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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：US20210294488A1
公开(公告)日：2021-09-23
申请号：US16824467
申请日：2020-03-19
Inventor： Vasileios VONIKAKIS , Yao ZHOU , Chandra Suwandi WIJAYA , Ariel BECK
IPC: G06F3/0484 , G06T7/00
Abstract: A method implemented in a computing-device with a display screen for image inspection. The method comprises displaying a distribution of a quality-indicia of at least one object in each of a plurality of images to be inspected, within a first area of the display screen. Within a second area of the display screen, a user-control is displayed to adjust a threshold-value with respect to an acceptance of at least one object in said plurality of images to be inspected. The threshold-value may be determined manually or automatically. A change in or update of threshold value is determined based on a user-operation performed over the user-control for adjusting the threshold value. Thereafter, a quality-indicia of at least one object in each the plurality of images is determined. Acceptable objects in respect of an image inspection procedure based on the updated threshold value and the determined quality-indicia are indicated.
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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：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：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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Patent Agency Ranking
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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公开(公告)号：US20250068982A1
公开(公告)日：2025-02-27
申请号：US18236754
申请日：2023-08-22
Inventor： Yizhou HUANG , Chandra Suwandi WIJAYA , Debdeep PAUL , Koji MIURA
IPC: G06N20/20
Abstract: According to an embodiment, a method for determining feature importance in an ensemble model including a plurality of Machine Learning (ML) models is disclosed. The method includes receiving a dataset comprising input features and a forecast result. The method also includes generating a ranking-based feature list based on the input features. Further, the method includes generating a feature importance output based on the ranking-based features lists. Furthermore, the method includes determining a weightage value corresponding to each of the plurality of ML models based on an accuracy value associated with the corresponding machine learning model. The method also includes determining a weightage-based feature importance value corresponding to each input feature corresponding to the feature importance output based on the determined weightage value corresponding to each ML model responsible for the corresponding input feature in the feature importance output.
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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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