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公开(公告)号：US20240233344A9
公开(公告)日：2024-07-11
申请号：US17973177
申请日：2022-10-25
Inventor： Yuya SUGASAWA , Hisaji MURATA , Nway Nway AUNG , Ariel BECK , Zong Sheng TANG
IPC: G06V10/776
CPC classification number: G06V10/776
Abstract: According to an embodiment, a method for estimating robustness of a trained machine learning model is disclosed. The method comprises receiving a labelled dataset, a model of an object for which defect detection is required, and the trained machine learning model. Further, the method comprises determining one or more parameters associated with image capturing conditions in the environment. Furthermore, the method comprises performing an auto extraction of one or more defects using the model of the object and the labelled dataset based on image processing. Furthermore, the method comprises generating one or more images based on the one or more parameters and the one or more defects. Additionally, the method comprises testing the trained machine learning model using the generated images. Moreover, the method comprises estimating a robustness report for the machine learning model based on the testing of the machine learning model.
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公开(公告)号：US20240135689A1
公开(公告)日：2024-04-25
申请号：US17973177
申请日：2022-10-24
Inventor： Yuya SUGASAWA , Hisaji MURATA , Nway Nway AUNG , Ariel BECK , Zong Sheng TANG
IPC: G06V10/776
CPC classification number: G06V10/776
Abstract: According to an embodiment, a method for estimating robustness of a trained machine learning model is disclosed. The method comprises receiving a labelled dataset, a model of an object for which defect detection is required, and the trained machine learning model. Further, the method comprises determining one or more parameters associated with image capturing conditions in the environment. Furthermore, the method comprises performing an auto extraction of one or more defects using the model of the object and the labelled dataset based on image processing. Furthermore, the method comprises generating one or more images based on the one or more parameters and the one or more defects. Additionally, the method comprises testing the trained machine learning model using the generated images. Moreover, the method comprises estimating a robustness report for the machine learning model based on the testing of the machine learning model.
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公开(公告)号：US20240248464A1
公开(公告)日：2024-07-25
申请号：US18561559
申请日：2022-03-15
Inventor： Jeffry NAINGGOLAN , Yuya SUGASAWA , Hisaji MURATA , Yoshinori SATOU
IPC: G05B19/418
CPC classification number: G05B19/41875 , G05B2219/32222
Abstract: A defect prediction system includes an acquirer, a defect-related number predictor, and a cause predictor. The defect-related number predictor predicts, based on a past defect-related number acquired by the acquirer, a future defect-related number of a product with respect to each of a plurality of production lines in each of a plurality of process steps. The cause predictor predicts, based on the future defect-related number predicted by the defect-related number predictor with respect to each of the plurality of production lines in each of the plurality of process steps, which of the plurality of production lines a cause of an increase in a total future defect-related number is attributable to.
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公开(公告)号：US20240185576A1
公开(公告)日：2024-06-06
申请号：US18284794
申请日：2022-03-14
Inventor： Yuya SUGASAWA , Yoshinori SATOU , Hisaji MURATA , Jeffery FERNANDO , Yao ZHOU , Nway Nway AUNG
IPC: G06V10/774 , G06V10/70 , G06V10/80
CPC classification number: G06V10/774 , G06V10/809 , G06V10/87
Abstract: An image determination device according to the present disclosure includes: a trainer that obtains one or more first models by training machine learning models of one or more types with use of a first training data set including first images and first labels, and obtains one or more second models by training machine learning models of one or more types with use of one or more second training data sets each including second images different from the first images, second labels, and at least part of the first training data set; an image obtainer that obtains a target image; and a determiner that outputs a determination result of a label of the target image obtained by the image obtainer, which is obtained by using, for the target image, at least two models including one of the one or more first models and one of the one or more second models.
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公开(公告)号：US20240054397A1
公开(公告)日：2024-02-15
申请号：US18255034
申请日：2021-10-14
Inventor： Jeffry NAINGGOLAN , Yuya SUGASAWA , Hisaji MURATA , Yoshinori SATOU , Hisashi AIKAWA
IPC: G06N20/00
CPC classification number: G06N20/00
Abstract: A processing system includes a first acquirer, a second acquirer, a third acquirer, an identifier, and an extractor. The first acquirer is configured to acquire a plurality of pieces of learning data to which labels have been assigned. The second acquirer is configured to acquire a learned model generated based on the plurality of pieces of learning data. The third acquirer is configured to acquire identification data to which a label has been assigned. The identifier is configured to identify the identification data on a basis of the learned model. The extractor is configured to extract, based on an index which is applied in the learned model and which relates to similarity between the identification data and each of the plurality of pieces of learning data, one or more pieces of learning data similar to the identification data from the plurality of pieces of learning data.
