Cloud Blacklist Screening for Mobile Analytical Measurement Compatibility
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Solution Overview
Problem
Existing methods for using mobile devices for analytical measurements, particularly optical detection reactions, fail to account for the individual hardware and software properties of diverse devices, leading to inconsistent and unreliable results.
Innovation Solution
A cloud-based blacklist database system that provides suitability information for mobile devices, considering both hardware and software, by evaluating and updating blacklist information based on individual check results from mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mobile devices are used for analytical measurements without centralized compatibility management, then device versatility and ease of operation are improved, but measurement reliability and consistency deteriorate due to hardware and software variations
Solution Approach 1:
The patent implements a universal blacklist database system that serves all mobile devices regardless of manufacturer, model, or operating system. The cloud server provides centralized compatibility management through a unified interface, allowing diverse mobile devices to be evaluated against the same suitability criteria while maintaining consistent measurement standards across the entire ecosystem.
Solution Approach 2:
The system establishes a feedback loop where measurement results and compatibility issues from mobile devices are transmitted back to the cloud server. The server evaluates this feedback and updates the blacklist database accordingly, continuously improving device suitability assessment. This closed-loop feedback mechanism ensures that reliability improvements are propagated across all devices using the system.
2Measurement precision
If individual device evaluation is performed for each mobile device, then measurement accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The patent segments the device evaluation process into distinct components: hardware evaluation, software evaluation, and combined suitability assessment. Each component is evaluated separately and stored as independent data in the blacklist database. This segmentation allows the system to maintain detailed evaluation criteria for measurement accuracy while organizing complexity into manageable, modular components that can be processed independently.
3Measurement precision
If comprehensive hardware and software evaluation is performed, then device suitability accuracy is improved, but evaluation time and computational resources increase
Solution Approach 1:
The patent performs preliminary evaluation of mobile device hardware and software characteristics before actual analytical measurements are conducted. The cloud server assesses camera specifications, processor capabilities, operating system version, and other relevant parameters in advance, storing the results in the blacklist database. This preliminary action ensures that when a device is selected for measurement, its suitability has already been verified, eliminating the need for time-consuming evaluations during the actual measurement process.
Data Source
AI summary
A computer-implemented method of operating a cloud server for providing blacklist information includes a cloud-based blacklist database on the cloud server that has blacklist information on a plurality of mobile devices with cameras. The blacklist information relates to suitability of the mobile devices' hardware and software for performing an analytical measurement. A mobile device may receive upon request blacklist information from the blacklist database concerning the suitability of the requesting mobile device's hardware and software for performing the analytical measurement. The cloud server may receive individual check result information from a sending mobile device concerning the suitability of the sending mobile device's hardware and software for performing the analytical measurement. The individual check result information may be evaluated to determine if the blacklist information on the blacklist database should be updated.