system

Abstract

The system according to this embodiment aims to prevent medication errors by integrating pharmaceutical inventory management and medication dispensing management. [Solution] The system according to the embodiment comprises an acquisition unit, an update unit, a collection unit, a management unit, a verification unit, and a prevention unit. The acquisition unit acquires information on pharmaceuticals. The update unit updates the inventory status of pharmaceuticals based on the information acquired by the acquisition unit. The collection unit collects patient medication information. The management unit manages the medication information collected by the collection unit. The verification unit verifies the medication information managed by the management unit with the inventory information updated by the update unit. The prevention unit prevents incorrect medication based on the information verified by the verification unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the inventory management and dosing management of pharmaceuticals are not sufficiently integrated, and it is difficult to prevent medication errors. [[ID=3|6]]

[0005] The system according to the embodiment aims to integrate the inventory management and dosing management of pharmaceuticals and prevent medication errors.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an update unit, a collection unit, a management unit, a verification unit, and a prevention unit. The acquisition unit acquires information on pharmaceuticals. The update unit updates the inventory status of pharmaceuticals based on the information acquired by the acquisition unit. The collection unit collects patient medication information. The management unit manages the medication information collected by the collection unit. The verification unit verifies the medication information managed by the management unit against the inventory information updated by the update unit. The prevention unit prevents incorrect medication based on the information verified by the verification unit. [Effects of the Invention]

[0007] The system according to this embodiment integrates pharmaceutical inventory management and medication management, and can prevent medication errors. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network)..

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The pharmaceutical management system according to an embodiment of the present invention is a system that uses generating AI to automate pharmaceutical inventory management and dispensing checks, allowing pharmacists to focus on their core duties. This pharmaceutical management system involves attaching IC tags to pharmaceuticals and reading them with an RFID reader. Next, the generating AI manages the inventory status of pharmaceuticals in real time and automatically replenishes the necessary pharmaceuticals. The generating AI also manages dispensing information for each patient and performs checks to prevent dispensing errors. As a result, pharmacists are freed from inventory management and can concentrate on dispensing medication to patients. For example, IC tags are attached to pharmaceuticals and read with an RFID reader. At this time, a unique ID is assigned to each pharmaceutical, and inventory information is centrally managed. For example, pharmaceutical A is assigned ID001, pharmaceutical B is assigned ID002, and so on. This allows for real-time monitoring of the pharmaceutical inventory status. Next, the generating AI manages the pharmaceutical inventory status in real time. The generating AI updates the inventory status based on the information read by the RFID reader. For example, if the inventory of drug A decreases from 10 to 9 units, the generating AI automatically updates the inventory information and replenishes the necessary drug. Furthermore, the generating AI manages medication information for each patient. Based on the patient's medication information, the generating AI performs checks to prevent medication errors. For example, if patient A needs to be prescribed drug A, the generating AI checks the ID of drug A and performs a check to prevent medication errors. This reduces the risk of medication errors. This system frees pharmacists from inventory management and allows them to concentrate on dispensing medication to patients. For example, pharmacists can provide optimal medication according to the patient's symptoms, thereby improving the effectiveness of the patient's treatment. In addition, inventory management by the generating AI improves the traceability of drugs and streamlines drug management. As a result, the drug management system allows pharmacists to focus on their core duties and automates drug inventory management and dispensing checks.

[0029] The pharmaceutical management system according to this embodiment comprises an acquisition unit, an update unit, a collection unit, a management unit, a verification unit, and a prevention unit. The acquisition unit acquires information about pharmaceuticals. This information includes, but is not limited to, the name, ingredients, efficacy, and side effects of the pharmaceuticals. The acquisition unit can, for example, attach an IC tag to a pharmaceutical and read it with an RFID reader. The IC tag is assigned a unique ID for the pharmaceutical, and by reading it with an RFID reader, information about the pharmaceutical can be acquired. The update unit updates the inventory status of pharmaceuticals based on the information acquired by the acquisition unit. The update unit updates the inventory status based on the information read by the RFID reader. The update unit can update the inventory status in real time and automatically replenish necessary pharmaceuticals. The collection unit collects patient medication information. The collection unit collects patient medication information from, for example, electronic medical records. This medication information includes the date and time of administration, the amount of medication administered, and the person administering the medication. The management unit manages the collected medication information. The management unit centrally manages the medication information using, for example, a database. The management unit manages medication information and performs checks to prevent medication errors. The verification unit verifies the medication information managed by the management unit against the inventory information updated by the update unit. The verification unit performs, for example, cross-database verification. The verification unit verifies the medication information against the inventory information and performs checks to prevent medication errors. The prevention unit prevents medication errors based on the information verified by the verification unit. The prevention unit prevents medication errors using, for example, an alert system. The prevention unit reduces the risk of medication errors and ensures patient safety. As a result, the pharmaceutical management system according to this embodiment allows pharmacists to focus on their core duties and automates pharmaceutical inventory management and medication checks.

[0030] The acquisition unit can attach IC tags to pharmaceuticals and read them with an RFID reader. IC tags include, but are not limited to, RFID tags and barcodes. For example, the acquisition unit can attach an RFID tag to a pharmaceutical and read it with an RFID reader. The RFID tag has a unique ID for the pharmaceutical, and by reading it with an RFID reader, information about the pharmaceutical can be obtained. The acquisition unit can also attach a barcode to a pharmaceutical and read it with a barcode reader. The barcode has a unique ID for the pharmaceutical, and by reading it with a barcode reader, information about the pharmaceutical can be obtained. This allows for accurate acquisition of pharmaceutical information and streamlines inventory management. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input the information read by the RFID reader into AI to acquire pharmaceutical information.

[0031] The update unit can update inventory status based on information read by an RFID reader. Inventory status includes, but is not limited to, the number of items in stock, storage location, and expiration date. The update unit can update inventory status in real time and automatically replenish necessary medicines. The update unit can also update inventory status periodically. For example, the update unit can update inventory status at a fixed time each day. This allows for real-time updates of inventory status and automatic replenishment of necessary medicines. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input information read by an RFID reader into AI and update inventory status.

[0032] The data collection unit can collect patient medication information. This information may include, but is not limited to, the date and time of administration, the dosage, and the person administering the medication. The data collection unit can collect patient medication information from, for example, electronic medical records. Electronic medical records contain patient medication information, and the data collection unit can collect this information from the electronic medical records. The data collection unit can also manually input medication information. For example, the data collection unit can allow the person administering the medication to manually input the medication information. This allows for the accurate collection of patient-specific medication information and provides basic data to prevent medication errors. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input medication information collected from electronic medical records into AI and collect the medication information.

[0033] The management department can manage the collected medication information. This management includes, but is not limited to, database management or paper-based management. For example, the management department can centrally manage medication information using a database. The database contains the collected medication information, and the management department can manage it using the database. The management department can also manage medication information using paper-based methods. For example, the management department can record and manage medication information on paper. This allows for centralized management of medication information and the implementation of checks to prevent medication errors. Some or all of the above processes in the management department may be performed using, for example, AI, or not. For example, the management department can input medication information recorded in the database into AI and manage the medication information.

[0034] The matching unit can match medication information with inventory information. Matching medication information with inventory information includes, but is not limited to, matching between databases or manual matching. For example, the matching unit can perform matching between databases. The databases record medication information and inventory information, and the matching unit can perform matching between databases. The matching unit can also manually match medication information with inventory information. For example, the matching unit can allow a medication provider to manually match medication information with inventory information. This helps prevent medication errors by matching medication information with inventory information. Some or all of the above-described processes in the matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can input the matching between databases into AI and match medication information with inventory information.

[0035] The prevention unit can prevent medication errors based on the verified information. Methods for preventing medication errors include, but are not limited to, alert systems and double-checking. For example, the prevention unit can prevent medication errors using an alert system. The alert system issues a warning when there is a risk of medication errors and draws the attention of the person administering the medication. The prevention unit can also perform double-checking. For example, the prevention unit can prevent medication errors by having the person administering the medication double-check. This reduces the risk of medication errors and ensures patient safety. Some or all of the above processes in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can input the alert system into AI to prevent medication errors.

