DataRobot. has filed a patent for a system that uses machine learning to identify n-grams in text data, generate predictions, compare impacts of n-grams, and present visual indications on a user interface. This innovative solution aims to enhance data analysis and decision-making processes. 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 January 2024 was 40%. Grant share is based on the ratio of number of grants to total number of patents.
Text analysis system with visual impact indication
The patent application (Publication Number: US20240028828A1) describes a system that utilizes machine learning to analyze text data sets by identifying n-grams at various locations, generating predictions, and assessing the impact of specific n-grams on the predictions. The system includes a data processing system with processors and memory to perform these functions. It generates visual indications on a user interface to represent the impact of specific n-grams, allowing users to visualize the influence of these elements on the data analysis process. The system can handle data in multiple modalities, including text, numerical values, and images, and employs tokenization techniques such as deep learning tokenizer, treebank tokenizer, or linguistic rules to process the data effectively.
Furthermore, the system can generate features based on specific n-grams, input them into the model for prediction generation, and differentiate visual indications for different n-grams based on a predefined map. The impact of the n-grams can be either positive or negative on the predictions, and the visual indications are applied using a color map to highlight the specific n-grams in the user interface. The patent also covers a method and a non-transitory computer-readable medium storing instructions for implementing the system's functionalities, emphasizing the importance of n-grams in data analysis and prediction generation processes. Overall, the patent application showcases a sophisticated system that leverages machine learning and visualization techniques to enhance the analysis of text data sets and improve prediction accuracy.
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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.
