Patents by Inventor Junjie Bai

Junjie Bai 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: 20190144541
    Abstract: The invention provides human monoclonal antibodies that specifically bind to PD-1 with high affinity. The anti-PD-1 monoclonal antibodies were screened from a synthetic antibody library, and affinity maturation was performed. The synthetic antibody libraries used to select for the high affinity anti-PD-1 monoclonal antibodies were made by replacing the light chain CDR1, CDR2 and CDR3 and heavy chain CDR1, CDR 2 and CDR 3 of phage libraries from the preliminary screening, and the high affinity anti-PD-1 monoclonal antibodies were selected. The human anti-PD-1 monoclonal antibodies have high affinity and inhibit the binding of PD-1 to its ligand PD-L1. The antibodies can be used for treating tumor, inflammation and autoimmune diseases.
    Type: Application
    Filed: January 18, 2019
    Publication date: May 16, 2019
    Inventors: Haiping ZHOU, Xiaomin LI, Junjie ZHOU, Shuang PEI, Yanlu ZAN, Yi BAI, Xianhong BAI
  • Publication number: 20190139218
    Abstract: Embodiments of the disclosure provide systems and methods for generating a report based on medical images of a patient. An exemplary system includes a communication interface configured to receive the medical images acquired by an image acquisition device. The system may further include at least one processor. The at least one processor is configured to receive a user selection of at least one medical image in at least one view. The at least one processor is further configured to automatically generate keywords describing the selected medical image based on a learning network including a convolutional neural network and a recursive neural network connected in series. The at least one processor is also configured to receive a keyword selection among the generated keywords and generate the report based on the keyword selection. The exemplary system additionally includes a display configured to display the selected medical image and the report.
    Type: Application
    Filed: November 4, 2018
    Publication date: May 9, 2019
    Applicant: BEIJING CURACLOUD TECHNOLOGY CO., LTD.
    Inventors: Qi Song, Feng Gao, Hanbo Chen, Shanhui Sun, Junjie Bai, Zheng Te, Youbing Yin
  • Publication number: 20190114766
    Abstract: Embodiments of the disclosure provide systems and methods for generating a diagnosis report based on a medical image of a patient. The system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system further includes at least one processor. The at least one processor is configured to detect a medical condition of the patient and parameters associated with the medical condition based on the medical image. The at least one processor is further configured to construct the diagnosis report based on the medical image, wherein the diagnosis report includes at least one view of the medical image and a description of the medical condition using the parameters. The system also includes a display configured to display the diagnosis report.
    Type: Application
    Filed: October 8, 2018
    Publication date: April 18, 2019
    Applicant: BEIJING CURACLOUD TECHNOLOGY CO., LTD.
    Inventors: Qi Song, Hanbo Chen, Zheng Te, Youbing Yin, Junjie Bai, Shanhui Sun
  • Publication number: 20190050981
    Abstract: A computer-implemented method for automatically detecting a target object from a 3D image is disclosed. The method may include receiving the 3D image acquired by an imaging device. The method may further include detecting, by a processor, a plurality of bounding boxes as containing the target object using a 3D learning network. The learning network may be trained to generate a plurality of feature maps of varying scales based on the 3D image. The method may also include determining, by the processor, a set of parameters identifying each detected bounding box using the 3D learning network, and locating, by the processor, the target object based on the set of parameters.
    Type: Application
    Filed: June 2, 2018
    Publication date: February 14, 2019
    Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Qi Song, Shanhui Sun, Hanbo Chen, Junjie Bai, Feng Gao, Youbing Yin
  • Publication number: 20190050982
    Abstract: The present disclosure is directed to a method and system for automatically detecting a physiological condition from a medical image of a patient. The method may include receiving the medical image acquired by an imaging device. The method may further include detecting, by a processor, target objects and obtaining the corresponding target object patches from the received medical image. And the method may further include determining, by the processor, a first parameter using a first learning network for each target object patch. The first parameter represents the physiological condition level of the corresponding target object, and the first learning network is trained by adding one or more auxiliary classification layers. This method can quickly, accurately, and automatically predict target object level and/or image (patient) level physiological condition from a medical image of a patient by means of a learning network, such as 3D learning network.
    Type: Application
    Filed: July 5, 2018
    Publication date: February 14, 2019
    Applicant: SHENZHEN KEYA MEDICAL TECHNOLOGY CORPORATION
    Inventors: Qi Song, Shanhui Sun, Feng Gao, Junjie Bai, Hanbo Chen, Youbing Yin