Patents by Inventor Elizaveta ULIANENKO

Elizaveta ULIANENKO 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).

  • MOLAR TRIMMING PREDICTION AND VALIDATION USING MACHINE LEARNING
    Publication number: 20240325125
    Abstract: Provided herein are systems and methods for determining if a 3D tooth model requires trimming or removal of incomplete or missing data (e.g., gingiva covering a portion of a tooth such as a molar). A patient's dentition may be scanned and/or segmented. Raw dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A classifier can identify and/or output probability that the 3D tooth model requires trimming. Trimming of the 3D tooth model can be implemented without human intervention.
    Type: Application
    Filed: June 5, 2024
    Publication date: October 3, 2024
    Inventors: Roman A. ROSCHIN, Evgenii Vladimirovich KARNYGIN, Sergey GREBENKIN, Dmitry GUSKOV, Dmitrii ISCHEYKIN, Ivan POTAPENKO, Denis DURDIN, Roman GUDCHENKO, Vasily PARAKETSOV, Mikhail GORODILOV, Alexey VLADYKIN, Roman SOLOVYEV, Alexander BELIAEV, Elizaveta ULIANENKO, Leonid TROFIMOV, Anzhelika SON, Nikolay ZHIRNOV, Alexander VOVCHENKO
  • MACHINE LEARNING DENTAL SEGMENTATION SYSTEM AND METHODS USING SPARSE VOXEL REPRESENTATIONS
    Publication number: 20240293205
    Abstract: Provided herein are systems and methods for automatically segmenting a 3D model of a patient's teeth. A patient's dentition may be scanned. The scan data may be converted into a 3D model, including a sparse voxel representation of the 3D model. Features can be extracted from the sparse voxel representation of the 3D model and input into a machine learning model to train the machine learning model to segment the 3D model into individual dental components.
    Type: Application
    Filed: January 5, 2024
    Publication date: September 5, 2024
    Inventors: Christopher E. CRAMER, Roman GUDCHENKO, Dmitrii ISCHEYKIN, Vasily PARAKETSOV, Sergey GREBENKIN, Denis DURDIN, Dmitry GUSKOV, Nikolay ZHIRNOV, Mikhail GORODILOV, Ivan POTAPENKO, Anton BASKANOV, Elizaveta ULIANENKO, Alexander VOVCHENKO, Roman SOLOVYEV, Aleksandr Sergeevich KARSAKOV, Aleksandr ANIKIN, Mikhail TOPORKOV
  • Molar trimming prediction and validation using machine learning
    Patent number: 12036093
    Abstract: Provided herein are systems and methods for determining if a 3D tooth model requires trimming or removal of incomplete or missing data (e.g., gingiva covering a portion of a tooth such as a molar). A patient's dentition may be scanned and/or segmented. Features of the patient's teeth can be determined, including a tooth center point of at least three of the patient's teeth. The features of the patient's teeth are used to access a parametric model. A trim plane normal vector is identified for a distal tooth. Trimming of the 3D tooth model is based on the trim plane normal vector.
    Type: Grant
    Filed: April 13, 2023
    Date of Patent: July 16, 2024
    Assignee: Align Technology, Inc.
    Inventors: Roman A. Roschin, Evgenii Vladimirovich Karnygin, Sergey Grebenkin, Dmitry Guskov, Dmitrii Ischeykin, Ivan Potapenko, Denis Durdin, Roman Gudchenko, Vasily Paraketsov, Mikhail Gorodilov, Alexey Vladykin, Roman Solovyev, Alexander Beliaev, Elizaveta Ulianenko, Leonid Trofimov, Anzhelika Son, Nikolay Zhirnov, Alexander Vovchenko
  • Machine learning dental segmentation methods using sparse voxel representations
    Patent number: 11903793
    Abstract: Methods for automatically segmenting a 3D model of a patient's teeth may include scanning a patient's dentition and converting the scan data into a 3D model, including a sparse voxel representation of the 3D model. Features can be extracted from the sparse voxel representation of the 3D model and input into a machine learning model to train the machine learning model to segment the 3D model into individual dental components.
