Company Filing History:
Years Active: 2025
Title: Innovations by Gurdit Chahal in Machine Learning for Commercial Lease Benchmarking
Introduction
Gurdit Chahal is an innovative inventor based in Elk Grove, CA (US). He has made significant contributions to the field of machine learning, particularly in the area of commercial lease benchmarking. His work focuses on developing methods and devices that enhance property analysis through advanced technology.
Latest Patents
Gurdit Chahal holds a patent for "Machine learning methods for commercial lease benchmarking and devices thereof." This patent describes methods, non-transitory computer-readable media, and property analysis server devices that generate a feature dataset. The dataset includes property and metric data, such as addresses and actual lease values. A machine learning model (MLM) is selected from various types of MLMs, and a determination is made that the selected MLM exceeds an accuracy threshold based on cross-validation using predicted lease values. The property data is stored in a lease benchmarking database, with addresses replaced by corresponding geographic coordinates and geohash values. The properties are associated in the database with predicted lease values, and one of these values is returned in response to a lease pricing request that includes an address.
Career Highlights
Gurdit Chahal is currently employed at Jones Lang Lasalle IP, Inc., where he applies his expertise in machine learning to improve commercial real estate analysis. His innovative approach has positioned him as a valuable asset in the industry.
Collaborations
Gurdit collaborates with talented coworkers, including Pradnya Nimkar and Utkarsh Porwal. Their combined efforts contribute to the advancement of technology in property analysis and benchmarking.
Conclusion
Gurdit Chahal's work in machine learning for commercial lease benchmarking showcases his innovative spirit and dedication to improving property analysis. His contributions are paving the way for more accurate and efficient methods in the real estate industry.