Davis, CA, United States of America

Josh Livni

USPTO Granted Patents = 2 


Average Co-Inventor Count = 7.2

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2021-2024

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2 patents (USPTO):

Title: Innovations by Josh Livni in Insect Classification

Introduction

Josh Livni is an innovative inventor based in Davis, CA (US). He has made significant contributions to the field of insect classification through his patented technologies. With a total of 2 patents, Livni's work focuses on utilizing advanced classification approaches to enhance the understanding and categorization of insects.

Latest Patents

Livni's latest patents include "Predictive classification of insects" and "Predictive models for visually classifying insects." The first patent describes a method for classifying insects into various categories, such as sex, species, and size, using different classification approaches, including industrial vision classifiers and machine learning classifiers. This technology allows for real-time decision-making and validation of earlier classifications. The second patent outlines a predictive model that localizes and classifies insects based on image data. This model evaluates samples of image data to determine the presence of insects and categorizes them accordingly, such as male or female and different species.

Career Highlights

Josh Livni is currently employed at Verily Life Sciences LLC, where he applies his expertise in insect classification. His work is instrumental in advancing the field and providing innovative solutions for real-time insect identification.

Collaborations

Livni collaborates with notable colleagues, including Mark Desnoyer and Yaniv Ovadia, to further enhance the research and development of insect classification technologies.

Conclusion

Josh Livni's contributions to insect classification through his innovative patents demonstrate his commitment to advancing technology in this field. His work not only aids in the understanding of insect categorization but also paves the way for future advancements in real-time classification methods.

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