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For the Inventor, By the Inventor

Mountain View, CA, United States of America

Oren Rippel

USPTO Granted Patents = 17 

Average Co-Inventor Count = 2.8

ph-index = 4

Forward Citations = 82(Granted Patents)


Location History:

  • Mountain View, CA (US) (2019 - 2023)
  • Vancouver, CA (2023)

Company Filing History:

goldMedal17 out of 19 
 
Waveone Inc.
 patents

Years Active: 2019-2025

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17 patents (USPTO):Explore Patents
1. 12425618 - Dynamic control for a machine learning autoencoder (2025-09-23),
2. 11849128 - Dynamic control for a machine learning autoencoder (2023-12-19),
3. 11593632 - Deep learning based on image encoding and decoding (2023-02-28),
4. 11570465 - Machine-learned in-loop predictor for video compression (2023-01-31),
5. 11423310 - Deep learning based adaptive arithmetic coding and codelength regularization (2022-08-23),
6. 11315011 - Using generative adversarial networks in compression (2022-04-26),
7. 11256984 - Data compression for machine learning tasks (2022-02-22),
8. 11100394 - Deep learning based adaptive arithmetic coding and codelength regularization (2021-08-24),
9. 11062211 - Deep learning based adaptive arithmetic coding and codelength regularization (2021-07-13),
10. 10977553 - Enhanced coding efficiency with progressive representation (2021-04-13),
11. 10860929 - Machine-learning based video compression (2020-12-08),
12. 10748062 - Deep learning based adaptive arithmetic coding and codelength regularization (2020-08-18),
13. 10685282 - Machine-learning based video compression (2020-06-16),
14. 10594338 - Adaptive quantization (2020-03-17),
15. 10565499 - Autoencoding image residuals for improving upsampled images (2020-02-18),

Title: Oren Rippel: Innovator in Machine Learning Technologies

Introduction

Oren Rippel is a prominent inventor based in Mountain View, CA (US). He has made significant contributions to the field of machine learning, holding a total of 17 patents. His work focuses on enhancing the efficiency and effectiveness of autoencoders, which are crucial in various applications of artificial intelligence.

Latest Patents

One of Oren Rippel's latest patents is titled "Dynamic control for a machine learning autoencoder." This innovative autoencoder is designed to encode content at different quality levels. It features an encoding system and a decoding system, with neural network layers forming both an encoder network and a decoder network. The architecture includes branching paths that consist of different subnetworks. During deployment, the encoding system receives content along with a quality signal that indicates the quality level for content reconstruction. This quality signal determines which paths through the encoder network are activated for encoding the content into one or more tensors. These tensors are then compressed into a bitstream, which the decoding system uses to reconstruct the content. The autoencoder is trained by selecting various combinations of tensors for encoding and backpropagating error values from loss functions through the associated network paths.

Career Highlights

Oren Rippel has established himself as a key figure in the tech industry, particularly through his work at Waveone Inc. His innovative approaches to machine learning have garnered attention and respect from peers and industry leaders alike.

Collaborations

Oren has collaborated with notable professionals in the field, including Lubomir Dimitrov Bourdev and Sanjay Nair. These collaborations have further enriched his work and contributed to advancements in machine learning technologies.

Conclusion

Oren Rippel's contributions to machine learning, particularly through his patented innovations, highlight his role as a leading inventor in the tech industry. His work continues to influence the development of advanced technologies that shape the future of artificial intelligence.

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