Location History:
- Albuquerque, NM (US) (2019 - 2023)
- Keller, TX (US) (2024)
Company Filing History:
Years Active: 2019-2025
Title: Innovations of James Bradley Aimone
Introduction
James Bradley Aimone is a notable inventor based in Albuquerque, NM (US). He holds a total of 11 patents, showcasing his significant contributions to the field of technology and machine learning. His work primarily focuses on enhancing user authentication and improving the efficiency of machine learning algorithms.
Latest Patents
One of Aimone's latest patents is titled "Secure authentication using recurrent neural networks." This invention presents a computer-implemented method of user authentication that combines a user recurrent neural network with a system recurrent neural network. The unique combined recurrent neural network generates a distinct combined key, which is used to authenticate users securely.
Another significant patent is "Devices and methods for increasing the speed or power efficiency of a computer when performing machine learning using spiking neural networks." This method enhances the speed and efficiency of computers during machine learning processes. It involves correlating input values with neuron response speeds, allowing for improved machine learning performance through the formation of equivalence relationships.
Career Highlights
Throughout his career, Aimone has worked with reputable organizations such as National Technology & Engineering Solutions of Sandia, LLC and Lewis Rhodes Labs, Inc. His experience in these companies has contributed to his expertise in developing innovative technologies.
Collaborations
Aimone has collaborated with notable individuals in his field, including William Mark Severa and Ojas D Parekh. These collaborations have likely enriched his work and led to further advancements in his inventions.
Conclusion
James Bradley Aimone's contributions to technology through his patents and collaborations highlight his role as an influential inventor. His innovative approaches to user authentication and machine learning efficiency continue to impact the industry positively.