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This invention describes a system that reads news articles and other text online to understand what's happening with publicly traded companies. It identifies company names and specific actions, then groups similar events together. By comparing these event patterns with past stock price movements, the system predicts whether a company's stock will go up or down, helping to inform trading decisions.
Why it matters: Filed before modern deep learning and large language models revolutionized natural language processing. The ability to extract nuanced information and fill templates from text, as described, is now far more accurate and scalable than in 2002.
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