Alpharetta, GA, United States of America

Michel Guillet


Average Co-Inventor Count = 5.0

ph-index = 1

Forward Citations = 3(Granted Patents)


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: Michel Guillet: Innovator in Neural Network Architectures

Introduction

Michel Guillet is a notable inventor based in Alpharetta, GA (US). He has made significant contributions to the field of neural network architectures, particularly in the context of email communication. His innovative approach focuses on enhancing the understanding of reply emails through advanced modeling techniques.

Latest Patents

Michel Guillet holds a patent titled "Methods and systems for cascading model architecture for providing information on reply emails." This patent outlines a method for training a receptivity neural network model using incoming reply emails. The model classifies new reply emails as either positive or non-positive. Additionally, it includes a process for augmenting sample data of non-positive reply emails and training an objection identification neural network model. This model helps in determining objection classifications for new non-positive reply emails. The patent also emphasizes the importance of explainability, providing key information and phrases used in the classification process.

Career Highlights

Michel Guillet is currently employed at Salesloft, Inc., where he applies his expertise in neural networks to improve email communication systems. His work has been instrumental in developing innovative solutions that enhance user experience and efficiency in handling email replies.

Collaborations

Michel collaborates with talented individuals such as Alec Lionel Delany and Carlos Roman Salas. Their combined efforts contribute to the advancement of technologies in the field of email communication and neural networks.

Conclusion

Michel Guillet's contributions to the field of neural network architectures demonstrate his commitment to innovation and technology. His patent on email reply classification showcases the potential of machine learning in improving communication systems.

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