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For the Inventor, By the Inventor

Gyeongsangbuk-do, South Korea

Wooju Ryu

USPTO Granted Patents = 155 

 

Average Co-Inventor Count = 11.4

ph-index = 14

Forward Citations = 814(Granted Patents)

Forward Citations (Not Self Cited) = 693(Dec 10, 2025)


Inventors with similar research interests:


Location History:

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

Company Filing History:

goldMedal152 out of 162 
 
Stradvision, Inc.
 patents
silverMedal1 out of 1 
 
Stadvision, Inc.
 patent
bronzeMedal1 out of 1 
 
Stradivision, Inc.
 patent
4. 1 out of 1 
 
Stradavision, Inc.
 patent

Years Active: 2018-2022

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Areas of Expertise:
Convolutional Neural Networks
On-Device Continual Learning
Autonomous Vehicle Object Detection
Deep Learning Training Methods
Reinforcement Learning
V2X Communication
Sensor Fusion
Explainable Ai
Trajectory-Based Behavior Analysis
Generative Adversarial Networks
Emergency Vehicle Detection
Adaptive Deep Learning Models
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. 11250298 - Methods for training and testing perception network by using images obtained from multiple imaging devices having diverse specifications and learning device and testing device using the same (2022-02-15),
5. 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),
6. 11113574 - Methods for performing self-supervised learning of deep-learning based detection network by using deep Q-network and devices using the same (2021-09-07),
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. 10970598 - Learning method and learning device for training an object detection network by using attention maps and testing method and testing device using the same (2021-04-06),
15. 10963792 - Method for training deep learning network based on artificial intelligence and learning device using the same (2021-03-30),

Title: Wooju Ryu: Innovator in Hardware Optimization and Neural Network Learning

Introduction:

In the realm of innovations and patents, Wooju Ryu has established himself as a visionary inventor. With a location in Gyeongsangbuk-do, South Korea, Wooju Ryu's contributions span diverse areas, including hardware optimization and on-device continual learning of neural networks. With an impressive portfolio of 155 patents, Wooju Ryu has made significant advancements in these fields. This article will delve into his latest patents, career highlights, collaborations, and highlight the value he brings to the world of technology.

Latest Patents:

Wooju Ryu's recent patents exemplify his expertise in hardware optimization and neural network learning. One of his latest inventions includes a "Learning method and learning device for CNN using 1xK or Kx1 convolution to be used for hardware optimization." This method enables the learning of parameters in a Convolutional Neural Network (CNN) using 1xK or Kx1 convolution operations to optimize hardware performance. By reshaping and adjusting feature maps, this technique meets the Key Performance Indicators (KPI) effectively.

Another groundbreaking patent is the "Method and device for on-device continual learning of a neural network." This method allows continual learning of a neural network on smart devices, drones, vessels, or military equipment. Through generative adversarial networks (GANs) and online learning, the neural network analyzes input data, preventing catastrophic forgetting, and ensuring privacy. This innovation has significant implications for resource-saving, data security, and preventing the loss of valuable knowledge.

Career Highlights:

Throughout his career, Wooju Ryu has made remarkable contributions to the field of technology. He has worked with renowned companies like Stradvision, Inc. and has been instrumental in developing cutting-edge solutions in computer vision. His expertise in hardware optimization has led to pioneering advancements, making technology more efficient and accessible.

Collaborations:

Wooju Ryu has had the privilege of collaborating with accomplished professionals in his field. Notably, he has worked closely with Hongmo Je and Yongjoong Kim. Together, they have pushed the boundaries of innovation, leveraging their combined expertise to solve complex technological challenges. Their collaborations have resulted in groundbreaking inventions, further solidifying Wooju Ryu's impact in the tech industry.

Conclusion:

Wooju Ryu stands out as a distinguished innovator in the fields of hardware optimization and neural network learning. With a plethora of patents under his name, his contributions have revolutionized technology, making it more efficient, secure, and accessible. From creating hardware optimization methods to enabling continual learning on smart devices, his inventions have a far-reaching impact. Wooju Ryu's collaborations with talented professionals have further elevated his stature within the industry. He continues to push the boundaries of what is possible, paving the way for advancements that enhance various aspects of our technological landscape.

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