Company Filing History:
Years Active: 2019-2020
Title: Jack Edward Neil: Innovator in Acoustic Detection Technologies
Introduction
Jack Edward Neil is a notable inventor based in Hendersonville, NC (US). He has made significant contributions to the field of acoustic detection and correction systems. With a total of 2 patents to his name, Neil's work showcases his innovative approach to solving complex problems in audio technology.
Latest Patents
Neil's latest patents include "Acoustic and other waveform event detection and correction systems and methods." This patent outlines systems and methods for detecting, classifying, and correcting acoustic events. In one embodiment, a computer-implemented method involves obtaining audio data from a source and accessing a machine-learned acoustic detection model. The method inputs the audio data into the model and obtains an output indicative of an acoustic event associated with the source. Furthermore, the system provides notifications to user devices regarding the acoustic event and potential responses. The computing system may also initiate autonomous actions to modify or halt the acoustic event through a continuously learned hierarchical process.
Another notable patent is for a "Virtual reality headset adapted to engage an anesthesia mask." This invention reflects Neil's commitment to enhancing user experiences in various technological applications.
Career Highlights
Jack Edward Neil is currently associated with Udifi, Inc., where he continues to innovate and develop new technologies. His work at Udifi emphasizes the integration of advanced audio detection systems into practical applications.
Collaborations
Neil collaborates with talented individuals in his field, including his coworker, Jenny E Freeman. Their combined expertise contributes to the advancement of technologies that address real-world challenges.
Conclusion
Jack Edward Neil's contributions to acoustic detection technologies highlight his innovative spirit and dedication to improving audio systems. His patents reflect a deep understanding of machine learning and its applications in real-world scenarios. Neil's work continues to influence the field and pave the way for future advancements.