Toronto, Canada

Brian Keng

This inventor holds 7 USPTO granted patents and 10 published patent applications, plus 1 CIPO patent. Top assignee: Kinaxis Inc.. Active years: 2024-2026.

USPTO Granted Patents = 7 

% Patents Active = 85.7

 

Average Co-Inventor Count = 2.6

ph-index = 1

Forward Citations = 2(Granted Patents)


Location History:

  • Toronto, CA (2024)
  • Ottawa, CA (2024)

Company Filing History:


Years Active: 2024-2026

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7 patents (USPTO):Explore Patents

Title: Brian Keng: Innovator in Machine Learning and Optimization

Introduction

Brian Keng is a notable inventor based in Toronto, Canada, recognized for his contributions to the fields of machine learning and optimization. With a total of five patents to his name, Keng has developed innovative methods that enhance predictive modeling and objective optimization.

Latest Patents

Among his latest patents is a "Method and system for model auto-selection using an ensemble of machine learning models." This invention outlines a system that receives historical data, trains candidate machine learning models, and determines an ensemble of models to make predictions based on previous outcomes. Another significant patent is the "Method and system for optimizing an objective having discrete constraints." This patent describes a method for optimizing objectives with discrete constraints by utilizing a dataset and iteratively determining optimized values.

Career Highlights

Brian Keng is currently employed at Kinaxis Inc., a company known for its advanced supply chain management solutions. His work at Kinaxis has allowed him to apply his innovative ideas in real-world applications, contributing to the company's success in the industry.

Collaborations

Keng collaborates with talented coworkers, including Kanchana Padmanabhan and Fan Zhang, who share his passion for innovation and technology.

Conclusion

Brian Keng's work in machine learning and optimization showcases his commitment to advancing technology through innovative solutions. His patents reflect a deep understanding of complex systems and a drive to improve predictive accuracy and optimization processes.

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