DataRobot has been granted a patent for a predictive modeling method that involves obtaining and fitting a first-order predictive model to predict output variables based on input variables, followed by a second-order modeling procedure to generate and test a second-order predictive model. The method aims to improve predictive accuracy. GlobalData’s report on DataRobot gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on DataRobot, Predictive modeling techniques was a key innovation area identified from patents. DataRobot's grant share as of February 2024 was 62%. Grant share is based on the ratio of number of grants to total number of patents.
Predictive modeling method with first and second-order models
A recently granted patent (Publication Number: US11922329B2) discloses a predictive modeling method that involves obtaining a first-order predictive model to predict output variables based on input variables and then performing a second-order predictive modeling procedure on this model. The second-order procedure includes generating second-order input data, training and testing data, fitting a second-order predictive model, and testing it on the testing data. The method also allows for blending two fitted predictive models, using different types of second-order predictive models like RuleFit or generalized additive models, and evaluating the accuracy of the models through cross-validation.
Furthermore, the patent describes a predictive modeling apparatus that stores a machine-executable module for executing the second-order predictive modeling procedure on a fitted first-order predictive model. The apparatus includes tasks for pre-processing and model-fitting, generating second-order input data, training and testing data, fitting a second-order predictive model, and testing it. Additionally, the apparatus can determine the computational efficiency of the second-order model compared to the first-order model based on resource utilization measurements. The apparatus can also handle different types of predictive models, determine accuracy scores, and deploy the most accurate model. Overall, the patent presents a comprehensive approach to predictive modeling that involves multiple steps and considerations for enhancing accuracy and efficiency in predictive modeling processes.
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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.
