Experimental Animal Inventory Optimization for Cage Space and Supply Matching

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Solution Overview

Problem

The management of experimental animals, particularly mice, is challenged by space constraints and mismatches between demand and supply due to high fertility and varying breeding cycles, leading to inefficient use of laboratory resources.

Innovation Solution

An experimental animal managing method utilizing an algorithm that calculates and optimizes the expected production amount, number of cages, and total number of cages based on user log data and individual data, adjusting these factors to minimize mismatches and maximize efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If the number of cages is increased to accommodate high fertility animals, then the production amount of experimental animals is improved, but the space availability deteriorates

Engineering Contradiction:

Improveproduction amount of experimental animalsVSAvoidspace availability

Core Design Contradiction:

ProductivityVSArea of stationary object

Solution Approach 1:

The system performs preliminary calculations of expected production amounts and required cage numbers based on historical data and breeding parameters. By predicting future animal populations in advance, the system allows proactive space planning and resource allocation, preventing space constraints before they become critical issues.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts breeding parameters such as mating pair allocations and litter management strategies based on real-time space availability and production targets. This dynamic optimization allows the system to maximize animal production within constrained space by continuously adapting breeding intensity and cage utilization.

Inventive Principle:

Principle #15Dynamics

2Productivity

If the breeding population is increased to meet demand, then the supply of experimental animals is improved, but the mismatch between demand and supply worsens due to breeding delays

Engineering Contradiction:

Improvesupply of experimental animalsVSAvoidmismatch between demand and supply

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system calculates expected production timelines based on gestation periods, weaning ages, and maturity requirements. By predicting when animals will be available for experimentation, the system enables advance demand planning and timing of breeding activities to ensure supply matches experimental schedules, reducing time mismatches.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors actual production outcomes against predicted timelines and adjusts breeding parameters accordingly. By incorporating feedback from previous breeding cycles, the system refines its predictions of animal availability, improving the alignment between supply timing and experimental demand.

Inventive Principle:

Principle #23Feedback

3Area of stationary object

If the number of cages is optimized to reduce space usage, then the space efficiency is improved, but the production amount of experimental animals deteriorates

Engineering Contradiction:

Improvespace efficiencyVSAvoidproduction amount of experimental animals

Core Design Contradiction:

Area of stationary objectVSProductivity

Solution Approach 1:

The system optimizes cage utilization by adjusting parameters such as animals per cage ratios, litter groupings, and breeding pair configurations. By changing these management parameters, the system maximizes the productive output per unit of space, achieving high production volumes within limited physical constraints.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system dynamically reconfigures cage assignments and breeding allocations based on real-time space availability and production targets. This allows continuous optimization of space utilization while maintaining or improving overall production output, adapting to changing constraints without sacrificing productivity.

Inventive Principle:

Principle #15Dynamics

4Device complexity

If manual management methods are used to control animal populations, then the system complexity is reduced, but the management efficiency deteriorates due to difficulty in controlling appropriate population

Engineering Contradiction:

Improvemanagement system complexityVSAvoidmanagement efficiency

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The system performs self-service by automatically calculating expected production amounts, determining required cage numbers, and optimizing breeding parameters based on input data. This automated self-management eliminates the need for complex manual tracking while improving population control accuracy and management efficiency through systematic computational optimization.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250272650A1Experimental animal managing method

Publication Date: 2025.08.28 CHUNGNAM NAT UNIV HOSPITAL

AI summary

The present invention relates to an experimental animal managing method. Disclosed is an experimental animal managing method comprising: a receiving step (S10) of receiving user log data and individual data of an experimental animal; and a processing step (S20) of calculating at least one of an expected production amount of individuals, the expected number of cages to be produced, and the expected total number of cages, on the basis of the user log data and the individual data, and optimizing at least one among the calculated expected production amount of individuals, the calculated expected number of cages to be produced, and the calculated expected total number of cages according to optimization requirement.