Intelligent control method for wireless keyboard of computer

By acquiring hand movement data, using machine learning and reinforcement learning to generate differentiated strategies, and combining dynamic game algorithms and inter-device communication, the problems of low behavior recognition accuracy and low resource utilization in the intelligent control of wireless keyboards are solved, achieving personalized adaptation and stable control.

CN122412959APending Publication Date: 2026-07-17SHENZHEN HENGCHANGTONG ELECTRONICS CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN HENGCHANGTONG ELECTRONICS CO LTD
Filing Date
2026-05-20
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing intelligent control technologies for wireless computer keyboards suffer from low accuracy in behavior recognition, weak model generalization ability, low resource utilization, and a lack of personalized adaptation capabilities, making them difficult to adapt to complex scenarios.

Method used

By acquiring hand movement data, using machine learning engine models to identify user behavior patterns, combining reinforcement learning to generate differentiated candidate strategies, optimizing control commands through dynamic game algorithms, and verifying and adjusting operating parameters through inter-device communication links, personalized control is achieved.

Benefits of technology

It improves the accuracy of understanding user intent, enhances the flexibility and adaptability of control strategies, ensures the reliability and stability of control operations, and optimizes the intelligent control experience of wireless keyboards.

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Abstract

The application belongs to the technical field of computer input devices, and particularly relates to a wireless keyboard intelligent control method for a computer, wherein hand operation data is acquired; a machine learning engine model is used to identify a user behavior mode, a model is trained according to preset rules and historical behavior data, and a user interaction intention is predicted; according to the user interaction intention, a plurality of sets of differentiated candidate strategies that adapt to a current scene are generated based on an exploration-exploitation mechanism of reinforcement learning, a target control strategy and a control instruction are generated through a dynamic game algorithm by combining real-time environment parameters and user preference weights; the control strategy is interactively verified through a communication link between devices, after verification, operation parameters are dynamically adjusted in combination with a current state of a host, and the keyboard is controlled in real time according to the control instruction. Thus, the problems of low behavior recognition accuracy, weak model generalization ability and low resource utilization in the prior art are solved.
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