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公开(公告)号：US11507252B2
公开(公告)日：2022-11-22
申请号：US16997441
申请日：2020-08-19
Inventor： Ariel Beck , Chandra Suwandi Wijaya , Khai Jun Kek
IPC: G06F3/0482 , G06F3/04845 , 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：US11521313B2
公开(公告)日：2022-12-06
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：US20240119470A1
公开(公告)日：2024-04-11
申请号：US17955053
申请日：2022-09-28
Inventor： Debdeep PAUL , Chandra Suwandi Wijaya , Yizhou Huang , Khai Jun Kek , Koji Miura
IPC: G06Q30/02
CPC classification number: G06Q30/0202
Abstract: According to an embodiment, a method for generating a forecast of a timeseries is disclosed. The method comprises receiving a set of features comprising data and timeseries to be used by each of a plurality of prediction models for generating the forecast. Further, the method comprises generating using the set of features, a plurality of forecast results based on an ensemble of the plurality of prediction models. Furthermore, the method comprises optimizing the plurality of forecast results associated with a respective forecast module. Additionally, the method comprises probabilistically combining the outputs of the plurality of optimization modules. Moreover, the method comprises outputting a final forecast based on the combination of the at least two forecast results.
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公开(公告)号：US11250356B2
公开(公告)日：2022-02-15
申请号：US16366963
申请日：2019-03-27
Inventor： Ariel Beck , Vasileios Vonikakis , Khai Jun Kek , Chandra Suwandi Wijaya
Abstract: The present disclosure relates to a method and system for apportioning tasks to person in an environment. The method comprises capturing a first-value indicating a sympathetic-nerve based activity and a second-value indicating a parasympathetic-nerve based activity for at least one person operating in an environment. Thereafter, a quantitative-relation is determined between the first and second values. At-least one task is assigned for execution by said person within the environment based on such quantitative relation.
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公开(公告)号：US20190354777A1
公开(公告)日：2019-11-21
申请号：US15984963
申请日：2018-05-21
Inventor： Ariel BECK , Khai Jun Kek , Vasileios Vonikakis , Chandra Suwandi Wijaya
Abstract: There is provided a computer-implemented method for generating an output with respect to a group of individuals. The method includes: identifying the group of individuals amongst a plurality of individuals in an area being monitored by one or more sensors; determining, for each individual in the group of individuals, one or more individual-based features associated with the individual based on sensing data obtained from the one or more sensors; determining a group characteristic information associated with the group of individuals based on the one or more individual-based features determined for each individual in the group; and generating the output based on the group characteristic information determined for the group of individuals. There is also provide a corresponding system for generating an output with respect to a group of individuals.
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Patent Agency Ranking
公开(公告)号：US10481304B2
公开(公告)日：2019-11-19
申请号：US15634071
申请日：2017-06-27
Inventor： Chandra Suwandi Wijaya , Jovia Jia Zhen Lee
Abstract: According to various embodiments, there is provided a lens sheet including an array of lenses arranged parallel to each other. Each lens includes a light redirecting portion having a light incident surface and a light reflecting surface, and includes a light refracting portion. The light reflecting surface is slanted relative to the light incident surface and relative to a plane interfacing the light redirecting portion and the light refracting portion, such that light of a first view image and light of a second view image transmitted through the light incident surface into the lens are directed to a first region and a second region of the light reflecting surface respectively and are reflected to the light refracting portion by the light reflecting surface. The first region is next to the second region. The light refracting portion is configured to refract the light of the first view image to a first view region and refract the light of the second view image to a second view region spaced apart from the first view region.
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公开(公告)号：US12094180B2
公开(公告)日：2024-09-17
申请号：US17063923
申请日：2020-10-06
Inventor： Chandra Suwandi Wijaya , Ariel Beck
IPC: G06N20/00 , G06F11/34 , G06F18/214 , G06F18/231 , G06V10/25 , G06V10/40
CPC classification number: G06V10/25 , G06F11/3409 , G06F18/214 , G06F18/231 , G06N20/00 , G06V10/40
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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8.发明申请公开(公告)号：US20230111765A1
公开(公告)日：2023-04-13
申请号：US17500833
申请日：2021-10-13
Inventor： Zong Sheng Tang , Ariel Beck , Khai Jun Kek , Chandra Suwandi Wijaya
IPC: G06K9/62
Abstract: The present subject matter describes a method for labeling data in a computing system based on artificial intelligent techniques. The method comprises receiving input data and ordering the received input-data in a plurality of classes inferred based on at-least one of clustering and anomaly detection. The method further comprises receiving one more manual annotated labels for the ordered data. A first machine-learning (ML) model is trained with respect to the ordered data and thereby generating new labels. The performance of the first ML model is computed based on a comparison between the manual labels and the new labels. The labels are automatically propagated to unlabelled-portion of the ordered data based on execution of the first ML model based on accuracy of first ML model being above a predefined threshold.
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公开(公告)号：US11568318B2
公开(公告)日：2023-01-31
申请号：US17064692
申请日：2020-10-07
Inventor： Ariel Beck , Chandra Suwandi Wijaya
IPC: G06F8/34 , G06N20/00 , G06Q10/10 , G06Q10/06 , G06T7/00 , G06T7/30 , G06K9/62 , G06F16/245 , G06V10/40 , G06V10/22 , G09B19/16
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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公开(公告)号：US11288822B2
公开(公告)日：2022-03-29
申请号：US16827468
申请日：2020-03-23
Inventor： Xibeijia Guan , Chandra Suwandi Wijaya , Vasileios Vonikakis , Ariel Beck
Abstract: The present subject matter refers a method for training image-alignment procedures in a computing environment. The method comprises communicating one or more images of an object to a user and receiving a plurality of user-selected zones within said one or more through a user-interface. An augmented data-set is generated based on said one or more images comprising the user-selected zones, wherein such augmented data set comprises a plurality of additional images defining variants of said one or more communicated images. Thereafter, a machine-learning based image alignment is trained based on at-least one of the augmented data set and the communicated images.
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