Patents by Inventor Zong Sheng TANG

Zong Sheng TANG has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240233344
    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.
    Type: Application
    Filed: October 25, 2022
    Publication date: July 11, 2024
    Inventors: Yuya SUGASAWA, Hisaji MURATA, Nway Nway AUNG, Ariel BECK, Zong Sheng TANG
  • Publication number: 20240135689
    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.
    Type: Application
    Filed: October 24, 2022
    Publication date: April 25, 2024
    Inventors: Yuya SUGASAWA, Hisaji MURATA, Nway Nway AUNG, Ariel BECK, Zong Sheng TANG
  • Publication number: 20240020944
    Abstract: A method and system for sampling and augmenting a dataset associated with a first class and a second class, respectively, to balance the dataset of images is described. The method includes receiving a required number of reduced set of dataset images associated with the first class, creating a plurality of clusters from a set of images associated with the first class, and selecting a representative image from each cluster to provide a reduced set of images. Further, a median image and a non-defect artifact mask is generated corresponding to the set of images associated with the first class. Additionally, a defect foreground is extracted based on the median image and each defect image of another set of images associated with the second class. Finally, the at least one non-defect artifact is removed from the defect foreground to provide a new synthetic defect image for each defect image for augmentation.
    Type: Application
    Filed: July 18, 2022
    Publication date: January 18, 2024
    Inventors: Ramdas KRISHNAKUMAR, Ariel BECK, Zong Sheng TANG, Khai Jun KEK, Satyam SATYAM, Masahiro ISHII, Yuto KITAGAWA
  • Publication number: 20230111765
    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.
    Type: Application
    Filed: October 13, 2021
    Publication date: April 13, 2023
    Inventors: Zong Sheng Tang, Ariel Beck, Khai Jun Kek, Chandra Suwandi Wijaya
  • Patent number: 11521313
    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.
    Type: Grant
    Filed: February 11, 2021
    Date of Patent: December 6, 2022
    Assignee: PANASONIC INTELLECTUAL PROPERTY MANAGEMENT CO., LTD.
    Inventors: Ariel Beck, Chandra Suwandi Wijaya, Athul M. Mathew, Nway Nway Aung, Ramdas Krishnakumar, Zong Sheng Tang, Yao Zhou, Pradeep Rajagopalan, Yuya Sugasawa
  • Publication number: 20220253995
    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.
    Type: Application
    Filed: February 11, 2021
    Publication date: August 11, 2022
    Inventors: Ariel BECK, Chandra Suwandi WIJAYA, Athul M. MATHEW, Nway Nway AUNG, Ramdas KRISHNAKUMAR, Zong Sheng TANG, Yao ZHOU, Pradeep RAJAGOPALAN, Yuya SUGASAWA