Datastax’s patented machine learning feature studio allows users to define and calculate features from historical or real-time data for entities. Users can interact with and export the features for machine learning models. The method involves receiving user inputs, retrieving data, generating features, storing data, and displaying visualizations. GlobalData’s report on Datastax 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 Datastax, Social media analytics was a key innovation area identified from patents. Datastax's grant share as of May 2024 was 34%. Grant share is based on the ratio of number of grants to total number of patents.
Machine learning feature studio for defining and exporting features
A recently granted patent (Publication Number: US11983384B2) outlines a method and system for generating machine learning features from data associated with entities. The method involves receiving user inputs through interfaces to define features, retrieving data from an event store, generating machine learning features based on the inputs and data, storing the features in an accessible data store, and creating visualizations from the features. These visualizations are then displayed via the user interfaces. Additionally, the system includes a data store, storage devices with computer-readable instructions, and computing nodes to process user inputs, generate machine learning features, and present them graphically for analysis.
Furthermore, the patent details the ability to display the data from the event store, calculate machine learning features using selected data, incorporate user-defined formulae in feature generation, apply transformations to update features, and display a history of changes made to the features over time. The visualizations can take various forms such as bar graphs, scatter plots, heat maps, or pair plots. The system also allows for the generation of feature vectors as part of a machine learning model based on the machine learning features, with the option to export these vectors to a production environment for model use. Overall, the method and system described in the patent aim to streamline the process of generating, visualizing, and analyzing machine learning features from entity data, enhancing the efficiency and effectiveness of machine learning model development.
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