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Patent Agency Ranking
公开(公告)号：US20240160196A1
公开(公告)日：2024-05-16
申请号：US18283411
申请日：2022-03-25
Inventor： Yao ZHOU , Athul M. MATHEW , Ariel BECK , Chandra Suwandi WIJAYA , Nway Nway AUNG , Khai Jun KEK , Yuya SUGASAWA , Jeffry FERNANDO , Yoshinori SATOU , Hisaji MURATA
IPC: G05B19/418 , G05B13/02
CPC classification number: G05B19/41875 , G05B13/0265 , G05B2219/32368
Abstract: First, a plurality of models that predict categories of input data are pooled. At least one of the plurality of models is a model trained by machine learning. Next, each of a plurality of hybrid model candidates that judge the categories are created by selecting and combining two or more models from among the plurality of pooled models. Then, by comparing the plurality of hybrid model candidates, one of the plurality of hybrid model candidates is selected as a hybrid model.
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公开(公告)号：US20230122673A1
公开(公告)日：2023-04-20
申请号：US17911614
申请日：2021-03-09
Inventor： Junko ONOZAKI , Koji OBATA , Hisashi AIKAWA , Yuya SUGASAWA
IPC: G06N20/00
Abstract: A data generation method includes a first acquisition step, a second acquisition step, and a generation step. The first acquisition step includes acquiring result information about a result of a classification executed by a living being on a target. The second acquisition step includes acquiring execution information about execution of the classification. The generation step includes generating data for machine learning based on the result information and the execution information. The data for machine learning includes learning data and evaluation information about evaluation of the learning data.
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公开(公告)号：US20220253995A1
公开(公告)日：2022-08-11
申请号：US17173822
申请日：2021-02-11
Inventor： Ariel BECK , Chandra Suwandi WIJAYA , Athul M. MATHEW , Nway Nway AUNG , Ramdas KRISHNAKUMAR , Zong Sheng TANG , Yao ZHOU , Pradeep RAJAGOPALAN , Yuya SUGASAWA
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.
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公开(公告)号：US20250117919A1
公开(公告)日：2025-04-10
申请号：US18834461
申请日：2023-01-17
Inventor： Yuya SUGASAWA , Hisaji MURATA , Jeffry NAINGGOLAN
IPC: G06T7/00
Abstract: A processing system includes an output processor which outputs criterion information applicable to an inspection algorithm. The criterion information includes information about a decision boundary to be defined based on identification results obtained by a plurality of identification algorithms that are different from each other. The decision boundary is used as a criterion for determining, by the inspection algorithm, whether the category of a target is a first category or a second category. Each of the plurality of identification algorithms identifies the category with respect to each of the plurality of image data sets. The decision boundary is a convex hull boundary to be defined based on a set of corresponding identification results, belonging to the identification results, about an image data set, to which a label indicating the second category is attached, out of the plurality of image data sets.
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公开(公告)号：US20240183793A1
公开(公告)日：2024-06-06
申请号：US18553827
申请日：2022-02-25
Inventor： Jeffry NAINGGOLAN , Yuya SUGASAWA , Hisaji MURATA , Yoshinori SATOU
IPC: G01N21/88
CPC classification number: G01N21/8851 , G01N2021/8877 , G01N2021/8887
Abstract: An inspection device includes an input portion and a determining portion. The input portion is configured to receive an input of an image taken of an object. The determining portion is configured to execute a first process on each of a plurality of inspection regions including a first inspection region and a second inspection region. The first process is a process relating to a determination as to quality of the object based on the image. The first inspection region includes a specific region not included in an inspection region other than the first inspection region of the plurality of inspection regions. The determining portion is configured to execute a second process. The second process is a process of determining, based on a result of the first process executed on each of the plurality of inspection regions, the quality of the object.
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