DataRobot had two patents in big data 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 with the trained model on a graphical user interface. The second patent discusses techniques for automatically deriving new spatial features, optimizing parameters, determining feature importance, and reducing spatial leakage to develop 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.
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DataRobot grant share with big data 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, provides blueprints with trained models from various projects, trains the selected model via machine learning, and displays the trained model on a graphical user interface. The system can compare performance levels, fit models to linear relationships, perform singular value decomposition, and search for additional blueprints based on user input.
The claims outline the specific functionalities of the system, such as receiving requests, identifying features, providing blueprints, training models, and displaying results on a graphical user interface. The system can handle multiple projects, compare performance levels, fit models to linear relationships, and perform various search functions based on user input. The method involves similar steps as the system, including receiving requests, identifying features, providing blueprints, training models, and displaying results. The computer-readable medium stores instructions for executing these processes, including receiving requests, identifying features, providing blueprints, training models, and displaying results on a graphical user interface. Overall, the patent focuses on a comprehensive system and method for efficient machine learning model searching using metadata.
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