Sunnyvale, CA, United States of America

Pararth Paresh Shah


Average Co-Inventor Count = 4.6

ph-index = 5

Forward Citations = 154(Granted Patents)


Company Filing History:


Years Active: 2022-2023

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6 patents (USPTO):Explore Patents

Title: Pararth Paresh Shah: Innovator in Machine Learning and Assistant Systems

Introduction

Pararth Paresh Shah is a notable inventor based in Sunnyvale, California. He has made significant contributions to the field of machine learning, particularly in the development of assistant systems. With a total of six patents to his name, Shah's work focuses on enhancing the interpretability and personalization of machine learning models.

Latest Patents

Shah's latest patents include "Interpretability of deep reinforcement learning models in assistant systems." This patent outlines a method for training a target machine-learning model iteratively by utilizing training data of content objects. The process involves generating state-indications that comprise user intents, system actions, and user actions. The model is trained based on contextual evaluation measurements and an action set of possible system actions. Additionally, he has developed a patent titled "Personalized conversational recommendations by assistant systems." This invention describes a method for generating personalized recommendations based on user requests and responses, enhancing user interaction with assistant systems.

Career Highlights

Throughout his career, Pararth Paresh Shah has worked with prominent companies such as Meta Platforms Technologies, LLC and Facebook Technologies, LLC. His experience in these organizations has allowed him to refine his skills and contribute to cutting-edge technologies in the field of artificial intelligence.

Collaborations

Shah has collaborated with talented individuals in the industry, including Seungwhan Moon and Anuj Kumar. These collaborations have further enriched his work and expanded the impact of his inventions.

Conclusion

Pararth Paresh Shah is a distinguished inventor whose work in machine learning and assistant systems continues to shape the future of technology. His innovative patents and career achievements highlight his commitment to advancing the field.

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