Company Filing History:
Years Active: 2025
Title: The Contributions of Guodong Xing to Customized Anomaly Detection
Introduction: Guodong Xing is a distinguished inventor based in Redmond, WA, known for his innovative contributions to the field of anomaly detection. With a focus on enhancing data analysis methodologies, Guodong has made significant strides in creating advanced detection models that utilize reinforcement learning.
Latest Patents: Guodong Xing holds a patent for "Customized Anomaly Detection - Methods and Apparatuses for Implementing Customized Anomaly Detection." This innovative patent outlines a method for obtaining time-series data comprised of multiple data points. It facilitates anomaly detection using a tailored model, which is refined through feedback received on the detection results. This process of updating the anomaly detection model through reinforcement learning demonstrates a sophisticated approach to capturing and responding to data anomalies in real-time.
Career Highlights: Guodong Xing currently works for Microsoft Technology Licensing, LLC, where he applies his expertise in developing cutting-edge technologies that address complex data challenges. His career is marked by a strong commitment to advancing the fields of artificial intelligence and machine learning, particularly in anomaly detection applications.
Collaborations: Throughout his career, Guodong has had the privilege of collaborating with talented colleagues such as Congrui Huang and Yujing Wang. These collaborations highlight his ability to work alongside other professionals in the field, fostering an environment of creativity and innovation.
Conclusion: Guodong Xing has made a noteworthy impact in the realm of anomaly detection through his patent and ongoing work at Microsoft. His innovative approach, particularly his application of reinforcement learning in refining detection models, positions him as a key contributor to advancing data analysis techniques. As technology continues to evolve, the implications of his work are likely to inspire further developments in the field.