Optum Technology, Inc.

Raghav Bali

Ninad D Sathaye

Gregory J Boss

V Kishore Ayyadevara

Devikiran Ramadas

Krishnamohan Nandiraju

Nitin Dwivedi

Piyush Gupta

Vineet Shukla

Kartik Chaudhary

Sree Harsha Ankem

Yerraguntla Yeshwanth Reddy

Rohan Khilnani

Saikumar Chintareddy

Ranraj Rana Singh

There is a need for more effective and efficient predictive document conversion. This need can be addressed by, for example, solutions for performing document conversion using a trained convolutional neural document conversion machine learning. In one example, the trained convolutional neural document conversion machine learning model is associated with a preprocessing block having a plurality of preprocessing subblocks, one or more main processing blocks each having a plurality of main processing subblocks, and a plurality of postprocessing subblocks each having one or more postprocessing subblocks, and the trained convolutional neural document conversion machine learning model is further associated with a preprocessing subblock repetition count hyper-parameter that defines a preprocessing subblock count of the plurality of preprocessing subblocks.

Predictive document conversion

An example method includes obtaining, by a computing system, first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system; generating, by the computing system, based on the first audio data, a prediction regarding whether a subsequent utterance of a user in the interactive voice session will contain sensitive information, wherein the subsequent utterance follows the one or more initial utterances in time; obtaining, by the computing system, second audio data representing the subsequent utterance; determining, by the computing system, based on the prediction and based on a risk profile of the interactive voice system, whether to transmit the second audio data to the interactive voice system; and based on the determination to transmit the second audio data to the interactive voice system, transmitting the second audio data to the interactive voice system.

Cybersecurity for sensitive-information utterances in interactive voice sessions using risk profiles

Various embodiments of the present disclosure performing conversation sentiment monitoring for a conversation data object. In various embodiments, a text block that can be resized is identified within a conversation data object and successive regularized sentiment profile generation iterations are performed until a regularized sentiment score of the block exceeds a regularized sentiment score threshold. A current regularized sentiment profile generation iteration involves determining a regularized sentiment score for the block based on an initial sentiment score, a subjectivity probability value, and, optionally, a stage-wise penalty factor. A determination is then made as to whether the score exceeds the threshold. If so, then a regularized sentiment profile of the conversation data object is updated based on the regularized sentiment score. If not, then the text block is resized and a subsequent regularized sentiment profile generation iteration is performed based on the resized block.

Sentiment progression analysis

A method comprises obtaining, by a computing system, first audio data representing one or more initial utterances during an interactive voice session with an interactive voice system; generating, by the computing system, based on the first audio data, a prediction regarding whether a subsequent utterance of a user during the interactive voice session will contain sensitive information, the subsequent utterance following the one or more initial utterances in time; obtaining, by the computing system, second audio data representing the subsequent utterance; determining, by the computing system, based on the prediction, whether to transmit the second audio data; and based on a determination not to transmit the second audio data: replacing, by the computing system, the second audio data with third audio data that is based on a voice of the user; and transmitting, by the computing system, the third audio data.

Cybersecurity for sensitive-information utterances in interactive voice sessions

There is a need for more effective and efficient prediction data analysis. This need can be addressed by, for example, solutions for performing first-occurrence multi-disease prediction. In one example, a method includes determining a per-event-type loss value for each event type of a group of event types; determining a cross-event-type loss value based at least in part on each per-event-type loss value; training a multi-event-type prediction model based at least in part on the cross-event type loss value; generating a first-occurrence prediction based at least in part on the multi-event-type prediction model, wherein the first occurrence-prediction comprises a first-occurrence prediction item for each event type of the group of event types; and performing one or more prediction-based actions based at least in part on the first-occurrence prediction.

Weighted adaptive filtering based loss function to predict the first occurrence of multiple events in a single shot

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Bengaluru, India

Raghav Bali