Topic predictions based on natural language processing of large corpora

This invention describes a computer system designed to predict which topics will appear in future documents. It works by analyzing a large collection of time-stamped text, such as news articles or social media posts, to identify recurring themes. By tracking how these topics change over time, the system can forecast their future relevance. This particular version requires analyzing at least 2,000 documents, each over 50 words, spanning more than a month, and detecting more than 5 distinct topics.

Why it matters: Filed before the widespread adoption of Transformer models and large language models. The core task of detecting and analyzing topics in vast unstructured text is now significantly more powerful and accessible due to these advancements in NLP since 2017.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedHeavy software system, NLP algorithms

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