Charlestown, MA, United States of America

Lauren Moores

USPTO Granted Patents = 3 

 

Average Co-Inventor Count = 33.0

ph-index = 2

Forward Citations = 23(Granted Patents)


Company Filing History:


Years Active: 2022-2025

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3 patents (USPTO):Explore Patents

Title: The Innovative Contributions of Lauren Moores

Introduction

Lauren Moores is a prominent inventor based in Charlestown, MA (US). She has made significant strides in the field of agricultural technology, particularly through her work in machine learning applications for crop production. With a total of 3 patents, her contributions are shaping the future of farming.

Latest Patents

One of her latest patents focuses on machine learning in agricultural planting, growing, and harvesting contexts. This crop prediction system performs various machine learning operations to predict crop production and identify a set of farming operations that, if performed, optimize crop production. The system utilizes crop prediction models trained on geographic and agronomic information. Upon receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, including the location of the land, weather conditions, and soil composition. By applying one or more crop prediction models to this accessed information, the system predicts crop production and identifies an optimized set of farming operations for the grower to perform.

Career Highlights

Lauren Moores is currently employed at Indigo Ag, Inc., where she continues to innovate in the agricultural sector. Her work is instrumental in enhancing the efficiency and productivity of farming practices through advanced technology.

Collaborations

Some of her notable coworkers include David Patrick Perry and Geoffrey Von Maltzahn, who contribute to the collaborative environment at Indigo Ag, Inc.

Conclusion

Lauren Moores is a trailblazer in the field of agricultural technology, with her innovative patents paving the way for smarter farming practices. Her work exemplifies the intersection of technology and agriculture, showcasing the potential for machine learning to transform crop production.

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