[0036] The acquisition unit can analyze the frequency of drug use and select the optimal acquisition method. Methods for analyzing usage frequency include, but are not limited to, analysis of past usage data and statistical methods. For example, the acquisition unit can analyze past usage data to analyze the frequency of drug use. Past usage data includes the date and time of use and the amount used. The acquisition unit can automatically acquire frequently used drugs on a regular basis. Drugs with low usage frequency can be acquired as needed. Furthermore, for drugs with large fluctuations in usage frequency, the optimal acquisition timing can be determined based on past data. This allows for the selection of the optimal acquisition method based on drug usage frequency, thereby streamlining inventory management. Some or all of the above-described processes in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input past usage data into AI to analyze the frequency of drug use.

[0037] The acquisition unit can filter pharmaceuticals based on their expiration date and storage conditions when acquiring them. Expiration dates and storage conditions include, but are not limited to, use-by dates and temperature control. For example, the acquisition unit can prioritize acquiring pharmaceuticals with approaching expiration dates. Pharmaceuticals with approaching expiration dates need to be used quickly, so they can be acquired preferentially. The acquisition unit can also acquire pharmaceuticals with strict storage conditions, taking appropriate storage locations into consideration. Pharmaceuticals with strict storage conditions need to have appropriate storage locations, so they can be acquired with these conditions in mind. Furthermore, the acquisition unit can acquire pharmaceuticals with long expiration dates depending on inventory levels. Acquiring pharmaceuticals with long expiration dates according to inventory levels can streamline inventory management. This allows for filtering based on pharmaceutical expiration dates and storage conditions, enabling the acquisition of appropriate pharmaceuticals. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not. For example, the acquisition unit can input expiration date and storage condition information into AI and perform pharmaceutical filtering.

[0038] The acquisition unit can prioritize the acquisition of highly relevant information when acquiring pharmaceuticals, taking into account the pharmaceutical manufacturer's information. Manufacturer information includes, but is not limited to, the name of the manufacturing company and the country of manufacture. For example, the acquisition unit can prioritize the acquisition of pharmaceutical information from a specific manufacturer. Pharmaceutical information from a specific manufacturer can be prioritized because it is highly reliable. The acquisition unit can also acquire pharmaceutical information considering the reliability of the manufacturer. If the manufacturer is highly reliable, its pharmaceutical information can be prioritized for acquisition. Furthermore, the acquisition unit can acquire pharmaceutical information based on the manufacturer's past quality data. If the manufacturer's past quality data is good, its pharmaceutical information can be prioritized for acquisition. This allows for the priority acquisition of highly relevant information, taking into account the pharmaceutical manufacturer's information, and enables appropriate pharmaceutical management. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input manufacturer information into AI and prioritize the acquisition of highly relevant information.

[0039] The acquisition unit can analyze the past usage history of a drug and obtain relevant information when acquiring it. Past usage history includes, but is not limited to, the date and time of use and the amount used. For example, the acquisition unit can analyze past usage history to understand the frequency of drug use. Drugs with high usage frequency can be acquired automatically on a regular basis. Drugs with low usage frequency can be acquired as needed. Furthermore, for drugs with large fluctuations in usage frequency, the optimal acquisition timing can be determined based on past data. In this way, appropriate drug management can be performed by analyzing the past usage history of drugs and obtaining relevant information. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input past usage history into AI and analyze the frequency of drug use.

[0040] The update unit can analyze the consumption rate of pharmaceuticals during inventory updates to determine the optimal replenishment timing. Methods for analyzing consumption rate include, but are not limited to, analyzing past consumption data and statistical methods. For example, the update unit can analyze past consumption data to understand the consumption rate of pharmaceuticals. Pharmaceuticals with a fast consumption rate can be replenished frequently. Pharmaceuticals with a slow consumption rate can be replenished as needed. Furthermore, for pharmaceuticals with large fluctuations in consumption rate, the optimal replenishment timing can be determined based on past data. This allows for the determination of optimal replenishment timing based on the consumption rate of pharmaceuticals, thereby streamlining inventory management. Some or all of the above processes in the update unit may be performed using, for example, AI, or not. For example, the update unit can input past consumption data into AI to analyze the consumption rate of pharmaceuticals.

[0041] The update unit can update inventory while considering the storage location information of pharmaceuticals. Storage location information includes, but is not limited to, the name and conditions of the storage location. For example, the update unit can update pharmaceuticals with limited storage locations by securing appropriate storage locations. Since pharmaceuticals with limited storage locations require appropriate storage, the update unit can consider the storage location when updating. The update unit can also update pharmaceuticals with strict storage conditions by considering appropriate storage locations. Since pharmaceuticals with strict storage conditions require appropriate storage, the update unit can consider the storage conditions when updating. Furthermore, if a change in storage location is necessary, the update unit can select an appropriate storage location and update. This allows for inventory updates that consider the storage location information of pharmaceuticals, enabling proper inventory management. Some or all of the above-described processes in the update unit may be performed using, for example, AI, or without AI. For example, the update unit can input storage location information into AI and perform inventory updates.

[0042] The update unit can update inventory while considering the manufacturing lot information of pharmaceuticals. Manufacturing lot information includes, but is not limited to, lot numbers and manufacturing dates. The update unit can, for example, prioritize updating pharmaceuticals from a specific manufacturing lot. Pharmaceuticals from a specific manufacturing lot can be prioritized for updating because they are more reliable. The update unit can also update inventory while considering the reliability of the manufacturing lot. If the manufacturing lot is highly reliable, that pharmaceutical can be prioritized for updating. Furthermore, the update unit can update inventory based on the historical quality data of the manufacturing lot. If the historical quality data of the manufacturing lot is good, that pharmaceutical can be prioritized for updating. This allows for inventory updates that consider the manufacturing lot information of pharmaceuticals, enabling appropriate inventory management. Some or all of the above processing in the update unit may be performed using, for example, AI, or not using AI. For example, the update unit can input manufacturing lot information into AI and perform inventory updates.

[0043] The update unit can improve the accuracy of inventory updates by referring to relevant literature on pharmaceuticals during inventory updates. Relevant literature includes, but is not limited to, academic papers and technical reports. For example, the update unit can determine the optimal inventory update method based on the relevant literature on pharmaceuticals. This literature includes the latest research findings and usage guidelines for pharmaceuticals, and the update unit can improve the accuracy of inventory updates by referring to this information. Furthermore, the update unit can adjust the timing and method of inventory updates based on the information in the relevant literature. This allows for improved inventory updates and appropriate inventory management by referring to relevant literature on pharmaceuticals. Some or all of the above processes in the update unit may be performed using, for example, AI, or not. For example, the update unit can input information from relevant literature into AI to improve the accuracy of inventory updates.

[0044] The data collection unit can analyze the patient's past medication history to select the optimal collection method when collecting medication information. Past medication history includes, but is not limited to, the date and time of administration and the amount of medication administered. The data collection unit can analyze past medication history to understand the patient's medication pattern. Based on the medication pattern, it can select the optimal collection method. For example, information on medications that have been frequently administered in the past can be automatically collected on a regular basis. It can also prioritize the collection of information on specific medications from past medication history. Furthermore, the collection method can be adjusted to take into account fluctuations in the medication history. This allows for appropriate information collection by analyzing the patient's past medication history and selecting the optimal collection method. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input past medication history into AI to analyze the medication pattern.

[0045] The data collection unit can filter medication information based on the patient's current health status. Current health status includes, but is not limited to, blood pressure and body temperature. For example, if a patient's health is deteriorating, the data collection unit prioritizes collecting important medication information. Since patients with deteriorating health require prompt attention, prioritizing the collection of important medication information is crucial. Furthermore, if a patient's health is stable, the data collection unit can collect medication information using the standard collection method. It can also adjust the collection method to account for fluctuations in health status. This allows for filtering based on the patient's current health status and prioritizes the collection of important information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input patient health information into AI to filter medication information.

[0046] The data collection unit can prioritize the collection of highly relevant information when collecting medication information, taking into account the patient's lifestyle. Lifestyle information includes, but is not limited to, dietary habits and exercise habits. For example, the data collection unit prioritizes the collection of medication information related to the patient's lifestyle. Medication information related to lifestyle has a significant impact on the patient's health condition, so it can be collected preferentially. The data collection unit can also adjust the collection method to account for changes in lifestyle. Furthermore, it can analyze lifestyle patterns and determine the optimal collection method. This allows for the priority collection of highly relevant information, taking into account the patient's lifestyle, and enables appropriate information management. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input lifestyle information into AI and prioritize the collection of highly relevant information.

