Iowa City, IA, United States of America

Michael Yudelson

This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: Act, Inc.. Active years: 2023.

USPTO Granted Patents = 1 

% Patents Active = 100.0

Average Co-Inventor Count = 4.0

ph-index = 1

Forward Citations = 1(Granted Patents)


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: Michael Yudelson: Innovator in Digital Learning Systems

Introduction

Michael Yudelson is an accomplished inventor based in Iowa City, IA (US). He has made significant contributions to the field of digital learning and tutoring systems. His innovative approach combines machine learning with knowledge tracing to enhance educational experiences.

Latest Patents

Yudelson holds a patent for "Deep knowledge tracing with transformers." This invention involves digital learning or tutoring systems that utilize a trained machine learning knowledge tracing engine. The system embeds an array for learner interactions into a static representation corresponding to prior learner interactions. It determines a contextualized interaction representation based on these interactions. The digital tutoring systems calculate attention weights based on the time gap between learner interactions, allowing for personalized recommendations to be displayed at the graphical user interface (GUI).

Career Highlights

Michael Yudelson is currently employed at Act, Inc., where he continues to develop innovative solutions in the realm of digital education. His work focuses on improving learner engagement and outcomes through advanced technology.

Collaborations

Yudelson collaborates with talented colleagues, including Shi Pu and Lu Ou, who contribute to the development of cutting-edge educational technologies.

Conclusion

Michael Yudelson's contributions to digital learning systems exemplify the intersection of technology and education. His innovative patent and ongoing work at Act, Inc. highlight his commitment to enhancing learning experiences through advanced machine learning techniques.

Profile summary based on public USPTO records.
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