system

Abstract

The system according to this embodiment aims to support the work of pharmacists and to operate an unmanned pharmacy. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a data provision unit, and an operation unit. The data collection unit collects data related to pharmacists. The analysis unit analyzes the data collected by the data collection unit and provides solutions to each problem. The data provision unit supports the work of pharmacists based on the analysis results obtained by the analysis unit. The operation unit operates an unmanned pharmacy based on the solutions provided by the data provision 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 character of the chatbot, 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] Conventional technologies have problems such as the workload of pharmacists, dispensing errors, the business risks of pharmacy operators, the difficulty for patients to obtain information, and the shortage of dispensing pharmacies in depopulated areas, leaving room for improvement.

[0005] The system according to the embodiment aims to support the work of pharmacists and operate a dispensing pharmacy without a pharmacist.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and an operation unit. The data collection unit collects data related to pharmacists. The analysis unit analyzes the data collected by the data collection unit and provides solutions to each problem. The data provision unit supports the work of pharmacists based on the analysis results obtained by the analysis unit. The operation unit operates an unmanned pharmacy based on the solutions provided by the data provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can support the work of pharmacists and operate an unmanned pharmacy. [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 such as 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 system according to the embodiment of the present invention is a system that uses an AI agent to solve problems that arise in various tasks performed by pharmacists, as well as problems faced by patients and pharmacy managers. This system accumulates various data related to pharmacists (relevant laws, information on drugs, patient medication history information, medical accident cases that have occurred in pharmacies, etc.) while updating them to the latest information. Next, the AI ​​agent processes the data and thoughts generated by a large-scale language model to provide solutions to each problem. This is expected to reduce the risk of medical accidents caused by pharmacist dispensing errors and physician prescription errors, alleviate the pharmacist shortage by reducing the workload of pharmacists, realize unmanned pharmacies in sparsely populated areas, and promote the development of groundbreaking new drugs through application in new drug research. For example, data related to pharmacists is collected and analyzed by the AI ​​agent. At this time, data such as relevant laws, information on drugs, patient medication history information, and medical accident cases that have occurred in pharmacies, etc. are collected. For example, pharmacists can refer to past medical accident cases to prevent dispensing errors and take measures to prevent similar mistakes. Next, the AI ​​agent analyzes the collected data and provides solutions to each problem. For example, to prevent pharmacists from making dispensing errors, an AI agent will judge the accuracy of prescriptions and their contents, preventing subsequent risks due to errors. Furthermore, a generative AI model specifically for pharmacists will be built to supplement knowledge specific to pharmacist duties. In addition, to address the shortage of pharmacists, the AI ​​agent will open unmanned pharmacies with licenses equivalent to those of a licensed pharmacist. This will alleviate the shortage of pharmacies in sparsely populated areas and reduce disparities in healthcare. For instance, the AI ​​agent can dispense and provide appropriate medications to patients based on their medication history. The AI ​​agent can also provide solutions to patient challenges. For example, for patients unsure about drug interactions, the AI ​​agent will learn data equivalent to that of a pharmacist and provide medication support services. This reduces the risk of patients taking medication incorrectly. Finally, as an application to new drug research, the AI ​​agent will analyze vast amounts of research data to support new drug research. For example, the AI ​​agent can refer to past research data to discover new drug candidates. This can significantly reduce the time and cost of new drug development.This will enable the system to support the work of pharmacists and realize the operation of unmanned pharmacies.

[0029] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and an operation unit. The data collection unit collects data related to pharmacists. For example, the data collection unit collects data such as relevant laws, information on drugs, patient medication history information, and medical accident cases that occurred at pharmacies, etc. For example, the data collection unit can collect medication history information and understand the patient's past prescription history and allergy information. The data collection unit can also collect medical accident cases and accumulate past cases of dispensing errors and prescription errors. Furthermore, the data collection unit can collect information on drugs and understand their efficacy, dosage and administration, and side effect information. The analysis unit analyzes the data collected by the data collection unit and provides solutions for each problem. For example, the analysis unit analyzes the collected data using data mining technology and provides solutions to reduce the risk of medical accidents caused by pharmacist dispensing errors or physician prescription errors. For example, the analysis unit can use machine learning algorithms to extract patterns from the collected data and predict the risk of medical accidents. Furthermore, the analysis unit can use statistical analysis to analyze trends in the collected data and take measures to reduce the risk of medical accidents. The provision department supports pharmacists' work based on the analysis results obtained by the analysis department. For example, the provision department builds a generative AI model specifically for pharmacists and uses AI to supplement knowledge specific to pharmacist work. For example, the provision department can use the generative AI model to support pharmacists in determining the correctness of prescription contents when reviewing prescriptions. The provision department can also use the generative AI model to provide appropriate advice to pharmacists when providing medication guidance to patients. The operations department operates unmanned pharmacies based on the solutions provided by the provision department. For example, the operations department opens unmanned pharmacies with licenses equivalent to those of a pharmacist, addressing the shortage of pharmacies in sparsely populated areas. For example, the operations department can use an AI agent to dispense and provide appropriate medications to patients based on their medication history information. The operations department can also use an AI agent to provide appropriate advice to patients when they consult about drug interactions. In this way, the system supports pharmacists' work and enables the operation of unmanned pharmacies.

