Optum, Inc.

Raghav Bali

Subhadip Maji

Veera Raghavendra Chikka

Sudheer Ponnala

M P S Jagannadha Rao

Amy L Jensen

Eric D Tryon

Nathan H Funk

Anudeep Srivatsav Appe

Gregory J Boss

V Kishore Ayyadevara

Kartik Chaudhary

Ninad D Sathaye

Devikiran Ramadas

Sudeep Choudhary

Anurag Das

Swaroop S Shekar

Fritz T Wilhelm

Vinod Burugupalli

Joseph C Cremaldi

Rohan Khilnani

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing intervention recommendation operations. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform intervention recommendations by using at least one of reinforcement learning machine learning models and event scoring machine learning models.

Reinforcement learning machine learning models for intervention recommendation

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

An example method includes obtaining first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system (IVS); generating, based on the first audio data, a prediction regarding whether a subsequent utterance of the user in the interactive voice session will contain sensitive information; obtaining second audio data representing the subsequent utterance; determining, based on the prediction, whether to transmit the second audio data to the IVS via a first communication channel; based on a determination not to transmit the second audio data to the IVS via the first communication channel: transmitting third audio data to the IVS via the first communication channel in place of the second audio data; and transmitting the second audio data to a server via a second communication channel that bypasses the IVS.

Routing of sensitive-information utterances through secure channels in interactive voice sessions

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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Delhi, India

Raghav Bali