[0047] The management department can select the optimal management method when managing medication information by referring to the patient's past treatment history. Past treatment history includes, but is not limited to, treatment dates and treatment details. The management department can, for example, refer to past treatment history to understand the patient's treatment patterns. Based on these treatment patterns, it can select the optimal management method. For example, medication information related to treatments that have been frequently performed in the past can be managed preferentially. It can also prioritize the management of medication information related to specific treatments based on past treatment history. Furthermore, the management method can be adjusted to take into account changes in the treatment history. This allows for the selection of the optimal management method by referring to the patient's past treatment history and ensuring appropriate information management. Some or all of the above processes in the management department may be performed using, for example, AI, or not. For example, the management department can input past treatment history into AI and analyze treatment patterns.

[0048] The management department can manage medication information while taking into account the patient's allergy information. Allergy information includes, but is not limited to, the type of allergen and the severity of the allergic reaction. For example, the management department manages medication information based on the patient's allergy information. Allergy information is important for ensuring patient safety and must be managed appropriately. The management department can also adjust its management methods in consideration of fluctuations in allergy information. Furthermore, it can analyze patterns in allergy information and determine the optimal management method. This allows for the management of medication information while taking the patient's allergy information into consideration, ensuring appropriate information management. Some or all of the above processes in the management department may be performed using, for example, AI, or not. For example, the management department can input allergy information into AI and manage medication information.

[0049] The management department can manage medication information while considering the patient's family history information. Family history information includes, but is not limited to, family medical history and genetic factors. The management department manages medication information based on the patient's family history information. Family history information needs to be managed appropriately because it affects the patient's health status. The management department can also adjust its management methods in consideration of changes in family history information. Furthermore, it can analyze patterns in family history information and determine the optimal management method. This allows for the management of medication information while considering the patient's family history information, enabling appropriate information management. Some or all of the above processes in the management department may be performed using, for example, AI, or not. For example, the management department can input family history information into AI and manage medication information.

[0050] The matching unit can improve the accuracy of the matching process by considering drug interaction information during the matching process. Interaction information includes, but is not limited to, drug interactions and food interactions. The matching unit improves the accuracy of the matching process based on drug interaction information, for example. Drugs with a high risk of interaction can be matched preferentially. The matching unit can also perform matching by referring to the latest data on interaction information. This improves the accuracy of the matching process by considering drug interaction information and enables appropriate information matching. Some or all of the above processing in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input interaction information into AI to improve the accuracy of the matching process.

[0051] The matching unit can cross-reference the patient's medication history and drug inventory information during the matching process. The medication history includes, but is not limited to, the date and time of administration and the amount administered. The matching unit can, for example, compare the inventory information based on the patient's medication history. It can perform the matching by confirming that the medication history and inventory information match. The matching unit can also compare the inventory information while taking into account changes in the medication history. This allows for appropriate information matching by cross-referencing the patient's medication history and drug inventory information. Some or all of the above processing in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input the medication history and inventory information into AI and perform the matching by cross-referencing.

[0052] The matching unit can perform matching while considering the manufacturer information of the pharmaceutical product. Manufacturer information includes, but is not limited to, the name of the manufacturing company and the country of manufacture. The matching unit can, for example, prioritize matching pharmaceutical products from a specific manufacturer. Pharmaceutical products from a specific manufacturer can be prioritized for matching because they are highly reliable. The matching unit can also perform matching while considering the reliability of the manufacturer. If the manufacturer is highly reliable, that pharmaceutical product can be prioritized for matching. Furthermore, the matching unit can also perform matching based on the manufacturer's past quality data. If the manufacturer's past quality data is good, that pharmaceutical product can be prioritized for matching. This allows for appropriate information matching by considering the manufacturer information of the pharmaceutical product. Some or all of the above processing in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input manufacturer information into AI and perform matching.

[0053] The matching unit can improve the accuracy of the matching process by referring to relevant literature on the drug during the matching process. Relevant literature includes, but is not limited to, academic papers and technical reports. The matching unit can improve the accuracy of the matching process based on relevant literature on the drug. This literature may contain the latest research findings and usage guidelines for the drug, and the matching unit can improve the accuracy of the matching process by referring to this information. Furthermore, the matching unit can adjust the timing and method of the matching process based on the information in the relevant literature. This allows for improved accuracy of the matching process by referring to relevant literature on the drug, enabling appropriate information matching. Some or all of the above-described processes in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input information from relevant literature into AI to improve the accuracy of the matching process.

[0054] The prevention unit can analyze a patient's past medication error history to select the optimal prevention method when preventing medication errors. Past medication error history includes, but is not limited to, the date and time of medication errors and the content of the medication errors. For example, the prevention unit selects the optimal prevention method based on past medication error history. The prevention method can be adjusted considering fluctuations in the medication error history. The prevention unit can also analyze patterns in the medication error history to determine the optimal prevention method. In this way, appropriate medication errors can be prevented by analyzing a patient's past medication error history and selecting the optimal prevention method. Some or all of the above processing in the prevention unit may be performed using, for example, AI, or without AI. For example, the prevention unit can input past medication error history into AI and analyze medication error patterns.

[0055] The prevention unit can implement preventive measures by referring to drug usage guidelines when preventing medication errors. These usage guidelines include, but are not limited to, information on administration methods and dosages. For example, the prevention unit can implement medication error prevention measures based on drug usage guidelines. It can also implement preventive measures by referring to the latest information on usage guidelines. Furthermore, the prevention unit can implement preventive measures in compliance with drug usage guidelines. This allows for the implementation of preventive measures by referring to drug usage guidelines and ensuring appropriate prevention of medication errors. Some or all of the above-described processes in the prevention unit may be performed using, for example, AI, or without AI. For example, the prevention unit can input information from usage guidelines into AI and implement medication error prevention measures.

[0056] The prevention unit can implement preventive measures when preventing drug misdispensing, taking into account the manufacturing lot information of the drug. Manufacturing lot information includes, but is not limited to, the lot number and manufacturing date and time. The prevention unit can, for example, prioritize the implementation of preventive measures for drugs from a specific manufacturing lot. Drugs from a specific manufacturing lot can be prioritized for preventive measures because they are highly reliable. The prevention unit can also implement preventive measures considering the reliability of the manufacturing lot. If the reliability of the manufacturing lot is high, preventive measures can be prioritized for that drug. Furthermore, the prevention unit can implement preventive measures based on the past quality data of the manufacturing lot. If the past quality data of the manufacturing lot is good, preventive measures can be prioritized for that drug. In this way, preventive measures can be implemented taking into account the manufacturing lot information of the drug, and appropriate drug misdispensing can be prevented. Some or all of the above processing in the prevention unit may be performed using, for example, AI, or without using AI. For example, the prevention unit can input manufacturing lot information into AI and implement preventive measures.

[0057] The prevention unit can improve the accuracy of preventive measures by referring to relevant literature on pharmaceuticals when preventing drug misadministration. Relevant literature includes, but is not limited to, academic papers and technical reports. For example, the prevention unit can improve the accuracy of drug misadministration prevention measures based on relevant literature on pharmaceuticals. This literature includes the latest research results and usage guidelines for pharmaceuticals, and the prevention unit can improve the accuracy of preventive measures by referring to this information. Furthermore, the prevention unit can adjust the timing and method of preventive measures based on the information in the relevant literature. This allows for improved accuracy of preventive measures by referring to relevant literature on pharmaceuticals, enabling appropriate drug misadministration prevention. Some or all of the above processing in the prevention unit may be performed using, for example, AI, or without AI. For example, the prevention unit can input information from relevant literature into AI to improve the accuracy of preventive measures.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The pharmaceutical management system also includes a forecasting unit. This unit analyzes historical inventory data and consumption patterns to predict future inventory shortages. For example, based on historical data, if a particular drug tends to be consumed in large quantities during a specific season, the system can automatically replenish it as that season approaches. The forecasting unit can also predict sudden increases in demand and secure inventory in advance. This prevents drug shortages due to insufficient stock and maintains a stable supply.

[0060] The acquisition unit can acquire information on drug side effects simultaneously with acquiring information on pharmaceuticals. For example, by acquiring drug side effect information from a database and comparing it with the patient's allergy information, the risk of allergic reactions can be identified in advance. Furthermore, based on the side effect information, the acquisition unit can evaluate the risk of administering medication to the patient and avoid using high-risk medications. This ensures patient safety and allows for appropriate medication administration.