[0030] The Data Collection Department collects data related to pharmacists. For example, it collects data on relevant laws, drug information, patient medication history, and medical accident cases that occurred at pharmacies, etc. Specifically, regarding relevant laws, it collects legal information such as the Pharmacist Act and the Pharmaceuticals and Medical Devices Act to understand the legal requirements that pharmacists must comply with. Regarding drug information, it collects information on efficacy, dosage and administration, side effects, and drug interactions to provide basic data for pharmacists to make appropriate drug selections. Regarding patient medication history information, it obtains data from electronic medical record systems and medication history management systems to understand patients' past prescription history, allergy information, and medical history. This allows pharmacists to select the optimal drug for each patient. Regarding medical accident cases, it collects cases of dispensing errors and prescription errors that occurred at pharmacies and hospitals, and stores these cases in a database. This allows for referencing past cases and taking measures to prevent similar errors. The Data Collection Department centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the Analysis Department and the Provision Department. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the collection department and provides solutions to each problem. For example, the analysis department uses data mining techniques to analyze the collected data and provide solutions to reduce the risk of medical accidents caused by pharmacist dispensing errors and physician prescription errors. Specifically, it uses machine learning algorithms to extract patterns from the collected data and predict the risk of medical accidents. For example, based on past dispensing error data, it identifies risks associated with specific drugs or prescription patterns and issues warnings to pharmacists. It also uses statistical analysis to analyze trends in the collected data and implement measures to reduce the risk of medical accidents. For example, it analyzes the frequency of dispensing errors during specific times of day or days of the week and reviews operations and optimizes staffing during high-risk periods. Furthermore, the analysis department can use AI to analyze the collected data in real time and detect medical accident risks early. For example, it can detect abnormal data or patterns that occur during dispensing and immediately issue warnings to pharmacists. In this way, the analysis department can quickly and accurately analyze the collected data and provide effective measures to reduce the risk of medical accidents.

[0032] The service provider supports pharmacists' work based on the analysis results obtained by the analysis department. For example, the service provider builds a generative AI model specifically for pharmacists and uses AI to supplement knowledge specific to pharmacist work. Specifically, it uses the generative AI model to help pharmacists determine the accuracy of prescriptions when reviewing them. For example, it checks whether the usage and dosage of the drugs listed on the prescription are appropriate and warns of the risk of overdose or drug interactions. It also uses the generative AI model to provide appropriate advice to patients when providing medication guidance. For example, it explains the timing of medication and precautions for side effects based on the patient's medication history, supporting patients in taking their medication safely. Furthermore, the service provider uses the generative AI model to provide educational content for pharmacists to learn about new drugs and treatments. This allows pharmacists to stay up-to-date with the latest medical information and apply it to their work. Through this support, the service provider can improve the efficiency of pharmacists' work and reduce the risk of medical errors.

[0033] The operations department will operate the unmanned pharmacies based on the solutions provided by the service provider department. For example, the operations department will open unmanned pharmacies that have received accreditation equivalent to that of a pharmacist, thereby alleviating the shortage of pharmacies in sparsely populated areas. Specifically, it will use an AI agent to dispense and provide appropriate medications to patients based on their medication history information. For example, when a patient submits a prescription, the AI ​​agent will check the prescription details, select the appropriate medication, and dispense it. The AI ​​agent will also provide appropriate advice when patients consult about drug interactions. For example, if a patient is taking multiple medications, it will assess the risk of interactions and suggest a safe way to take them. Furthermore, the operations department will monitor the operation of the unmanned pharmacies and provide maintenance and support as needed. For example, it will regularly manage drug inventory and inspect equipment to ensure that the unmanned pharmacies are always in proper working order. In this way, the operations department can alleviate the shortage of pharmacies in sparsely populated areas and provide an environment where patients can receive safe and appropriate medical services.

[0034] The data collection unit can collect data such as relevant laws, information on drugs, patient medication history information, and medical accident cases that occurred at pharmacies, etc. For example, the data collection unit can collect medication history information to understand a patient's past prescription history and allergy information. For example, the data collection unit can collect medical accident cases to accumulate past cases of dispensing errors and prescription errors. For example, the data collection unit can collect information on drugs to understand their efficacy, dosage and administration, and side effect information. In this way, the data collection unit can reduce the risk of medical accidents by collecting various data related to pharmacists. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input medication history information into AI, and the AI ​​can analyze and collect the data.

