San Diego, CA, United States of America

Nicholas Molyneux

USPTO Granted Patents = 1 

Average Co-Inventor Count = 6.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Innovations by Nicholas Molyneux in Image Analysis Systems

Introduction

Nicholas Molyneux is an accomplished inventor based in San Diego, California. He has made significant contributions to the field of image analysis, particularly in determining the age of building roofs through innovative methods and systems.

Latest Patents

Molyneux holds a patent for "Image analysis systems and methods for determining building roof age." This patent describes methods, non-transitory computer-readable media, and roof analysis systems that preprocess overhead images obtained based on geographic location requests. The overhead images depict a building at various historical points in time. A neural network is utilized to analyze input data structures derived from these images. The neural network is trained to identify relationships between features indicating changes in the roof of the building. It generates an output data structure that represents these changes. Patterns in the output data structure are then analyzed to determine instances of roof change, ultimately providing a roof age via a user interface based on the likelihood of these changes or the time interval between the overhead images and the current time.

Career Highlights

Molyneux is currently employed at Nearmap US, Inc., where he continues to develop and refine his innovative technologies. His work focuses on enhancing the accuracy and efficiency of image analysis systems, contributing to advancements in the field.

Collaborations

Some of his notable coworkers include Julius Simonelli and Ilsoo Seong, who collaborate with him on various projects at Nearmap US, Inc.

Conclusion

Nicholas Molyneux's contributions to image analysis systems demonstrate his commitment to innovation and technology. His patent for determining building roof age showcases the potential of neural networks in analyzing historical data for practical applications.

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