Case Matrix Clustering for Data Management Support Queries
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Solution Overview
Problem
Traditional data management systems fail to accurately group customer assistance calls at a fine-grained level, leading to inefficient customer self-help and excessive resource utilization, as they struggle to identify the primary factors driving user support requests.
Innovation Solution
A method involving the generation of a case matrix from retrieved documents, followed by a truncated singular value decomposition process with latent semantic analysis, and agglomerative clustering to group similar cases based on vector similarity, allowing for the identification of common topics that drive user support inquiries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data management systems use fine-grained clustering on course-level metadata or free text to group user questions, then the grouping precision is improved, but the computational resources and time required become excessive
Solution Approach 1:
The patent extracts only the most relevant features from assistance document interactions (document IDs, interaction counts, recency) to create a compact case representation. This selective extraction achieves sufficient grouping precision without processing entire free-text transcripts or comprehensive metadata, thereby reducing computational time and resources while maintaining effective case clustering.
2Measurement precision
If traditional systems perform comprehensive searches for assistance topics, then the accuracy of identifying user questions is improved, but the computational resources consumed increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-processing assistance document interactions to create structured case data including document IDs, interaction counts, and recency metrics before clustering. This preliminary structuring enables efficient case grouping and identification of user questions without requiring resource-intensive searches during runtime, as the relevant information is already organized and ready for analysis.
3Measurement precision
If data management systems rely on customer support agents to manually identify driving factors, then the accuracy of understanding user needs is improved, but the operational complexity and costs increase
Solution Approach 1:
The patent implements self-service by automatically analyzing assistance document interaction patterns to identify case clusters and driving factors without human intervention. The system autonomously processes case data, performs clustering based on document interactions, and generates insights about user questions, eliminating the need for manual analysis by customer support agents while maintaining high accuracy in understanding user needs.
Data Source
AI summary
Systems and methods are disclosed. An example method may be performed by one or more processors of a system and include retrieving case data indicating, for each respective case of a number of cases, one or more documents retrieved to assist a system user associated with the respective case, generating, from the case data, a case matrix including a plurality of rows each corresponding to a respective case of the number of cases and a plurality of columns each corresponding to the documents retrieved to assist the system user associated with the respective case, and identifying groups of similar cases among the plurality of cases based on a clustering process performed on at least a portion of the case matrix.