Dynamic Time Windowing for IoT Device Troubleshooting
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Solution Overview
Problem
The increasing complexity of IoT devices has led to a rise in technical issues, making it challenging for companies to efficiently troubleshoot and debug their products due to the complex interdependencies between system-on-a-chips (SoCs), passive components, and other modules.
Innovation Solution
A machine learning and natural language processing-based system that receives performance data from IoT devices, filters it within a dynamic time window to include event data and dependent event data sharing interdependencies, and outputs troubleshooting analysis determined from these interdependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional troubleshooting methods are used for complex IoT devices, then comprehensive analysis of all components can be performed, but the time and resources required for debugging increase significantly
Solution Approach 1:
The patent segments the performance data into discrete events with specific timestamps, organizing them into a structured format that can be analyzed individually. By dividing the complex interdependency analysis into smaller event-based units, the system can process troubleshooting data more efficiently without losing comprehensive analysis capability
Solution Approach 2:
The system performs preliminary organization of performance data into events with timestamps and interdependency relationships before actual troubleshooting analysis. This pre-processing structure enables faster query and analysis during debugging, reducing the time required when issues actually occur
2Reliability
If all performance data from multiple electrical components is analyzed, then complete troubleshooting coverage is achieved, but data processing complexity and resource requirements increase
Solution Approach 1:
The patent divides performance data from multiple electrical components into discrete, timestamped events. Each event represents a specific state or occurrence from a particular component, making the data manageable and analyzable in structured units rather than as an overwhelming continuous stream
Solution Approach 2:
The system dynamically adjusts the time window for analysis based on the specific troubleshooting needs and interdependency relationships detected. This dynamic approach allows the system to focus computational resources on relevant time periods and components, reducing overall processing complexity while maintaining comprehensive coverage
3Productivity
If a fixed time window is used for analyzing performance data, then data processing is simplified, but the ability to capture relevant interdependent events is reduced
Solution Approach 1:
The patent implements a dynamic time window that adapts its boundaries based on the interdependency relationships between events. When analyzing a primary event, the time window automatically extends to include dependent events that occur before or after it, ensuring no relevant information is lost while maintaining processing efficiency through targeted analysis
Data Source
AI summary
This disclosure provides methods, devices, and systems for troubleshooting operation of an electronic device, the method comprising by receiving performance data from a plurality of electrical components, and including a portion of the performance data within a dynamic time window. The portion includes event data. The dynamic time window may be manipulated to include dependent event data within the portion. The dependent event data shares an interdependency with the event data. Troubleshooting analysis determined from the interdependency may be output.