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:
Dec. 20, 2022

Filed:

Apr. 23, 2020
Applicant:

Nec Corporation, Tokyo, JP;

Inventors:

Lu Feng, Beijing, CN;

Lvye Cui, Beijing, CN;

Wenjuan Wei, Beijing, CN;

Chunchen Liu, Beijing, CN;

Assignee:

NEC CORPORATION, Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06N 7/00 (2006.01); G06V 10/75 (2022.01);
U.S. Cl.
CPC ...
G06K 9/6256 (2013.01); G06K 9/6296 (2013.01); G06N 7/005 (2013.01); G06V 10/751 (2022.01);
Abstract

Embodiments of the present disclosure relate to a method, a device and a computer-readable storage medium for data processing. The method for data processing comprises: obtaining a set of observation samples regarding a plurality of factors, one of the set of observation samples comprising respective observed values of the plurality of factors. The method further comprises: estimating, for each of the plurality of factors and based on the set of observation samples, a distribution that differences between observed values of the factor and estimated values of the factor follow. The method further comprises determining, based at least on the estimated distribution, a causal structure representing a causal relationship among the plurality of factors. Embodiments of the present disclosure further provide a device and a computer-readable storage medium for implementing the above method. The embodiments of the present disclosure can accurately and robustly discover the causal relationship among a plurality of factors without making any assumptions about the relationship between the data distribution and the factors, and affect the observed value of the target factor based on the causal relationship.


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