Automated Inquiry Response System Sentiment Clustering
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Solution Overview
Problem
Current automated inquiry response systems fail to accurately determine the sentiment and level of sophistication of customer inquiries, leading to ineffective interactions and increased customer dissatisfaction, as they treat all customers similarly regardless of their emotional state or technical understanding.
Innovation Solution
The system forms clusters based on historical inquiries, assigning levels of sophistication and sentiment, and generates sub-clusters to tailor responses accordingly, using crowdsourced data to improve accuracy and adapt responses to the specific sentiment and sophistication of each inquiry.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If automated systems use standardized question lists and number-based responses, then resource allocation for training representatives is reduced, but the system cannot address the tone or sentiment of the customer
Solution Approach 1:
The patent segments the automated response system into multiple specialized modules: a sentiment analysis module that detects customer emotional state, a sophistication level detection module that assesses technical understanding, and a response generation module that selects appropriate responses based on both sentiment and sophistication level. This segmentation allows the system to handle multiple dimensions of customer inquiry simultaneously without requiring extensive representative training.
Solution Approach 2:
The patent introduces natural language processing algorithms as intermediaries between the customer's input and the automated response system. These NLP intermediaries analyze the customer's query to detect sentiment, tone, and sophistication level, then translate this analysis into parameters that guide response selection. This intermediary layer enables the automated system to understand and adapt to customer emotional state without requiring human representatives.
2Ease of operation
If manual representative analysis is used to determine substantive issues, then customer interactions can be personalized, but the system becomes expensive and resource intensive
Solution Approach 1:
The patent implements a self-service automated system that independently performs sentiment analysis, sophistication level assessment, and response selection without human intervention. The system uses machine learning models trained on historical customer interactions to automatically detect patterns and generate personalized responses. This self-service capability eliminates the need for expensive human representatives while maintaining personalized interaction quality.
Solution Approach 2:
The patent changes the operational parameters of the customer service system by transitioning from human-based analysis to algorithm-based analysis. The system uses computational parameters such as sentiment scores, sophistication levels, and response confidence metrics to replace human judgment. This parameter transformation enables automated personalized interactions at scale without the resource intensity of manual representative analysis.
3Device complexity
If automated systems treat all customers similarly, then operational simplicity is maintained, but customer satisfaction decreases due to lack of personalized attention
Solution Approach 1:
The patent applies local quality by tailoring the response characteristics to match the specific sentiment and sophistication level of each customer inquiry. The system selects different response templates, tones, and levels of technical detail based on the analyzed attributes of each customer's query. This local customization ensures that each customer receives appropriately personalized attention while the overall system maintains automated operational simplicity.
Data Source
AI summary
A method of automated inquiry response includes forming clusters that represent a meaning. The method includes assigning a level of sophistication to the clusters and generating sub-clusters within the clusters that represent a sentiment or a level of sophistication. The method includes assigning responses to the sub-clusters that address the meaning of the cluster and are modified based on the sentiment or a level of sophistication. The method includes computing a substantive issue, a sentiment, and a level of sophistication of a received inquiry. The method includes identifying clusters to which the inquiry pertains and generating an order of the identified clusters based on the assigned level of sophistication. The method includes crowdsourcing a comparison of the substantive issue of the inquiry to meanings of the identified clusters. The method includes offering a response associated with one of the identified clusters and with the sub-cluster for the identified sentiment.