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:
Sep. 17, 2024

Filed:

Oct. 06, 2020
Applicant:

Panasonic Intellectual Property Management Co., Ltd., Osaka, JP;

Inventors:

Chandra Suwandi Wijaya, Singapore, SG;

Ariel Beck, Singapore, SG;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 11/34 (2006.01); G06F 18/214 (2023.01); G06F 18/231 (2023.01); G06V 10/25 (2022.01); G06V 10/40 (2022.01);
U.S. Cl.
CPC ...
G06V 10/25 (2022.01); G06F 11/3409 (2013.01); G06F 18/214 (2023.01); G06F 18/231 (2023.01); G06N 20/00 (2019.01); G06V 10/40 (2022.01);
Abstract

The present subject matter refers a method for developing machine-learning (ML) based tool. The method comprises initializing an input dataset for undergoing ML based processing. The input dataset is pre-processed by a first model to harmonize features across the dataset. Thereafter, the dataset is annotated by a second model to define a labelled data set. A plurality of features are extracted with respect to the data set through a feature extractor. A selection of at-least a machine-learning classifier is received through an ML training module to operate upon the extracted features and classify the dataset with respect to one or more labels. A meta controller communicated with one or more of the first model, the second model, the feature extractor and the selected classifier for assessing a performance of at least one of first model and the feature extractor, a comparison of operation among the one or more selected classifier, and diagnosis of an unexpected operation with respect to one or more of the first model, the feature extractor and the selected classifier.


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