Menlo Park, CA, United States of America

Peter Elliot Schmidt-Nielsen


Average Co-Inventor Count = 6.0

ph-index = 2

Forward Citations = 5(Granted Patents)


Company Filing History:


Years Active: 2021

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3 patents (USPTO):Explore Patents

Title: Innovations of Peter Elliot Schmidt-Nielsen

Introduction

Peter Elliot Schmidt-Nielsen is a notable inventor based in Menlo Park, CA. He has made significant contributions to the field of machine learning and conversation analysis. With a total of three patents to his name, Schmidt-Nielsen is recognized for his innovative approaches to enhancing communication through technology.

Latest Patents

One of his latest patents focuses on systems and methods for identifying a behavioral target during a conversation. This invention allows for real-time monitoring of conversations using a trained machine learning model. The model detects various attributes of a conversation, such as its type and state, which helps specify the context. Contextually appropriate behavioral targets are provided to agents participating in the conversation. The behavioral targets are identified by applying a set of rules to the identified conversation attributes. This facilitates real-time monitoring and guidance for agents based on machine-identified behavioral targets.

Career Highlights

Peter Schmidt-Nielsen is currently employed at Cresta Intelligence Inc., where he continues to develop innovative solutions in the realm of conversation analysis. His work has been instrumental in advancing the capabilities of machine learning applications in real-world scenarios.

Collaborations

Some of his notable coworkers include Tianlin Shi and Navjot Matharu, who contribute to the collaborative environment at Cresta Intelligence Inc. Their combined expertise fosters innovation and drives the development of cutting-edge technologies.

Conclusion

Peter Elliot Schmidt-Nielsen's work exemplifies the intersection of technology and communication. His patents reflect a commitment to enhancing conversational interactions through machine learning. His contributions are paving the way for future advancements in this exciting field.

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