[0035] The analysis unit can analyze the collected data and provide solutions to reduce the risk of medical accidents caused by pharmacist dispensing errors or physician prescription errors. For example, the analysis unit can analyze the collected data using data mining techniques to provide solutions to reduce the risk of medical accidents caused by pharmacist dispensing errors or physician prescription errors. For example, the analysis unit can use machine learning algorithms to extract patterns from the collected data and predict the risk of medical accidents. For example, the analysis unit can use statistical analysis to analyze trends in the collected data and take measures to reduce the risk of medical accidents. In this way, the analysis unit can provide solutions to reduce the risk of medical accidents. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can analyze the data and provide solutions.

[0036] The service provider can build a generative AI model specifically for pharmacists and use AI to support the supplementation of knowledge specific to pharmacist duties. For example, the service provider can use the generative AI model to support pharmacists in determining the correctness of prescription contents when reviewing prescriptions. For example, the service provider can use the generative AI model to provide appropriate advice when pharmacists provide medication guidance to patients. In this way, the service provider can use AI to support the supplementation of knowledge specific to pharmacist duties. The generative AI model can be implemented using, for example, a natural language processing model or a machine learning model. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input a generative AI model into an AI, and the AI ​​can build and support the generative AI model.

[0037] The operations department can establish unmanned pharmacies with licenses equivalent to those of a pharmacist, thereby alleviating the shortage of pharmacies in sparsely populated areas. For example, the operations department can use an AI agent to dispense and provide appropriate medications to patients based on their medication history information. For example, the operations department can use an AI agent to provide appropriate advice when patients consult about drug interactions. In this way, the operations department can alleviate the shortage of pharmacies in sparsely populated areas. Some or all of the above processes performed by the operations department may be carried out using AI, for example, or without AI. For example, the operations department can input an AI agent into an AI, and the AI ​​can operate an unmanned pharmacy.

[0038] The service provider can provide medication support services based on the patient's medication history information. For example, the service provider can provide medication guidance based on the patient's medication history information. For example, the service provider can provide reminder services based on the patient's medication history information. In this way, the service provider can provide medication support services based on the patient's medication history information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's medication history information into AI, and the AI ​​can provide medication support services.

[0039] The analysis unit can analyze vast amounts of research data and support new drug research. For example, the analysis unit can analyze clinical trial data to evaluate the effectiveness of new drugs. For example, the analysis unit can analyze drug efficacy data to identify new drug candidates. For example, the analysis unit can analyze past research data to discover new drug candidates. In this way, the analysis unit can analyze vast amounts of research data and support new drug research. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input research data into AI, and the AI ​​can analyze the data to support new drug research.

[0040] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past data collection history and reflect this in future data collection. For example, the data collection unit can analyze past data collection history, find areas for improvement in collection methods, and optimize them. For example, the data collection unit can learn patterns in collection methods based on past data collection history and propose the optimal collection method. This allows the data collection unit to analyze past data collection history and select the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI, which can analyze the data and select the optimal collection method.

[0041] The data collection unit can evaluate the reliability of the data to be collected and prioritize the collection of highly reliable data. For example, the data collection unit can verify the source of the data and prioritize the collection of highly reliable data. For example, the data collection unit can check the consistency of the data and select highly reliable data. For example, the data collection unit can evaluate the timeliness of the data and prioritize the collection of the most recent data. As a result, the data collection unit can evaluate the reliability of the data to be collected and prioritize the collection of highly reliable data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the reliability of the data into the AI, which can then evaluate the data and collect highly reliable data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. For example, the data collection unit can prioritize the collection of data related to a specific region. For example, the data collection unit can select the optimal data collection point based on geographical location information. This allows the data collection unit to prioritize the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI, and the AI ​​can analyze the data and collect highly relevant data.

[0043] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze trends on social media and collect relevant data. For example, the data collection unit can analyze user posts on social media and collect relevant data. For example, the data collection unit can analyze hashtags on social media and collect relevant data. In this way, the data collection unit can analyze social media activity and collect relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into AI, and the AI ​​can analyze the data and collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can evaluate the data and adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a medical-specific analysis algorithm to medical data. For example, the analysis unit can apply a legal-specific analysis algorithm to legal data. For example, the analysis unit can apply a medical history-specific analysis algorithm to medical history data. In this way, the analysis unit can apply different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can analyze the data and apply an appropriate algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can postpone the analysis of older data. For example, the analysis unit can perform analysis with an appropriate priority on data of moderate recency. In this way, the analysis unit can determine the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can analyze the data and determine the priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of data with high relevance. For example, the analysis unit can postpone the analysis of data with low relevance. For example, the analysis unit can perform the analysis of data with moderate relevance in an appropriate order. In this way, the analysis unit can adjust the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can analyze the data and adjust the order.

