Context-Vector Troubleshooting Across Multiple Device Models
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Solution Overview
Problem
Existing troubleshooting systems for electronic devices require tedious and time-consuming manual creation of support documents for each device model, and existing machine learning models are not scalable for a large number of device models, necessitating retraining for each new device model introduced.
Innovation Solution
A machine learning model is trained using context vectors generated from support documents and device feature vectors, allowing it to identify troubleshooting solutions for multiple device models without requiring retraining for new models, by leveraging common functional features across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual creation of support documents is performed for each device model, then troubleshooting accuracy is improved, but time consumption and labor costs increase
Solution Approach 1:
The system enables self-service troubleshooting by having the electronic device automatically generate and send diagnostic information to the server. The server then automatically generates and sends troubleshooting solutions without requiring manual intervention for each case, thus improving accuracy while reducing time consumption.
Solution Approach 2:
The patent replaces the manual mechanical process of creating support documents with an automated information processing system. The machine learning model automatically analyzes device information, generates context vectors, and produces troubleshooting solutions, substituting human manual work with automated computational processes.
2Measurement precision
If machine learning models are trained for each device model, then troubleshooting precision is improved, but system complexity and training requirements increase
Solution Approach 1:
The patent implements a universal machine learning model that can handle multiple device models simultaneously. The model uses device feature vectors and context vectors to adapt to different device types without requiring separate training for each model, thus maintaining precision while reducing system complexity.
Solution Approach 2:
The system changes parameters by using device feature vectors to represent different device models and context vectors to capture device-specific characteristics. This allows the single machine learning model to adapt to various device models by adjusting its input parameters rather than requiring separate models for each device type.
3Reliability
If separate support documents are created for each device model, then troubleshooting reliability is improved, but data management complexity increases
Solution Approach 1:
The patent merges the management of support documents for multiple device models into a single unified system. The server consolidates troubleshooting data and uses a single machine learning model to serve multiple device models, reducing data management complexity while maintaining reliability through the use of device-specific context vectors.
4Measurement precision
If traditional troubleshooting systems are used, then accuracy is maintained, but resource consumption and scalability worsen
Solution Approach 1:
The system performs preliminary actions by pre-processing device information into device feature vectors and context vectors before the actual troubleshooting query. This preliminary structuring of data reduces the computational resources required during the actual troubleshooting process and improves scalability.
Data Source
AI summary
System for providing troubleshooting solutions for an electronic device is described. The system includes a query engine, that may receive a troubleshoot query from a user of the electronic device. The troubleshoot query indicates a device model of the electronic device and an issue with the electronic device. Further, the query engine may identify a context vector corresponding to the device model based on a mapping table and the device model. The context vector describes a relationship between the device model, a plurality of troubleshoot support documents, and functional features of the device model. Further, the system includes a machine learning engine to determine a troubleshooting solution for the issue based on the issue and the context vector corresponding to the device.