Laptop adaptation test method and system in combination with user habits

By constructing a typical user habit model library and a set of simulated operation habit sequences, and combining multi-dimensional analysis, the problem of failing to consider user habits in traditional laptop adaptation testing methods has been solved. This has enabled a precise match between system performance and user needs, improving the actual performance and market competitiveness of laptops.

CN120909893AActive Publication Date: 2025-11-07SHENZHEN ZHUO CHUANG INTELLIGENT TECH CO LTD
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Patent Information

Application Number
CN202511431062.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-09
Publication Date
2025-11-07
Estimated Expiration
2045-10-09

AI Technical Summary

Technical Problem

Traditional laptop compatibility testing methods fail to fully consider the diverse user habits, often resulting in laptops exhibiting performance mismatches with user needs in actual use, and failing to effectively detect issues such as excessive resource consumption and response latency.

Method used

Collect user habit data from multiple scenarios, construct a typical user habit model library through clustering algorithms, generate a set of simulated operation habit sequences, inject them into the laptop system, collect system adaptation response data synchronously, perform multi-dimensional correlation analysis, calculate user habit adaptation indicators and key adaptation bottleneck paths, and generate a user habit adaptation optimization report.

Benefits of technology

Accurately characterize the behavioral differences among different user groups, reveal potential problems in actual system use, improve the targeting and effectiveness of testing, clarify the correlation of abnormal system responses, provide key clues for optimization, and enhance the system's adaptability to user habits and user experience.

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Abstract

The invention relates to a notebook adaptation test method and system in combination with user habits. According to the method, user habit data including software use preferences, input mode characteristics and environment setting behaviors in multiple scenes are collected, and a typical user habit model library is constructed through a clustering algorithm; generating a simulation operation habit sequence set based on the model library, injecting the simulation operation habit sequence set into a notebook system, and synchronously collecting resource occupation feature vectors and operation response delay data as system adaptation response data; and then performing multi-dimensional correlation analysis on the response data and the model library to obtain a user habit adaptation degree index and a key adaptation bottleneck path. And finally, a user habit adaptation optimization report is generated based on an analysis result and is used for guiding software and hardware parameter adaptation optimization of the notebook computer system, and the suitability of the system and user habits is improved.
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