Diagnostic Data Inference Model for Unmanaged Device Quality
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Solution Overview
Problem
The challenge lies in managing diagnostic data collection from both managed and unmanaged devices, where unmanaged devices lack quality control and maintenance, leading to unreliable data that can impact accurate diagnosis and treatment.
Innovation Solution
A system that registers and qualifies unmanaged devices, uses inference models to process and rate diagnostic data, and implements access controls to ensure data quality and security, thereby reducing overhead and improving data reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If diagnostic data is collected from unmanaged devices, then the quantity of diagnostic data is increased, but the reliability of the diagnostic data deteriorates
Solution Approach 1:
The patent introduces a data management system as an intermediary between unmanaged devices and the diagnostic process. This system qualifies devices, monitors data quality, and selectively accepts or rejects data based on predefined criteria, thereby mediating between the need for large data quantities and the requirement for data reliability
Solution Approach 2:
The system performs preliminary qualification of unmanaged devices before allowing them to contribute diagnostic data. This preliminary action includes assessing device capabilities, establishing quality thresholds, and setting monitoring parameters in advance, ensuring that only data meeting reliability standards is collected
2Reliability
If quality control measures are implemented for unmanaged devices, then the reliability of diagnostic data is improved, but the device complexity increases
Solution Approach 1:
The data management system is segmented into distinct functional modules: device qualification module, data quality monitoring module, and data acceptance/rejection module. This segmentation allows each component to perform its specific function independently, reducing overall system complexity while maintaining comprehensive quality control
Solution Approach 2:
Unmanaged devices are required to self-qualify by providing information about their capabilities and operating parameters. The system automatically assesses this information against predefined criteria, reducing the need for manual configuration and simplifying the quality control process
3Quantity of substance
If all diagnostic data from unmanaged devices is collected and stored, then the quantity of diagnostic data is increased, but the loss of time for data processing increases
Solution Approach 1:
The system implements partial action by selectively collecting only those portions of diagnostic data that meet quality criteria and are relevant to specific diagnostic purposes. Rather than collecting all data excessively, the system filters and accepts only the necessary subset, reducing processing time while maintaining data quantity sufficiency
4Reliability
If access controls are implemented for diagnostic data, then the security of diagnostic data is improved, but the device complexity increases
Solution Approach 1:
Access controls are implemented with local quality by assigning different permission levels to different user roles and data types. Rather than applying uniform complex access control to all data, the system tailors access restrictions to specific diagnostic data categories and user needs, improving security while managing complexity
Data Source
AI summary
Methods and systems for managing collection of diagnostic data are disclosed. To collect diagnostic data, unmanaged devices may be used. The unmanaged devices may be registered with a data management system. During the registration process, procedures for processing diagnostic data from the unmanaged devices may be established. The procedures may be established based on data collection performance of the unmanaged devices. The procedures may reduce the likelihood of use of diagnostic data that is unreliable. To manage overhead for obtaining and maintaining data, a distributed inference model may be used to selective some diagnostic data for retention and other diagnostic data for removal. Once obtained, the diagnostic data may be rated for different uses based on the performance of the hardware used to obtain the diagnostic data.