Optum Technology, Inc.

Oracle International Corporation

Rahul Dutta

Amit Krishna

Love Hasija

Atul Kumar

Ashish Kumar

Atul S Kumar

Kapil Mohan

Shubhendu Shekhar

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

Methods, apparatus, systems, computing devices, computing entities, and/or the like for programmatically generating text strings are provided. An example method may include receiving an input string; generating a first utterance set based at least in part on the input string and a randomness indicator via a generative machine learning model; generating at least one grammar context score for the first utterance set; generating at least one relevance context score for the first utterance set; adjusting the randomness indicator based at least in part on one or more of the at least one grammar context score, a grammar context threshold, the at least one relevance context score, and a relevance context threshold; and generating a second utterance set based at least in part on the input string and the adjusted randomness indicator.

Method, apparatus and computer program product for generating text strings

An aspect of the present disclosure facilitates processing of natural language (NL) messages. In one embodiment, upon receiving a NL message from a user directed to a chatbot operational in a digital processing system, a data set pertinent for responding to the NL message is formed. An interactive widget that facilitates examination of the formed data set in different views is constructed and sent for display as a response to the NL message. Accordingly, when the interactive widget is displayed (on a display unit), the user is facilitated to examine the data set (pertinent to the NL message) in different views by interacting with the interactive widget.

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Bengaluru, India

Rahul Dutta