Distributed ML Diagnostics With Multi-Model Switching for User Support
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Solution Overview
Problem
Current broadband operator network diagnostics and services rely on fixed analytic algorithms that often confuse and frustrate users due to technical jargon and lack of understanding, necessitating improved communication and diagnostic solutions.
Innovation Solution
Employing multi-model switching machine learning techniques to enhance user interaction through distributed ML models across network hierarchies, including CPE, edge devices, and cloud resources, utilizing chat engines for natural language processing and prompt/response augmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed analytic algorithms are used for network diagnostics, then the system structure is simple and easy to implement, but the user understanding and satisfaction deteriorate due to technical jargon and lack of explanation
Solution Approach 1:
The patent introduces an intermediary layer (explanation generation module) between the fixed analytic algorithms and the user interface. This intermediary translates technical diagnostic results into user-friendly explanations, resolving the contradiction by maintaining simple algorithms while improving user understanding through adaptive natural language generation.
Solution Approach 2:
The system segments the diagnostic process into distinct modules: the fixed analytic algorithm layer for technical analysis and the explanation generation layer for user communication. This segmentation allows each layer to specialize - maintaining algorithmic simplicity while enhancing user understanding through dedicated explanation mechanisms.
2Adaptability or versatility
If distributed ML models are deployed across network hierarchies, then the adaptability and diagnostic capability are improved, but the device complexity and resource requirements increase
Solution Approach 1:
The patent segments the ML modeling function into two parts: a centralized model training facility that develops diagnostic models, and distributed model deployment across network elements. This segmentation enables enhanced diagnostic capability through specialized models while managing complexity by separating model development from deployment.
Solution Approach 2:
The patent creates a universal ML platform that can deploy the same diagnostic framework across diverse network elements (CPE, edge devices, core network). This multi-functionality approach enhances adaptability across different device types while using a standardized platform to manage complexity.
3Ease of operation
If chat engines with natural language processing are implemented, then the user interaction quality is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-compiling and caching explanation templates and natural language responses during off-peak periods. When users query the system, pre-generated explanations can be quickly retrieved and customized, improving interaction quality while minimizing real-time processing time.
Solution Approach 2:
The system applies local quality by generating detailed natural language explanations only when needed and for specific diagnostic results, rather than processing all data uniformly. This selective approach improves user interaction quality for critical information while reducing overall processing time through targeted NLP application.
Data Source
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AI summary
Improved solutions that enable more effective and efficient communications with users, in particular with respect to field diagnostics and services. Some solutions can enable users to better communicate with a provider to obtain more useful diagnostic and service information. Certain solutions can employ multi-model switching machine learning techniques to enhance a user's communication with the provider and/or the provider's response.