Automated Cell Selection for Specimen Loading
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Solution Overview
Problem
Current automated detection systems for microbial growth in biological samples often experience false positives due to temperature fluctuations caused by newly intaken specimen containers being loaded next to occupied cells, which can affect sensor readings and induce false positive results.
Innovation Solution
The system electronically evaluates available cells to select the one with the lowest risk of inducing a false positive by considering the test status and temperature stability of neighboring cells, using a neighboring factor to rank cells and prioritize those with lower risk for loading newly intaken specimen containers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If newly intaken specimen containers are loaded into available cells without evaluation, then loading speed and productivity are improved, but false positive test results increase due to temperature fluctuations
Solution Approach 1:
The system performs preliminary evaluation of available cells by assessing neighboring cell temperatures and occupancy status before loading a new specimen container. This advance assessment identifies cells at risk of causing false positives and selects safer alternative cells, preventing the harmful effect before it occurs while maintaining efficient loading operations.
Solution Approach 2:
The system continuously monitors temperature data from neighboring cells and uses this feedback to dynamically evaluate the risk of loading a new container in a particular cell. The controller adjusts cell selection based on real-time temperature fluctuations and occupancy patterns, creating a closed-loop system that prevents false positives while maintaining productivity.
2Adaptability or versatility
If specimen containers are loaded into cells with occupied neighboring cells, then cell utilization is improved, but temperature-induced false positives increase
Solution Approach 1:
The system applies different selection criteria based on the local characteristics of each cell and its neighbors. Instead of uniform treatment, the controller evaluates the specific thermal environment of each available cell by examining the temperature and occupancy status of neighboring cells, selecting cells with favorable local thermal conditions to minimize false positives while maximizing overall cell utilization.
3Ease of operation
If automated cell selection is implemented without evaluation, then operation simplicity is maintained, but measurement precision of microbial growth detection deteriorates
Solution Approach 1:
The system performs self-evaluation by automatically assessing the thermal and occupancy characteristics of available cells without requiring manual intervention. The controller independently monitors temperature data, evaluates neighboring cell conditions, and selects appropriate cells autonomously, maintaining operational simplicity while ensuring measurement precision through intelligent automated decision-making.
Data Source
Figure 1
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Figure 5A~5B
AI summary
Automated test instruments electronically determine and/or obtain cell availability of cells of a holding structure in an incubated test chamber and for each of a plurality of open and available cells and electronically identify neighboring cells, electronically determining whether each of the identified neighboring cells are occupied or empty and, if occupied, electronically evaluating at least one of a test status or a time from load of a specimen container held therein. The instruments then electronically rank each of the plurality of open and available cells based at least in part on whether the identified neighboring cells are occupied or empty and the at least one of test status or time from load of the occupied cells of the identified neighboring cells. The loading mechanism is then directed to load an incoming specimen container into a selected one of the open and available cells based on the ranking.