San Jose, CA, United States of America

Rashmi Raghu


Average Co-Inventor Count = 2.4

ph-index = 3

Forward Citations = 23(Granted Patents)


Company Filing History:


Years Active: 2017-2023

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5 patents (USPTO):Explore Patents

Title: Rashmi Raghu: Innovator in Data Clustering and Visualization

Introduction

Rashmi Raghu, based in San Jose, CA, is a prominent inventor with a notable portfolio of five patents. Her work primarily focuses on innovative methods for clustering and visualizing large volumes of unstructured textual data, enhancing the way data is analyzed and interpreted.

Latest Patents

Rashmi's latest patents include groundbreaking technologies that address complex data management challenges. One of her significant inventions is a system for clustering and visualizing alerts and incidents. This innovation provides methods, systems, and apparatus for organizing large datasets into cohesive categories, ultimately presenting them graphically for better comprehension. Additionally, Rashmi has developed a patent that focuses on the analysis of smart meter data based on frequency content. This technology transforms time series data into the frequency domain, enabling the detection of anomalies in resource consumption, contributing significantly to fields such as smart energy management.

Career Highlights

Throughout her career, Rashmi has contributed her expertise to well-known companies such as EMC IP Holding Company LLC and Pivotal Software, Inc. Her work in these organizations has allowed her to pioneer essential technologies that enhance data reliability and visualization techniques.

Collaborations

Rashmi has worked alongside talented professionals like Kaushik K Das and Derek Chin-Teh Lin, fostering a collaborative environment that has facilitated innovative breakthroughs in their respective fields.

Conclusion

Rashmi Raghu's contributions to data clustering and visualization have positioned her as a significant player in innovation. Her patents not only showcase her inventiveness but also have the potential to substantially influence data analysis methodologies across various industries.

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