Patents by Inventor Hyo Jun MOON

Hyo Jun MOON 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: 20240324452
    Abstract: The present disclosure relates to a plurality of host materials comprising at least one first host compound and at least one second host compound, an organic electroluminescent compound, and an organic electroluminescent device comprising the same. It is possible to produce an organic electroluminescent device having low driving voltage, high luminous efficiency, and/or excellent lifetime properties, either by utilizing the plurality of host materials comprising a specific combination of host compounds, or by utilizing the organic electroluminescent compound represented by a specific Formula.
    Type: Application
    Filed: February 8, 2024
    Publication date: September 26, 2024
    Inventors: Yea-Mi SONG, Hyo-Soon PARK, Jeong-Eun YANG, Tae-Jun HAN, Mi-Ja LEE, Kyoung-Jin PARK, Doo-Hyeon MOON
  • Patent number: 12049468
    Abstract: The present disclosure relates to a plurality of host materials comprising a compound represented by formula 1 and a compound represented by formula 2, and an organic electroluminescent device comprising the same. By comprising the plurality of host materials comprising a specific combination of compounds, it is possible to provide an organic electroluminescent device having long lifespan characteristics.
    Type: Grant
    Filed: May 9, 2019
    Date of Patent: July 30, 2024
    Assignee: Rohm and Haas Electronic Materials Korea Ltd.
    Inventors: Bitnari Kim, Su-Hyun Lee, Doo-Hyeon Moon, Hyo-Soon Park, Tae-Jun Han, Sang-Hee Cho
  • Patent number: 11593877
    Abstract: An order execution server for stock trading includes a data collection unit configured to collect trading data on at least one item, a subsidiary prediction value generation unit configured to generate a subsidiary prediction value by inputting the trading data into a pre-trained first deep learning model based on supervised learning, an order execution strategy deriving unit configured to derive an order execution strategy for the at least one item during a current period of time based on the trading data and the subsidiary prediction value by using a pre-trained second deep learning model based on reinforcement learning; and an order execution instruction unit configured to instruct order execution for the at least one item during the current period of time by using order information including the order execution strategy.
    Type: Grant
    Filed: November 27, 2019
    Date of Patent: February 28, 2023
    Assignee: QRAFT TECHNOLOGIES INC.
    Inventors: Seong Min Kim, Tae Hee Cho, Hyo Jun Moon
  • Patent number: 11593878
    Abstract: An order execution server for stock trading includes a data collection unit configured to collect trading data on at least one item, a model generation unit configured to generate a reinforcement learning-based deep learning model including two or more actors which are neural networks that determine an action policy of a reinforcement learning agent and a critic which is a neural network that estimates an action value of the reinforcement learning agent and train the reinforcement learning-based deep learning model to derive an order execution strategy for the at least one item based on the trading data and an order execution unit configured to perform order execution for the at least one item during a current period of time by using order information including the order execution strategy.
    Type: Grant
    Filed: November 27, 2019
    Date of Patent: February 28, 2023
    Assignee: QRAFT TECHNOLOGIES INC.
    Inventors: Seong Min Kim, Tae Hee Cho, Hyo Jun Moon
  • Publication number: 20210035214
    Abstract: An order execution server for stock trading includes a data collection unit configured to collect trading data on at least one item, a model generation unit configured to generate a reinforcement learning-based deep learning model including two or more actors which are neural networks that determine an action policy of a reinforcement learning agent and a critic which is a neural network that estimates an action value of the reinforcement learning agent and train the reinforcement learning-based deep learning model to derive an order execution strategy for the at least one item based on the trading data and an order execution unit configured to perform order execution for the at least one item during a current period of time by using order information including the order execution strategy.
    Type: Application
    Filed: November 27, 2019
    Publication date: February 4, 2021
    Inventors: Seong Min KIM, Tae Hee CHO, Hyo Jun MOON
  • Publication number: 20210035213
    Abstract: An order execution server for stock trading includes a data collection unit configured to collect trading data on at least one item, a subsidiary prediction value generation unit configured to generate a subsidiary prediction value by inputting the trading data into a pre-trained first deep learning model based on supervised learning, an order execution strategy deriving unit configured to derive an order execution strategy for the at least one item during a current period of time based on the trading data and the subsidiary prediction value by using a pre-trained second deep learning model based on reinforcement learning; and an order execution instruction unit configured to instruct order execution for the at least one item during the current period of time by using order information including the order execution strategy.
    Type: Application
    Filed: November 27, 2019
    Publication date: February 4, 2021
    Inventors: Seong Min KIM, Tae Hee CHO, Hyo Jun MOON