DataRobot had one patents in digitalization during Q4 2023. DataRobot Inc filed a patent for automated spatial feature engineering techniques that optimize parameters, derive new spatial features, determine feature importance, and reduce spatial leakage for more accurate spatial models. This involves treating distinct location coordinate features as a single location feature for determining feature importance. 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 digitalization 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: Data analytics methods for spatial data, and related systems and devices (Patent ID: US20230316137A1)

The patent filed by DataRobot Inc. describes automated spatial feature engineering techniques that involve deriving new features based on spatial relationships between observations, optimizing parameters of the spatial feature engineering process, deriving new spatial features representing geometric properties and spatial statistics, determining feature importance of location features, and reducing spatial leakage in spatial datasets to develop more accurate spatial models. The techniques may treat distinct location coordinate features jointly for determining feature importance.

The patent includes claims for a method and system that involve identifying data corresponding to locations in a coordinate space, generating observations based on sets of coordinates associated with objects, creating features representing the locations of objects, generating data based on observations, and training models using machine learning processes. The method further includes evaluating features based on hyperparameters, adjusting hyperparameter values, selecting features with low correlation, and presenting visualizations of data portions. The system is configured to perform these functions and identify data portions using machine learning models different from those used for training.

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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.