Patents by Inventor Yiqiao Liu

Yiqiao Liu 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: 20250086467
    Abstract: The described method may include receiving user input indicating a configuration identifying a large language model (LLM) and a subset of documents indicated in the configuration as being available to a tenant. The method may include generating one or more vectorizations of content of the subset of documents. The method may include receiving a request to generate a generative response. The method may include generating the generative artificial intelligence (AI) prompt using the content to ground the generative AI prompt. The subset of documents may be identified based on a comparison between a vectorization of the request and the one or more vectorizations and based at least in part on a determination that a user associated with the tenant is permitted to access the subset of documents. The method may include presenting a response to the generative AI prompt, the response generated by the LLM using the generative AI prompt.
    Type: Application
    Filed: January 30, 2024
    Publication date: March 13, 2025
    Inventors: Victor Yee, Yiqiao Liu, Shashank Harinath, Fermin Ordaz, Adam Smith, Suhail Barot, Tuan Nguyen
  • Patent number: 12175679
    Abstract: A method or system for training a convolutional neural network (CNN) for medical imaging analysis. The system pre-trains the CNN's encoder using a dataset of unlabeled 3D medical images. Each 3D image includes an annotated slice delineating a boundary of a lesion and multiple non-annotated 2D slices above and below the annotated slice. The system then fine-tunes the pre-trained encoder using an annotated 2D image dataset. The annotated 2D image dataset includes multiple 2D slices of lesions, each including an annotation that delineates a boundary of a corresponding lesion.
    Type: Grant
    Filed: November 28, 2023
    Date of Patent: December 24, 2024
    Assignee: Merck Sharp & Dohme LLC
    Inventors: Yiqiao Liu, Antong Chen, Gregory Goldmacher
  • Publication number: 20240321461
    Abstract: Systems, methods, and apparatus are provided for determining a risk prediction for major adverse cardiovascular event (MACE) for a patient based on a computed tomography (CT) calcium score image of the patient's chest. In one example, a method includes receiving a computed tomography (CT) calcium score image of a chest; identifying tissue of interest in the CT calcium score image; analyzing the CT calcium score image to determine features of the identified tissue of interest; and determining a risk prediction of MACE based on the features.
    Type: Application
    Filed: March 19, 2024
    Publication date: September 26, 2024
    Inventors: David L. Wilson, Sadeer Al-Kindi, Yingnan Song, Ammar Hoori, Hao Wu, Yiqiao Liu
  • Publication number: 20240177320
    Abstract: A method or system for training a convolutional neural network (CNN) for medical imaging analysis. The system pre-trains the CNN's encoder using a dataset of unlabeled 3D medical images. Each 3D image includes an annotated slice delineating a boundary of a lesion and multiple non-annotated 2D slices above and below the annotated slice. The system then fine-tunes the pre-trained encoder using an annotated 2D image dataset. The annotated 2D image dataset includes multiple 2D slices of lesions, each including an annotation that delineates a boundary of a corresponding lesion.
    Type: Application
    Filed: November 28, 2023
    Publication date: May 30, 2024
    Inventors: Yiqiao Liu, Antong Chen, Gregory Goldmacher
  • Publication number: 20240161490
    Abstract: A system and method of multi-stage training of a transformer-based machine-learning model. The system performs at least two stages of the following three stages of training: During a first stage, the system pre-trains a transformer encoder via a first machine-learning network using an unlabeled 3D image dataset. During a second stage, the system fine-tunes the pre-trained transformer encoder via a second machine-learning network via a labeled 2D image dataset. During a third stage, the system further fine-tunes the previously pre-trained transformer encoder or fine-tuned transformer encoder via a third machine-learning network using a labeled 3D image dataset.
    Type: Application
    Filed: November 7, 2023
    Publication date: May 16, 2024
    Inventors: Shaoyan Pan, Yiqiao Liu, Antong Chen, Gregory Goldmacher
  • Patent number: D1027513
    Type: Grant
    Filed: June 10, 2023
    Date of Patent: May 21, 2024
    Assignee: Falcon Innovations Technology (Shenzhen) Co., Ltd
    Inventors: Yiqiao Liu, Bin Zheng