Clustering-Based Support Solution Recommendation System
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Solution Overview
Problem
Existing customer support systems face challenges in efficiently managing and responding to a large volume of user feedback items, often requiring significant human intervention and lacking automated solutions to provide timely and accurate responses to user issues.
Innovation Solution
A method and system that utilize clustering techniques to identify associations between user feedback items and corresponding support solutions, creating an items-solutions model to automatically determine and provide relevant support solutions to new user feedback items, reducing the need for human involvement and improving response efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing of user feedback items is used, then accuracy of support solutions can be maintained through human judgment, but productivity decreases due to the large volume of feedback items requiring significant human intervention
Solution Approach 1:
The system enables self-service by automatically processing user feedback items through clustering techniques and domain-specific dictionaries to generate support solutions without human intervention. The automated classification and solution recommendation system allows the feedback processing to serve itself, eliminating the need for manual review of each feedback item while maintaining consistent accuracy through predefined classification rules and confidence scoring mechanisms.
Solution Approach 2:
The patent replaces the mechanical human judgment system with an automated computational system. Instead of relying on human operators to manually analyze and respond to feedback items, the system uses clustering algorithms, domain-specific dictionaries, and confidence scoring to automatically classify feedback and generate solutions, substituting mechanical human processing with automated information processing.
2Productivity
If automated solutions are implemented to improve productivity, then response efficiency increases, but device complexity increases due to the need for clustering techniques and domain-specific dictionaries
Solution Approach 1:
The system segments the complex task of feedback processing into distinct modular components: feedback item reception, domain-specific dictionary matching, clustering analysis, confidence scoring, and solution recommendation. Each component handles a specific aspect of the processing pipeline, making the overall system more manageable and maintainable despite the increased automation capability.
Solution Approach 2:
The automated system performs multiple functions within a unified framework: it classifies feedback items, generates support solutions, ranks solutions by confidence, and provides recommendations all through the same clustering and analysis infrastructure. This multi-functionality reduces the need for separate specialized systems for each task.
3Measurement precision
If clustering techniques are applied to filter and analyze feedback items, then the accuracy of matching feedback with solutions improves, but loss of time increases during the initial model building phase
Solution Approach 1:
The system performs preliminary action by pre-processing feedback items through domain-specific dictionary filtering and clustering before the actual solution matching occurs. This preliminary classification and organization of feedback items into clusters based on domain terminology establishes a structured foundation that accelerates subsequent solution retrieval and matching operations.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium and a method for automatically providing support solutions in response to user feedback items. The method comprises receiving user feedback items and corresponding support solutions. The method further comprises identifying, using clustering techniques, associations between the user feedback items and the corresponding support solutions. The method further comprises storing the identified associations as an items-solutions model that correlates the user feedback items with the corresponding support solutions. The method further comprises receiving a new user feedback item. The method further comprises automatically determining, using the items-solutions model, at least one support solution that corresponds to the new user feedback item. The method further comprises providing the at least one support solution in response to the received new user feedback item.