Sample Tube Identification for Accurate Probe Positioning

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Solution Overview

Problem

Existing automated analyzers face challenges in accurately determining the type or class of containers, particularly sample tubes, due to variations in geometry and hematocrit levels, leading to potential probe crashes and incorrect liquid aspiration, which can result in contamination or insufficient sample analysis.

Innovation Solution

A method involving image capture, edge detection, and database aggregation to determine container type or class, along with hematocrit assessment using convolutional neural networks, to accurately identify container dimensions and separate layers in centrifuged blood samples.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual sorting of tube types is used, then user can place tubes in correct rack positions, but human errors lead to wrong tube placement and contamination

Engineering Contradiction:

Improvetube placement accuracyVSAvoidmanual sorting complexity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system enables automated identification of tube types through image capture and convolutional neural network processing. The analyzer automatically determines tube characteristics (diameter, height, false bottom presence) without requiring manual sorting by the user, eliminating human error while maintaining ease of operation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical sorting process with an automated optical and computational system. Image capture devices and neural network algorithms substitute for human visual inspection and manual placement, achieving both high reliability and ease of operation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If probe moves quickly to liquid surface, then throughput is improved, but liquid level detection requires slower velocity reducing efficiency

Engineering Contradiction:

ImprovethroughputVSAvoidliquid level detection accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary tube identification and geometry determination before the probing phase. By pre-determining tube diameter, height, and false bottom presence through image analysis, the system can calculate expected liquid levels in advance, allowing the probe to move quickly without sacrificing detection accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent separates the measurement process into distinct phases: rapid tube identification using image capture and neural networks, followed by targeted liquid level detection. This segmentation allows different velocities for different tasks, maintaining both throughput and measurement precision.

Inventive Principle:

Principle #1Segmentation

3Measurement precision

If wrong tube bottom geometry is used for calculation, then probe stop position is incorrect, but probe crash occurs

Engineering Contradiction:

Improveprobe stop position accuracyVSAvoidprobe crash risk

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary identification of tube bottom geometry (distinguishing false bottom from real bottom) through image capture and neural network analysis before the probing operation. This advance knowledge allows correct calculation of probe stop positions, preventing probe crashes while ensuring measurement precision.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

By identifying false bottom tubes in advance through image analysis, the system can adjust probe motion parameters beforehand to prevent crashes. The neural network classification of tube types enables preparatory adjustments to probing depth and velocity, cushioning against potential harmful effects.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

4Measurement precision

If automated image processing is used, then tube identification accuracy is improved, but system complexity increases

Engineering Contradiction:

Improvetube type identification accuracyVSAvoidimage processing system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent employs convolutional neural networks and automated image processing algorithms to replace complex manual identification procedures. While the computational system adds complexity, it eliminates the need for multiple manual sorting steps and human judgment, achieving net simplification of the overall process while improving accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12626522B2Instrument parameter determination based on sample tube identification

Publication Date: 2026.05.12 INSTRUMENTATION LABORATORY COMPANY

AI summary

A system and method for reducing the responsibility of the user significantly by applying an optical system that can identify container like sample tubes with respect to their characteristics, e.g., shapes and inner dimensions, from their visual properties by capturing images from a rack comprising container and processing said images for reliably identifying a container tyle.