Optum Services (ireland) Limited

Optum, Inc.

Bruno Ohana

Damian Kelly

Gregory Buckley

Jack Sullivan

Mariah Sonja Pereira Penha

Yangcheng Huang

Raja Mukherji

Thomas R Gilbertson

Dominik Dahlem

Mallory B Van Horn

Scott D Johnson

David A Chennisi

Michael B Wentzien

Joshua L Taylor

Systems and methods are configured for predictive prompt generation for an interaction between a party and an automated prompt system. In various embodiments, metadata is received on the interaction and provided as input to a multi-label predictive model to generate interaction probabilities for a plurality of prompt information data objects. Each probability generated by the predictive model provides a likelihood a particular information data object needs to be provided to the first party during the interaction. Accordingly, one or more of the prompt information data objects are identified based on the probability of each piece found in the one or more prompt information data objects that satisfy a set threshold and one or more notifications are provided so that the automated prompt system provides at least one of the prompt information data objects to the first party during the interaction.

Predictive prompt generation by an automated prompt system

There is a need for faster and more accurate predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a computer-implemented method for generating a predictive output with respect to a primary audio data embedding data object associated with a primary audio data object, is provided. The method includes generating, using one or more computer processors, by utilizing a similarity determination machine learning model and based at least in part on the primary audio data embedding data object, the predictive output for the primary audio data embedding data object; generating, by the one or more computer processors, a forwarding recommendation prediction based at least in part on the predictive output; and performing, by the one or more computer processors, one or more prediction-based actions based at least in part on the forwarding recommendation prediction.

Audio-based machine learning frameworks utilizing similarity determination machine learning models

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and/or computing entities for processing a call drop likelihood prediction for an interactive call data object. In accordance with one embodiment, a method is provided that includes: identifying a group of interactive call feature data objects associated with the interactive call data object that comprises an interactive call audio data object and an interactive call metadata object; processing the call feature data objects using a real-time call monitoring machine learning framework to generate the prediction by: processing the call audio data object using an audio data processing machine learning model to generate an audio-based embedding data object, processing the call audio data object using an audio transcript processing machine learning model to generate a transcript-based embedding data object, and generating the prediction based at least in part on the audio-based and transcript-based embedding data objects and the metadata object.

Conformational framework for call drop likelihood from interactive voice response system

Methods, apparatus, systems, computing devices, computing entities, and/or the like for optimizing interaction flows. For example, upon receiving an inbound interaction, the interaction is routed to either a baseline interaction flow or one of a plurality of experimental interaction flows. Using one or more target metrics, the experimental interaction flows can be monitored and further configured to optimize the interaction flows and parameters.

Optimizing interaction flows

There is a need for solutions that increase efficiency and reliability of medical record processing systems. This need can be addressed, for example, by receiving a free-form primary provider input from a primary provider profile; detecting one or more information deficiencies in the primary provider input, wherein each information deficiency indicates a respective query; for each identified information deficiency of the one or more information deficiencies, identifying a supportive profile associated with the respective information deficiency, causing the supportive profile computing entity associated with the respective supportive provider profile to present an audiovisual interface that includes an indication of the respective query associated with the information deficiency, and receiving a supportive provider input from each supportive provider profile; and generating a collaborative record for the primary provider input based on the primary provider input and each received supportive provider input.

Concepts for iterative and collaborative generation of data reports via distinct computing entities

A system and method for tokenizing sentences in transcriptions of electronic communication audio files includes one or more electronic data storage systems coupled in electronic communication with an audio receiver. A collection of audio files is stored in the electronic data storage system(s). The audio receiver is configured to receive one or more audio files from the electronic data storage systems. The system also includes a speech-to-text processing engine configured to transcribe the one or more audio files received by the audio receiver to produce input transcriptions. In addition, the system includes a call tokenization engine that stores a list of candidate expressions and expression pairs that are indicative of turn taking and change of context in phone conversations. The call tokenization engine also receives input transcriptions of the one or more audio files from the speech-to-text processing engine, scans each input transcription for the one or more candidate expressions and expression pairs, inserts a punctuation marker adjacent a detected expression when the detected expression is one of the one or more candidate expressions and expression pairs, and outputs a tokenized transcription when the call tokenization engine reaches an end of the input transcription.

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Dublin, Ireland

Bruno Ohana