[0048] The service provider can adjust the level of detail provided based on the importance of the solution at the time of provision. For example, the service provider can provide a detailed explanation for high-importance solutions. For example, the service provider can provide a simplified explanation for low-importance solutions. For example, the service provider can provide an explanation with a moderate level of detail for medium-importance solutions. In this way, the service provider can adjust the level of detail provided based on the importance of the solution. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the importance of the solution into the AI, and the AI ​​can analyze the data to adjust the level of detail provided.

[0049] The service provider can apply different service provision algorithms depending on the category of the solution at the time of provision. For example, the service provider can apply a service provision algorithm specifically for medical solutions. For example, the service provider can apply a service provision algorithm specifically for legal solutions. For example, the service provider can apply a service provision algorithm specifically for medication history solutions. In this way, the service provider can apply different service provision algorithms depending on the category of the solution. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category of the solution into the AI, and the AI ​​can analyze the data and apply an appropriate algorithm.

[0050] The service provider can adjust the order of delivery based on the relevance of the solutions. For example, the service provider may prioritize the delivery of highly relevant solutions. For example, it may postpone the delivery of less relevant solutions. For example, it may deliver solutions of moderate relevance in an appropriate order. In this way, the service provider can adjust the order of delivery based on the relevance of the solutions. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of the solutions into the AI, and the AI ​​can analyze the data and adjust the order.

[0051] The service provider may, at the time of delivery, provide additional relevant data to broaden the scope of application of the solution. For example, the service provider may provide additional relevant data to broaden the scope of application of the solution. For example, the service provider may provide additional relevant case studies to broaden the scope of application of the solution. For example, the service provider may provide additional relevant reference materials to broaden the scope of application of the solution. In this way, the service provider can provide additional relevant data to broaden the scope of application of the solution. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input relevant data into AI, and the AI ​​may analyze the data and provide additional information.

[0052] The operations department can analyze past operational data during operations to select the optimal operating method. For example, the operations department can analyze past operational data to identify the most efficient operating method. For example, the operations department can find areas for improvement in operating methods based on past operational data and optimize them. For example, the operations department can learn patterns in operating methods by referring to past operational data and propose the optimal operating method. This allows the operations department to analyze past operational data and select the optimal operating method. Some or all of the above processes in the operations department may be performed using AI, for example, or without AI. For example, the operations department can input past operational data into AI, and the AI ​​can analyze the data to select the optimal operating method.

[0053] The operations department can customize its operational methods based on regional characteristics during operation. For example, the operations department can customize operational methods according to the population density of the region. For example, the operations department can customize operational methods according to the medical needs of the region. For example, the operations department can customize operational methods according to the culture and customs of the region. In this way, the operations department can customize operational methods based on regional characteristics. Some or all of the above processes in the operations department may be performed using AI, for example, or without AI. For example, the operations department can input regional characteristic data into AI, and the AI ​​can analyze the data to customize operational methods.

[0054] The operations department can select the optimal operating method while considering geographical location information during operations. For example, the operations department can select the optimal operating method based on geographical location information. For example, the operations department can customize operating methods while considering geographical location information. For example, the operations department can identify areas for improvement in operating methods and optimize them based on geographical location information. In this way, the operations department can select the optimal operating method while considering geographical location information. Some or all of the above processes in the operations department may be performed using AI, for example, or without AI. For example, the operations department can input geographical location information into AI, and the AI ​​can analyze the data to select the optimal operating method.

[0055] The operations department can analyze social media activity and propose operational methods during operations. For example, the operations department can analyze trends on social media and propose operational methods. For example, the operations department can analyze user posts on social media and propose operational methods. For example, the operations department can analyze hashtags on social media and propose operational methods. In this way, the operations department can analyze social media activity and propose operational methods. Some or all of the above processing by the operations department may be performed using AI, for example, or not using AI. For example, the operations department can input social media data into AI, and the AI ​​can analyze the data and propose operational methods.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can evaluate the reliability of data and prioritize the collection of highly reliable data when collecting data related to pharmacists' work. For example, the data collection unit can verify the source of the data and select reliable data. Furthermore, the data collection unit can check the consistency of the data and prioritize the collection of consistent data. In addition, the data collection unit can evaluate the timeliness of the data and prioritize the collection of the most up-to-date information. As a result, the data collection unit can more accurately support pharmacists' work by collecting highly reliable data.

[0058] The analysis unit can adjust the level of detail in the analysis based on the importance of the collected data. For example, it can perform a detailed analysis on highly important data and a simplified analysis on less important data. It can also perform an analysis with an appropriate level of detail on data of moderate importance. As a result, the analysis unit can provide solutions to efficiently reduce the risk of medical errors by performing analyses according to the importance of the data.

