The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
May. 05, 2026
Filed:
Feb. 25, 2022
Chongqing University, Chongqing, CN;
Star Institute of Intelligent Systems, Chongqing, CN;
Db (Chongqing) Intelligent Technology Research Institute Co., Ltd, Chongqing, CN;
University of Electronic Science and Technology of China, Chengdu City, CN;
Yongduan Song, Chongqing, CN;
Feng Yang, Chengdu City, CN;
Rui Li, Chongqing, CN;
Hongyu Xia, Chongqing, CN;
Qin Chen, Chongqing, CN;
Shichun Wang, Chongqing, CN;
Liangjie Li, Chongqing, CN;
Haoyuan Zhong, Chongqing, CN;
Chongqing University, Chongqing, CN;
Star Institute of Intelligent Systems, Chongqing, CN;
DB (Chongqing) Intelligent Technology Research Institute Co., Ltd, Chongqing, CN;
University of Electronic Science and Technology of China, Chengdu City, CN;
Abstract
The present disclosure provides a student performance evaluation method and system based on artificial intelligence (AI) identification data, and relates to the field of intelligent education. A lightweight network model suitable for student performance evaluation takes the AI identification data as an input and evaluation results as an output. A training data generation algorithm is provided, and multidimensional AI identification data and labels are uniformly processed into training data suitable for the network model through the above algorithm, which can solve the problems that dimensions between any AI identification data and various labels are not uniform, and original data cannot meet training of a multidimensional and cross-time prediction model. A simulated data generation algorithm and a simulated label generation algorithm are provided, and simulated training data is generated using these algorithms in conjunction with the training data generation algorithm.
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