Operational Support Data Selection for Network Malfunction Resolution
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Solution Overview
Problem
Users face challenges in diagnosing and resolving malfunctions in interconnected devices and networks, requiring extensive technical expertise and time-consuming interactions with multiple technical representatives, especially when multiple malfunctions occur simultaneously.
Innovation Solution
A computer-implemented method and apparatus that utilize device activity data and malfunction text description data to apply an operational support processing data model, selecting predicted operational support data objects in real-time to assist in resolving malfunctions, reducing the need for user expertise and minimizing the initiation of support sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users seek technical support for device malfunctions through conventional methods, then they can receive assistance from technical representatives, but the process becomes time-consuming and requires extensive user-technical representative interactions
Solution Approach 1:
The system enables self-service by allowing users to input malfunction descriptions and receive automated diagnostic results through the processing data model, eliminating the need for extensive interactions with technical representatives while maintaining effective malfunction resolution
Solution Approach 2:
The processing data model is trained in advance using historical device activity data and malfunction information, enabling it to perform diagnostics immediately when a user submits a malfunction description, thus reducing support session duration without compromising resolution effectiveness
2Measurement precision
If users with limited technical expertise attempt to diagnose malfunctions, then they may struggle to identify the problem accurately, but seeking expert help increases time consumption and complexity
Solution Approach 1:
The processing data model acts as an intermediary between the user's malfunction description and the diagnostic conclusion, automatically analyzing the input and providing accurate malfunction identification without requiring the user to have technical expertise or navigate complex diagnostic procedures
Solution Approach 2:
The system uses historical malfunction data and device activity patterns as training copies to build the processing data model, enabling it to replicate expert diagnostic reasoning and provide accurate malfunction identification while keeping the user interface simple and straightforward
3Adaptability or versatility
If multiple malfunctions occur simultaneously in interconnected devices, then the diagnostic complexity increases significantly, but conventional support methods require users to interact with multiple technical representatives
Solution Approach 1:
The processing data model is designed with multi-functionality to handle various types of malfunctions across different devices simultaneously, analyzing multiple malfunction descriptions and device interactions through a single unified diagnostic process, thereby managing diagnostic complexity without requiring multiple separate support sessions
Data Source
AI summary
Embodiments of the present disclosure provide for identification and output of improved predicted operational support data object(s). Embodiments utilize particular data sets and data model implementations to identify and select predicted operational support data object(s) determined as associated with the highest confidence to assist in resolving a particular malfunction affecting a networked device in a dynamic home communications network. Such embodiments enable resolution of the malfunction, utilizing the predicted operational support data object(s), with improved success rates and without requiring performance of additional and/or alternative support processes.