Patents by Inventor Cheol-Kyun RHO

Cheol-Kyun RHO 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).

  • Classification system and method based on generative adversarial network
    Patent number: 12019711
    Abstract: A generative adversarial network-based classification system and method that can generate missing data as missing data imputation values similar to real data using a generative adversarial network (GAN) and allowing training with labeled data sets with labels, as well as and irregular data sets such as non-labeled data sets without labels.
    Type: Grant
    Filed: March 17, 2020
    Date of Patent: June 25, 2024
    Assignee: AGILESODA INC.
    Inventors: Cheol-Kyun Rho, Ye-Rin Min, Pham-Tuyen Le
  • REINFORCEMENT LEARNING DEVICE AND METHOD USING CONDITIONAL EPISODE CONFIGURATION
    Publication number: 20230206079
    Abstract: Disclosed are a reinforcement learning device and method using a conditional episode configuration. The present invention imparts conditions on individual decision making, and terminates an episode if the imparted conditions are not met, thereby maximizing the total sum of rewards reflecting the current values. Accordingly, reinforcement learning can be easily applied even to problems using a non-continuous state.
    Type: Application
    Filed: August 21, 2020
    Publication date: June 29, 2023
    Applicant: AGILESODA INC.
    Inventors: Cheol-Kyun RHO, Seong-Ryeong LEE, Ye-Rin MIN, Pham-Tuyen LE
  • DEVICE AND METHOD FOR DATA-BASED REINFORCEMENT LEARNING
    Publication number: 20220230097
    Abstract: Disclosed is a device for data-based reinforcement learning. The disclosure allows an agent to learn a reinforcement learning model so as to maximize a reward for an action selectable according to a current state in a random environment, wherein a difference between a total variation rate and an individual variation rate for each action is provided as a reward for the agent.
    Type: Application
    Filed: February 28, 2020
    Publication date: July 21, 2022
    Applicant: AGILESODA INC.
    Inventors: Yong CHA, Cheol-Kyun RHO, Kwon-Yeol LEE
  • CLASSIFICATION SYSTEM AND METHOD BASED ON GENERATIVE ADVERSARIAL NETWORK
    Publication number: 20220207300
    Abstract: Disclosed are generative adversarial network-based classification system and method. The present invention can generate missing data as missing data imputation values similar to real data using a generative adversarial network (GAN), thus allowing the overall quality of the data to be improved, and allowing training with labeled data sets with labels, as well as irregular data sets such as non-labeled data sets without labels.
    Type: Application
    Filed: March 17, 2020
    Publication date: June 30, 2022
    Applicant: AGILESODA INC.
    Inventors: Cheol-Kyun RHO, Ye-Rin MIN, Pham-Tuyen LE
  • DECISION-MAKING AGENT HAVING HIERARCHICAL STRUCTURE
    Publication number: 20220138656
    Abstract: Disclosed is a decision-making agent having a hierarchical structure. The present invention allows a user without knowledge about reinforcement learning to learn by easily setting and applying core factors of the reinforcement learning to business problems.
    Type: Application
    Filed: October 25, 2021
    Publication date: May 5, 2022
    Applicant: AGILESODA INC.
    Inventors: Pham-Tuyen LE, Cheol-Kyun RHO, Seong-Ryeong LEE, Ye-Rin MIN