NLP Embedding Clustering for Predicting Service Issues
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Solution Overview
Problem
The complexity of modern computing devices makes it difficult to diagnose and resolve service issues, leading to dissatisfaction among users, as existing methods lack efficiency in identifying and remediating issues across various devices and contexts.
Innovation Solution
A system utilizing natural language processing (NLP) to generate embeddings from service requests and attributional data, clustering similar issues, and predicting trending problems to aid helpdesk personnel in providing timely and effective solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual diagnosis methods are used for service issues, then device complexity is managed by human expertise, but response time and diagnostic efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical diagnosis processes with an automated NLP-based system. The system uses natural language processing to automatically analyze service requests, extract issues, and generate diagnostic information, eliminating the need for manual analysis of each service request and significantly improving response time and diagnostic efficiency
Solution Approach 2:
The system enables self-service through automated diagnostic capabilities. The NLP system automatically processes service requests, identifies issues, and provides diagnostic information without requiring manual intervention from support personnel for each individual case, allowing the system to serve itself in the diagnostic process
2Productivity
If detailed analysis of each service request is performed manually, then diagnostic precision is maintained, but operational efficiency deteriorates
Solution Approach 1:
The patent replaces manual detailed analysis with automated NLP processing that maintains diagnostic precision. The system uses natural language processing algorithms to accurately extract and analyze service request details, ensuring precise diagnostic results while processing multiple requests simultaneously to improve operational efficiency
Solution Approach 2:
The system creates a standardized digital representation of service requests through NLP processing. By converting unstructured service requests into structured data representations, the system can efficiently process and analyze multiple requests using consistent algorithms, maintaining diagnostic precision while improving throughput and operational efficiency
3Reliability
If comprehensive service issue analysis is conducted, then service quality improves, but system complexity increases
Solution Approach 1:
The patent replaces complex manual analysis procedures with a standardized NLP-based system. The system uses natural language processing to automatically perform comprehensive service issue analysis, maintaining high service quality through consistent and thorough analysis while reducing system complexity by using a unified automated approach rather than multiple manual processes
Data Source
AI summary
Examples are described herein for facilitating responses to computing device service requests. In various examples, natural language processing may be performed on a plurality of incoming computing device service requests associated with a plurality of computing devices. A plurality of embeddings corresponding to the plurality of computing device service requests may be generated based on the natural language processing. Based on distances between each of a particular subset of the embeddings in an embedding space, a trending service issue associated with the computing device service requests corresponding to the particular subset may be predicted. The trending service issue may be provided to a computing device servicer associated with resolving a new incoming computing device service request.