Gourav Awasthi

Title: Innovations by Gourav Awasthi

Introduction

Gourav Awasthi is an accomplished inventor based in Gurugram, India. He has made significant contributions to the field of data analysis and machine learning, holding a total of 2 patents. His work focuses on developing innovative solutions that enhance predictive data analysis and emotional sentiment scoring.

Latest Patents

One of Gourav's latest patents is titled "Natural language processing techniques using target composite sentiment designation." This invention addresses the need for more effective and efficient predictive data analysis solutions. It involves generating an emotional sentiment score without the use of labeled data. The process includes receiving an input text sequence, generating an intermediate emotional sentiment score object using a machine learning model, and performing prediction-based actions based on the overall emotional sentiment score.

Another notable patent is "Machine learning techniques for hierarchical-workflow risk score prediction using multi-party communication data." This invention provides methods and systems for performing predictive data analysis operations. It utilizes a hybrid space classification machine learning model to generate a risk score based on multi-party communication transcript data. The invention aims to enhance the accuracy of risk score predictions through a hierarchical risk score adjustment workflow.

Career Highlights

Gourav has worked with reputable companies such as Optum, Inc. and Optum Services (Ireland) Limited. His experience in these organizations has allowed him to refine his skills in data analysis and machine learning, contributing to his innovative inventions.

Collaborations

Gourav has collaborated with talented professionals in his field, including Rajesh Sabapathy and Sumeet Jain. These collaborations have fostered a creative environment that encourages the development of groundbreaking solutions.

Conclusion

Gourav Awasthi is a notable inventor whose work in machine learning and data analysis has led to significant advancements in predictive technologies. His patents reflect a commitment to innovation and a desire to improve data analysis methodologies.