{
  "patent_number": "US 11922469",
  "country": "US",
  "title": "How Computers Rank Financial News for Analysts",
  "original_title": "Automated news ranking and recommendation system",
  "summary": "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.",
  "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."
  ],
  "filed": "2022-04-01",
  "granted": "2024-03-05",
  "expires": "2042-04-01",
  "status": "active",
  "holder": "S&P Global",
  "holder_url": "https://patentbrief.org/company/sp-global",
  "inventors": [
    {
      "name": "Steven Pomerville",
      "url": "https://patentbrief.org/inventor/steven-pomerville"
    },
    {
      "name": "Xiaomo Liu",
      "url": "https://patentbrief.org/inventor/xiaomo-liu"
    },
    {
      "name": "Russell Kociuba",
      "url": "https://patentbrief.org/inventor/russell-kociuba"
    },
    {
      "name": "Lisa Kim",
      "url": "https://patentbrief.org/inventor/lisa-kim"
    },
    {
      "name": "Chong Wang",
      "url": "https://patentbrief.org/inventor/chong-wang"
    },
    {
      "name": "Grace Bang",
      "url": "https://patentbrief.org/inventor/grace-bang"
    },
    {
      "name": "Zhiqiang Ma",
      "url": "https://patentbrief.org/inventor/zhiqiang-ma"
    },
    {
      "name": "Himani Singh",
      "url": "https://patentbrief.org/inventor/himani-singh"
    }
  ],
  "times_cited": 0,
  "tags": [
    "software",
    "ai_ml",
    "telecommunications",
    "finance"
  ],
  "abstract": "A framework for an automated news recommendation system for financial analysis. The system includes the automated ingestion, relevancy, clustering, and ranking of news events for financial analysts in the capital markets. The framework is adaptable to any form of input news data and can seamlessly integrate with other data used for analysis like financial data.",
  "url": "https://patentbrief.org/patent/us/11922469/automated-news-ranking-and-recommendation-system",
  "markdown_url": "https://patentbrief.org/patent/us/11922469/automated-news-ranking-and-recommendation-system/md",
  "google_patents_url": "https://patents.google.com/patent/US11922469",
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}