NLP Dialogue System for Irregularity Validation
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Solution Overview
Problem
Current automated systems, such as IVR, struggle to resolve user issues that are dissimilar to those previously encountered, leading to inefficiencies in cost and time for both organizations and users.
Innovation Solution
A system utilizing Natural Language Processing (NLP) and a dialogue learning module to autonomously identify irregularities in user utterances, generate hypotheses, and provide computer-generated dialogue responses for validation and resolution, including the ability to retrieve user information and update confidence measurements based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems use pre-scripted responses to resolve user issues, then cost efficiency and time efficiency improve for common issues, but the system fails to handle dissimilar or novel issues effectively
Solution Approach 1:
The system enables automated self-service by allowing the automated dialogue system to independently generate hypotheses, retrieve relevant information, and resolve issues without human intervention. The system autonomously validates irregularities and provides resolutions for both common and novel issues, eliminating the need for human representatives to handle routine cases while maintaining adaptability to new problem types.
Solution Approach 2:
The system dynamically adjusts its operational parameters by generating multiple hypotheses with associated confidence measurements and selectively retrieving information based on the specific issue context. This allows the system to adapt its information retrieval strategy and hypothesis validation approach depending on the nature of the user issue, enabling effective handling of both scripted and novel scenarios.
2Measurement precision
If the system retrieves and processes additional user information to validate hypotheses, then measurement precision and validation accuracy improve, but loss of time increases due to additional information gathering
Solution Approach 1:
The system performs partial information retrieval by selectively obtaining only the specific pieces of information needed to validate or refute each hypothesis. Rather than comprehensively gathering all possible user data, the system retrieves information on-demand based on hypothesis requirements, updating confidence measurements only when additional information is necessary to reach a validation decision.
Solution Approach 2:
The system employs feedback mechanisms where confidence measurements are continuously updated based on retrieved information and user responses. This feedback loop allows the system to determine when sufficient validation has been achieved and when to cease information gathering, balancing accuracy requirements with time efficiency by stopping retrieval once confidence thresholds are met.
Data Source
AI summary
Systems and methods for identifying irregularities during an automated user interaction are disclosed. The system may receive a communication and extract a perceived irregularity from the communication. The system may generate a first explanatory hypothesis associated with the perceived irregularity having an associated confidence measurement. The system may selectively retrieve user information based on the generated hypothesis and generate an investigational strategy associated with the hypothesis. In response to the investigational strategy, the system may receive a user communication, and the system may update the confidence measurement based on the user communication. When the confidence measurement exceeds the predetermined confidence threshold the system may validate the perceived irregularity as a true irregularity and provide a computer-generated dialogue response indicative of a proposed resolution of the irregularity. When no existing hypothesis has a confidence measurement exceeding the threshold, the system may generate a novel hypothesis to be validated.