Optum, Inc.

Subhadip Maji

Raghav Bali

Veera Raghavendra Chikka

Eric D Tryon

Nathan H Funk

Anudeep Srivatsav Appe

Amy L Jensen

M P S Jagannadha Rao

Sudheer Ponnala

Kartik Chaudhary

Sudeep Choudhary

Anurag Das

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.

Machine learning techniques for natural language processing using predictive entity scoring

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing augmented reality assistance mode functionalities. Certain embodiments utilize systems, methods, and computer program products that perform augmented reality assistance mode functionalities by using at least one of environment familiarity predictions, assistance mode triggering need determinations, and threat detection machine learning models.

Dynamic triggering of augmented reality assistance mode functionalities

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Medinipur, India

Subhadip Maji