PERSONALIZED DOCUMENT FIELD PREDICTION BASED ON LEARNING FROM USER FEEDBACK
卷宗概要
发明人
Max STEPIN; Mohsen SARDARI; Gaurav TANDON; Changlin ZHANG; Nakul CHAKRAPANI
IPC 分类
CPC 分类
Particular embodiments relate to personalized document field prediction based on user behavior and feature generation. Specifically, various embodiments have the technical effect of improved accuracy with respect to field/entity value prediction (e.g., predicting that the amount due is X via a Gradient Boosting Model) relative to document processing technologies by learning through user behavior data or feedback (e.g., through continuous reinforcement learning from human feedback (RLHF)). This is at least partially because of the technical solution of accessing or generating unique features from one or more documents previously used by a user.
原文（中文）
Particular embodiments relate to personalized document field prediction based on user behavior and feature generation. Specifically, various embodiments have the technical effect of improved accuracy with respect to field/entity value prediction (e.g., predicting that the amount due is X via a Gradient Boosting Model) relative to document processing technologies by learning through user behavior data or feedback (e.g., through continuous reinforcement learning from human feedback (RLHF)). This is at least partially because of the technical solution of accessing or generating unique features from one or more documents previously used by a user.