Datastax. has been granted a patent for a machine learning feature studio that enables users to define and visualize features associated with entities using historical or real-time data. The system allows for user interaction, feature commitment to projects, and export of feature vectors to production environments. 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 June 2024 was 69%. Grant share is based on the ratio of number of grants to total number of patents.
Machine learning feature generation and visualization interface
The patent US11983384B2 outlines a method and system for generating machine learning features from data associated with various entities. The process begins with user inputs received through interfaces, which indicate a request for feature generation and define the specific features to be created. The system retrieves relevant data from an event store and generates the requested machine learning features based on this data and user-defined parameters. Additionally, the generated features are stored in a data store for access by other users, and visualizations such as bar graphs, scatter plots, and heat maps are created to represent these features graphically. The method also allows for the display of historical changes to the features and the application of transformations to update them.
The system includes a computing node that executes computer-readable instructions to facilitate the feature generation process. It processes user inputs to define and generate machine learning features, which are then presented in graphical form for analysis. The system can also display data from the event store and utilize selected data for feature generation. Furthermore, it supports the inclusion of formulae in defining features, the application of transformations, and the generation of feature vectors for machine learning models. The patent emphasizes the importance of user interaction in defining features and visualizing data, thereby enhancing the usability and functionality of machine learning applications.
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