Adaptive Content Rendering Using Local Prediction Under Low Bandwidth
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Solution Overview
Problem
In low-bandwidth or unstable network environments, static content rendering methods lead to slow or failed rendering of target content, resulting in inefficient and unstable rendering effects.
Innovation Solution
A content rendering method and apparatus that utilizes a local prediction model to determine the display form with the highest attribute prediction value based on terminal device state information, dynamically selecting the display form for rendering to improve efficiency and effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static rendering method is used, then rendering process is simple, but rendering efficiency is low and rendering stability is poor in low-bandwidth network environments
Solution Approach 1:
The patent implements dynamic rendering by selecting different display forms (full-screen, picture-in-picture, floating window) based on real-time network conditions and user behavior predictions, transforming the static rendering process into a dynamic adaptive system that responds to changing environmental conditions
Solution Approach 2:
The system changes rendering parameters (display form selection, content loading strategy) based on network bandwidth conditions and predicted user behavior, allowing the rendering process to adapt its parameters dynamically rather than using fixed static rendering settings
2Reliability
If static rendering method is used, then implementation is straightforward, but rendering stability is poor in unstable network environments
Solution Approach 1:
The system performs preliminary actions by predicting user behavior and network conditions in advance, pre-selecting appropriate display forms and rendering strategies before actual content delivery, which stabilizes rendering performance by preparing adaptive responses ahead of time
Solution Approach 2:
The patent implements feedback mechanisms that monitor network conditions and user interactions in real-time, using this feedback to dynamically adjust rendering decisions and select optimal display forms, thereby maintaining rendering stability through continuous adaptation to changing conditions
3Productivity
If dynamic display form selection is implemented, then rendering efficiency is improved, but system complexity increases
Solution Approach 1:
The system performs self-service by using on-device machine learning models to predict user behavior and autonomously select optimal display forms without requiring complex server-side coordination or manual intervention, simplifying the overall system architecture while maintaining dynamic adaptability
Solution Approach 2:
The patent uses simplified copies or proxies for complex predictions by employing lightweight machine learning models that run locally on the terminal device, capturing essential user behavior patterns without requiring full-blown complex analysis systems
Data Source
AI summary
The present disclosure relates to a content rendering method and apparatus, a readable medium, and an electronic device. The method includes: obtaining a target content, where a plurality of display forms correspond to the target content; determining state information of the terminal device; determining, by using a local prediction model, an attribute prediction value of each display form based on the state information, where the local prediction model is configured to predict an attribute value of the display form based on the state information; and determining, from the plurality of display forms, a display form with a highest attribute prediction value as a target display form, and rendering the target content according to the target display form.