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For the Inventor, By the Inventor

Gyeongsangbuk-do, South Korea

Yongjoong Kim

USPTO Granted Patents = 155 

 

Average Co-Inventor Count = 11.6

ph-index = 13

Forward Citations = 797(Granted Patents)

Forward Citations (Not Self Cited) = 678(Dec 10, 2025)


Inventors with similar research interests:


Location History:

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

Company Filing History:

goldMedal153 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
On-Device Learning
Autonomous Vehicles
Reinforcement Learning
Object Detection
V2X Communication
Deep Learning
Sensor Fusion
Generative Adversarial Networks
Trajectory Analysis
Explainable Ai
Adaptive Learning
155 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. 11254331 - Learning method and learning device for updating object detector, based on deep learning, of autonomous vehicle to adapt the object detector to driving circumstance, and updating method and updating device using the same (2022-02-22),
4. 11203361 - Method for performing on-device learning of machine learning network on autonomous vehicle by using multi-stage learning with adaptive hyper-parameter sets and device using the same (2021-12-21),
5. 11157813 - Method and device for on-vehicle active learning to be used for training perception network of autonomous vehicle (2021-10-26),
6. 11132607 - Method for explainable active learning, to be used for object detector, by using deep encoder and active learning device using the same (2021-09-28),
7. 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),
8. 11080544 - Method and device for calibrating pitch of camera on vehicle and method and device for continual learning of vanishing point estimation model to be used for calibrating the pitch (2021-08-03),
9. 11074507 - Method for performing adjustable continual learning on deep neural network model by using selective deep generative replay module and device using the same (2021-07-27),
10. 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),
11. 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),
12. 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),
13. 10984262 - Learning method and testing method for monitoring blind spot of vehicle, and learning device and testing device using the same (2021-04-20),
14. 10970645 - Method for explainable active learning, to be used for object detector, by using Bayesian dual autoencoder and active learning device using the same (2021-04-06),
15. 10970633 - Method for optimizing on-device neural network model by using sub-kernel searching module and device using the same (2021-04-06),

Title: Yongjoong Kim: Innovator Enhancing Hardware Optimization in Neural Networks

Introduction:

Yongjoong Kim, a prolific inventor hailing from Gyeongsangbuk-do, South Korea, has made significant contributions to the field of hardware optimization in neural networks. With an impressive count of 155 patents under his belt, Kim has been at the forefront of technological advancements in machine learning. This article delves into Kim's latest patents, career highlights, notable collaborations, and the profound impact of his work.

Latest Patents:

Yongjoong Kim's recent patents showcase his expertise in developing learning methods, learning devices, and testing methodologies for convolutional neural networks (CNN). Notably, his patent titled "Learning method and learning device for CNN using 1xK or Kx1 convolution to be used for hardware optimization" introduces an innovative approach to optimize hardware performance while maintaining key performance indicators (KPIs). This method involves reshaping and adjusting feature maps, allowing for efficient learning and hardware utilization.

In another patent titled "Method and device for on-device continual learning of a neural network," Kim addresses the challenge of continual learning in neural networks for devices such as smartphones, drones, vessels, and military applications. This invention utilizes generative adversarial networks (GANs) and online learning techniques to enable efficient on-device learning while ensuring resource-saving, preventing catastrophic forgetting, and maintaining privacy.

Career Highlights:

Throughout his career, Yongjoong Kim has contributed to various cutting-edge companies, propelling advancements in the field of machine learning. Notably, he has made notable contributions at StradVision, Inc., a renowned computer vision company. StradVision has gained recognition for its work on advanced camera perception technology for autonomous vehicles, and Kim's involvement in the company demonstrates his commitment to tackling real-world challenges through technology.

Collaborations:

Collaboration plays a crucial role in driving innovation, and Yongjoong Kim has worked closely with esteemed colleagues to enhance his inventions. Among his collaborators, Hongmo Je and Wooju Ryu stand out. Through synergistic teamwork, they have contributed to the development of groundbreaking methodologies and devices for neural networks.

Conclusion:

Yongjoong Kim's expertise in hardware optimization for neural networks has yielded remarkable advancements in machine learning. With 155 patents to his credit, his contributions have significantly impacted the field, paving the way for efficient learning methods and hardware utilization. As he continues to collaborate with fellow innovators and contribute to prestigious companies like StradVision, Inc., Yongjoong Kim's work is sure to shape the future of machine learning and its applications in various industries.

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