Seattle, WA, United States of America

Siddhartha Agarwal

This inventor holds 1 USPTO granted patent. Top assignee: Amazon Technologies, Inc.. Active years: 2023.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Siddhartha Agarwal. Retrieved from https://idiyas.com/inventor/siddhartha-agarwal-m5soybbx

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


% Patents Active = 100.0

Average Co-Inventor Count = 6.0

ph-index = 1

Forward Citations = 9(Granted Patents)


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: Siddhartha Agarwal: Innovator in Automated Learning Systems

Introduction

Siddhartha Agarwal is a prominent inventor based in Seattle, WA. He has made significant contributions to the field of automated learning systems. His innovative approach focuses on enhancing model training and deployment processes.

Latest Patents

Siddhartha holds a patent for "Iterative model training and deployment for automated learning systems." This patent describes techniques for iterative model training and deployment. The method involves generating training data based on inference data from a model hosted at a machine learning service and feedback data from a client application. It also includes generating a second version of the model using the training data and deploying it to the machine learning service endpoint. He has 1 patent to his name.

Career Highlights

Siddhartha is currently employed at Amazon Technologies, Inc. His work at Amazon focuses on advancing machine learning technologies. He has played a crucial role in developing systems that improve the efficiency of automated learning.

Collaborations

Siddhartha collaborates with talented professionals in his field, including Vineet Khare and Saurabh Gupta. Their teamwork fosters innovation and drives advancements in automated learning systems.

Conclusion

Siddhartha Agarwal is a key figure in the realm of automated learning systems, with a focus on iterative model training and deployment. His contributions continue to shape the future of machine learning technologies.

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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