Patents by Inventor Dani Kiyasseh

Dani Kiyasseh 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: 20250117705
    Abstract: Disclosed systems and methods provide a framework for evaluating AI systems without ground-truth annotations. The disclosed embodiments may assign temporary labels to data points in sets of working data and use the temporarily labeled data to train one or more distinct models. These models may be evaluated to determine which has the highest performance and is thus indicative of the temporary labels most likely to be correct.
    Type: Application
    Filed: October 9, 2024
    Publication date: April 10, 2025
    Inventors: Dani Kiyasseh, Aaron Cohen, Chengsheng Jiang, Nicholas Altieri
  • Patent number: 12002202
    Abstract: Methods and systems are described for image segmentation. A machine learning model is applied to a set of images to generate results. The results may be obtained as a probability map for each image in the set of images. The model may be trained by accessing a set of labeled images, each image associated with a label indicating a location of a feature within a respective image. An initial set of parameters is accessed. An encoder is initialized with the initial set of parameters. The encoder is applied to the set of labeled images to generate a prediction of a feature location within each image. The initial set of parameters are updated based on the predictions and the label associated with the labeled images. The updated set of parameters and an additional set of parameters generated using a set of unlabeled images are aggregated.
    Type: Grant
    Filed: August 9, 2021
    Date of Patent: June 4, 2024
    Assignee: Merck Sharp & Dohme LLC
    Inventors: Dani Kiyasseh, Antong Chen, Albert Joseph Swiston, Jr., Ronghua Chen
  • Publication number: 20230040908
    Abstract: Methods and systems are described for image segmentation. A machine learning model is applied to a set of images to generate results. The results may be obtained as a probability map for each image in the set of images. The model may be trained by accessing a set of labeled images, each image associated with a label indicating a location of a feature within a respective image. An initial set of parameters is accessed. An encoder is initialized with the initial set of parameters. The encoder is applied to the set of labeled images to generate a prediction of a feature location within each image. The initial set of parameters are updated based on the predictions and the label associated with the labeled images. The updated set of parameters and an additional set of parameters generated using a set of unlabeled images are aggregated.
    Type: Application
    Filed: August 9, 2021
    Publication date: February 9, 2023
    Inventors: Dani Kiyasseh, Antong Chen, Albert Joseph Swiston, JR., Ronghua Chen