公开(公告)号：US12093651B1

公开(公告)日：2024-09-17

申请号：US17650457

申请日：2022-02-09

申请人： Optum, Inc.

发明人： Nathan H. Funk ,  Eric D. Tryon ,  Amy L. Jensen ,  Sudheer Ponnala ,  M. P. S. Jagannadha Rao ,  Raghav Bali ,  Veera Raghavendra Chikka ,  Subhadip Maji ,  Anudeep Srivatsav Appe

IPC分类号： G06F40/279 ,  G06F40/295 ,  G06F40/30 ,  G06N20/00 ,  G06F40/284

CPC分类号： G06F40/295 ,  G06F40/30 ,  G06N20/00 ,  G06F40/284

摘要： There is a need for more accurate and more efficient natural language solutions with greater semantic intelligence. This need can be addressed, for example, by natural language processing techniques that utilize predictive entity scoring. In one example, a method includes determining an overall prevalence score for the input entity data object with respect to a scored document corpus and a target section; determining a qualified prevalence score for the input entity data object with respect to a high-scoring subset of the scored document corpus; processing the input entity data object using an entity scoring machine learning model to generate the predicted entity score, wherein the entity scoring machine learning model may characterized by a plurality of multiplicative hyper-parameters and one or more additive hyper-parameters; and performing one or more prediction-based actions based at least in part on the predicted entity score.

公开(公告)号：US11853700B1

公开(公告)日：2023-12-26

申请号：US18161969

申请日：2023-01-31

申请人： Optum, Inc.

发明人： Nathan H. Funk ,  Eric D. Tryon ,  Amy L. Jensen ,  Sudheer Ponnala ,  M. P. S. Jagannadha Rao ,  Raghav Bali ,  Veera Raghavendra Chikka ,  Subhadip Maji ,  Anudeep Srivatsav Appe

IPC分类号： G10L17/00 ,  G06F40/295 ,  G06N20/00 ,  G06F40/30 ,  G06F40/284

CPC分类号： G06F40/295 ,  G06F40/30 ,  G06N20/00 ,  G06F40/284

摘要： There is a need for more accurate and more efficient natural language solutions with greater semantic intelligence. This need can be addressed, for example, by natural language processing techniques that utilize predictive entity scoring. In one example, a method includes determining an overall prevalence score for the input entity data object with respect to a scored document corpus and a target section; determining a qualified prevalence score for the input entity data object with respect to a high-scoring subset of the scored document corpus; processing the input entity data object using an entity scoring machine learning model to generate the predicted entity score, wherein the entity scoring machine learning model may characterized by a plurality of multiplicative hyper-parameters and one or more additive hyper-parameters; and performing one or more prediction-based actions based at least in part on the predicted entity score.