network bandwidth is limited, then network resources are conserved, but client performance and user experience deteriorate

Engineering Contradiction:

Improvenetwork bandwidth usageVSAvoidclient rendering performance

Core Design Contradiction:

Quantity of substanceVSProductivity

Solution Approach 1:

The system dynamically changes rendering parameters based on real-time network conditions. The AI/ML model analyzes network resources data and adjusts LOD schedules, thread counts, and GPU memory usage accordingly. This allows the system to maintain high rendering performance even under network constraints by intelligently allocating resources rather than uniformly reducing quality.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The rendering system transitions from static quality settings to dynamic adaptive quality adjustment. The AI/ML model continuously monitors network conditions and client resources, then dynamically adjusts rendering parameters in real-time. This dynamic approach enables the system to optimize the balance between network bandwidth consumption and rendering performance based on actual conditions rather than using fixed quality levels.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12548231B2Graphics rendering optimization service for thin client applications

Publication Date: 2026.02.10 HEXAGON TECH CENT GMBH

AI summary

Systems and methods to automatically provide graphics rendering optimization settings in real-time to a client device for rendering of graphics by a client application such as running on a browser or hand-held device. Specifically, a specially-configured Graphics Rendering Optimization Client application running on a client device sends requests to a corresponding Graphics Rendering Optimization Service running on a server system to obtain graphics rendering settings in real-time for rendering of graphics by the client application running on the client device, thereby providing automated and real-time graphics rendering optimization with little impact on the performance of the client application and client device. In certain embodiments, the Graphics Rendering Optimization Service employs an AI-based model that is trained and used to predict the graphics rendering settings in real time to improve the rendering performance.