Patents by Inventor LIYU GONG

LIYU GONG 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).

  • Patent number: 12056944
    Abstract: An efficient and robust high-speed neural networks for cell image classification is described herein. The neural networks for cell image classification utilize a difference between soft-max scores to determine if a cell is ambiguous or not. If the cell is ambiguous, then the class is classified in a pseudo class, and if the cell is not ambiguous, the cell is classified in the class corresponding to the highest class score. The neural networks for cell image classification enable a high speed, high accuracy and high recall implementation.
    Type: Grant
    Filed: July 9, 2021
    Date of Patent: August 6, 2024
    Assignees: SONY GROUP CORPORATION, SONY CORPORATION OF AMERICA
    Inventors: Liyu Gong, Ming-Chang Liu
  • Patent number: 12008732
    Abstract: A method for super-resolution of block-compressed texture is provided. The method includes receiving a first texture block of a first block size. Based on application of a first block compression (BC) scheme on the received first texture block, coded-texture values are generated in a compressed domain. Further, a first machine learning model is applied on the generated coded-texture values to generate super-resolution coded-texture values in the compressed domain. The generated super-resolution coded-texture values are processed to generate a second texture block of a second block size. The second block size is greater than the first block size.
    Type: Grant
    Filed: March 30, 2021
    Date of Patent: June 11, 2024
    Assignee: SONY GROUP CORPORATION
    Inventors: Ming-Chang Liu, Liyu Gong, Ko-Kai Albert Huang, Joshua Scott Hobson
  • Patent number: 12008825
    Abstract: An image-based classification workflow uses unsupervised clustering to help a user identify subpopulations of interest for sorting. Labeled cell images are used to fine-tune the supervised classification network for a specific experiment. The workflow allows the user to select the populations to sort in the same manner for a variety of applications. The supervised classification network is very fast, allowing it to make real-time sort decisions as the cell travels through a device. The workflow is more automated and has fewer user steps, which improves the ease of use. The workflow uses machine learning to avoid human error and bias from manual gating. The workflow does not require the user to be an expert in image processing, thus increasing the ease of use.
    Type: Grant
    Filed: November 19, 2021
    Date of Patent: June 11, 2024
    Assignees: SONY GROUP CORPORATION, SONY CORPORATION OF AMERICA
    Inventors: Ming-Chang Liu, Michael Zordan, Ko-Kai Albert Huang, Su-Hui Chiang, Liyu Gong
  • Publication number: 20230316792
    Abstract: Techniques are described for automatically, and substantially without human intervention, generating training data where the training data includes a set of training images containing text content and associated label data. Both the training images and the associated label data are automatically generated. The label data that is automatically generated for a training image includes one or more labels identifying locations of one or more text portions within the training image, and for each text portion, a label indicative of the text content in the text portion. By automating both the generation of training images and the generation of associated label data, the techniques described herein are very scalable and repeatable and can be used to generate large amounts of training data, which in turn enables building more reliable and accurate language models.
    Type: Application
    Filed: March 11, 2022
    Publication date: October 5, 2023
    Applicant: Oracle International Corporation
    Inventors: Yazhe Hu, Yuying Wang, Liyu Gong, Iman Zadeh, Jun Qian
  • Publication number: 20230066922
    Abstract: The present embodiments relate to identifying a native language of text included in an image-based document. A cloud infrastructure node (e.g., one or more interconnected computing devices implementing a cloud infrastructure) can utilize one or more deep learning models to identify a language of an image-based document (e.g., a scanned document) that is formed of pixels. The cloud infrastructure node can detect text lines that are bounded by bounding boxes in the document, determine a primary script classification of the text in the document, and derive a primary language for the document. Various document management tasks can be performed responsive to determining the language, such as perform optical character recognition (OCR) or derive insights into the text.
    Type: Application
    Filed: August 26, 2022
    Publication date: March 2, 2023
    Applicant: Oracle International Corporation
    Inventors: Liyu Gong, Yuying Wang, Zhonghai Deng, Iman Zadeh, Jun Qian
  • Publication number: 20230067033
    Abstract: The present embodiments relate to a language identification system for predicting a language and text content of text lines in an image-based document. The language identification system uses a trainable neural network model that integrates multiple neural network models in a single unified end-to-end trainable architecture. A CNN and an RNN of the model can process text lines and derive visual and contextual features of the text lines. The derived features can be used to predict a language and text content for the text line. The CNN and the RNN can be jointly trained by determining losses based on the predicted language and content and corresponding language labels and text labels for each text line.
    Type: Application
    Filed: August 26, 2022
    Publication date: March 2, 2023
    Applicant: Oracle International Corporation
    Inventors: Liyu Gong, Yuying Wang, Zhonghai Deng, Iman Zadeh, Jun Qian
  • Publication number: 20220156482
    Abstract: An image-based classification workflow uses unsupervised clustering to help a user identify subpopulations of interest for sorting. Labeled cell images are used to fine-tune the supervised classification network for a specific experiment. The workflow allows the user to select the populations to sort in the same manner for a variety of applications. The supervised classification network is very fast, allowing it to make real-time sort decisions as the cell travels through a device. The workflow is more automated and has fewer user steps, which improves the ease of use. The workflow uses machine learning to avoid human error and bias from manual gating. The workflow does not require the user to be an expert in image processing, thus increasing the ease of use.
    Type: Application
    Filed: November 19, 2021
    Publication date: May 19, 2022
    Inventors: Michael Zordan, Ming-Chang Liu, Ko-Kai Albert Huang, Su-Hui Chiang, Liyu Gong
  • Publication number: 20220156481
    Abstract: An efficient and robust high-speed neural networks for cell image classification is described herein. The neural networks for cell image classification utilize a difference between soft-max scores to determine if a cell is ambiguous or not. If the cell is ambiguous, then the class is classified in a pseudo class, and if the cell is not ambiguous, the cell is classified in the class corresponding to the highest class score. The neural networks for cell image classification enable a high speed, high accuracy and high recall implementation.
    Type: Application
    Filed: July 9, 2021
    Publication date: May 19, 2022
    Inventors: Liyu Gong, Ming-Chang Liu
  • Publication number: 20210312592
    Abstract: A method for super-resolution of block-compressed texture is provided. The method includes receiving a first texture block of a first block size. Based on application of a first block compression (BC) scheme on the received first texture block, coded-texture values are generated in a compressed domain. Further, a first machine learning model is applied on the generated coded-texture values to generate super-resolution coded-texture values in the compressed domain. The generated super-resolution coded-texture values are processed to generate a second texture block of a second block size. The second block size is greater than the first block size.
    Type: Application
    Filed: March 30, 2021
    Publication date: October 7, 2021
    Inventors: MING-CHANG LIU, LIYU GONG, KO-KAI ALBERT HUANG, JOSHUA SCOTT HOBSON