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:
Mar. 12, 2024

Filed:

Feb. 22, 2021
Applicant:

Hitachi, Ltd., Tokyo, JP;

Inventors:

Kanako Esaki, Tokyo, JP;

Tadayuki Matsumura, Tokyo, JP;

Hiroyuki Mizuno, Tokyo, JP;

Kiyoto Ito, Tokyo, JP;

Assignee:

Hitachi, Ltd., Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 18/20 (2023.01); G06F 18/2413 (2023.01); G06F 18/2415 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06V 10/82 (2022.01); G06V 30/10 (2022.01); G06V 30/18 (2022.01); G06V 30/19 (2022.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 18/2413 (2023.01); G06F 18/2415 (2023.01); G06F 18/285 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06V 10/82 (2022.01); G06V 30/18057 (2022.01); G06V 30/19173 (2022.01); G06V 30/10 (2022.01);
Abstract

An information processing apparatus and an information processing method capable of accurately recognizing an object to be sensed are provided. One or a plurality of learning models are selected from among learning models corresponding to a plurality of categories, a priority of each of the selected learning models is set, observation data obtained by sequentially compounding pieces of sensor data applied from a sensor is analyzed using the learning model and the priority of the learning model, a setting of sensing at a next cycle is selected based on an analysis result, predetermined control processing is executed in such a manner that the sensor performs sensing at the selected setting of sensing, and the learning model corresponding to the category estimated as the category to which a current object to be sensed belongs with a highest probability is selected based on the categories to each of which the previously recognized object to be sensed belongs.


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