Architecture and processes for computer learning and understanding

This invention describes a computer system designed to learn and understand natural language text by working with people. It reads text, figures out an initial meaning by analyzing its grammar and semantics, specifically by creating a predicate argument structure. Then, it asks humans questions about the text, and uses their answers to improve its understanding and knowledge over time, allowing it to comprehend more complex stories.

Why it matters: Filed before the widespread adoption of large language models (LLMs), the core challenge of inferring meaning and generating intelligent questions for human collaboration is now significantly more feasible. LLMs have dramatically advanced natural language understanding and dialog generation capabilities 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Status: Not in forceListed as no longer active.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, semantic analysis, dialog.

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