UI element priority dynamic adjustment method based on user behavior prediction

By using a Transformer-based user behavior prediction model and a multi-dimensional scoring mechanism, the priority of UI elements and resource allocation are dynamically adjusted, solving the problem of the disconnect between resource allocation and interaction logic in existing technologies, and achieving more efficient and personalized user interface optimization.

CN122284876APending Publication Date: 2026-06-26LIANKE YUNCHUANG (BEIJING) TECH CO LTD
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Patent Information

Application Number
CN202610542817.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-23
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing technologies lack the ability to predict the temporal correlations and intent evolution inherent in the dynamic sequences of user behavior, leading to a disconnect between resource allocation and interaction logic. Especially in resource-constrained scenarios, low-probability interaction elements consume critical resources, while high-probability elements suffer from insufficient resources, resulting in a poor user experience.

Method used

We employ a Transformer-based machine learning model to predict user behavior, and combine it with a multi-dimensional scoring mechanism to dynamically adjust the priority and resource allocation of UI elements. Through real-time environmental monitoring and closed-loop optimization, we can proactively adjust the priority and resource allocation of UI elements.

Benefits of technology

It significantly improves interaction efficiency and user experience smoothness, increases resource utilization, provides highly personalized interface loading strategies, and enhances the system's robustness and adaptability in complex environments.

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Abstract

The invention discloses a UI element priority dynamic adjustment method based on user behavior prediction. The method comprises the following steps: S101, system initialization and baseline establishment; s102, carrying out real-time data acquisition and preprocessing; s103, user behavior prediction is carried out; s104, carrying out multi-dimensional priority scoring; s105, adaptive resource scheduling and rendering execution are carried out; and S106, carrying out performance monitoring and closed loop optimization. According to the method, the user behavior can be predicted in a prospective manner, and the priority, resource allocation and rendering strategy of the UI elements are dynamically and intelligently adjusted, so that the transformation from passive response to active prediction is realized, and the interaction efficiency, the resource utilization rate and the personalized user experience are remarkably improved.
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