Singapore, Singapore

Penny Chong

This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: International Business Machines Corporation. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Penny Chong. Retrieved from https://idiyas.com/inventor/penny-chong

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

USPTO Granted Patents = 1 

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Penny Chong and Multi-Resolution Audio Defect Detection in Welding.

Introduction

Penny Chong is an inventor associated with Singapore and International Business Machines Corporation. Her patent work applies machine learning to the detection of defects in welding audio.

Latest Patents

Chong is associated with 1 patent, titled “Multi-resolution audio defect detection in welding.” The invention describes receiving and formatting labeled and unlabeled audio data, training a foundation model on unlabeled data using a self-supervised reconstruction loss, and then training it on labeled data using a classification loss. The trained model is used to detect welding defects across multiple audio resolutions.

Career Highlights

Chong’s listed patent reflects work at the intersection of artificial intelligence, audio analysis, and industrial quality control. The disclosed approach uses both labeled and unlabeled data to train a foundation model for welding defect detection.

Collaborations

Chong is associated with International Business Machines Corporation. Her listed coworker is Laura Wynter.

Conclusion

Penny Chong’s patent describes a foundation-model approach to identifying welding defects from audio, combining self-supervised and classification-based training.

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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