Lawrenceville, NJ, United States of America

Patrick Charles Kyllonen


Average Co-Inventor Count = 7.0

ph-index = 1

Forward Citations = 17(Granted Patents)


Company Filing History:


Years Active: 2020

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1 patent (USPTO):

Title: The Innovations of Patrick Charles Kyllonen

Introduction

Patrick Charles Kyllonen is an accomplished inventor based in Lawrenceville, NJ. He has made significant contributions to the field of assessment through his innovative patent. His work focuses on utilizing technology to enhance the interview process, making it more efficient and effective.

Latest Patents

Kyllonen holds a patent for "Systems and methods for assessing structured interview responses." This patent describes systems and methods that utilize supervised machine learning to generate a model for scoring interview responses. The system can access a training response, which may include an audiovisual recording of a person responding to an interview question. The training response is assigned a human-determined score. The system extracts delivery and content features from the audiovisual recording and uses these features along with the human-determined score to train a response scoring model. This model is designed to automatically assign scores to audiovisual recordings of interview responses, which can be used by interviewers to assess candidates effectively.

Career Highlights

Kyllonen is currently associated with Educational Testing Service, where he applies his expertise in assessment and technology. His work has been instrumental in advancing the methodologies used in structured interviews, thereby improving the overall candidate evaluation process.

Collaborations

Some of Kyllonen's notable coworkers include Lei Chen and Michelle Paulette Martin. Their collaborative efforts contribute to the innovative projects at Educational Testing Service.

Conclusion

Patrick Charles Kyllonen's contributions to the field of assessment through his patent demonstrate his commitment to improving interview processes. His innovative approach using machine learning sets a new standard for evaluating candidates in various settings.

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