[0061] The update unit can update inventory status while taking drug price information into consideration. For example, if drug prices fluctuate, the timing of inventory updates can be adjusted based on the price information. Furthermore, for drugs whose prices are soaring, maintaining a larger inventory can help reduce costs. In addition, for drugs whose prices are falling, inventory can be reduced as needed. This allows for inventory management that takes drug price information into account, thereby improving cost efficiency.

[0062] The data collection unit can simultaneously collect information on patients' lifestyles when collecting medication information. For example, by collecting information on patients' diets and exercise habits and analyzing it in combination with medication information, a more appropriate medication plan can be created. Furthermore, the effectiveness of medication can be evaluated based on lifestyle information, and the medication plan can be adjusted as needed. This enables personalized medicine tailored to the patient's lifestyle, thereby improving treatment effectiveness.

[0063] The management department can manage medication information while considering the patient's treatment goals. For example, it can prioritize the management of medication information based on the patient's treatment goals and create an optimal medication plan to achieve those goals. It can also monitor the progress towards treatment goals and adjust the medication plan as needed. This allows for medication management that aligns with the patient's treatment goals and maximizes treatment effectiveness.

[0064] The matching unit can compare medication information with inventory information while considering the expiration date of the drugs. For example, by prioritizing the use of drugs with approaching expiration dates, it is possible to reduce drug waste. It can also automatically exclude expired drugs to prevent incorrect dispensing. Furthermore, it can adjust the timing of inventory replenishment based on expiration date information. This allows for appropriate inventory management that takes drug expiration dates into account, thereby reducing drug waste.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The acquisition unit acquires information about the drug. This information includes the drug's name, ingredients, efficacy, and side effects. The acquisition unit, for example, attaches an IC tag to the drug and reads it with an RFID reader. The IC tag has a unique ID for the drug, and by reading it with the RFID reader, the drug's information can be acquired. Step 2: The update unit updates the drug inventory status based on the information acquired by the acquisition unit. For example, the update unit updates the inventory status based on information read by an RFID reader. The update unit can update the inventory status in real time and automatically replenish necessary drugs. Step 3: The collection unit collects patient medication information. The collection unit collects patient medication information, for example, from electronic medical records. Medication information includes the date and time of administration, dosage, and the person administering the medication. Step 4: The management department manages the collected medication information. The management department centrally manages medication information using a database, for example. The management department manages the medication information and performs checks to prevent medication errors. Step 5: The matching unit compares the medication information managed by the management unit with the inventory information updated by the update unit. The matching unit performs, for example, a comparison between databases. The matching unit compares the medication information with the inventory information and performs checks to prevent incorrect medication administration. Step 6: The prevention unit prevents medication errors based on the information verified by the verification unit. The prevention unit prevents medication errors using, for example, an alert system. The prevention unit reduces the risk of medication errors and ensures patient safety.

[0067] (Example of form 2) The pharmaceutical management system according to an embodiment of the present invention is a system that uses generating AI to automate pharmaceutical inventory management and dispensing checks, allowing pharmacists to focus on their core duties. This pharmaceutical management system involves attaching IC tags to pharmaceuticals and reading them with an RFID reader. Next, the generating AI manages the inventory status of pharmaceuticals in real time and automatically replenishes the necessary pharmaceuticals. The generating AI also manages dispensing information for each patient and performs checks to prevent dispensing errors. As a result, pharmacists are freed from inventory management and can concentrate on dispensing medication to patients. For example, IC tags are attached to pharmaceuticals and read with an RFID reader. At this time, a unique ID is assigned to each pharmaceutical, and inventory information is centrally managed. For example, pharmaceutical A is assigned ID001, pharmaceutical B is assigned ID002, and so on. This allows for real-time monitoring of the pharmaceutical inventory status. Next, the generating AI manages the pharmaceutical inventory status in real time. The generating AI updates the inventory status based on the information read by the RFID reader. For example, if the inventory of drug A decreases from 10 to 9 units, the generating AI automatically updates the inventory information and replenishes the necessary drug. Furthermore, the generating AI manages medication information for each patient. Based on the patient's medication information, the generating AI performs checks to prevent medication errors. For example, if patient A needs to be prescribed drug A, the generating AI checks the ID of drug A and performs a check to prevent medication errors. This reduces the risk of medication errors. This system frees pharmacists from inventory management and allows them to concentrate on dispensing medication to patients. For example, pharmacists can provide optimal medication according to the patient's symptoms, thereby improving the effectiveness of the patient's treatment. In addition, inventory management by the generating AI improves the traceability of drugs and streamlines drug management. As a result, the drug management system allows pharmacists to focus on their core duties and automates drug inventory management and dispensing checks.

[0068] The pharmaceutical management system according to this embodiment comprises an acquisition unit, an update unit, a collection unit, a management unit, a verification unit, and a prevention unit. The acquisition unit acquires information about pharmaceuticals. This information includes, but is not limited to, the name, ingredients, efficacy, and side effects of the pharmaceuticals. The acquisition unit can, for example, attach an IC tag to a pharmaceutical and read it with an RFID reader. The IC tag is assigned a unique ID for the pharmaceutical, and by reading it with an RFID reader, information about the pharmaceutical can be acquired. The update unit updates the inventory status of pharmaceuticals based on the information acquired by the acquisition unit. The update unit updates the inventory status based on the information read by the RFID reader. The update unit can update the inventory status in real time and automatically replenish necessary pharmaceuticals. The collection unit collects patient medication information. The collection unit collects patient medication information from, for example, electronic medical records. This medication information includes the date and time of administration, the amount of medication administered, and the person administering the medication. The management unit manages the collected medication information. The management unit centrally manages the medication information using, for example, a database. The management unit manages medication information and performs checks to prevent medication errors. The verification unit verifies the medication information managed by the management unit against the inventory information updated by the update unit. The verification unit performs, for example, cross-database verification. The verification unit verifies the medication information against the inventory information and performs checks to prevent medication errors. The prevention unit prevents medication errors based on the information verified by the verification unit. The prevention unit prevents medication errors using, for example, an alert system. The prevention unit reduces the risk of medication errors and ensures patient safety. As a result, the pharmaceutical management system according to this embodiment allows pharmacists to focus on their core duties and automates pharmaceutical inventory management and medication checks.

[0069] The acquisition unit can attach IC tags to pharmaceuticals and read them with an RFID reader. IC tags include, but are not limited to, RFID tags and barcodes. For example, the acquisition unit can attach an RFID tag to a pharmaceutical and read it with an RFID reader. The RFID tag has a unique ID for the pharmaceutical, and by reading it with an RFID reader, information about the pharmaceutical can be obtained. The acquisition unit can also attach a barcode to a pharmaceutical and read it with a barcode reader. The barcode has a unique ID for the pharmaceutical, and by reading it with a barcode reader, information about the pharmaceutical can be obtained. This allows for accurate acquisition of pharmaceutical information and streamlines inventory management. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input the information read by the RFID reader into AI to acquire pharmaceutical information.

[0070] The update unit can update inventory status based on information read by an RFID reader. Inventory status includes, but is not limited to, the number of items in stock, storage location, and expiration date. The update unit can update inventory status in real time and automatically replenish necessary medicines. The update unit can also update inventory status periodically. For example, the update unit can update inventory status at a fixed time each day. This allows for real-time updates of inventory status and automatic replenishment of necessary medicines. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input information read by an RFID reader into AI and update inventory status.

[0071] The data collection unit can collect patient medication information. This information may include, but is not limited to, the date and time of administration, the dosage, and the person administering the medication. The data collection unit can collect patient medication information from, for example, electronic medical records. Electronic medical records contain patient medication information, and the data collection unit can collect this information from the electronic medical records. The data collection unit can also manually input medication information. For example, the data collection unit can allow the person administering the medication to manually input the medication information. This allows for the accurate collection of patient-specific medication information and provides basic data to prevent medication errors. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input medication information collected from electronic medical records into AI and collect the medication information.

[0072] The management department can manage the collected medication information. This management includes, but is not limited to, database management or paper-based management. For example, the management department can centrally manage medication information using a database. The database contains the collected medication information, and the management department can manage it using the database. The management department can also manage medication information using paper-based methods. For example, the management department can record and manage medication information on paper. This allows for centralized management of medication information and the implementation of checks to prevent medication errors. Some or all of the above processes in the management department may be performed using, for example, AI, or not. For example, the management department can input medication information recorded in the database into AI and manage the medication information.

