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:
Aug. 29, 2023
Filed:
Jan. 17, 2020
Advanced New Technologies Co., Ltd., Grand Cayman, KY;
Fan Chen, Hangzhou, CN;
Xiang Qi, Hangzhou, CN;
Desheng Wang, Hangzhou, CN;
Hanbin Wang, Hangzhou, CN;
Qilin Guo, Hangzhou, CN;
ADVANCED NEW TECHNOLOGIES CO., LTD., George Town, KY;
Abstract
Disclosed are a data sample label processing method and apparatus. The data sample label processing method comprises: obtaining a first set of data samples without determined labels and a second set of data samples with determined labels; performing an iteration with the following steps until an accuracy rate meets a preset requirement: training a prediction model based on a combination of the first set of data samples and the second set of data samples; inputting data samples from the first set of data samples into the prediction model to obtain prediction values as learning labels for each data sample, and associating the learning labels with the data samples respectively; obtaining a subset from the first set of data samples, wherein the subset comprise data samples associated with learning labels; obtaining determined labels for the data samples in the subset; obtaining the accuracy rate based at least on the learning labels of the data samples in the subset and the determined labels of the data samples in the subset; and if the accuracy rate does not meet the preset requirement, labeling the data samples in the subset with the determined labels for the data samples in the subset, and moving the subset from the first set of data samples to the second set of data samples; and after the iteration ends, labeling the remaining data samples in the first set with the associated learning labels.