Forward Citations = 25

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4 inventors (with patents filed for the assignee):

Kye-Hyeon Kim (8 out of 144 patents)

Kye Hyeon Kim (5 out of 5 patents)

Jung Kwon Lee (1 out of 2 patents)

4. Hyundong Lee (1 out of 1 patent)

13 patents: Sort by Forward Citations

1. 12450884 - Method for training custom model based on pre-trained base model and learning device using the same (2025-10-21),

2. 12367663 - Method for selecting training data to train a deep learning model and training data selecting device using the same (2025-07-22),

3. 11955272 - Method for generating object detector based on deep learning capable of detecting extended object class and detector generation device using the same (2024-04-09),

4. 11954898 - Learning method and learning device for performing transfer learning on an object detector that has been trained to detect first object classes such that the object detector is able to detect second object classes, and testing method and testing device using the same (2024-04-09),

5. 11941820 - Method for tracking an object in a low frame-rate video and object tracking device using the same (2024-03-26),

6. 11113573 - Method for generating training data to be used for training deep learning network capable of analyzing images and auto labeling device using the same (2021-09-07),

7. 11030489 - Method for auto-labeling test image by using class-agnostic refinement module, and auto labeling device using the same (2021-06-08),

8. 11023780 - Methods for training auto labeling device and performing auto labeling related to object detection while performing automatic verification by using uncertainty scores and devices using the same (2021-06-01),

9. 11023779 - Methods for training auto labeling device and performing auto labeling related to segmentation while performing automatic verification by using uncertainty scores and devices using the same (2021-06-01),

10. 11023776 - Methods for training auto-labeling device and performing auto-labeling by using hybrid classification and devices using the same (2021-06-01),

11. 10902291 - Methods for training auto labeling device and performing auto labeling related to segmentation while performing automatic verification by using uncertainty scores and devices using the same (2021-01-26),

12. 10902290 - Methods for training auto labeling device and performing auto labeling related to object detection while performing automatic verification by using uncertainty scores and devices using the same (2021-01-26),

13. 10885388 - Method for generating training data to be used for training deep learning network capable of analyzing images and auto labeling device using the same (2021-01-05),

Company Overview

Superb Ai Co., Ltd., based in Seoul, South Korea, specializes in artificial intelligence solutions that enhance various industries through advanced machine learning technologies. The company focuses on developing innovative AI applications that streamline processes, improve decision-making, and drive efficiency across sectors such as healthcare, finance, and logistics. With a commitment to research and development, Superb Ai aims to push the boundaries of AI capabilities, fostering a future where intelligent systems seamlessly integrate into everyday operations.

Profile summary synthesized from public USPTO records and patent filings.

Company Badge

USPTO Data Jan 1 1976 - Oct 20 2026

13 Patents

#319 in Seoul

#878 in South Korea

#45,081 in the World

Seoul, South Korea

Superb Ai Co., Ltd.

Forward Citations = 25

4 inventors:

Kye-Hyeon Kim (8 out of 144 patents)

Kye Hyeon Kim (5 out of 5 patents)

Jung Kwon Lee (1 out of 2 patents)

4. Hyundong Lee (1 out of 1 patent)

IDiyas Innovation Intelligence. (2026). Company Patent Portfolio: Superb Ai Co., Ltd.. Retrieved from https://idiyas.com/company/superb-ai-company

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]