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:
Jun. 27, 2023

Filed:

Sep. 20, 2019
Applicant:

Nippon Telegraph and Telephone Corporation, Tokyo, JP;

Inventors:

Takashi Isezaki, Musashino, JP;

Ryosuke Aoki, Musashino, JP;

Tomoki Watanabe, Musashino, JP;

Tomohiro Yamada, Musashino, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/26 (2019.01); G06F 16/28 (2019.01); G06F 16/22 (2019.01); A61B 5/11 (2006.01); A61B 5/00 (2006.01);
U.S. Cl.
CPC ...
G06F 16/26 (2019.01); A61B 5/11 (2013.01); A61B 5/7264 (2013.01); G06F 16/2264 (2019.01); G06F 16/2282 (2019.01); G06F 16/287 (2019.01);
Abstract

An embodiment of the present invention is provided with a projective transform model including a plurality of nodes and a projection table, the plurality of nodes each holding a reference vector having a dimension corresponding to the dimension of multi-dimensional data. The projection table indicates the correspondence relation between the number of each node and a coordinate in a two-dimensional space as a projection target of the reference vector held by the node. First in a learning phase, multi-dimensional input data of a positive example and a negative example is acquired, the amplitude characteristic amounts thereof are calculated, and this amplitude characteristic amount data is learned as the reference vectors of the nodes for each sample. Subsequently, the Euclidean distance between coordinates when the nodes learned based on the amplitude characteristic amount data of the positive example and the nodes learned based on the amplitude characteristic amount data of the negative example are projected into the two-dimensional space in accordance with the projection table is calculated, and coordinates in the projection table are updated so that the calculated Euclidean distance becomes equal to or larger than a threshold value.


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