[0059] The operations department can customize the operational methods of an unmanned pharmacy based on the characteristics of the region. For example, they can adjust the operational methods according to the population density of the region, enabling efficient operation in sparsely populated areas. They can also customize the services provided according to the local medical needs. Furthermore, they can adjust the operational methods according to the local culture and customs. In this way, the operations department can provide operational methods tailored to the characteristics of the region, thereby achieving effective operation of the unmanned pharmacy.

[0060] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, it can analyze trends on social media and collect relevant data. It can also analyze user posts on social media and collect relevant data. Furthermore, it can analyze hashtags on social media and collect relevant data. In this way, the data collection unit can support the work of pharmacists by analyzing social media activity and collecting relevant data.

[0061] The service provider can adjust the level of detail provided based on the importance of the solution being offered. For example, detailed explanations can be provided for high-importance solutions, while simplified explanations can be provided for low-importance solutions. Furthermore, explanations of moderate importance can be provided. This allows the service provider to effectively support pharmacists' work by providing solutions according to their importance.

[0062] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, it can prioritize the collection of highly relevant data based on the user's current location. It can also prioritize the collection of data related to a specific region. Furthermore, it can select the optimal data collection points based on geographical location information. As a result, the data collection unit can effectively support the work of pharmacists by collecting highly relevant data while considering geographical location information.

[0063] The analysis unit can determine the priority of analysis based on the data collection date. For example, it can prioritize the analysis of the most recent data and postpone the analysis of older data. It can also analyze data of moderate recency with an appropriate priority. By determining the priority of analysis based on the data collection date, the analysis unit can efficiently provide solutions to reduce the risk of medical errors.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects data related to pharmacists. The data collection unit collects data such as relevant laws, information on drugs, patient medication history information, and medical accident cases that occurred at pharmacies, etc. The data collection unit can collect medication history information and understand the patient's past prescription history and allergy information. In addition, the data collection unit can collect medical accident cases and accumulate past cases of dispensing errors and prescription errors. Furthermore, the data collection unit can collect information on drugs and understand information such as drug efficacy, dosage and administration, and side effect information. Step 2: The analysis unit analyzes the data collected by the collection unit and provides solutions for each problem. For example, the analysis unit can analyze the collected data using data mining techniques to provide solutions to reduce the risk of medical errors caused by pharmacist dispensing errors or physician prescription errors. The analysis unit can use machine learning algorithms to extract patterns from the collected data and predict the risk of medical errors. In addition, the analysis unit can use statistical analysis to analyze trends in the collected data and take measures to reduce the risk of medical errors. Step 3: The service provider supports pharmacists' work based on the analysis results obtained by the analysis unit. For example, the service provider builds a generative AI model specifically for pharmacists and uses AI to supplement knowledge specific to pharmacist work. The service provider can use the generative AI model to support pharmacists in determining the correctness of prescription contents when reviewing prescriptions. The service provider can also use the generative AI model to provide appropriate advice to pharmacists when providing medication guidance to patients. Step 4: The operations department operates the unmanned pharmacy based on the solutions provided by the supply department. For example, the operations department may open an unmanned pharmacy with a license equivalent to that of a pharmacist to alleviate the shortage of pharmacies in sparsely populated areas. The operations department can use an AI agent to dispense and provide appropriate medications to patients based on their medication history information. The operations department can also use the AI ​​agent to provide appropriate advice to patients when they consult about drug interactions.

[0066] (Example of form 2) The system according to the embodiment of the present invention is a system that uses an AI agent to solve problems that arise in various tasks performed by pharmacists, as well as problems faced by patients and pharmacy managers. This system accumulates various data related to pharmacists (relevant laws, information on drugs, patient medication history information, medical accident cases that have occurred in pharmacies, etc.) while updating them to the latest information. Next, the AI ​​agent processes the data and thoughts generated by a large-scale language model to provide solutions to each problem. This is expected to reduce the risk of medical accidents caused by pharmacist dispensing errors and physician prescription errors, alleviate the pharmacist shortage by reducing the workload of pharmacists, realize unmanned pharmacies in sparsely populated areas, and promote the development of groundbreaking new drugs through application in new drug research. For example, data related to pharmacists is collected and analyzed by the AI ​​agent. At this time, data such as relevant laws, information on drugs, patient medication history information, and medical accident cases that have occurred in pharmacies, etc. are collected. For example, pharmacists can refer to past medical accident cases to prevent dispensing errors and take measures to prevent similar mistakes. Next, the AI ​​agent analyzes the collected data and provides solutions to each problem. For example, to prevent pharmacists from making dispensing errors, an AI agent will judge the accuracy of prescriptions and their contents, preventing subsequent risks due to errors. Furthermore, a generative AI model specifically for pharmacists will be built to supplement knowledge specific to pharmacist duties. In addition, to address the shortage of pharmacists, the AI ​​agent will open unmanned pharmacies with licenses equivalent to those of a licensed pharmacist. This will alleviate the shortage of pharmacies in sparsely populated areas and reduce disparities in healthcare. For instance, the AI ​​agent can dispense and provide appropriate medications to patients based on their medication history. The AI ​​agent can also provide solutions to patient challenges. For example, for patients unsure about drug interactions, the AI ​​agent will learn data equivalent to that of a pharmacist and provide medication support services. This reduces the risk of patients taking medication incorrectly. Finally, as an application to new drug research, the AI ​​agent will analyze vast amounts of research data to support new drug research. For example, the AI ​​agent can refer to past research data to discover new drug candidates. This can significantly reduce the time and cost of new drug development.This will enable the system to support the work of pharmacists and realize the operation of unmanned pharmacies.

