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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：US20220108210A1
公开(公告)日：2022-04-07
申请号：US17063923
申请日：2020-10-06
Inventor： Chandra Suwandi WIJAYA , Ariel BECK
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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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：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：US20220057901A1
公开(公告)日：2022-02-24
申请号：US16997441
申请日：2020-08-19
Inventor： Ariel BECK , Chandra Suwandi WIJAYA , Khai Jun KEK
IPC: G06F3/0482 , G06F3/0484 , G06K9/62 , G06N20/00
Abstract: A graphical user interface (GUI) for forming hierarchically arranged clusters of items and operating thereupon through an electronic device equipped with an input-device and a display-screen is provided. The GUI comprises a first area configured to display a graphical-tree representation having a plurality of hierarchical levels, each of said level corresponds to at least one cluster of content-items formed by execution of a machine-learning classifier over a plurality of input content items. A second area is configured to display a dataset corresponding to the content-items classified within the clusters. A third area is configured to display a plurality of types of content representations with respect to each selected cluster, said representations corresponding to content-items classified within the cluster.
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Patent Agency Ranking
公开(公告)号：US20250086389A1
公开(公告)日：2025-03-13
申请号：US18367310
申请日：2023-09-12
Inventor： Gayathri SARANATHAN , Nway Nway AUNG , Ariel BECK , Chandra Suwandi WIJAYA , Jianyu CHEN , Debdeep PAUL , Sahim YAMAURA , Koji MIURA
IPC: G06F40/279 , G06N20/00
Abstract: According to an embodiment, a method for generating textual features corresponding to text documents from a raw dataset is disclosed. The method includes preprocessing the text documents and determining topic probability scores (TPS) and confidence scores (CS) using unsupervised and supervised machine learning models, respectively. The combination of TPS and CS is used to generate a compound distribution score (CDS), which forms a comprehensive representation of the output of the machine learning models. The determined TPS, CS, and CDS are then used to generate a set of textual features, which serve as independent variables for a forecasting model.
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公开(公告)号：US20220409075A1
公开(公告)日：2022-12-29
申请号：US17358401
申请日：2021-06-25
Inventor： Ramdas KRISHNAKUMAR , Muhammad USMAN , Pradeep RAJAGOPALAN , Ariel BECK , Khai Jun KEK , Yasufumi SHIRAKAWA
Abstract: A system (101) for monitoring a physiological condition of a user (104) is disclosed herein. The system (101) includes a receiving module (110) configured to receive a plurality of short-term segments of Heart Rate Variability (HMI) (302) or short-term electrocardiogram (ECG) segments (402) or short voice recordings (602) from the user (104) recorded at different time points. The system includes a stitching module (114) for stitching the plurality of short-term segments and creating a stitched segment. The system further includes an extracting module (116) extracting feature from the stitched segment and a predicting module (118) for predict the physiological condition, based on the feature.
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公开(公告)号：US20190289225A1
公开(公告)日：2019-09-19
申请号：US15924490
申请日：2018-03-19
Inventor： Vasileios VONIKAKIS , Ariel BECK , Chandra Suwandi WIJAYA
Abstract: A system and method for combining individual faces from a collection of group photos into a single photo based on optimal characteristics and user preferences is described. The system obtains a collection of group photos and conducts an analysis on the collection of group photos. A user may input a desired a desired facial expression and/or context. The system selects a base image and individual faces from the collection of group photos according to the desired facial expression and/or context of the user. The selected faces can be incorporated onto the base image to generate an optimal composite group photo. Multiple composite photos can be generated from the collection based on the user input.
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公开(公告)号：US20240395004A1
公开(公告)日：2024-11-28
申请号：US18795979
申请日：2024-08-06
Inventor： Chandra Suwandi WIJAYA , Ariel BECK
IPC: G06V10/25 , G06F11/34 , G06F18/214 , G06F18/231 , G06N20/00 , G06V10/40
Abstract: A method for developing machine-learning (ML) based tool including 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. Features are extracted with respect to the data set. A selection of a machine-learning classifier is received through an ML training module to operate upon the extracted features and classify the dataset. A meta controller communicates with one or more of the first model, the second model, the feature extractor and the selected classifier for assessing 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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公开(公告)号：US20220107788A1
公开(公告)日：2022-04-07
申请号：US17064692
申请日：2020-10-07
Inventor： Ariel BECK , Chandra Suwandi WIJAYA
IPC: G06F8/34 , G06F16/245 , G06N20/00 , G06Q10/10 , G06Q10/06 , G06T7/00 , G06T7/30 , G06K9/62 , G06K9/46 , G06K9/20
Abstract: A method for developing machine-learning (ML) based tool including initializing an input dataset, which is pre-processed by a first model to harmonize the dataset. Historical data similar to the input data set is fetched from a historical database. Based thereupon a controller recommends a method and a control-setting associated with the identified model for the visual inspection process to a user. 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 machine-learning classifier operates upon the extracted features and classifies the dataset with respect to one or more labels. A meta controller communicates 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.
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