Feedback Prioritization via Semantic Clustering
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Solution Overview
Problem
Traditional methods for aggregating and prioritizing customer feedback in network-based service providers are time-consuming and laborious, requiring manual processing and specific labeling, which underutilizes the vast amount of unstructured and voluminous feedback received.
Innovation Solution
A feedback processing service that aggregates semantically similar customer feedback into clusters using techniques like SentenceBERT and Deep Embedded Clustering, identifies representative themes, and prioritizes clusters based on inertia scores, customer size, and time intervals without requiring user-provided labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing and labeling methods are used for customer feedback, then processing accuracy can be maintained, but processing time and labor requirements increase significantly
Solution Approach 1:
The patent replaces manual mechanical processing of feedback with automated machine learning models. The system uses pre-trained language models to automatically cluster, summarize, and prioritize feedback without human intervention, thereby reducing processing time while maintaining accuracy through algorithmic consistency.
Solution Approach 2:
The feedback processing system performs self-service through automated clustering and summarization. The machine learning models independently process feedback data, identify patterns, generate summaries, and prioritize items without requiring manual labeling or human analysis, enabling the system to serve itself in processing large volumes of feedback efficiently.
2Productivity
If automated clustering techniques are used for feedback aggregation, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The system performs preliminary action by using pre-trained language models that have already learned linguistic patterns and semantics before deployment. These pre-trained models can be directly applied to feedback clustering without extensive customization or training, reducing the complexity of implementing automated processing while maintaining high efficiency.
3Loss of information
If comprehensive feedback analysis is performed on all customer inputs, then insight quality improves, but computational resources and processing time increase
Solution Approach 1:
The system extracts only the most important and representative feedback items through automated clustering and prioritization. By identifying semantically similar feedback and selecting representative examples with high priority scores, the system extracts key insights without processing every single feedback item in detail, thereby reducing computational resource consumption while maintaining insight quality.
Data Source
AI summary
The present disclosure generally relates to a feedback processing service that can receive customer input, as customer feedback corresponds to a service context. The feedback processing service aggregates semantically similar feedback as a cluster. Then, the feedback processing service can prioritize each of the clusters by ranking each of the clusters.