This inventor holds 2 USPTO granted patents and 1 published patent application. Top assignees: Ncr Atleos Corporation, Ncr Voyix Corporation. Active years: 2026.
Company Filing History:
Years Active: 2026
Title: Innovations by Yingying Cai in Power Supply Detection
Introduction
Yingying Cai is an accomplished inventor based in Atlanta, GA (US). He has made significant contributions to the field of power supply detection through his innovative patent. His work focuses on enhancing the reliability of terminal power supply units, which is crucial for various applications.
Latest Patents
Yingying Cai holds a patent titled "Terminal power supply degradation detection." This invention involves preprocessing event streams of terminals over a specified time interval to label event types and identify predefined time-based or sequence-based patterns associated with power supply unit (PSU) failures. The labeled event streams are then input into a trained machine-learning model (MLM), which generates a score for each terminal. This score indicates the likelihood of a terminal experiencing a PSU failure. The scores are compared against threshold values, classifying each terminal as low risk, medium risk, or high risk for PSU failure. Additionally, the scores and classifications are reported to the associated enterprise at predefined intervals.
Career Highlights
Yingying Cai is currently employed at Ncr Atleos Corporation, where he continues to develop innovative solutions in the field of power supply detection. His expertise and contributions have positioned him as a valuable asset to his organization.
Collaborations
Yingying collaborates with talented individuals such as Ryan Albert Breeze and Virginia-May Risebrough Barnes, who contribute to the innovative environment at Ncr Atleos Corporation.
Conclusion
Yingying Cai's work in power supply degradation detection exemplifies the importance of innovation in technology. His patent not only addresses critical issues in terminal reliability but also showcases the potential of machine learning in predictive maintenance.