San Jose, CA, United States of America

Wei Jiang

This inventor holds 110 USPTO granted patents and 17 published patent applications and 16 EPO patents, primarily in Neural Image Compression (CPC class G06N3-08). Top assignees: Tencent America LLC, Innogrit Technologies Co., Ltd., Future Wei Technologies, Inc.. Active years: 2016-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Wei Jiang. Retrieved from https://idiyas.com/inventor/wei-jiang

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

USPTO Granted Patents = 110 

% Patents Active = 98.2

 

Average Co-Inventor Count = 3.4

ph-index = 5

Forward Citations = 306(Granted Patents)

Forward Citations (Not Self Cited) = 259(Dec 10, 2025)


Inventors with similar research interests:

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Location History:

  • Bridgewater, NJ (US) (2016)
  • Belle Mead, NJ (US) (2017)
  • Princeton, NJ (US) (2016 - 2019)
  • Santa Clara, CA (US) (2019 - 2022)
  • Palo Alto, CA (US) (2022 - 2023)
  • Fremont, CA (US) (2016 - 2024)
  • San Jose, CA (US) (2019 - 2024)
  • Sunnyvale, CA (US) (2022 - 2024)

Company Filing History:


Years Active: 2016-2026

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Areas of Expertise:
Neural Compression
Deep Reinforcement Learning
Video Coding
Adaptive Thermal Calibration
Multi-task Neural Network
Quantization
Entropy Coding
DNN-based Prediction
Post-filtering
Parameter Sharing
Model Compression
Super Resolution
110 patents (USPTO):Explore Patents

Title: Wei Jiang: Innovator in Neural Image Compression and Video Coding

Introduction

Wei Jiang, based in San Jose, CA, is a prolific inventor with an impressive portfolio of 93 patents. His work primarily focuses on advancements in neural image compression and video encoding techniques, contributing significantly to the fields of artificial intelligence and multimedia technologies.

Latest Patents

Among Wei Jiang's latest inventions is a method and apparatus for multi-learning rates of substitution in neural image compression. This innovative approach involves a neural network-based substitutional end-to-end (E2E) image compression system that operates on input images within an E2E NIC framework. The process includes determining a substitute image based on a training model, encoding the substitute image to generate a bitstream, and mapping this bitstream to create a compressed representation of the input image. Additionally, the input image may be partitioned into blocks, facilitating individual substitute representation and encoding for each block rather than the entire substitute image.

Another notable patent involves utilizing a hierarchical structure for neural network-based tools in video coding. This method encompasses video encoding and decoding processes where video data, including a current picture, is received. A virtual reference frame is generated for the current picture based on the hierarchical level associated with it and the nearest decoded picture. The video data is subsequently decoded using the generated reference frame, enhancing the efficiency of video processing.

Career Highlights

Wei Jiang has made considerable contributions to the technology sector through his work at leading companies such as Tencent America LLC and Future Wei Technologies, Inc. His expertise in multimedia technology and neural networks has positioned him as a prominent figure in innovation, driving advancements in image and video processing.

Collaborations

Throughout his career, Wei Jiang has collaborated with notable colleagues, including Shan Liu and Wei W Wang. These partnerships have led to groundbreaking research and developments in their respective fields, further expanding the impact of their inventions.

Conclusion

Wei Jiang's extensive patent portfolio and innovative work in neural image compression and video coding underscore his role as a key inventor in modern technology. His contributions not only advance the field of multimedia technology but also pave the way for future innovations in artificial intelligence and beyond.

Profile summary based on public USPTO records.
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]
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