Patents by Inventor Irene Chavlytko

Irene Chavlytko 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).

  • Self-adaptive, self-trained computer engines based on machine learning and methods of use thereof
    Patent number: 10896387
    Abstract: In some embodiments, the present invention provides for a computer system which includes a content database storing initial content data and a vocabulary data set; a processor configured to applying a machine learning model to transform the initial content data into a N-dimensional vector space; self-training the machine learning model based on the vocabulary data set; applying a clustering technique to the N-dimensional vector space to generate a cluster model of clusters, where each cluster includes a plurality of word representations; associating each cluster with a cluster identifier; obtaining subsequent content data; associating each data element of the subsequent content data with each cluster to generate a content data cluster mapping model; continuously tracking, for each user, each respective cluster identifier of each respective cluster associated with each action performed by each user with each data element to continuously self-adapt each user-specific, time-specific dynamic cluster mapping model
    Type: Grant
    Filed: June 24, 2019
    Date of Patent: January 19, 2021
    Assignee: Capital Com SV Investments Limited
    Inventors: Viktor Prokopenya, Irene Chavlytko, Alexei Shpikat, Maksim Vatkin
  • SELF-ADAPTIVE, SELF-TRAINED COMPUTER ENGINES BASED ON MACHINE LEARNING AND METHODS OF USE THEREOF
    Publication number: 20190311294
    Abstract: In some embodiments, the present invention provides for a computer system which includes a content database storing initial content data and a vocabulary data set; a processor configured to applying a machine learning model to transform the initial content data into a N-dimensional vector space; self-training the machine learning model based on the vocabulary data set; applying a clustering technique to the N-dimensional vector space to generate a cluster model of clusters, where each cluster includes a plurality of word representations; associating each cluster with a cluster identifier; obtaining subsequent content data; associating each data element of the subsequent content data with each cluster to generate a content data cluster mapping model; continuously tracking, for each user, each respective cluster identifier of each respective cluster associated with each action performed by each user with each data element to continuously self-adapt each user-specific, time-specific dynamic cluster mapping model
    Type: Application
    Filed: June 24, 2019
    Publication date: October 10, 2019
    Inventors: Viktor Prokopenya, Irene Chavlytko, Alexei Shpikat, Maksim Vatkin
  • Self-adaptive, self-trained computer engines based on machine learning and methods of use thereof
    Patent number: 10332034
    Abstract: In some embodiments, the present invention provides for a computer system which includes a content database storing initial content data and a vocabulary data set; a processor configured to applying a machine learning model to transform the initial content data into a N-dimensional vector space; self-training the machine learning model based on the vocabulary data set; applying a clustering technique to the N-dimensional vector space to generate a cluster model of clusters, where each cluster includes a plurality of word representations; associating each cluster with a cluster identifier; obtaining subsequent content data; associating each data element of the subsequent content data with each cluster to generate a content data cluster mapping model; continuously tracking, for each user, each respective cluster identifier of each respective cluster associated with each action performed by each user with each data element to continuously self-adapt each user-specific, time-specific dynamic cluster mapping model
    Type: Grant
    Filed: April 10, 2018
    Date of Patent: June 25, 2019
    Assignee: Capital Com SV Investments Limited
    Inventors: Viktor Prokopenya, Irene Chavlytko, Alexei Shpikat, Maksim Vatkin
  • SELF-ADAPTIVE, SELF-TRAINED COMPUTER ENGINES BASED ON MACHINE LEARNING AND METHODS OF USE THEREOF
    Publication number: 20180293515
    Abstract: In some embodiments, the present invention provides for a computer system which includes a content database storing initial content data and a vocabulary data set; a processor configured to applying a machine learning model to transform the initial content data into a N-dimensional vector space; self-training the machine learning model based on the vocabulary data set; applying a clustering technique to the N-dimensional vector space to generate a cluster model of clusters, where each cluster includes a plurality of word representations; associating each cluster with a cluster identifier; obtaining subsequent content data; associating each data element of the subsequent content data with each cluster to generate a content data cluster mapping model; continuously tracking, for each user, each respective cluster identifier of each respective cluster associated with each action performed by each user with each data element to continuously self-adapt each user-specific, time-specific dynamic cluster mapping model
    Type: Application
    Filed: April 10, 2018
    Publication date: October 11, 2018
    Inventors: Viktor Prokopenya, Irene Chavlytko, Alexei Shpikat, Maksim Vatkin