DataRobot had nine patents in artificial intelligence during Q1 2024. DataRobot Inc’s patents filed in Q1 2024 focus on methods and systems for generating and executing applications that provide insights into model operation without requiring coding knowledge, customizing automated machine learning systems, and deploying virtual sensors to machine learning projects. These innovations aim to simplify the process of utilizing machine learning models and optimizing their performance for users without extensive technical expertise. GlobalData’s report on DataRobot gives a 360-degree view of the company including its patenting strategy. Buy the report here.
DataRobot grant share with artificial intelligence as a theme is 11% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Systems and related methods for developing artificial intelligence applications based on machine learned models (Patent ID: US20240086775A1)
The patent filed by DataRobot Inc. describes methods and systems for generating and executing applications that provide insights into a model's operation without requiring coding or AI knowledge. The method involves deploying a model using input data to generate a predicted dataset, presenting applications for generating new scenarios and optimizing features, analyzing model features, and executing applications based on user selections. The system ranks features by importance, filters features based on impact value, and generates error messages for non-compliant values.
The computer system described in the patent includes a server configured to deploy models, present applications for scenario generation and feature optimization, analyze model features, and execute applications based on user selections. The system ranks features by importance, filters features based on impact value, and generates error messages for non-compliant values. Additionally, the system can generate electronic documents based on the predicted datasets and provide graphical indicators for comparing values. Overall, the patent outlines a user-friendly approach to interacting with machine learning models without requiring advanced technical knowledge.
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