# How Computers Rank Financial News for Analysts > This patent describes a computer system that automatically collects financial news, groups similar stories, and ranks them for financial analysts using advanced text analysis and machine learning. - **Patent:** US 11922469 - **Original title:** Automated news ranking and recommendation system - **Owner:** S&P Global - **Granted:** 2024 - **Status:** Active - **Times cited:** 0 - **Field:** software, ai_ml, telecommunications, finance ## What it does This system first "ingests" (takes in) news articles from many different sources. It then "extracts named entities" (like company names or people) from each article to create a "one-hot vector" for initial grouping. The articles are then "clustered" based on these vectors. For each cluster, a "representative news article" is chosen. The system then uses a "machine learning model" with "character embeddings" and a "convolutional layer followed by a max-pool layer" to understand the meaning of words and sentences in these representative articles. Similar clusters are then "merged" based on their semantic meaning. Finally, the system generates a "set of ranked clusters," which it "digitally displays" in a user interface, allowing analysts to interact with the ranked news. For example, a financial analyst could see a cluster of news about a specific company's earnings report, with the most important articles ranked at the top, and then filter these results. ## What it does NOT cover - Does not cover news ranking systems that do not use 'one-hot vectors' generated from 'named entities' for initial clustering. - Does not cover systems that do not employ 'character embeddings' to create 'word representations' for representative articles. - Does not cover systems that do not use a 'convolutional layer followed by a max-pool layer' to generate input representations for articles. - Does not cover ranking news articles for general audiences, as it specifically targets 'financial analysts in the capital markets'. - Does not cover ranking articles within a cluster solely based on publication date without considering 'trustworthiness and linking volume' of the news sources. ## The clever bit The novelty lies in combining specific natural language processing techniques, like named entity extraction for initial clustering and character embeddings with convolutional neural networks for deeper semantic understanding, to create a hierarchical, ranked view of financial news tailored for analysts. ## Real-world examples 1. S&P Global Market Intelligence platform 2. Bloomberg Terminal news feeds 3. Refinitiv Eikon news analysis 4. FactSet Research Systems news aggregation ## Why it matters Financial analysts need to quickly process vast amounts of news to make informed decisions. This system automates the complex task of sifting through, categorizing, and prioritizing financial news. By providing relevant, clustered, and ranked information, it helps analysts save time and potentially identify critical market movements or risks faster, which is crucial in fast-paced financial markets. ## Frequently asked questions ### What does How Computers Rank Financial News for Analysts cover? This patent describes a computer system that automatically collects financial news, groups similar stories, and ranks them for financial analysts using advanced text analysis and machine learning. ### Who owns patent US 11922469? S&P Global owns this patent, granted in 2024. ### When does this patent expire? This patent is expected to expire on April 1, 2042, when the invention enters the public domain. ### What problem does this patent solve? Financial analysts need to quickly process vast amounts of news to make informed decisions. This system automates the complex task of sifting through, categorizing, and prioritizing financial news. By providing relevant, clustered, and ranked information, it helps analysts save time and potentially identify critical market movements or risks faster, which is crucial in fast-paced financial markets. ### What does this patent NOT cover? Does not cover news ranking systems that do not use 'one-hot vectors' generated from 'named entities' for initial clustering. **Full plain-English explainer:** https://patentbrief.org/patent/us/11922469/automated-news-ranking-and-recommendation-system **Original patent:** https://patents.google.com/patent/US11922469 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How an AI System Answers Financial Questions Using Specialized Bots](https://patentbrief.org/patent/us/12307349/systems-and-methods-of-large-language-model-driven-orchestration-of-task-specifi) — This patent describes an AI system where a large language model (LLM) directs specialized machine learning agents to answer natural language questions about financial data, then refines the answers using an adversarial AI. - [Teaching Computers to Understand Document Similarity Using AI](https://patentbrief.org/patent/us/10909459/federated-learning) — This patent describes a way to train a computer program (a neural network) to understand how similar documents are to each other, by showing it examples and teaching it to group similar ones together and separate dissimilar ones. - [How Computers Find Hidden Connections Between Different Fields of Knowledge](https://patentbrief.org/patent/us/6523026/google-search-query-processing) — A method for finding related ideas in completely different subjects by using math to map how words appear together, even when the subjects use different vocabulary. - [How Computers Find Similar Text Using Compact Data Structures](https://patentbrief.org/patent/us/10878335/bert-bidirectional-encoder-representations) — This patent describes a method for efficiently identifying similar text records, like documents or product reviews, by using special compact data structures that store text terms probabilistically and then analyzing them with machine learning. - [Using Non-AI Systems to Improve AI Text Generation](https://patentbrief.org/patent/us/12353827/computer-implemented-methods-for-the-automated-analysis-or-use-of-data-including) — This patent describes a method where a traditional, rule-based computer system helps a Large Language Model (LLM) generate more accurate and reliable text by providing it with better context and fact-checking.