KR-102961014-B1 - METHOD FOR OPTIMIZING DRUG CABINET ARRANGEMENT USING AI AND SERVER FOR PERFORMING THEREOF

KR102961014B1KR 102961014 B1KR102961014 B1KR 102961014B1KR-102961014-B1

Abstract

The method for optimizing the placement of medicine cabinets using AI according to the present invention comprises the steps of: collecting medicine identification codes and location identification codes to generate mapping data between medicines and locations; collecting usage history information of medicines to derive correlation information between medicines; analyzing pharmacist movement history information to calculate spatial movement cost information; determining possible placement locations for the medicines according to the storage requirements of the medicines; performing AI-based calculations based on the mapping data, correlation information, movement cost information, and possible placement location information to calculate appropriate placement locations for each medicine; and visually displaying the calculated placement results or providing them as an interface for user review.

Inventors

김병주
장경일

Assignees

주식회사 참약사

Dates

Publication Date: 20260507
Application Date: 20250804

Claims (4)

A step of collecting drug identification codes and location identification codes to generate mapping data between drugs and locations; A step of collecting drug usage history information to derive correlation information between drugs; A step of calculating spatial movement cost information by analyzing the pharmacist's movement history information; A step of determining a possible placement location for the drug based on the storage requirements of the drug; A step of calculating an appropriate placement location for each drug by performing an AI-based calculation based on the above mapping data, correlation information, movement cost information, and possible placement location information; and Characterized by including a step of visually representing the calculated batch results or providing them as an interface for user review. Method for Optimizing Medicine Cabinet Placement Using AI.
In paragraph 1, The step of collecting the above drug identification code and location identification code to generate mapping data between the drug and the location A step of receiving a location identification code extracted through a process of scanning the location identification code label using a pharmacy terminal after printing the location identification code as a location identification code label through a printing device and attaching it to each of a plurality of stacking shelves of a pharmaceutical display case; A step of determining whether storage location information is pre-registered in the above location identification code; and Characterized by including the step of generating mapping data between the drug and the location by mapping the corresponding location identification code and the drug identification code according to the above judgment result. Method for Optimizing Medicine Cabinet Placement Using AI.
A mapping database generation unit that collects drug identification codes and location identification codes to generate mapping data between drugs and locations; A drug usage tracking unit that collects and analyzes drug usage history data to track the frequency and usage patterns of each drug; A pharmacist movement tracking unit that analyzes pharmacist movement history information to calculate spatial movement cost information; A storage condition filtering unit that determines possible placement locations for the drug according to the storage requirements of the drug; An AI-based placement optimization unit that calculates an appropriate placement location for each drug by performing AI-based calculations based on the above mapping data, correlation information, movement cost information, and possible placement location information; Characterized by including a batch visualization unit that visually represents the calculated batch results or provides them as an interface for user review. AI-based medal placement optimization server.
In paragraph 3, The above mapping database generation unit The method is characterized by printing a location identification code as a location identification code label using a printing device and attaching it to each of a plurality of stacking shelves in a pharmaceutical display case, receiving a location identification code extracted through a process of scanning the location identification code label using a pharmacy terminal, determining whether storage location information is pre-registered in the location identification code, and mapping the corresponding location identification code and pharmaceutical identification code according to the determination result to generate mapping data between the pharmaceutical and the location. AI-based medal placement optimization server.

Description

Method for Optimizing Drug Cabinet Arrangement Using AI and Server for Performing Thereof The present invention relates to a method for optimizing the placement of medicine cabinets using AI and a server for executing the same. More specifically, the invention relates to a method for optimizing the placement of medicine cabinets using AI and a server for executing the same, wherein the AI learns factors such as the frequency of use, concomitant use relationships, pharmacist movement patterns, and storage conditions of medicines based on mapping data between medicine cabinet location identification codes and medicine identification codes, and thereby enables the automatic generation of a medicine cabinet placement plan optimized for the internal spatial structure of a pharmacy. In environments for drug storage and dispensing, such as pharmacies and hospital pharmacy departments, hundreds of different types of medications are densely arranged and managed within structures like shelves, drawers, and cabinets. In such environments, pharmacists must quickly and accurately locate and retrieve necessary medications during dispensing; consequently, the accurate management of not only the inventory status but also the actual physical location information is critical. Traditionally, while computerized pharmacy inventory management systems effectively managed product names, quantities, expiration dates, and batch numbers, most failed to precisely manage location information regarding the actual storage of medications, such as shelves, cabinets, and drawers. Consequently, pharmacists frequently had to repeatedly search multiple locations to find necessary medications during dispensing, leading to increased dispensing time and decreased operational efficiency. To address these issues, a medicine cabinet map generation system was proposed that digitizes and manages medicine cabinet maps that visually represent the physical structure of pharmacies and the status of medicine storage. By assigning a Location ID to each medicine cabinet and establishing mapping information with the Product IDs of the medicines stored at each location, the system provides real-time storage status and location-based inventory information, thereby contributing to improving pharmacists' visual perception and the accuracy of inventory management. However, the above-mentioned system has the following limitations. First, the system can only record and retrieve static mapping information between locations and drugs, and cannot provide spatial layout optimization functions that reflect the frequency of drug use or the structure of concomitant prescriptions in the actual dispensing site. Second, pharmacists in pharmacies handle multiple concomitant medications simultaneously when performing a single dispensing task. If medications with a high frequency of concomitant use are physically placed far apart from each other, pharmacists are required to move excessively during dispensing, which leads to increased work fatigue and a higher possibility of dispensing errors. Third, the structure of a pharmacy is not fixed but continuously changes due to the addition of shelves, relocation of shelves, and the introduction of new medications. Existing systems could not flexibly respond to these dynamic structural changes; they could only handle modified medicine cabinet structures manually or by resetting the system, which caused inconvenience in practical application. Fourth, the system lacks the ability to learn or improve layout based on actual usage data, such as pharmacists' dispensing patterns, distribution of drug usage by time of day, and pharmacists' physical movement paths, and thus lacks the capability for long-term and continuous improvement of space efficiency. Consequently, while current technology is useful for identifying “where something is stored,” decisions regarding “where to place something optimally” still rely on the pharmacist’s subjective judgment or experience, which limits the standardization and optimization of pharmacy operations. Korean Registered Patent No. 10-0821612 relates to a modular exhaust reagent cabinet. It discloses that the indoor space of a laboratory can be used efficiently by allowing reagent cabinets of various sizes to be detachably assembled according to the size of the laboratory and the type and quantity of experimental materials to be stored in the cabinet; however, it does not disclose a solution to the aforementioned problem. FIG. 1 is a network configuration diagram for explaining a system for optimizing the placement of medicines through AI according to one embodiment of the present invention. FIG. 2 is a block diagram illustrating the internal structure of a server for optimizing the placement of medicines through AI according to an embodiment of the present invention. FIG. 3 is a flowchart illustrating an embodiment of a method for optimizing the placement of medicine boxes using AI according to the present