Unitedhealth Group Incorporated

Optum Services (Ireland) Limited

Chirag Mittal

Rajesh Sabapathy

Gourav Awasthi

Aditya Teja Josyula

Ankur Gulati

Arjun Rana

Lubna Khan

Erin Conway

Arushi Parganiha

Tarun Bansal

Sudeep Vishnumurthy

Rebin Raju

Sharenna D Gonzalez

As described herein, various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations for generating guided summaries using summarization templates that are mapped to hybrid classes of a hybrid classification space for a hybrid classification machine learning model. In some embodiments, by using summarization templates, a proposed summarization framework is able to vastly reduce the computational complexity of performing summarization on an input document data object, such as an input multi-party communication transcript data object, by defining the set of dynamic data fields that apply to the input document data object based at least in part on an assigned class/category of the input document data object.

Natural language processing techniques for machine-learning-guided summarization using hybrid class templates

Methods, apparatuses, systems, computing entities, and/or the like are provided. An example method may include retrieving a scheme data object; generating a tabular data object; generating a graph data object including a plurality of nodes and a plurality of edges; calculating, based on the graph data object, a plurality of node attributes for the plurality of nodes and a plurality of edge attributes for the plurality of edges; identifying at least one irregular member node from the plurality of member nodes; identifying at least one irregular referring provider node from the plurality of referring provider nodes; identifying at least one irregular servicing provider node from the plurality of servicing provider nodes based at least in part on the at least one irregular member node and the at least one irregular referring provider node; and performing one or more responsive actions.

Method, apparatus and computer program product for predictive graph-based network analysis

There is a need for more effective and efficient predictive natural language summarization. This need is addressed by applying hybrid extractive and abstractive summarization techniques in a unique processing pipeline to generate a cohesive and comprehensive summary of a multi-party interaction. A method for generating the summary of a multi-party interaction includes receiving a multi-party interaction transcript data object comprising a plurality of interaction utterances from at least two participants; using an extractive summarization model to identify a key sentence of the multi-party interaction transcript data object; identifying an interaction utterance from the multi-party interaction transcript data object that corresponds to the key sentence; generating a contextual summary for the multi-party interaction transcript data object based at least in part on the interaction utterance; and generating a reported contextual summary for the multi-party interaction transcript data object based at least in part on the contextual summary.

Machine-learning based transcript summarization

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by generating a hybrid class for a multi-party communication transcript data object associated with a predictive entity utilizing a hybrid space classification machine learning model, generating a machine learning-based risk score utilizing a hybrid-class-based risk scoring machine learning model, and generating a hierarchical-workflow risk score using a hierarchical risk score adjustment workflow.

Machine learning techniques for hierarchical-workflow risk score prediction using multi-party communication data

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Gurgaon, India

Chirag Mittal