Optum Services (ireland) Limited

Optum, Inc.

Unitedhealth Group Incorporated

Damian Kelly

Gregory Buckley

Megan O'Brien

Bruno Ohana

Jack Sullivan

Mariah Sonja Pereira Penha

Randy Olinger

Peter Joseph Ross

Rick A Hamilton, Ii

Gregory J Boss

Vijay S Nori

Atul Kumar

Daniel J Mulcahy

Adam Russell

Love Hasija

Ninad D Sathaye

Darragh Hanley

Dominik Dahlem

Jonathan Lawrence Herke

Julie Zhu

Jesse Hultgren

Kimberly A Vorse

Ronan McCormack

Colleen B Caveney

Meihua S Eddy

Ronald Matthew Hoffner

Kelsey Isenberg

Melissa Jeanne Umenhofer

Anthony A Nguyen

Subhash Seelam

Rahul Dutta

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations for parasomnia episode management. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations for parasomnia episode management using at least one of pre-sleep parasomnia episode likelihood prediction machine learning models, in-sleep parasomnia episode likelihood prediction machine learning models, augmented parasomnia episode likelihood prediction machine learning models that are configured to generate conditional likelihood scores for candidate parasomnia reduction interventions, deep reinforcement learning machine learning models that are configured to generate recommended parasomnia reduction interventions, and dynamically-deployable parasomnia episode likelihood prediction machine learning models.

Machine learning techniques for parasomnia episode management

Various embodiments provide methods, apparatus, systems, computing entities, and/or the like, for identifying deviated behavior of an individual indicated by outlying activity counts and outlying activity timings classified by a prediction interval profile generated from historical behavior data. In an embodiment, an example method comprises accessing sensor data describing behavioral activities of an individual during historical time periods and generating a prediction interval profile for the behavioral activities comprising a predicted count interval within a prediction time period for at least some behavioral activities. The method further includes receiving sensor data for the prediction time period and extracting an observed activity count and an observed activity timing for each behavioral activity. The method then includes classifying a particular behavioral activity as a behavioral deviation if the observed activity count does not satisfy the predicted count interval and/or if the observed activity timing does not satisfy the predicted timing interval.

Behavior deviation detection with activity timing prediction

There is a need for more effective and efficient heightened need prediction. In one example, a method comprises, during an online prediction phase: receiving incoming monitoring data associated with the user profile; performing anomaly reduction on the incoming monitoring data to generate anomaly-reduced incoming monitoring data; generating an incoming monitoring window based on the anomaly-reduced incoming monitoring data; processing the incoming monitoring window using a trained heightened need prediction machine learning model to determine a heightened need prediction for the incoming monitoring window; and in response to determining that the heightened need prediction is positive, performing one or more responsive actions by communicating with one or more client devices associated with the user profile.

Passive heightened need prediction

An example method includes obtaining, by a computing system, audio data representing sounds generated by a user; applying, by the computing system, a machine learning (ML) model that generates output data based on non-semantic content of the audio data; and generating, by the computing system, based on the output data generated by the ML model, a recommended healthcare provider list for the user, the recommended healthcare provider list for the user including one or more healthcare providers recommended for the user.

Use of audio data for matching patients with healthcare providers

There is a need for more effective and efficient predictive data analysis. This need can be addressed by, for example, solutions for performing/executing hybrid input predictive data analysis. In one example, a method includes identifying a vocabulary data object associated with one or more prediction input text data objects; determining, based at least in part on the vocabulary data object, a per-input-entity tokenized representation for the one or more prediction input text data objects; determining, based at least in part on the per-input-entity tokenized representation and one or more prediction input structured data objects and using a hybrid-input predictive model, a prediction score; determining, based at least in part on one or more threshold determination configuration criteria, a predictive threshold for the hybrid-input predictive model; generating, based at least in part on the prediction score and the predictive threshold, a predictive output; and performing one or more prediction-based actions based at least in part on the predictive output.

Hybrid-input predictive data analysis

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

Methods, apparatus, systems, computing devices, computing entities, and/or the like for contextually optimizing routings for interactions. This may include receiving an interaction, wherein the interaction is selected from the group consisting of a voice-based interaction and a textual-based interaction; receiving an interaction problem statement for the interaction; generating, based at least in part on the interaction problem statement, an interaction problem statement summary, wherein the interaction problem statement comprises the context of the interaction; identifying one or more features for the interaction, wherein the features are input for one or more machine learning models; predicting an optimal route for the interaction, wherein the optimality of each route, hence, the optimal route is determined by the one or more machine learning models; and routing the interaction to the optimal route.

Contextually optimizing routings for interactions

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

Various embodiments of the present disclosure facilitate recommendation prediction using machine learning. In one example, an embodiment provides for generating embeddings data related to one or more provider entities, predicting a set of provider entities for a patient entity based on a provider machine learning model, ranking provider entities in the set of provider entities to generate a ranked set of provider entities, and performing one or more actions to provide a recommendation for the patient entity based on the ranked set of provider entities.

Artificial intelligence recommendation system

A computing system may train an autoencoder to generate a first set of codes from a first set of thermal video images of activities of a user in an environment. The activities may represent routine behaviors of the user in the environment. The computing system may use an unsupervised machine-learning algorithm to categorize the first set of codes into a set of clusters. The computing system may use the autoencoder to determine a code representative of a second set of thermal video images of an activity in the environment. Based on the code not being associated with any cluster in the set of clusters, the computing system may determine that the code is an anomalous code. The computing system may perform an alert action based on the anomalous code.

Identifying anomalous activity from thermal images

There is a need for more accurate and more efficient hybrid-input prediction steps/operations. This need can be addressed by, for example, techniques for efficient joint processing of data objects. In one example, a method includes: processing an audio data object using an audio processing machine learning model to generate an audio-based feature data object, processing an acceleration data object using an acceleration processing machine learning model to generate an acceleration-based feature data object, processing the audio-based feature data object and the acceleration-based feature data object using an feature synthesis machine learning model in order to generate a hybrid-input prediction data object; and performing one or more prediction-based actions based at least in part on the hybrid-input prediction data object.

Hybrid input machine learning frameworks

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

The utility usage of a particular individual occupying a residence may give insight into the individual's current cognitive health and/or to enable provision of various services within the facility for the individual, particularly when monitoring patterns in utility usage over time. To enable accurate and non-invasive utility monitoring, a single-point utility sensor may be secured relative to a utility supply line, and generated signals may be utilized to monitor utility usage and to distinguish between utility fixtures. A centralized computing entity may identify frequency characteristics within the generated data, and may automatically generate one or more machine-learning algorithms to distinguish between utility usage events, without requiring substantial user input.

Growing community of inventors

Kildare, Ireland

Damian Kelly