Capital One Services, LLC

International Business Machines Corporation

Adam R Faulkner

Kenneth James Barker

Sara Rosenthal

Maury Courtland

Aysu Ezen Can

Branimir Konstantinov Boguraev

Mihaela Ancuta Bornea

Sugato Bagchi

Pei-Yun S Hsueh

Siddharth A Patwardhan

Subhro Das

Gayle McElvain

Ching-Hua Chen

Chandramouli Maduri

Yanpeng Li

Jonathan Galsurkar

John Qui

Gayle McELVAIN

Gema Almoguera

David Chen

Embodiments disclosed are directed to a computing system that performs steps to automatically generate fine-grained call reasons from customer service call transcripts. The computing system extracts, using a natural language processing (NLP) technique, a set of events from a set of text strings of speaker turns. The computing system then identifies a set of clusters of events based on the set of events and labels each cluster of events in the set of clusters of events to generate a set of labeled clusters of events. Subsequently, the computing system assigns each event in the set of events to a respective labeled cluster of events in the set of labeled clusters of events.

Automated generation of fine-grained call reasons from customer service call transcripts

A system and method of operating a system for automatically punctuating text using non-recurrent neural networks is disclosed. The system and method at least: applying a text string to a first component of a non-recurrent neural network trained to generate one or more contextualized vectors, wherein the first component determines the contextualized vectors by processing each word in the text string in parallel with one another; applying the contextualized vectors to a second component of the non-recurrent neural network trained to generate a set of probability values for each word in the text string, wherein the second component determines the set of probability values by processing the contextualized vectors in parallel with one another; and transmitting the set of probability values to a text generation engine to generate a formatted text string based on the set of probability values.

Efficient automatic punctuation with robust inference

Disclosed herein are system, method, and computer program product embodiments for machine learning systems to process incoming call-center calls based on inferred sentiments. An incoming call is routed to a call agent based on an inferred topic, classified based on one or more inferred sentiments of a current caller's speech, determining, based on the call classification, that a complaint has been articulated and initiating an automated assistance by searching for one or more similar callers to the current caller. Based on finding a successful call outcome associated with one or more similar callers, the system suggests one or more phrases to the call agent for use in a dialog with the current caller to improve the one or more inferred sentiments.

System and method for identifying complaints in interactive communications and providing feedback in real-time

A mechanism is provided in a data processing system to implement a personalized patient engagement engine. The personalized patient engagement engine develops a set of models for a plurality of behavioral phenotypes based on anonymized unstructured and structured patient-care management records for a plurality of patients over a period of time; matches a given patient to a behavioral phenotype; estimates a propensity of positive and/or negative behavioral responses of each of a plurality of targeted behaviors; dynamically updates personalized intervention effectiveness rankings in context for care manager and patient decision-making based on what has been shown to lead to positive responses for individuals with a similar behavioral profile; generates an intervention recommendation for the given patient based on the personalized intervention effectiveness rankings relative to the patient given an assigned goal and an individual intervention effect estimation; and provides the intervention recommendation to the care manager.

Personalized patient engagement in care management using explainable behavioral phenotypes

Provided herein are techniques for processing a context-situated inquiry to provide results satisfying the inquiry. An inquiry and its supporting context are processed using natural language processing to determine an interpretation for the inquiry and context, and the interpretation is presented to receive corrective information for the interpretation. The corrective information is applied to the interpretation and content is retrieved based on the interpretation of inquiry and context to produce candidate results for the inquiry. Supplemental information is iteratively received to generate updated candidate results.

Processing context-based inquiries for knowledge retrieval

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New York, NY, United States of America

Adam R Faulkner