Karnataka, India

Krishnaraju Thangaraju


Average Co-Inventor Count = 1.0


Company Filing History:


Years Active: 2025

where 'Filed Patents' based on already Granted Patents

1 patent (USPTO):

Title: Innovations by Krishnaraju Thangaraju in Machine Learning

Introduction

Krishnaraju Thangaraju is an accomplished inventor based in Karnataka, India. He has made significant contributions to the field of machine learning, particularly in the area of dataset preparation for storage devices. His innovative approach has the potential to enhance the quality of datasets used in various machine learning applications.

Latest Patents

Krishnaraju holds a patent for "Machine learning facets for dataset preparation in storage devices." This patent describes methods for preparing datasets in a storage device specifically for machine learning applications. The invention includes maintaining mappings between machine learning facets and dataset preparation tags, deriving machine learning facets from stored datasets, and generating filtered datasets that exhibit improved quality compared to unfiltered datasets. The storage device is capable of transmitting these filtered datasets to machine learning applications upon request. Additionally, it can recommend machine learning facets based on performance metrics, further optimizing the dataset preparation process.

Career Highlights

Krishnaraju is currently employed at Hewlett Packard Enterprise Development LP, where he continues to work on innovative solutions in the tech industry. His expertise in machine learning and data management has positioned him as a valuable asset to his team and the company.

Collaborations

One of his notable coworkers is Chaitra Kallianpur, with whom he collaborates on various projects related to machine learning and data storage solutions.

Conclusion

Krishnaraju Thangaraju's contributions to machine learning and dataset preparation demonstrate his commitment to innovation in technology. His patent reflects a significant advancement in the field, showcasing the importance of quality datasets in machine learning applications.

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