S&P Global has been granted a patent for an automated news recommendation system for financial analysis. The system ingests news articles, clusters them based on content, and ranks them for financial analysts. It utilizes machine learning models to enhance the recommendation process and can integrate with other data sources seamlessly. GlobalData’s report on S&P Global 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 S&P Global, was a key innovation area identified from patents. S&P Global's grant share as of April 2024 was 59%. Grant share is based on the ratio of number of grants to total number of patents.
Automated news recommendation system for financial analysis
A recently granted patent (Publication Number: US11922469B2) outlines a computer-implemented method for recommending news articles. The method involves ingesting news articles from various sources, extracting named entities, clustering the articles, selecting representative articles, and converting words into character embeddings using a machine learning model. The system then generates input and sentence representations for each article, merges clusters based on semantic similarities, and ranks the clusters for display in a graphical user interface. Users can interact with the displayed clusters through controls on the interface.
Furthermore, the patent details additional features such as ranking news articles based on trustworthiness and linking volume, ingesting articles based on user subscriptions, receiving user feedback on ranked clusters, and learning morphological features of words. The system also includes a computer system with multiple processor units to execute the program instructions for implementing the method. This system clusters news articles based on pairwise distances, merges clusters based on semantic similarities, and selects representative articles based on publication date and source significance. Overall, the patented method aims to provide users with personalized and relevant news recommendations through a sophisticated machine learning and clustering process, enhancing the user experience in accessing and interacting with news content.
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