[0073] The matching unit can match medication information with inventory information. Matching medication information with inventory information includes, but is not limited to, matching between databases or manual matching. For example, the matching unit can perform matching between databases. The databases record medication information and inventory information, and the matching unit can perform matching between databases. The matching unit can also manually match medication information with inventory information. For example, the matching unit can allow a medication provider to manually match medication information with inventory information. This helps prevent medication errors by matching medication information with inventory information. Some or all of the above-described processes in the matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can input the matching between databases into AI and match medication information with inventory information.

[0074] The prevention unit can prevent medication errors based on the verified information. Methods for preventing medication errors include, but are not limited to, alert systems and double-checking. For example, the prevention unit can prevent medication errors using an alert system. The alert system issues a warning when there is a risk of medication errors and draws the attention of the person administering the medication. The prevention unit can also perform double-checking. For example, the prevention unit can prevent medication errors by having the person administering the medication double-check. This reduces the risk of medication errors and ensures patient safety. Some or all of the above processes in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can input the alert system into AI to prevent medication errors.

[0075] The acquisition unit can estimate the user's emotions and adjust the timing of drug information acquisition based on the estimated user emotions. Methods for estimating user emotions include, but are not limited to, facial expression recognition and voice analysis. For example, the acquisition unit can estimate user emotions using facial expression recognition. Facial expression recognition can analyze the user's facial expressions and estimate emotions. The acquisition unit can also estimate user emotions using voice analysis. Voice analysis can analyze the tone and speed of the user's voice and estimate emotions. This allows the timing of drug information acquisition to be adjusted according to the user's emotions, reducing the burden on the user. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input the results of facial expression recognition or voice analysis into AI to adjust the timing of drug information acquisition.

[0076] The acquisition unit can analyze the frequency of drug use and select the optimal acquisition method. Methods for analyzing usage frequency include, but are not limited to, analysis of past usage data and statistical methods. For example, the acquisition unit can analyze past usage data to analyze the frequency of drug use. Past usage data includes the date and time of use and the amount used. The acquisition unit can automatically acquire frequently used drugs on a regular basis. Drugs with low usage frequency can be acquired as needed. Furthermore, for drugs with large fluctuations in usage frequency, the optimal acquisition timing can be determined based on past data. This allows for the selection of the optimal acquisition method based on drug usage frequency, thereby streamlining inventory management. Some or all of the above-described processes in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input past usage data into AI to analyze the frequency of drug use.

[0077] The acquisition unit can filter pharmaceuticals based on their expiration date and storage conditions when acquiring them. Expiration dates and storage conditions include, but are not limited to, use-by dates and temperature control. For example, the acquisition unit can prioritize acquiring pharmaceuticals with approaching expiration dates. Pharmaceuticals with approaching expiration dates need to be used quickly, so they can be acquired preferentially. The acquisition unit can also acquire pharmaceuticals with strict storage conditions, taking appropriate storage locations into consideration. Pharmaceuticals with strict storage conditions need to have appropriate storage locations, so they can be acquired with these conditions in mind. Furthermore, the acquisition unit can acquire pharmaceuticals with long expiration dates depending on inventory levels. Acquiring pharmaceuticals with long expiration dates according to inventory levels can streamline inventory management. This allows for filtering based on pharmaceutical expiration dates and storage conditions, enabling the acquisition of appropriate pharmaceuticals. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not. For example, the acquisition unit can input expiration date and storage condition information into AI and perform pharmaceutical filtering.

[0078] The acquisition unit can estimate the user's emotions and determine the priority of drug information to acquire based on the estimated user emotions. Methods for estimating user emotions include, but are not limited to, facial expression recognition and voice analysis. For example, the acquisition unit can estimate user emotions using facial expression recognition. Facial expression recognition can analyze the user's facial expressions and estimate emotions. The acquisition unit can also estimate user emotions using voice analysis. Voice analysis can analyze the tone and speed of the user's voice and estimate emotions. This allows the acquisition unit to determine the priority of drug information according to the user's emotions and acquire important information preferentially. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input the results of facial expression recognition or voice analysis into AI to determine the priority of drug information.

[0079] The acquisition unit can prioritize the acquisition of highly relevant information when acquiring pharmaceuticals, taking into account the pharmaceutical manufacturer's information. Manufacturer information includes, but is not limited to, the name of the manufacturing company and the country of manufacture. For example, the acquisition unit can prioritize the acquisition of pharmaceutical information from a specific manufacturer. Pharmaceutical information from a specific manufacturer can be prioritized because it is highly reliable. The acquisition unit can also acquire pharmaceutical information considering the reliability of the manufacturer. If the manufacturer is highly reliable, its pharmaceutical information can be prioritized for acquisition. Furthermore, the acquisition unit can acquire pharmaceutical information based on the manufacturer's past quality data. If the manufacturer's past quality data is good, its pharmaceutical information can be prioritized for acquisition. This allows for the priority acquisition of highly relevant information, taking into account the pharmaceutical manufacturer's information, and enables appropriate pharmaceutical management. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input manufacturer information into AI and prioritize the acquisition of highly relevant information.

[0080] The acquisition unit can analyze the past usage history of a drug and obtain relevant information when acquiring it. Past usage history includes, but is not limited to, the date and time of use and the amount used. For example, the acquisition unit can analyze past usage history to understand the frequency of drug use. Drugs with high usage frequency can be acquired automatically on a regular basis. Drugs with low usage frequency can be acquired as needed. Furthermore, for drugs with large fluctuations in usage frequency, the optimal acquisition timing can be determined based on past data. In this way, appropriate drug management can be performed by analyzing the past usage history of drugs and obtaining relevant information. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input past usage history into AI and analyze the frequency of drug use.

[0081] The update unit can estimate the user's emotions and adjust the frequency of inventory status updates based on the estimated user emotions. Methods for estimating user emotions include, but are not limited to, facial recognition and voice analysis. For example, the update unit can estimate user emotions using facial recognition. Facial recognition can analyze the user's facial expressions and estimate their emotions. The update unit can also estimate user emotions using voice analysis. Voice analysis can analyze the tone and speed of the user's voice and estimate their emotions. This allows for appropriate inventory management by adjusting the frequency of inventory status updates according to the user's emotions. Some or all of the above processing in the update unit may be performed using, for example, AI, or without AI. For example, the update unit can input the results of facial recognition or voice analysis into AI to adjust the frequency of inventory status updates.

[0082] The update unit can analyze the consumption rate of pharmaceuticals during inventory updates to determine the optimal replenishment timing. Methods for analyzing consumption rate include, but are not limited to, analyzing past consumption data and statistical methods. For example, the update unit can analyze past consumption data to understand the consumption rate of pharmaceuticals. Pharmaceuticals with a fast consumption rate can be replenished frequently. Pharmaceuticals with a slow consumption rate can be replenished as needed. Furthermore, for pharmaceuticals with large fluctuations in consumption rate, the optimal replenishment timing can be determined based on past data. This allows for the determination of optimal replenishment timing based on the consumption rate of pharmaceuticals, thereby streamlining inventory management. Some or all of the above processes in the update unit may be performed using, for example, AI, or not. For example, the update unit can input past consumption data into AI to analyze the consumption rate of pharmaceuticals.

[0083] The update unit can update inventory while considering the storage location information of pharmaceuticals. Storage location information includes, but is not limited to, the name and conditions of the storage location. For example, the update unit can update pharmaceuticals with limited storage locations by securing appropriate storage locations. Since pharmaceuticals with limited storage locations require appropriate storage, the update unit can consider the storage location when updating. The update unit can also update pharmaceuticals with strict storage conditions by considering appropriate storage locations. Since pharmaceuticals with strict storage conditions require appropriate storage, the update unit can consider the storage conditions when updating. Furthermore, if a change in storage location is necessary, the update unit can select an appropriate storage location and update. This allows for inventory updates that consider the storage location information of pharmaceuticals, enabling proper inventory management. Some or all of the above-described processes in the update unit may be performed using, for example, AI, or without AI. For example, the update unit can input storage location information into AI and perform inventory updates.