[0067] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, and an operation unit. The data collection unit collects data related to pharmacists. For example, the data collection unit collects data such as relevant laws, information on drugs, patient medication history information, and medical accident cases that occurred at pharmacies, etc. For example, the data collection unit can collect medication history information and understand the patient's past prescription history and allergy information. The data collection unit can also collect medical accident cases and accumulate past cases of dispensing errors and prescription errors. Furthermore, the data collection unit can collect information on drugs and understand their efficacy, dosage and administration, and side effect information. The analysis unit analyzes the data collected by the data collection unit and provides solutions for each problem. For example, the analysis unit analyzes the collected data using data mining technology and provides solutions to reduce the risk of medical accidents caused by pharmacist dispensing errors or physician prescription errors. For example, the analysis unit can use machine learning algorithms to extract patterns from the collected data and predict the risk of medical accidents. Furthermore, the analysis unit can use statistical analysis to analyze trends in the collected data and take measures to reduce the risk of medical accidents. The provision department supports pharmacists' work based on the analysis results obtained by the analysis department. For example, the provision department builds a generative AI model specifically for pharmacists and uses AI to supplement knowledge specific to pharmacist work. For example, the provision department can use the generative AI model to support pharmacists in determining the correctness of prescription contents when reviewing prescriptions. The provision department can also use the generative AI model to provide appropriate advice to pharmacists when providing medication guidance to patients. The operations department operates unmanned pharmacies based on the solutions provided by the provision department. For example, the operations department opens unmanned pharmacies with licenses equivalent to those of a pharmacist, addressing the shortage of pharmacies in sparsely populated areas. For example, the operations department can use an AI agent to dispense and provide appropriate medications to patients based on their medication history information. The operations department can also use an AI agent to provide appropriate advice to patients when they consult about drug interactions. In this way, the system supports pharmacists' work and enables the operation of unmanned pharmacies.

[0068] The Data Collection Department collects data related to pharmacists. For example, it collects data on relevant laws, drug information, patient medication history, and medical accident cases that occurred at pharmacies, etc. Specifically, regarding relevant laws, it collects legal information such as the Pharmacist Act and the Pharmaceuticals and Medical Devices Act to understand the legal requirements that pharmacists must comply with. Regarding drug information, it collects information on efficacy, dosage and administration, side effects, and drug interactions to provide basic data for pharmacists to make appropriate drug selections. Regarding patient medication history information, it obtains data from electronic medical record systems and medication history management systems to understand patients' past prescription history, allergy information, and medical history. This allows pharmacists to select the optimal drug for each patient. Regarding medical accident cases, it collects cases of dispensing errors and prescription errors that occurred at pharmacies and hospitals, and stores these cases in a database. This allows for referencing past cases and taking measures to prevent similar errors. The Data Collection Department centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the Analysis Department and the Provision Department. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0069] The analysis department analyzes the data collected by the collection department and provides solutions to each problem. For example, the analysis department uses data mining techniques to analyze the collected data and provide solutions to reduce the risk of medical accidents caused by pharmacist dispensing errors and physician prescription errors. Specifically, it uses machine learning algorithms to extract patterns from the collected data and predict the risk of medical accidents. For example, based on past dispensing error data, it identifies risks associated with specific drugs or prescription patterns and issues warnings to pharmacists. It also uses statistical analysis to analyze trends in the collected data and implement measures to reduce the risk of medical accidents. For example, it analyzes the frequency of dispensing errors during specific times of day or days of the week and reviews operations and optimizes staffing during high-risk periods. Furthermore, the analysis department can use AI to analyze the collected data in real time and detect medical accident risks early. For example, it can detect abnormal data or patterns that occur during dispensing and immediately issue warnings to pharmacists. In this way, the analysis department can quickly and accurately analyze the collected data and provide effective measures to reduce the risk of medical accidents.

