Patents by Inventor Konstantin Vyacheslavovich VORONTSOV

Konstantin Vyacheslavovich VORONTSOV 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).

  • Method of and system for generating a prediction model and determining an accuracy of a prediction model
    Patent number: 11341419
    Abstract: A computer-implemented method of and a system for generating a prediction model and determining an accuracy parameter of a trained decision tree prediction model. The method comprises accessing the trained decision tree prediction model having been generated at least partially based on a set of training objects; generating a subset of random parameters of interest; associating the subset of random parameters of interests with a given leaf; determining a leaf accuracy parameter based on (i) the parameters of interest associated with the given leaf and (ii) the subset of random parameters of interest of the given leaf; and determining the accuracy parameter of the trained decision tree prediction model based on the determined leaf accuracy parameter for each of the leafs of the decision tree.
    Type: Grant
    Filed: August 9, 2019
    Date of Patent: May 24, 2022
    Assignee: YANDEX EUROPE AG
    Inventors: Andrey Vladimirovich Gulin, Andrey Sergeevich Mishchenko, Konstantin Vyacheslavovich Vorontsov, Yevgeny Andreevich Sokolov
  • METHOD OF AND SYSTEM FOR GENERATING A PREDICTION MODEL AND DETERMINING AN ACCURACY OF A PREDICTION MODEL
    Publication number: 20190362267
    Abstract: A computer-implemented method of and a system for generating a prediction model and determining an accuracy parameter of a trained decision tree prediction model. The method comprises accessing the trained decision tree prediction model having been generated at least partially based on a set of training objects; generating a subset of random parameters of interest; associating the subset of random parameters of interests with a given leaf; determining a leaf accuracy parameter based on (i) the parameters of interest associated with the given leaf and (ii) the subset of random parameters of interest of the given leaf; and determining the accuracy parameter of the trained decision tree prediction model based on the determined leaf accuracy parameter for each of the leafs of the decision tree.
    Type: Application
    Filed: August 9, 2019
    Publication date: November 28, 2019
    Inventors: Andrey Vladimirovich GULIN, Andrey Sergeevich MISHCHENKO, Konstantin Vyacheslavovich VORONTSOV, Yevgeny Andreevich SOKOLOV
  • Method of and system for generating a prediction model and determining an accuracy of a prediction model
    Patent number: 10387801
    Abstract: A computer-implemented method of and a system for generating a prediction model and determining an accuracy parameter of a trained decision tree prediction model. The method comprises accessing the trained decision tree prediction model having been generated at least partially based on a set of training objects; generating a subset of random parameters of interest; associating the subset of random parameters of interests with a given leaf; determining a leaf accuracy parameter based on (i) the parameters of interest associated with the given leaf and (ii) the subset of random parameters of interest of the given leaf; and determining the accuracy parameter of the trained decision tree prediction model based on the determined leaf accuracy parameter.
    Type: Grant
    Filed: September 13, 2016
    Date of Patent: August 20, 2019
    Assignee: YANDEX EUROPE AG
    Inventors: Andrey Vladimirovich Gulin, Andrey Sergeevich Mishchenko, Konstantin Vyacheslavovich Vorontsov, Yevgeny Andreevich Sokolov
  • METHOD OF AND SYSTEM FOR GENERATING A PREDICTION MODEL AND DETERMINING AN ACCURACY OF A PREDICTION MODEL
    Publication number: 20170091670
    Abstract: A computer-implemented method of and a system for generating a prediction model and determining an accuracy parameter of a trained decision tree prediction model. The method comprises accessing the trained decision tree prediction model having been generated at least partially based on a set of training objects; generating a subset of random parameters of interest; associating the subset of random parameters of interests with a given leaf; determining a leaf accuracy parameter based on (i) the parameters of interest associated with the given leaf and (ii) the subset of random parameters of interest of the given leaf; and determining the accuracy parameter of the trained decision tree prediction model based on the determined leaf accuracy parameter.
    Type: Application
    Filed: September 13, 2016
    Publication date: March 30, 2017
    Inventors: Andrey Vladimirovich GULIN, Andrey Sergeevich MISHCHENKO, Konstantin Vyacheslavovich VORONTSOV, Yevgeny Andreevich SOKOLOV