Tokyo, Japan

Qingci Zhao

This inventor holds 1 USPTO granted patent. Top assignee: Ntt Data Japan Corporation. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Qingci Zhao. Retrieved from https://idiyas.com/inventor/qingci-zhao

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile


% Patents Active = 100.0

Average Co-Inventor Count = 4.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Qingci Zhao - Innovator in Interactive Learning Systems

Introduction

Qingci Zhao is a prominent inventor based in Tokyo, Japan. He has made significant contributions to the field of interactive learning systems, particularly through his innovative patent that leverages machine learning techniques.

Latest Patents

Qingci Zhao holds a patent for a "Predictor Interactive Learning System, Predictor Interactive Learning Method, and Program." This invention includes a machine learning unit designed to perform learning of a predictor that outputs a predicted value indicating the likelihood of a predetermined intrinsic expression. The system utilizes teacher data and labels, along with an interest score calculation unit that assesses statistical data of corresponding words in a corpus. The interactive learning frame unit extracts teacher data for subsequent learning, while the question-response unit engages users by asking whether the extracted data represents an intrinsic expression.

Career Highlights

Qingci Zhao is currently employed at NTT Data Japan Corporation, where he continues to develop and refine his innovative ideas. His work focuses on enhancing interactive learning methodologies through advanced technology.

Collaborations

Qingci Zhao collaborates with talented colleagues, including Hiroyuki Nemoto and Ayaka Iwamoto, who contribute to the development of cutting-edge solutions in the field of interactive learning.

Conclusion

Qingci Zhao's contributions to interactive learning systems exemplify the potential of machine learning in education. His innovative patent reflects a commitment to advancing technology for improved learning experiences.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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