[0084] The update unit can estimate the user's emotions and determine the priority of inventory updates based on the estimated user emotions. Methods for estimating user emotions include, but are not limited to, facial recognition and voice analysis. For example, the update unit can estimate user emotions using facial recognition. Facial recognition can analyze the user's facial expressions and estimate their emotions. The update unit can also estimate user emotions using voice analysis. Voice analysis can analyze the tone and speed of the user's voice and estimate their emotions. This allows the system to determine the priority of inventory updates according to the user's emotions and prioritize the updating of important medicines. Some or all of the above processing in the update unit may be performed using, for example, AI, or not using AI. For example, the update unit can input the results of facial recognition and voice analysis into AI to determine the priority of inventory updates.

[0085] The update unit can update inventory while considering the manufacturing lot information of pharmaceuticals. Manufacturing lot information includes, but is not limited to, lot numbers and manufacturing dates. The update unit can, for example, prioritize updating pharmaceuticals from a specific manufacturing lot. Pharmaceuticals from a specific manufacturing lot can be prioritized for updating because they are more reliable. The update unit can also update inventory while considering the reliability of the manufacturing lot. If the manufacturing lot is highly reliable, that pharmaceutical can be prioritized for updating. Furthermore, the update unit can update inventory based on the historical quality data of the manufacturing lot. If the historical quality data of the manufacturing lot is good, that pharmaceutical can be prioritized for updating. This allows for inventory updates that consider the manufacturing lot information of pharmaceuticals, enabling appropriate inventory management. Some or all of the above processing in the update unit may be performed using, for example, AI, or not using AI. For example, the update unit can input manufacturing lot information into AI and perform inventory updates.

[0086] The update unit can improve the accuracy of inventory updates by referring to relevant literature on pharmaceuticals during inventory updates. Relevant literature includes, but is not limited to, academic papers and technical reports. For example, the update unit can determine the optimal inventory update method based on the relevant literature on pharmaceuticals. This literature includes the latest research findings and usage guidelines for pharmaceuticals, and the update unit can improve the accuracy of inventory updates by referring to this information. Furthermore, the update unit can adjust the timing and method of inventory updates based on the information in the relevant literature. This allows for improved inventory updates and appropriate inventory management by referring to relevant literature on pharmaceuticals. Some or all of the above processes in the update unit may be performed using, for example, AI, or not. For example, the update unit can input information from relevant literature into AI to improve the accuracy of inventory updates.

[0087] The data collection unit can estimate the user's emotions and adjust the timing of medication information collection based on the estimated emotions. Methods for estimating user emotions include, but are not limited to, facial recognition and voice analysis. For example, the data collection unit can estimate user emotions using facial recognition. Facial recognition can analyze the user's facial expressions and estimate emotions. The data collection unit can also estimate user emotions using voice analysis. Voice analysis can analyze the tone and speed of the user's voice and estimate emotions. This allows the timing of medication information collection to be adjusted according to the user's emotions, enabling appropriate information collection. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the results of facial recognition and voice analysis into AI to adjust the timing of medication information collection.

[0088] The data collection unit can analyze the patient's past medication history to select the optimal collection method when collecting medication information. Past medication history includes, but is not limited to, the date and time of administration and the amount of medication administered. The data collection unit can analyze past medication history to understand the patient's medication pattern. Based on the medication pattern, it can select the optimal collection method. For example, information on medications that have been frequently administered in the past can be automatically collected on a regular basis. It can also prioritize the collection of information on specific medications from past medication history. Furthermore, the collection method can be adjusted to take into account fluctuations in the medication history. This allows for appropriate information collection by analyzing the patient's past medication history and selecting the optimal collection method. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input past medication history into AI to analyze the medication pattern.

[0089] The data collection unit can filter medication information based on the patient's current health status. Current health status includes, but is not limited to, blood pressure and body temperature. For example, if a patient's health is deteriorating, the data collection unit prioritizes collecting important medication information. Since patients with deteriorating health require prompt attention, prioritizing the collection of important medication information is crucial. Furthermore, if a patient's health is stable, the data collection unit can collect medication information using the standard collection method. It can also adjust the collection method to account for fluctuations in health status. This allows for filtering based on the patient's current health status and prioritizes the collection of important information. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input patient health information into AI to filter medication information.

[0090] The data collection unit can estimate the user's emotions and determine the priority of medication information to collect based on the estimated emotions. Methods for estimating user emotions include, but are not limited to, facial recognition and voice analysis. For example, the data collection unit can estimate user emotions using facial recognition. Facial recognition can analyze the user's facial expressions and estimate emotions. The data collection unit can also estimate user emotions using voice analysis. Voice analysis can analyze the tone and speed of the user's voice and estimate emotions. This allows the system to determine the priority of medication information according to the user's emotions and collect important information preferentially. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the results of facial recognition and voice analysis into AI to determine the priority of medication information.

[0091] The data collection unit can prioritize the collection of highly relevant information when collecting medication information, taking into account the patient's lifestyle. Lifestyle information includes, but is not limited to, dietary habits and exercise habits. For example, the data collection unit prioritizes the collection of medication information related to the patient's lifestyle. Medication information related to lifestyle has a significant impact on the patient's health condition, so it can be collected preferentially. The data collection unit can also adjust the collection method to account for changes in lifestyle. Furthermore, it can analyze lifestyle patterns and determine the optimal collection method. This allows for the priority collection of highly relevant information, taking into account the patient's lifestyle, and enables appropriate information management. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input lifestyle information into AI and prioritize the collection of highly relevant information.

[0092] The management department can estimate the user's emotions and adjust the medication information management method based on the estimated user emotions. Methods for estimating user emotions include, but are not limited to, facial expression recognition and voice analysis. For example, the management department can estimate user emotions using facial expression recognition. Facial expression recognition can analyze the user's facial expressions and estimate emotions. The management department can also estimate user emotions using voice analysis. Voice analysis can analyze the tone and speed of the user's voice and estimate emotions. This allows the management department to adjust the medication information management method according to the user's emotions and perform appropriate information management. Some or all of the above processing in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input the results of facial expression recognition and voice analysis into AI and adjust the medication information management method.

[0093] The management department can select the optimal management method when managing medication information by referring to the patient's past treatment history. Past treatment history includes, but is not limited to, treatment dates and treatment details. The management department can, for example, refer to past treatment history to understand the patient's treatment patterns. Based on these treatment patterns, it can select the optimal management method. For example, medication information related to treatments that have been frequently performed in the past can be managed preferentially. It can also prioritize the management of medication information related to specific treatments based on past treatment history. Furthermore, the management method can be adjusted to take into account changes in the treatment history. This allows for the selection of the optimal management method by referring to the patient's past treatment history and ensuring appropriate information management. Some or all of the above processes in the management department may be performed using, for example, AI, or not. For example, the management department can input past treatment history into AI and analyze treatment patterns.

[0094] The management department can manage medication information while taking into account the patient's allergy information. Allergy information includes, but is not limited to, the type of allergen and the severity of the allergic reaction. For example, the management department manages medication information based on the patient's allergy information. Allergy information is important for ensuring patient safety and must be managed appropriately. The management department can also adjust its management methods in consideration of fluctuations in allergy information. Furthermore, it can analyze patterns in allergy information and determine the optimal management method. This allows for the management of medication information while taking the patient's allergy information into consideration, ensuring appropriate information management. Some or all of the above processes in the management department may be performed using, for example, AI, or not. For example, the management department can input allergy information into AI and manage medication information.

[0095] The management department can estimate the user's emotions and determine the priority of medication information management based on the estimated emotions. Methods for estimating user emotions include, but are not limited to, facial recognition and voice analysis. For example, the management department can estimate user emotions using facial recognition. Facial recognition can analyze the user's facial expressions and estimate their emotions. The management department can also estimate user emotions using voice analysis. Voice analysis can analyze the tone and speed of the user's voice and estimate their emotions. This allows the management department to determine the priority of medication information management according to the user's emotions and prioritize the management of important information. Some or all of the above processing in the management department may be performed using, for example, AI, or not using AI. For example, the management department can input the results of facial recognition and voice analysis into AI to determine the priority of medication information management.

[0096] The management department can manage medication information while considering the patient's family history information. Family history information includes, but is not limited to, family medical history and genetic factors. The management department manages medication information based on the patient's family history information. Family history information needs to be managed appropriately because it affects the patient's health status. The management department can also adjust its management methods in consideration of changes in family history information. Furthermore, it can analyze patterns in family history information and determine the optimal management method. This allows for the management of medication information while considering the patient's family history information, enabling appropriate information management. Some or all of the above processes in the management department may be performed using, for example, AI, or not. For example, the management department can input family history information into AI and manage medication information.

