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For the Inventor, By the Inventor

Gyeongsangbuk-do, South Korea

Donghun Yeo

USPTO Granted Patents = 146 

 

Average Co-Inventor Count = 13.2

ph-index = 13

Forward Citations = 762(Granted Patents)

Forward Citations (Not Self Cited) = 663(Oct 12, 2025)


Inventors with similar research interests:


Location History:

  • Geongsangbuk-do, KR (2019)
  • Pohang-si, KR (2019 - 2021)
  • Gyeongsangbuk-do, KR (2018 - 2022)

Company Filing History:

goldMedal144 out of 162 
 
Stradvision, Inc.
 patents
silverMedal1 out of 1 
 
Stadvision, Inc.
 patent
bronzeMedal1 out of 1 
 
Stradivision, Inc.
 patent

Years Active: 2018-2022

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Areas of Expertise:
Convolutional Neural Networks
Reinforcement Learning
Autonomous Driving
V2X Communication
Generative Adversarial Networks
Sensor Fusion
Deep Learning
Object Detection
Trajectory Analysis
Adaptive Deep Learning
Emergency Vehicle Detection
Blind Spot Monitoring
146 patents (USPTO):Explore Patents
1. 11461653 - Learning method and learning device for CNN using 1xK or Kx1 convolution to be used for hardware optimization, and testing method and testing device using the same (2022-10-04),
2. 11315021 - Method and device for on-device continual learning of a neural network which analyzes input data, and method and device for testing the neural network to be used for smartphones, drones, vessels, or military purpose (2022-04-26),
3. 11087175 - Learning method and learning device of recurrent neural network for autonomous driving safety check for changing driving mode between autonomous driving mode and manual driving mode, and testing method and testing device using them (2021-08-10),
4. 11074480 - Learning method and learning device for supporting reinforcement learning by using human driving data as training data to thereby perform personalized path planning (2021-07-27),
5. 11017673 - Method and device for learning generating lane departure warning (LDW) alarm by referring to information on driving situation to be used for ADAS, V2X or driver safety required to satisfy level 4 and level 5 of autonomous vehicles (2021-05-25),
6. 11010668 - Method and device for attention-driven resource allocation by using reinforcement learning and V2X communication to thereby achieve safety of autonomous driving (2021-05-18),
7. 10984262 - Learning method and testing method for monitoring blind spot of vehicle, and learning device and testing device using the same (2021-04-20),
8. 10919543 - Learning method and learning device for determining whether to switch mode of vehicle from manual driving mode to autonomous driving mode by performing trajectory-based behavior analysis on recent driving route (2021-02-16),
9. 10890916 - Location-specific algorithm selection for optimized autonomous driving (2021-01-12),
10. 10872297 - Learning method and learning device for generating training data from virtual data on virtual world by using generative adversarial network, to thereby reduce annotation cost required in training processes of neural network for autonomous driving, and a testing method and a testing device using the same (2020-12-22),
11. 10861183 - Method and device for short-term path planning of autonomous driving through information fusion by using V2X communication and image processing (2020-12-08),
12. 10843728 - Method and device for delivering steering intention of autonomous driving module or driver to steering apparatus of subject vehicle more accurately (2020-11-24),
13. 10838418 - Method for providing autonomous driving service platform to be used for supporting autonomous driving of vehicles by using competitive computing and information fusion, and server using the same (2020-11-17),
14. 10832140 - Method and device for providing information for evaluating driving habits of driver by detecting driving scenarios occurring during driving (2020-11-10),
15. 10831189 - Learning method and learning device for providing functional safety by warning driver about potential dangerous situation by using explainable AI which verifies detection processes of autonomous driving network, and testing method and testing device using the same (2020-11-10),

Title: Spotlight on Donghun Yeo: Innovating Hardware Optimization and Continual Learning

Introduction:

In the world of technological advancements, Donghun Yeo has emerged as a prominent figure in the field of innovations and patents. Hailing from Gyeongsangbuk-do, Korea, Yeo's contributions have significantly impacted hardware optimization and continual learning for neural networks. With an impressive portfolio of 146 patents, Yeo's expertise and dedication have propelled him to the forefront of the industry.

Latest Patents:

Donghun Yeo's latest patents showcase his penchant for cutting-edge technologies and their applications in various domains. Two key patents include:

1. Learning method and learning device for CNN using 1xK or Kx1 convolution to be used for hardware optimization:

This patent introduces a novel method for learning the parameters of a Convolutional Neural Network (CNN) using 1xK or Kx1 convolution operations. The technique aims to optimize hardware performance by reshaping features and applying convolutional operations. By employing this method, Donghun Yeo contributes to advancing hardware efficiency for deep learning applications.

2. Method and device for on-device continual learning of a neural network:

Yeo's second recent patent focuses on on-device continual learning of neural networks for smartphones, drones, vessels, and military purposes. This method includes sampling new data, instructing an original data generator network, and generating output information aligned with current-learning. This approach enables continual learning without sacrificing storage resources, enhancing privacy, and preventing catastrophic forgetting.

Career Highlights:

During his professional journey, Donghun Yeo has made significant contributions to organizations like StradVision, Inc., and StadVision, Inc. (written as StradVision and StadVision, respectively). With his expertise in hardware optimization and continual learning, Yeo has played a pivotal role in shaping the future of these companies. His dedication and commitment to innovation have led to the development of groundbreaking technologies.

Collaborations:

Throughout his career, Donghun Yeo has collaborated with esteemed professionals, including Yongjoong Kim and SukHoon Boo. Working alongside these talented individuals, Yeo has leveraged their collective knowledge and expertise to drive exceptional outcomes. These collaborations have fostered an environment of creativity and exploration, resulting in cutting-edge inventions and patents.

Conclusion:

Donghun Yeo's groundbreaking contributions in hardware optimization and continual learning have made him an invaluable figure in the world of innovations and patents. His latest patents attest to his commitment to advancing technological solutions and improving hardware efficiency. With an illustrious career and notable collaborations, Yeo's work continues to shape the industry and drive progress towards a more advanced future.

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