Patents by Inventor Alex Volkovitsky

Alex Volkovitsky 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).

  • Patent number: 11721230
    Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
    Type: Grant
    Filed: April 20, 2022
    Date of Patent: August 8, 2023
    Assignee: CEREGO JAPAN KABUSHIKI KAISHA
    Inventors: Andrew Smith Lewis, Paul Mumma, Alex Volkovitsky, Iain Harlow, Kyle Stewart
  • Publication number: 20220246052
    Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
    Type: Application
    Filed: April 20, 2022
    Publication date: August 4, 2022
    Inventors: Andrew SMITH LEWIS, Paul MUMMA, Alex VOLKOVITSKY, Iain HARLOW, Kyle STEWART
  • Patent number: 11348476
    Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
    Type: Grant
    Filed: November 23, 2021
    Date of Patent: May 31, 2022
    Assignee: CEREGO JAPAN KABUSHIKI KAISHA
    Inventors: Andrew Smith Lewis, Paul Mumma, Alex Volkovitsky, Iain Harlow, Kyle Stewart
  • Publication number: 20220084429
    Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
    Type: Application
    Filed: November 23, 2021
    Publication date: March 17, 2022
    Inventors: Andrew SMITH LEWIS, Paul MUMMA, Alex VOLKOVITSKY, Iain HARLOW, Kyle STEWART
  • Patent number: 11217110
    Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
    Type: Grant
    Filed: June 17, 2021
    Date of Patent: January 4, 2022
    Assignee: CEREGO JAPAN KABUSHIKI KAISHA
    Inventors: Andrew Smith Lewis, Paul Mumma, Alex Volkovitsky, Iain Harlow, Kyle Stewart
  • Publication number: 20210312826
    Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
    Type: Application
    Filed: June 17, 2021
    Publication date: October 7, 2021
    Inventors: Andrew SMITH LEWIS, Paul MUMMA, Alex VOLKOVITSKY, Iain HARLOW, Kyle STEWART
  • Patent number: 11081018
    Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
    Type: Grant
    Filed: November 11, 2020
    Date of Patent: August 3, 2021
    Assignee: CEREGO LLC.
    Inventors: Andrew Smith Lewis, Paul Mumma, Alex Volkovitsky, Iain Harlow, Kyle Stewart
  • Publication number: 20210158712
    Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
    Type: Application
    Filed: November 11, 2020
    Publication date: May 27, 2021
    Inventors: Andrew SMITH LEWIS, Paul MUMMA, Alex VOLKOVITSKY, Iain HARLOW, Kyle STEWART
  • Patent number: 10861344
    Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
    Type: Grant
    Filed: December 8, 2017
    Date of Patent: December 8, 2020
    Assignee: CEREGO, LLC.
    Inventors: Andrew Smith Lewis, Paul Mumma, Alex Volkovitsky, Iain Harlow, Kyle Stewart
  • Publication number: 20180218627
    Abstract: Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
    Type: Application
    Filed: December 8, 2017
    Publication date: August 2, 2018
    Applicant: CEREGO LLC.
    Inventors: Andrew SMITH LEWIS, Paul MUMMA, Alex VOLKOVITSKY, Iain HARLOW, Kyle STEWART
  • Publication number: 20170075881
    Abstract: Various techniques are disclosed for providing a learning system. In one example, such a learning system includes a content editor processor configured or programmed to receive content data packets from a number of learner devices. The learning system is configured to identify a number of items from digital materials based on the content data packets. The learning system may include an adaptive engine configured to transmit interactions to the learner devices based on the identified items. The adaptive engine is also configured to receive respective responses from the learner devices based on the interactions. The learning system is also configured generate an electronic copy of the digital materials with highlighted items based on the received responses. Other examples of learning systems and related methods are also provided.
    Type: Application
    Filed: September 13, 2016
    Publication date: March 16, 2017
    Inventors: Andrew Smith Lewis, Paul Mumma, Alex Volkovitsky, Kit Richert