This invention describes a computer system that automatically reads vast amounts of text from the internet and other sources. Its purpose is to identify cause-and-effect relationships between different concepts, events, or people, organizing these into a network where connections show what leads to what. The system can then rank the importance of these concepts within the network. The system specifically extracts these cause-effect links either by looking for patterns in words and punctuation or by using advanced artificial intelligence models trained to recognize such relationships.
Why it matters: Filed before the widespread adoption of large language models. Modern LLMs could significantly enhance the accuracy and scalability of causality extraction, which was a major challenge in 2017.
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