Redmond, WA, United States of America

Bharathiraja Krishnamoorthy

This inventor holds 4 USPTO granted patents and 1 published patent application. Top assignee: Bank of America Corporation. Active years: 2018-2025.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Bharathiraja Krishnamoorthy. Retrieved from https://idiyas.com/inventor/bharathiraja-krishnamoorthy

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

USPTO Granted Patents = 4 

% Patents Active = 100.0

Average Co-Inventor Count = 4.4

ph-index = 1

Forward Citations = 5(Granted Patents)


Location History:

  • Redmond, WA (US) (2018)
  • Plano, TX (US) (2022 - 2024)

Company Filing History:


Years Active: 2018-2025

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4 patents (USPTO):Explore Patents

Title: Innovations by Bharathiraja Krishnamoorthy

Introduction

Bharathiraja Krishnamoorthy is an accomplished inventor based in Redmond, WA (US). He has made significant contributions to the field of machine learning, holding a total of four patents. His work focuses on enhancing the efficiency and effectiveness of machine learning model training systems.

Latest Patents

Bharathiraja's latest patents include a "Machine Learning Model Training System" and a "Machine Learning Model Builder." The Machine Learning Model Training System provides a solution for horizontal scaling of retraining machine learning models across operational domains. This system aims to reduce the computational overhead associated with model retraining. It features an artificial intelligence (AI) engine that identifies target machine learning models requiring retraining due to changes in training data. The AI engine assigns daemons to gather retraining requirements, such as source code and training data, and schedules the target models for retraining on either CPU or GPU-based systems.

The Machine Learning Model Builder offers a comprehensive solution for automating the development, testing, and updating of machine learning models in various operational environments. This system determines which models need retraining in response to changes in training data. By focusing on only the target models, it results in significant savings in computing resources. Additionally, it reduces the number of machine learning models generated for testing environments, further conserving computational resources.

Career Highlights

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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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