[0097] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. Methods for estimating user emotions include, but are not limited to, facial expression recognition and voice analysis. For example, the matching unit can estimate user emotions using facial expression recognition. Facial expression recognition can analyze the user's facial expressions and estimate emotions. The matching unit can also estimate user emotions using voice analysis. Voice analysis can analyze the tone and speed of the user's voice and estimate emotions. This allows the matching criteria to be adjusted according to the user's emotions, enabling appropriate information matching. Some or all of the above processing in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input the results of facial expression recognition or voice analysis into AI and adjust the matching criteria.

[0098] The matching unit can improve the accuracy of the matching process by considering drug interaction information during the matching process. Interaction information includes, but is not limited to, drug interactions and food interactions. The matching unit improves the accuracy of the matching process based on drug interaction information, for example. Drugs with a high risk of interaction can be matched preferentially. The matching unit can also perform matching by referring to the latest data on interaction information. This improves the accuracy of the matching process by considering drug interaction information and enables appropriate information matching. Some or all of the above processing in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input interaction information into AI to improve the accuracy of the matching process.

[0099] The matching unit can cross-reference the patient's medication history and drug inventory information during the matching process. The medication history includes, but is not limited to, the date and time of administration and the amount administered. The matching unit can, for example, compare the inventory information based on the patient's medication history. It can perform the matching by confirming that the medication history and inventory information match. The matching unit can also compare the inventory information while taking into account changes in the medication history. This allows for appropriate information matching by cross-referencing the patient's medication history and drug inventory information. Some or all of the above processing in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input the medication history and inventory information into AI and perform the matching by cross-referencing.

[0100] The matching unit can estimate the user's emotions and adjust the display order of the matching results based on the estimated emotions. Methods for estimating user emotions include, but are not limited to, facial expression recognition and voice analysis. For example, the matching unit can estimate user emotions using facial expression recognition. Facial expression recognition can analyze the user's facial expressions and estimate emotions. The matching unit can also estimate user emotions using voice analysis. Voice analysis can analyze the tone and speed of the user's voice and estimate emotions. This allows the display order of the matching results to be adjusted according to the user's emotions, prioritizing the display of important information. Some or all of the above processing in the matching unit may be performed using, for example, AI, or not using AI. For example, the matching unit can input the results of facial expression recognition or voice analysis into AI and adjust the display order of the matching results.

[0101] The matching unit can perform matching while considering the manufacturer information of the pharmaceutical product. Manufacturer information includes, but is not limited to, the name of the manufacturing company and the country of manufacture. The matching unit can, for example, prioritize matching pharmaceutical products from a specific manufacturer. Pharmaceutical products from a specific manufacturer can be prioritized for matching because they are highly reliable. The matching unit can also perform matching while considering the reliability of the manufacturer. If the manufacturer is highly reliable, that pharmaceutical product can be prioritized for matching. Furthermore, the matching unit can also perform matching based on the manufacturer's past quality data. If the manufacturer's past quality data is good, that pharmaceutical product can be prioritized for matching. This allows for appropriate information matching by considering the manufacturer information of the pharmaceutical product. Some or all of the above processing in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input manufacturer information into AI and perform matching.

[0102] The matching unit can improve the accuracy of the matching process by referring to relevant literature on the drug during the matching process. Relevant literature includes, but is not limited to, academic papers and technical reports. The matching unit can improve the accuracy of the matching process based on relevant literature on the drug. This literature may contain the latest research findings and usage guidelines for the drug, and the matching unit can improve the accuracy of the matching process by referring to this information. Furthermore, the matching unit can adjust the timing and method of the matching process based on the information in the relevant literature. This allows for improved accuracy of the matching process by referring to relevant literature on the drug, enabling appropriate information matching. Some or all of the above-described processes in the matching unit may be performed using, for example, AI, or without AI. For example, the matching unit can input information from relevant literature into AI to improve the accuracy of the matching process.

[0103] The prevention unit can estimate the user's emotions and adjust the medication error prevention method based on the estimated user emotions. Methods for estimating user emotions include, but are not limited to, facial expression recognition and voice analysis. For example, the prevention unit can estimate user emotions using facial expression recognition. Facial expression recognition can analyze the user's facial expressions and estimate emotions. The prevention unit can also estimate user emotions using voice analysis. Voice analysis can analyze the tone and speed of the user's voice and estimate emotions. This allows the prevention unit to adjust the medication error prevention method according to the user's emotions, enabling appropriate medication error prevention. Some or all of the above-described processes in the prevention unit may be performed using, for example, AI, or without AI. For example, the prevention unit can input the results of facial expression recognition and voice analysis into AI to adjust the medication error prevention method.

[0104] The prevention unit can analyze a patient's past medication error history to select the optimal prevention method when preventing medication errors. Past medication error history includes, but is not limited to, the date and time of medication errors and the content of the medication errors. For example, the prevention unit selects the optimal prevention method based on past medication error history. The prevention method can be adjusted considering fluctuations in the medication error history. The prevention unit can also analyze patterns in the medication error history to determine the optimal prevention method. In this way, appropriate medication errors can be prevented by analyzing a patient's past medication error history and selecting the optimal prevention method. Some or all of the above processing in the prevention unit may be performed using, for example, AI, or without AI. For example, the prevention unit can input past medication error history into AI and analyze medication error patterns.

[0105] The prevention unit can implement preventive measures by referring to drug usage guidelines when preventing medication errors. These usage guidelines include, but are not limited to, information on administration methods and dosages. For example, the prevention unit can implement medication error prevention measures based on drug usage guidelines. It can also implement preventive measures by referring to the latest information on usage guidelines. Furthermore, the prevention unit can implement preventive measures in compliance with drug usage guidelines. This allows for the implementation of preventive measures by referring to drug usage guidelines and ensuring appropriate prevention of medication errors. Some or all of the above-described processes in the prevention unit may be performed using, for example, AI, or without AI. For example, the prevention unit can input information from usage guidelines into AI and implement medication error prevention measures.

[0106] The prevention unit can estimate the user's emotions and determine the priority of medication error prevention based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, facial recognition and voice analysis. For example, the prevention unit can estimate the user's emotions using facial recognition. Facial recognition can analyze the user's facial expressions and estimate their emotions. The prevention unit can also estimate the user's emotions using voice analysis. Voice analysis can analyze the tone and speed of the user's voice and estimate their emotions. This allows the prevention unit to determine the priority of medication error prevention according to the user's emotions and implement important preventive measures first. Some or all of the above processing in the prevention unit may be performed using, for example, AI, or not using AI. For example, the prevention unit can input the results of facial recognition and voice analysis into AI to determine the priority of medication error prevention.

[0107] The prevention unit can implement preventive measures when preventing drug misdispensing, taking into account the manufacturing lot information of the drug. Manufacturing lot information includes, but is not limited to, the lot number and manufacturing date and time. The prevention unit can, for example, prioritize the implementation of preventive measures for drugs from a specific manufacturing lot. Drugs from a specific manufacturing lot can be prioritized for preventive measures because they are highly reliable. The prevention unit can also implement preventive measures considering the reliability of the manufacturing lot. If the reliability of the manufacturing lot is high, preventive measures can be prioritized for that drug. Furthermore, the prevention unit can implement preventive measures based on the past quality data of the manufacturing lot. If the past quality data of the manufacturing lot is good, preventive measures can be prioritized for that drug. In this way, preventive measures can be implemented taking into account the manufacturing lot information of the drug, and appropriate drug misdispensing can be prevented. Some or all of the above processing in the prevention unit may be performed using, for example, AI, or without using AI. For example, the prevention unit can input manufacturing lot information into AI and implement preventive measures.

