Generating customized digital documents using artificial intelligence
Abstract:
Methods and apparatuses are described in which unstructured computer text is analyzed for generation of customized digital documents. A server tokenizes and encodes historical user interactions and historical digital documents into multidimensional vectors. The server trains an interaction classification model using the multidimensional vectors as input to generate a classification for an input user interaction, and trains a language generation model using the multidimensional vectors as input to generate a customized digital document based upon an input user interaction. The server receives a new user interaction and encodes the new user interaction into a new multidimensional vector. The server executes the trained interaction classification model using the new vector as input to generate a digital document classification. The server executes the trained language generation model using the new vector and the classification as input to generate a customized digital document.
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