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:
Jan. 04, 2022

Filed:

Apr. 10, 2019
Applicant:

Fujitsu Limited, Kawasaki, JP;

Inventors:

Vivek Lakshmanan, San Jose, CA (US);

Jeffrey Fischer, Sunnyvale, CA (US);

Suhas Chelian, San Jose, CA (US);

Ajay Chander, San Francisco, CA (US);

Assignee:

FUJITSU LIMITED, Kawasaki, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/00 (2019.01); G06F 16/906 (2019.01); G06F 16/901 (2019.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 16/906 (2019.01); G06F 16/9024 (2019.01); G06N 20/00 (2019.01);
Abstract

A method may include directing display of a dataset menu listing datasets representative of graphs. The method may include identifying features in the datasets as corresponding to nodes and edges. The method may include selecting local or global mapping to map categorical feature values to numeric values. Local mapping may be selected in response to a distribution of feature values not corresponding across different graphs. Global mapping may be selected in response to a distribution of the feature values corresponding across different graphs. The method may include directing display of configuration settings that indicate the selection between local and global mapping for training a classification model. The method may include obtaining selected configuration settings. The method may include providing the selected configuration settings and datasets to a machine learning backend, which may utilize the machine learning algorithm, datasets, and selected configuration settings to train the classification model.


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