Optum Technology, Inc.

Unitedhealth Group Incorporated

Love Hasija

Atul Kumar

Amit Krishna

Rahul Dutta

Damian Kelly

Ninad D Sathaye

Kimberly A Vorse

Piyush Gupta

Atul S Kumar

Shubhendu Shekhar

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

A computing system initializes a score for each chatbot profile of a plurality of chatbot profiles. The chatbot profiles correspond to different personas. For each chatbot profile, the computing system collects biometric response data for a user while the user has an interaction session with the chatbot profile. The computing system updates the score for the chatbot profile based on the biometric response data for the user collected while the user has the interaction session with the chatbot profile. The computing system ranks the chatbot profiles based on the scores and selects a chatbot profile from the plurality of chatbot profiles for a subsequent interaction session with the user based on the ranking of the chatbot profiles.

Ranking chatbot profiles

Various embodiments provide for decentralized crowd sourced generation of recommendation data objects. An example apparatus receives, originating from an external computing device, a recommendation data object request, the recommendation data object request comprising a user identifier and one or more user attributes. The example apparatus may further retrieve, based on a predictive recommendation model, one or more therapy identifiers associated with a therapy efficacy score exceeding a therapy efficacy score threshold for attributes of a first attributes set associated with a first cluster identifier, the first attributes set comprising one or more of the one or more user attributes. The predictive recommendation model is trained based at least in part on trusted efficacy blocks of a distributed ledger, where the trusted efficacy blocks are added to the distributed ledger when a number of therapy efficacy transaction blocks associated with a given therapy identifier and having a therapy efficacy score exceeding a therapy efficacy score threshold meets or exceeds a therapy efficacy transaction block threshold. The example apparatus may further transmit, to the external computing device, a recommendation data object configured for rendering for display via a display device of the external computing device, where the recommendation data object comprises one or more of the one or more therapy identifiers.

Generating multi-dimensional recommendation data objects based on decentralized crowd sourcing

Systems and methods are configured to enable guided interaction with query assistant software using brainwave data. In various embodiments, a client device presents a query assistant user interface to a monitored end-user that describes a query associated with a plurality of response options and an intended physiological action for each response option. Accordingly, one or more user monitoring data objects associated with the monitored end-user are received that include user brainwave monitoring data objects. These user monitoring data objects are processed using one or more response designation machine learning models to generate response designators based on the user monitoring data objects that includes a physiological response designator describing a selected intended physiological action that is deemed to be related to the user monitoring data objects. Accordingly, a user response is then determined based on the response designators and the user interface may be updated based on the user response.

Guided interaction with a query assistant software using brainwave data

A method, apparatus and computer program product predict run-time to completion of workflows executing in a shared multi-tenant distributed compute clusters. The method, apparatus and computer program product receive a MapReduce workflow. The MapReduce workflow includes one or more MapReduce jobs for execution. The method, apparatus and computer program product generates one or more workflow profile summary reports for one or more currently executing workflows on the multi-tenant distributed computing system; generates one or more compact historical workflow profile summary reports of the received MapReduce workflow and the one or more currently executing workflows using the historical reports; generates one or more compact historical cluster contention summary reports of all workflow executing compute clusters in the multi-tenant distributed computing system; and generates a run-time prediction for the MapReduce workflow based at least in part on the one or more currently executing workflow profile summary reports, one or more compact historical workflow profile summary reports, and the one or more compact historical cluster contention summary reports.

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Sonepat, India

Love Hasija