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.

Date of Patent:
Oct. 28, 2025

Filed:

Oct. 04, 2021
Applicant:

Emc Ip Holding Company Llc, Hopkinton, MA (US);

Inventors:

Zijia Wang, WeiFang, CN;

Jiacheng Ni, Shanghai, CN;

Zhen Jia, Shanghai, CN;

Wenbin Yang, Shanghai, CN;

Assignee:

EMC IP Holding Company LLC, Hopkinton, MA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 18/2413 (2023.01); G06F 18/2132 (2023.01); G06F 18/22 (2023.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 18/2413 (2023.01); G06F 18/21322 (2023.01); G06F 18/22 (2023.01); G06F 18/21328 (2023.01); G06N 20/00 (2019.01);
Abstract

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for analyzing samples. The method includes acquiring a set of feature representations associated with a set of samples. The set of samples illustratively have classification information for indicating classifications of the set of samples. The method further includes adjusting the set of feature representations so that distances between feature representations of samples corresponding to the same classification are less than a first distance threshold. The method further includes training a classification model based on the adjusted set of feature representations and the classification information. The classification model is illustratively configured to receive an input sample and determine a classification of the input sample. In this manner, a relatively accurate classification model can be trained using a small number of samples, thereby reducing computation time and required computation capacity.


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