[0070] The service provider supports pharmacists' work based on the analysis results obtained by the analysis department. For example, the service provider builds a generative AI model specifically for pharmacists and uses AI to supplement knowledge specific to pharmacist work. Specifically, it uses the generative AI model to help pharmacists determine the accuracy of prescriptions when reviewing them. For example, it checks whether the usage and dosage of the drugs listed on the prescription are appropriate and warns of the risk of overdose or drug interactions. It also uses the generative AI model to provide appropriate advice to patients when providing medication guidance. For example, it explains the timing of medication and precautions for side effects based on the patient's medication history, supporting patients in taking their medication safely. Furthermore, the service provider uses the generative AI model to provide educational content for pharmacists to learn about new drugs and treatments. This allows pharmacists to stay up-to-date with the latest medical information and apply it to their work. Through this support, the service provider can improve the efficiency of pharmacists' work and reduce the risk of medical errors.

[0071] The operations department will operate the unmanned pharmacies based on the solutions provided by the service provider department. For example, the operations department will open unmanned pharmacies that have received accreditation equivalent to that of a pharmacist, thereby alleviating the shortage of pharmacies in sparsely populated areas. Specifically, it will use an AI agent to dispense and provide appropriate medications to patients based on their medication history information. For example, when a patient submits a prescription, the AI ​​agent will check the prescription details, select the appropriate medication, and dispense it. The AI ​​agent will also provide appropriate advice when patients consult about drug interactions. For example, if a patient is taking multiple medications, it will assess the risk of interactions and suggest a safe way to take them. Furthermore, the operations department will monitor the operation of the unmanned pharmacies and provide maintenance and support as needed. For example, it will regularly manage drug inventory and inspect equipment to ensure that the unmanned pharmacies are always in proper working order. In this way, the operations department can alleviate the shortage of pharmacies in sparsely populated areas and provide an environment where patients can receive safe and appropriate medical services.

[0072] The data collection unit can collect data such as relevant laws, information on drugs, patient medication history information, and medical accident cases that occurred at pharmacies, etc. For example, the data collection unit can collect medication history information to understand a patient's past prescription history and allergy information. For example, the data collection unit can collect medical accident cases to accumulate past cases of dispensing errors and prescription errors. For example, the data collection unit can collect information on drugs to understand their efficacy, dosage and administration, and side effect information. In this way, the data collection unit can reduce the risk of medical accidents by collecting various data related to pharmacists. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input medication history information into AI, and the AI ​​can analyze and collect the data.

[0073] The analysis unit can analyze the collected data and provide solutions to reduce the risk of medical accidents caused by pharmacist dispensing errors or physician prescription errors. For example, the analysis unit can analyze the collected data using data mining techniques to provide solutions to reduce the risk of medical accidents caused by pharmacist dispensing errors or physician prescription errors. For example, the analysis unit can use machine learning algorithms to extract patterns from the collected data and predict the risk of medical accidents. For example, the analysis unit can use statistical analysis to analyze trends in the collected data and take measures to reduce the risk of medical accidents. In this way, the analysis unit can provide solutions to reduce the risk of medical accidents. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can analyze the data and provide solutions.

[0074] The service provider can build a generative AI model specifically for pharmacists and use AI to support the supplementation of knowledge specific to pharmacist duties. For example, the service provider can use the generative AI model to support pharmacists in determining the correctness of prescription contents when reviewing prescriptions. For example, the service provider can use the generative AI model to provide appropriate advice when pharmacists provide medication guidance to patients. In this way, the service provider can use AI to support the supplementation of knowledge specific to pharmacist duties. The generative AI model can be implemented using, for example, a natural language processing model or a machine learning model. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input a generative AI model into an AI, and the AI ​​can build and support the generative AI model.

[0075] The operations department can establish unmanned pharmacies with licenses equivalent to those of a pharmacist, thereby alleviating the shortage of pharmacies in sparsely populated areas. For example, the operations department can use an AI agent to dispense and provide appropriate medications to patients based on their medication history information. For example, the operations department can use an AI agent to provide appropriate advice when patients consult about drug interactions. In this way, the operations department can alleviate the shortage of pharmacies in sparsely populated areas. Some or all of the above processes performed by the operations department may be carried out using AI, for example, or without AI. For example, the operations department can input an AI agent into an AI, and the AI ​​can operate an unmanned pharmacy.

[0076] The service provider can provide medication support services based on the patient's medication history information. For example, the service provider can provide medication guidance based on the patient's medication history information. For example, the service provider can provide reminder services based on the patient's medication history information. In this way, the service provider can provide medication support services based on the patient's medication history information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the patient's medication history information into AI, and the AI ​​can provide medication support services.

