DataRobot had two patents in artificial intelligence during Q4 2023. DataRobot Inc’s patents in Q4 2023 focus on machine learning model searching using meta data and automated spatial feature engineering techniques. The first patent describes a system that searches for blueprints with models to add to a project, trains the selected model via machine learning, and displays the blueprint including the trained model on a graphical user interface. The second patent discusses techniques for automatically deriving new spatial features, optimizing parameters, determining feature importance of location features, and reducing spatial leakage for more accurate spatial models. GlobalData’s report on DataRobot gives a 360-degreee view of the company including its patenting strategy. Buy the report here.

DataRobot grant share with artificial intelligence as a theme is 0% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Machine learning model search using meta data (Patent ID: US20230394361A1)

The patent filed by DataRobot Inc. describes a system and method for machine learning model searching using metadata. The system receives requests from client devices to search for blueprints with models to add to a project, identifies features for the search, and provides blueprints with trained models from various projects. The models are trained using machine learning to determine targets based on features, and the system generates data to display indications of the selected blueprints on a graphical user interface. The claims detail various aspects of the system, including comparing performance levels of models, selecting projects based on comparisons, fitting performance levels to linear relationships, and using vectors and angles to select projects and blueprints.

Overall, the patent outlines a comprehensive system for efficiently searching, selecting, and training machine learning models for projects based on metadata. The system's capabilities include comparing models, selecting projects based on performance levels, and utilizing mathematical methods like vectors and angles for selection. By providing a user-friendly graphical interface and utilizing machine learning techniques, the system streamlines the process of finding and incorporating models into projects, ultimately enhancing the efficiency and effectiveness of machine learning applications.

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