SoC Troubleshooting Query Clustering With NLP and ML
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Solution Overview
Problem
The increasing complexity of system-on-chips (SoCs) leads to a higher number of troubleshooting issues, overwhelming available resources and requiring a more efficient method to address these problems.
Innovation Solution
A machine learning and natural language processing-based system that clusters troubleshooting queries into semantically similar categories, retrieves resolution data from an expert system library, and generates recommendations for troubleshooting SoCs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual troubleshooting methods are used for complex SoCs, then customer service resources can address each issue individually, but the turnaround time increases and resource strain increases due to the high volume of troubleshooting queries
Solution Approach 1:
The patent implements an automated troubleshooting system that processes customer queries independently without requiring human analyst intervention for each case. The natural language processing and machine learning components enable the system to autonomously categorize queries, retrieve relevant resolutions, and generate recommendations, allowing the troubleshooting process to serve itself rather than relying on manual customer service resources
Solution Approach 2:
The patent introduces an automated processing system as an intermediary between customer queries and resolution databases. This intermediary system uses natural language processing to interpret queries, machine learning to categorize them appropriately, and automated retrieval to fetch relevant resolutions, thereby mediating the interaction between customers and the extensive resolution database without requiring direct human involvement in each troubleshooting case
2Productivity
If more customer service resources are allocated to troubleshooting, then more issues can be addressed, but the cost and resource complexity increase
Solution Approach 1:
The patent replaces the mechanical system of human customer service analysts with an automated computational system. Instead of relying on human expertise and manual processing, the system uses natural language processing algorithms, machine learning models, and automated database retrieval mechanisms to handle troubleshooting queries, thereby substituting human resources with an automated technological infrastructure that scales without proportional increases in resource complexity
Solution Approach 2:
The patent creates a universal troubleshooting system that can handle multiple types of queries across different categories simultaneously. The natural language processing component understands various query formats, the machine learning component categorizes diverse issues into appropriate groups, and the retrieval system accesses a comprehensive resolution database, enabling a single system to perform multiple troubleshooting functions without requiring specialized resources for each issue type
Data Source
AI summary
A method for processor-implemented method includes receiving an integrated circuit (IC) troubleshooting query for an IC. The IC troubleshooting query is received from a user. The method also includes performing natural language processing and machine learning to cluster the IC troubleshooting query into one of a number of semantically similar troubleshooting categories. The method further includes retrieving resolution data from an expert system library, based on a mapping between categories of user solutions and a topic of the IC troubleshooting query. The method also includes generating a recommendation in response to the IC troubleshooting query, based on the resolution data. The method outputs the recommendation to the user.