United Services Automobile Association (usaa)

Michael Martin Amati

Reynaldo Medina, Iii

James Warren Middleton

David Sokolowski

Aspects of the present disclosure are directed to resolving a latent status of document(s) that contain dense information using machine learning. Documents can be organized as a set of informational pages that together convey data associated with an organizational concept. Resolving a latent status from the pages of documents comprising dense information can be challenging, for example when indicator(s) of this latent status are hidden within the dense information. Implementations predict the latent status of document(s) via machine learning by performing a feature extraction processing pipeline. For example, a feature extraction processing pipeline can reduce the dimensionality of the document data, thus providing the machine learning model(s) a simplified problem. The machine learning model(s) can process the extracted data and, given the simplified problem, accurately predict the latent status of the document(s). In some implementations, the machine learning model(s) are trained using real-world training instances and synthetic training instances.

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Geneseo, NY, United States of America

Michael Martin Amati