公开(公告)号：US11989226B2

公开(公告)日：2024-05-21

申请号：US17756863

申请日：2020-12-17

Applicant: Panasonic Intellectual Property Management Co., Ltd.

Inventor： Hisaji Murata ,  Hideto Motomura ,  Jeffry Fernando ,  Yuya Sugasawa

IPC: G06F16/00 ,  G06F16/55 ,  G06Q50/04

CPC classification number: G06F16/55 ,  G06Q50/04

Abstract: Reliability regarding a class determination for an object is improved. Classification system includes first classification part, second classification part, and determination part. First classification part classifies first target data into at least one of a plurality of first classes. Second classification part classifies second target data into at least one of a plurality of second classes. Determination part decides whether to use one or both of a first classification result that is a classification result obtained by first classification part and a second classification result that is a classification result obtained by second classification part, and determines a class of object based on one or both of them. The first target data is image data of object. The second target data is manufacturing data regarding a manufacturing condition of object.

公开(公告)号：US11521313B2

公开(公告)日：2022-12-06

申请号：US17173822

申请日：2021-02-11

Applicant: PANASONIC INTELLECTUAL PROPERTY MANAGEMENT CO., LTD.

Inventor： Ariel Beck ,  Chandra Suwandi Wijaya ,  Athul M. Mathew ,  Nway Nway Aung ,  Ramdas Krishnakumar ,  Zong Sheng Tang ,  Yao Zhou ,  Pradeep Rajagopalan ,  Yuya Sugasawa

IPC: G06V10/75 ,  G06T7/00 ,  G06V10/60 ,  G06V10/46

Abstract: A method and system for checking data gathering conditions or image capturing conditions associated with images during AI based visual-inspection process. The method comprises generating a first representative (FR1) image for a first group of images and a second representative image (FR2) for a second group of images. A difference image data is generated between FR1 image and the FR2 image based on calculating difference between luminance values of pixels with same coordinate values. Thereafter, one or more of a plurality of white pixels or intensity-values are determined within the difference image based on acquiring difference image data formed of luminance difference-values of pixels. An index representing difference of data-capturing conditions across the FR1 image and the FR2 image is determined, said index having been determined at least based on the plurality of white pixels or intensity-values, for example, based on application of a plurality of AI or ML techniques.

公开(公告)号：US12141176B2

公开(公告)日：2024-11-12

申请号：US17762886

申请日：2020-08-28

Applicant: Panasonic Intellectual Property Management Co., Ltd.

Inventor： Jeffry Fernando ,  Hisaji Murata ,  Hideto Motomura ,  Yuya Sugasawa

IPC: G06F16/28

Abstract: Even when data that can belong to a new class that is not in an existing class is input, this data can be easily classified appropriately. Classification system includes input reception part, classification part, calculation part, determination part, and presentation part. Input reception part receives an input of target data. Classification part classifies the target data into any one of a plurality of classes. Calculation part calculates a feature amount of the target data. Determination part determines a possibility that the target data is classified into the new class based on a classification result in classification part and the feature amount of the target data calculated by calculation part. When determination part determines that there is a possibility that the target data is classified into the new class, presentation part presents a determination result of determination part.

公开(公告)号：US11977033B2

公开(公告)日：2024-05-07

申请号：US17053107

申请日：2019-05-13

Applicant: Panasonic Intellectual Property Management Co., Ltd.

Inventor： Yuya Sugasawa ,  Hideyuki Arai ,  Hisashi Aikawa

IPC: G01N21/00 ,  G01N21/88 ,  G06N3/08 ,  G06N20/00 ,  G06T7/00 ,  G06V10/143 ,  G06V10/764 ,  G06V10/774 ,  G06V20/00

CPC classification number: G01N21/8851 ,  G01N21/8806 ,  G06N3/08 ,  G06N20/00 ,  G06T7/0006 ,  G06V10/143 ,  G06V10/764 ,  G06V10/7747 ,  G06V20/00 ,  G01N2021/8845 ,  G01N2021/8854 ,  G01N2021/8887

Abstract: A learning device includes a camera configured to acquire image data by imaging a sample of a product, a physical property information acquisition unit configured to acquire physical property information of the sample, and a processing unit configured to generate a learning model. The processing unit is configured to identify a category of the sample based on rule information relating the physical property information to the category, to generate teacher data by relating the identified category to the image data, and to generate a learning model by machine learning using the teacher data. The learning model outputs the category of the sample in response to an input of the image data of the sample.