S&P Global has been granted a patent for an automated news recommendation system for financial analysis. The system ingests news articles, extracts named entities, clusters articles, selects representatives, and uses machine learning to rank and display clusters in a user-friendly interface. GlobalData’s report on S&P Global gives a 360-degree view of the company including its patenting strategy. Buy the report here.

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 February 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

Source: United States Patent and Trademark Office (USPTO). Credit: S&P Global Inc

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, converting words into representations, and generating ranked clusters based on semantic analysis. The system utilizes machine learning models to process and analyze the content of news articles to provide users with personalized and relevant news recommendations. Additionally, the method includes features such as user feedback integration, ranking based on trustworthiness and linking volume, and the creation of user portfolios to enhance the news recommendation process.

Furthermore, the patent describes a computer system that executes program instructions to perform the method outlined in the claims. The system includes processor units responsible for ingesting news articles, clustering based on semantic analysis, selecting representative articles, and generating ranked clusters for display in a graphical user interface. The system also stores relational information between clusters, news articles, and user subscriptions. By utilizing advanced machine learning models and semantic analysis techniques, the system aims to improve the efficiency and accuracy of news article recommendations for users based on their preferences and interests. The patent highlights the importance of factors such as news source significance and publication date in selecting representative articles and merging clusters to enhance the overall user experience.

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