Chatbot Response Quality Measurement via Intent Matching
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Solution Overview
Problem
Current methods for measuring chatbot response quality are time-consuming and dependent on user feedback or click tracking, which are often incomplete or unreliable.
Innovation Solution
A system and method using algorithms and AI models to automatically assess chatbot interactions by filtering questions based on relevance, matching questions for similarity and intent, and calculating classification scores to generate an accuracy report without requiring user feedback or click tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user feedback or click tracking is used to measure chatbot response quality, then measurement can be obtained, but the process becomes time-consuming and the data becomes incomplete or unreliable
Solution Approach 1:
The system enables self-service by having the chatbot automatically analyze its own interactions and generate quality assessments without requiring external user feedback or manual evaluation. The chatbot processes its conversation logs, applies classification models, and produces accuracy reports autonomously, eliminating the time-consuming dependency on user responses.
Solution Approach 2:
The system performs preliminary action by pre-processing and analyzing chatbot interactions in real-time as they occur, rather than waiting for user feedback after the fact. Classification models are applied immediately to conversation logs, and quality metrics are calculated proactively, enabling timely assessments without delays.
2Measurement precision
If user feedback or click tracking is used to measure chatbot response quality, then measurement can be obtained, but the data becomes incomplete or unreliable
Solution Approach 1:
The system introduces an intermediary classification model that acts as a mediator between the chatbot's responses and the quality assessment. Instead of relying directly on potentially biased or incomplete user feedback, the classification model objectively analyzes conversation patterns, intent matching, and response relevance to generate reliable quality metrics.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where the chatbot's performance is continuously assessed through automated analysis of its own interactions. The generated accuracy reports provide actionable feedback that can be used to improve the chatbot's responses, creating a self-improving system with reliably measured performance.
3Productivity
If automated algorithms and AI models are used to assess chatbot interactions, then measurement efficiency is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by breaking down the complex quality assessment task into distinct modular components: interaction logging, question filtering, classification model application, metadata generation, and report compilation. Each module handles a specific aspect of the analysis, making the overall system more manageable and maintainable while achieving high measurement efficiency.
Data Source
AI summary
A computer-implemented method, computer system, and computer program product for measuring a quality of a chatbot response. The present invention may include receiving one or more classifications, receiving a set of questions in a chatbot to be analyzed, filtering any question from the received set of questions that is not related to an area of expertise of the chatbot, matching at least two questions from the received set of questions to each other, and applying at least one of the one or more classifications to the at least two matched questions. The one or more classifications may be based on a similarity of words and synonyms used in the at least two matched questions. The one or more classifications may be based on a similarity of intents of the at least two matched questions.