[0108] The prevention unit can improve the accuracy of preventive measures by referring to relevant literature on pharmaceuticals when preventing drug misadministration. Relevant literature includes, but is not limited to, academic papers and technical reports. For example, the prevention unit can improve the accuracy of drug misadministration prevention measures based on relevant literature on pharmaceuticals. This literature includes the latest research results and usage guidelines for pharmaceuticals, and the prevention unit can improve the accuracy of preventive measures by referring to this information. Furthermore, the prevention unit can adjust the timing and method of preventive measures based on the information in the relevant literature. This allows for improved accuracy of preventive measures by referring to relevant literature on pharmaceuticals, enabling appropriate drug misadministration prevention. Some or all of the above processing in the prevention unit may be performed using, for example, AI, or without AI. For example, the prevention unit can input information from relevant literature into AI to improve the accuracy of preventive measures. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, update unit, collection unit, management unit, verification unit, and prevention unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires drug information using the RFID reader of the smart device 14. The update unit updates the inventory status in real time using the identification processing unit 290 of the data processing unit 12. The collection unit collects patient medication information using the control unit 46A of the smart device 14. The management unit manages the medication information using the identification processing unit 290 of the data processing unit 12. The verification unit verifies the medication information and inventory information using the identification processing unit 290 of the data processing unit 12. The prevention unit prevents incorrect medication administration using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, update unit, collection unit, management unit, verification unit, and prevention unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires drug information using the RFID reader of the smart glasses 214. The update unit updates the inventory status in real time using the identification processing unit 290 of the data processing unit 12. The collection unit collects patient medication information using the control unit 46A of the smart glasses 214. The management unit manages the medication information using the identification processing unit 290 of the data processing unit 12. The verification unit verifies the medication information and inventory information using the identification processing unit 290 of the data processing unit 12. The prevention unit prevents incorrect medication using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, update unit, collection unit, management unit, verification unit, and prevention unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires drug information using the RFID reader of the headset terminal 314. The update unit updates the inventory status in real time using the identification processing unit 290 of the data processing unit 12. The collection unit collects patient medication information using the control unit 46A of the headset terminal 314. The management unit manages the medication information using the identification processing unit 290 of the data processing unit 12. The verification unit verifies the medication information and inventory information using the identification processing unit 290 of the data processing unit 12. The prevention unit prevents incorrect medication administration using the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, update unit, collection unit, management unit, verification unit, and prevention unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires drug information using the RFID reader of the robot 414. The update unit updates the inventory status in real time using the identification processing unit 290 of the data processing unit 12. The collection unit collects patient medication information using the control unit 46A of the robot 414. The management unit manages the medication information using the identification processing unit 290 of the data processing unit 12. The verification unit verifies the medication information and inventory information using the identification processing unit 290 of the data processing unit 12. The prevention unit prevents incorrect medication administration using the control unit 46A of the robot 414.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The pharmaceutical management system also includes a forecasting unit. This unit analyzes historical inventory data and consumption patterns to predict future inventory shortages. For example, based on historical data, if a particular drug tends to be consumed in large quantities during a specific season, the system can automatically replenish it as that season approaches. The forecasting unit can also predict sudden increases in demand and secure inventory in advance. This prevents drug shortages due to insufficient stock and maintains a stable supply.

[0111] The acquisition unit can acquire information on drug side effects simultaneously with acquiring information on pharmaceuticals. For example, by acquiring drug side effect information from a database and comparing it with the patient's allergy information, the risk of allergic reactions can be identified in advance. Furthermore, based on the side effect information, the acquisition unit can evaluate the risk of administering medication to the patient and avoid using high-risk medications. This ensures patient safety and allows for appropriate medication administration.

[0112] The update unit can update inventory status while taking drug price information into consideration. For example, if drug prices fluctuate, the timing of inventory updates can be adjusted based on the price information. Furthermore, for drugs whose prices are soaring, maintaining a larger inventory can help reduce costs. In addition, for drugs whose prices are falling, inventory can be reduced as needed. This allows for inventory management that takes drug price information into account, thereby improving cost efficiency.

[0113] The data collection unit can simultaneously collect information on patients' lifestyles when collecting medication information. For example, by collecting information on patients' diets and exercise habits and analyzing it in combination with medication information, a more appropriate medication plan can be created. Furthermore, the effectiveness of medication can be evaluated based on lifestyle information, and the medication plan can be adjusted as needed. This enables personalized medicine tailored to the patient's lifestyle, thereby improving treatment effectiveness.

[0114] The management department can manage medication information while considering the patient's treatment goals. For example, it can prioritize the management of medication information based on the patient's treatment goals and create an optimal medication plan to achieve those goals. It can also monitor the progress towards treatment goals and adjust the medication plan as needed. This allows for medication management that aligns with the patient's treatment goals and maximizes treatment effectiveness.

[0115] The matching unit can compare medication information with inventory information while considering the expiration date of the drugs. For example, by prioritizing the use of drugs with approaching expiration dates, it is possible to reduce drug waste. It can also automatically exclude expired drugs to prevent incorrect dispensing. Furthermore, it can adjust the timing of inventory replenishment based on expiration date information. This allows for appropriate inventory management that takes drug expiration dates into account, thereby reducing drug waste.

[0116] The prevention unit can estimate the patient's emotions and adjust the alert intensity based on those emotions to prevent medication errors. For example, if the patient is feeling anxious or stressed, the alert intensity can be increased to draw attention. Conversely, if the patient is relaxed, the alert intensity can be lowered to reduce stress. This creates an alert system that responds to the patient's emotions, thereby reducing the risk of medication errors.

[0117] The information acquisition unit can estimate the user's emotions and adjust the timing of drug information acquisition based on the estimated emotions. For example, if the user is stressed, the information acquisition can be delayed to reduce the user's burden. Conversely, if the user is relaxed, information acquisition can be performed quickly to improve efficiency. This enables flexible information acquisition that responds to the user's emotions, thereby reducing the user's burden.

[0118] The update unit can estimate the user's emotions and adjust the frequency of inventory status updates based on those emotions. For example, if the user is busy, the update frequency can be lowered to reduce the user's burden. Conversely, if the user has more free time, the update frequency can be increased to improve the accuracy of inventory management. This enables flexible inventory management that responds to the user's emotions and reduces the user's burden.

[0119] The management department can estimate the user's emotions and adjust the medication information management method based on the estimated emotions. For example, if the user is stressed, the management method can be simplified to reduce the user's burden. Conversely, if the user is relaxed, a more detailed management method can be adopted to improve the accuracy of the information. This enables flexible information management that responds to the user's emotions, thereby reducing the user's burden.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The acquisition unit acquires information about the drug. This information includes the drug's name, ingredients, efficacy, and side effects. The acquisition unit, for example, attaches an IC tag to the drug and reads it with an RFID reader. The IC tag has a unique ID for the drug, and by reading it with the RFID reader, the drug's information can be acquired. Step 2: The update unit updates the drug inventory status based on the information acquired by the acquisition unit. For example, the update unit updates the inventory status based on information read by an RFID reader. The update unit can update the inventory status in real time and automatically replenish necessary drugs. Step 3: The collection unit collects patient medication information. The collection unit collects patient medication information, for example, from electronic medical records. Medication information includes the date and time of administration, dosage, and the person administering the medication. Step 4: The management department manages the collected medication information. The management department centrally manages medication information using a database, for example. The management department manages the medication information and performs checks to prevent medication errors. Step 5: The matching unit compares the medication information managed by the management unit with the inventory information updated by the update unit. The matching unit performs, for example, a comparison between databases. The matching unit compares the medication information with the inventory information and performs checks to prevent incorrect medication administration. Step 6: The prevention unit prevents medication errors based on the information verified by the verification unit. The prevention unit prevents medication errors using, for example, an alert system. The prevention unit reduces the risk of medication errors and ensures patient safety.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires information on pharmaceuticals, An update unit updates the inventory status of pharmaceuticals based on the information acquired by the acquisition unit, A collection unit that collects patient medication information, A management unit manages the medication information collected by the aforementioned collection unit, A matching unit that compares medication information managed by the management unit with inventory information updated by the update unit, The system includes a prevention unit that prevents incorrect medication administration based on the information verified by the verification unit. A system characterized by the following features.

2. The acquisition unit is, Attach IC tags to pharmaceuticals and read them with an RFID reader. The system according to feature 1.

3. The aforementioned update unit is The inventory status is updated based on the information read by the RFID reader. The system according to feature 1.

4. The aforementioned collection unit is Collect patient medication information The system according to feature 1.

5. The aforementioned management department, Manage collected medication information The system according to feature 1.

6. The aforementioned verification unit is Match medication information with inventory information. The system according to feature 1.

7. The aforementioned prevention unit is Preventing medication errors based on verified information The system according to feature 1.

8. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of drug information acquisition based on those estimated emotions. The system according to feature 1.

9. The acquisition unit is, Analyze the frequency of drug use and select the optimal method of acquisition. The system according to feature 1.

10. The acquisition unit is, When acquiring pharmaceuticals, filtering is performed based on the drug's expiration date and storage conditions. The system according to feature 1.

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