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For the Inventor, By the Inventor

Foster City, CA, United States of America

Tomas Jon Pfister

USPTO Granted Patents = 17 

 

Average Co-Inventor Count = 3.4

ph-index = 2

Forward Citations = 13(Granted Patents)


Company Filing History:

goldMedal14 out of 32,429 
 
Google Inc.
 patents
silverMedal1 out of 40,816 
 
Apple Inc.
 patents
bronzeMedal2 out of 832,680 
Other
 patents

Years Active: 2021-2025

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17 patents (USPTO):
1. 12468897 - Self-improving LLMs through consistency-based self-generated demonstrations (2025-11-11),
2. 12327395 - Aggregating nested vision transformers (2025-06-10),
3. 12271822 - Active learning via a sample consistency assessment (2025-04-08),
4. 12125265 - Reinforcement learning based locally interpretable models (2024-10-22),
5. 12106223 - Data valuation using reinforcement learning (2024-10-01),
6. 12039443 - Distance-based learning confidence model (2024-07-16),
7. 12026614 - Interpretable tabular data learning using sequential sparse attention (2024-07-02),
8. 11941531 - Attention-based prototypical learning (2024-03-26),
9. 11941084 - Self-supervised learning for anomaly detection and localization (2024-03-26),
10. 11907850 - Training image-to-image translation neural networks (2024-02-20),
11. 11823058 - Data valuation using reinforcement learning (2023-11-21),
12. 11487970 - Distance-based learning confidence model (2022-11-01),
13. 11475276 - Generating more realistic synthetic data with adversarial nets (2022-10-18),
14. 11437039 - Intelligent software agent (2022-09-06),
15. 11403490 - Reinforcement learning based locally interpretable models (2022-08-02),

Title: Innovations of Tomas Jon Pfister

Introduction

Tomas Jon Pfister is a prominent inventor based in Foster City, CA, known for his significant contributions to the field of machine learning and image processing. With a total of 16 patents to his name, he has made remarkable advancements that have influenced various technological applications.

Latest Patents

Among his latest patents is "Aggregating Nested Vision Transformers." This method involves receiving image data that includes a series of image patches. It generates higher-order feature representations using a vision transformer model and aggregates these representations into smaller sets for improved image classification. Another notable patent is "Active Learning via a Sample Consistency Assessment." This method focuses on obtaining unlabeled training samples, perturbing them to create augmented samples, and determining inconsistency values to enhance the training of machine learning models.

Career Highlights

Tomas has worked with renowned companies, including Google Inc., where he has applied his expertise in developing innovative solutions. His work has significantly impacted the advancement of machine learning technologies.

Collaborations

Throughout his career, Tomas has collaborated with talented individuals such as Sercan Omer Arik and Jinsung Yoon, contributing to various projects that push the boundaries of technology.

Conclusion

Tomas Jon Pfister's innovative work in machine learning and image processing has established him as a leading inventor in his field. His patents reflect a commitment to advancing technology and improving the efficiency of machine learning applications.

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