This invention takes raw text, like documents or conversations, and processes it to answer a specific question, turning the information into an organized format. It then calculates how confident it is in that answer; if the confidence is low, it shows the user the most relevant pieces of information it used, ranked by importance, for review and correction. Once the user modifies the information, the system re-evaluates and provides a more accurate answer.
Why it matters: Filed before the widespread availability of powerful large language models. These models now make the initial NLP processing and conclusion generation significantly easier, shifting the focus to refining confidence analysis and user override for accuracy.
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