Seattle, WA, United States of America

Kamran Rajabi Zargahi

This inventor holds 25 USPTO granted patents and 13 published patent applications and 8 EPO patents, primarily in Synthetic Data Solutions. Top assignee: Microsoft Technology Licensing, LLC. Active years: 2011-2025.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Kamran Rajabi Zargahi. Retrieved from https://idiyas.com/inventor/kamran-rajabi-zargahi

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

Calculating rank
USPTO Granted Patents = 25 

% Patents Active = 84.0

 

Average Co-Inventor Count = 2.7

ph-index = 4

Forward Citations = 125(Granted Patents)


Location History:

  • San Francisco, CA (US) (2014 - 2017)
  • Seattle, WA (US) (2011 - 2021)
  • Bellevue, WA (US) (2020 - 2023)
  • Redmond, WA (US) (2023)

Company Filing History:


Years Active: 2011-2025

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Areas of Expertise:
Data Enrichment
Synthetic Datasets
Machine Learning
Distributed Computing
Resource Allocation
Mobile Applications
On-Platform Analytics
Secure Authentication
Digital Content Billing
Crowdsourcing Engine
Multimodal Requests
Application Store Concierge
25 patents (USPTO):Explore Patents

Title: Spotlight on Kamran Rajabi Zargahi: Innovator in Machine Learning and Synthetic Data Solutions

Introduction: Kamran Rajabi Zargahi, based in Seattle, WA, is an accomplished inventor and a prominent figure in the field of machine learning and synthetic data solutions. With an impressive portfolio of 23 patents, he has made significant contributions to the development of technologies that enhance the efficiency and efficacy of machine learning models.

Latest Patents: Kamran's latest patents include groundbreaking innovations such as the "Automatic suggestion of variation parameters and pre-packaged synthetic datasets." This invention describes various techniques for automatically suggesting variation parameters crucial for generating tailored synthetic datasets used in training specific machine learning models. The process employs a seeding taxonomy that associates multiple machine learning scenarios with their respective subsets of variation parameters. By utilizing feedback loops, this adaptable seeding taxonomy can track selected variation parameters and update accordingly. Additionally, the invention provides a user interface for presenting available packages of synthetic data tailored for various industry sectors.

Another notable patent is the "Machine-learning training service for synthetic data," which offers various methods and systems for a distributed computing system that enables real-time generation of synthetic data assets. This service permits the machine learning model to be retrained using a combination of synthetic and non-synthetic data assets, thereby enhancing the model's performance and adaptability.

Career Highlights: Kamran currently works at Microsoft Technology Licensing, LLC, where he continues to innovate and push the boundaries of machine learning technologies. His work focuses on refining data solutions that cater to a range of industry-specific needs, demonstrating a robust understanding of the complex dynamics in machine learning applications.

Collaborations: Throughout his career, Kamran has collaborated with notable peers in the industry, including John Bruno and Michael Ebstyne. These collaborations have played a pivotal role in driving forward innovative solutions within the domain of synthetic data and machine learning.

Conclusion: Kamran Rajabi Zargahi stands out as an influential inventor whose patents contribute significantly to the advancement of machine learning and synthetic data solutions. His dedication to creating efficient tools and methodologies continues to impact various sectors, establishing him as a key player in the technological landscape.

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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