    Type: Grant
    Filed: December 30, 2020
    Date of Patent: February 20, 2024
    Assignee: Align Technology, Inc.
    Inventors: Christopher E. Cramer, Roman Gudchenko, Dmitrii Ischeykin, Vasily Paraketsov, Sergey Grebenkin, Denis Durdin, Dmitry Guskov, Nikolay Zhirnov, Mikhail Gorodilov, Ivan Potapenko, Anton Baskanov, Elizaveta Ulianenko, Alexander Vovchenko, Roman Solovyev, Aleksandr Sergeevich Karsakov, Aleksandr Anikin, Mikhail Toporkov
  • MOLAR TRIMMING PREDICTION AND VALIDATION USING MACHINE LEARNING
    Publication number: 20230320824
    Abstract: Provided herein are systems and methods for determining if a 3D tooth model requires trimming or removal of incomplete or missing data (e.g., gingiva covering a portion of a tooth such as a molar). A patient's dentition may be scanned and/or segmented. Raw dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A classifier can identify and/or output probability that the 3D tooth model requires trimming. Trimming of the 3D tooth model can be implemented without human intervention.
    Type: Application
    Filed: April 13, 2023
    Publication date: October 12, 2023
    Inventors: Roman A. ROSCHIN, Evgenii Vladimirovich KARNYGIN, Sergey GREBENKIN, Dmitry GUSKOV, Dmitrii ISCHEYKIN, Ivan POTAPENKO, Denis DURDIN, Roman GUDCHENKO, Vasily PARAKETSOV, Mikhail GORODILOV, Alexey VLADYKIN, Roman SOLOVYEV, Alexander BELIAEV, Elizaveta ULIANENKO, Leonid TROFIMOV, Anzhelika SON, Nikolay ZHIRNOV, Alexander Novchenko
  • Molar trimming prediction and validation using machine learning
    Patent number: 11654001
    Abstract: Provided herein are systems and methods for determining if a 3D tooth model requires trimming or removal of incomplete or missing data (e.g., gingiva covering a portion of a tooth such as a molar). A patient's dentition may be scanned and/or segmented. Raw dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A classifier can identify and/or output probability that the 3D tooth model requires trimming. Trimming of the 3D tooth model can be implemented without human intervention.
    Type: Grant
    Filed: October 4, 2019
    Date of Patent: May 23, 2023
    Assignee: Align Technology, Inc.
    Inventors: Roman A. Roschin, Evgenii Vladimirovich Karnygin, Sergey Grebenkin, Dmitry Guskov, Dmitrii Ischeykin, Ivan Potapenko, Denis Durdin, Roman Gudchenko, Vasily Paraketsov, Mikhail Gorodilov, Alexey Vladykin, Roman Solovyev, Alexander Beliaev, Elizaveta Ulianenko, Leonid Trofimov, Anzhelika Son, Nikolay Zhirnov, Alexander Vovchenko
  • MOLAR TRIMMING PREDICTION AND VALIDATION USING MACHINE LEARNING
    Publication number: 20200107915
    Abstract: Provided herein are systems and methods for determining if a 3D tooth model requires trimming or removal of incomplete or missing data (e.g., gingiva covering a portion of a tooth such as a molar). A patient's dentition may be scanned and/or segmented. Raw dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A classifier can identify and/or output probability that the 3D tooth model requires trimming. Trimming of the 3D tooth model can be implemented without human intervention.
    Type: Application
    Filed: October 4, 2019
    Publication date: April 9, 2020
    Inventors: Roman A. ROSCHIN, Evgenii Vladimirovich KARNYGIN, Sergey GREBENKIN, Dmitry GUSKOV, Dmitrii ISCHEYKIN, Ivan POTAPENKO, Denis DURDIN, Roman GUDCHENKO, Vasily PARAKETSOV, Mikhail GORODILOV, Alexey VLADYKIN, Roman SOLOVYEV, Alexander BELIAEV, Elizaveta ULIANENKO, Leonid TROFIMOV, Anzhelika SON, Nikolay ZHIRNOV, Alexander VOVCHENKO