[0077] The analysis unit can analyze vast amounts of research data and support new drug research. For example, the analysis unit can analyze clinical trial data to evaluate the effectiveness of new drugs. For example, the analysis unit can analyze drug efficacy data to identify new drug candidates. For example, the analysis unit can analyze past research data to discover new drug candidates. In this way, the analysis unit can analyze vast amounts of research data and support new drug research. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input research data into AI, and the AI ​​can analyze the data to support new drug research.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. In this way, the data collection unit can reduce the user's burden by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of data collection.

[0079] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most efficient collection method from past data collection history and reflect this in future data collection. For example, the data collection unit can analyze past data collection history, find areas for improvement in collection methods, and optimize them. For example, the data collection unit can learn patterns in collection methods based on past data collection history and propose the optimal collection method. This allows the data collection unit to analyze past data collection history and select the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI, which can analyze the data and select the optimal collection method.

[0080] The data collection unit can evaluate the reliability of the data to be collected and prioritize the collection of highly reliable data. For example, the data collection unit can verify the source of the data and prioritize the collection of highly reliable data. For example, the data collection unit can check the consistency of the data and select highly reliable data. For example, the data collection unit can evaluate the timeliness of the data and prioritize the collection of the most recent data. As a result, the data collection unit can evaluate the reliability of the data to be collected and prioritize the collection of highly reliable data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the reliability of the data into the AI, which can then evaluate the data and collect highly reliable data.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting only important data. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit may prioritize collecting data that can be collected quickly. This allows the data collection unit to determine the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of the data.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. For example, the data collection unit can prioritize the collection of data related to a specific region. For example, the data collection unit can select the optimal data collection point based on geographical location information. This allows the data collection unit to prioritize the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI, and the AI ​​can analyze the data and collect highly relevant data.

[0083] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can analyze trends on social media and collect relevant data. For example, the data collection unit can analyze user posts on social media and collect relevant data. For example, the data collection unit can analyze hashtags on social media and collect relevant data. In this way, the data collection unit can analyze social media activity and collect relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into AI, and the AI ​​can analyze the data and collect relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result. This allows the analysis unit to adjust the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can evaluate the data and adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a medical-specific analysis algorithm to medical data. For example, the analysis unit can apply a legal-specific analysis algorithm to legal data. For example, the analysis unit can apply a medical history-specific analysis algorithm to medical history data. In this way, the analysis unit can apply different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can analyze the data and apply an appropriate algorithm.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. If the user is excited, the analysis unit can perform a visually stimulating analysis. This allows the analysis unit to adjust the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can postpone the analysis of older data. For example, the analysis unit can perform analysis with an appropriate priority on data of moderate recency. In this way, the analysis unit can determine the priority of analysis based on the data collection timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can analyze the data and determine the priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of data with high relevance. For example, the analysis unit can postpone the analysis of data with low relevance. For example, the analysis unit can perform the analysis of data with moderate relevance in an appropriate order. In this way, the analysis unit can adjust the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can analyze the data and adjust the order.

[0090] The service provider can estimate the user's emotions and adjust the way the solutions are presented based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and easily understandable solution. If the user is relaxed, the service provider can provide a detailed solution. If the user is in a hurry, the service provider can provide a concise solution. This allows the service provider to adjust the way the solutions are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the way the solutions are presented.

[0091] The service provider can adjust the level of detail provided based on the importance of the solution at the time of provision. For example, the service provider can provide a detailed explanation for high-importance solutions. For example, the service provider can provide a simplified explanation for low-importance solutions. For example, the service provider can provide an explanation with a moderate level of detail for medium-importance solutions. In this way, the service provider can adjust the level of detail provided based on the importance of the solution. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the importance of the solution into the AI, and the AI ​​can analyze the data to adjust the level of detail provided.

[0092] The service provider can apply different service provision algorithms depending on the category of the solution at the time of provision. For example, the service provider can apply a service provision algorithm specifically for medical solutions. For example, the service provider can apply a service provision algorithm specifically for legal solutions. For example, the service provider can apply a service provision algorithm specifically for medication history solutions. In this way, the service provider can apply different service provision algorithms depending on the category of the solution. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category of the solution into the AI, and the AI ​​can analyze the data and apply an appropriate algorithm.

[0093] The service provider can estimate the user's emotions and determine the priority of solutions to offer based on the estimated emotions. For example, if the user is stressed, the service provider can prioritize offering only important solutions. For example, if the user is relaxed, the service provider can prioritize offering detailed solutions. For example, if the user is in a hurry, the service provider can prioritize offering solutions that can be delivered quickly. In this way, the service provider can determine the priority of solutions to offer based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of solutions.

[0094] The service provider can adjust the order of delivery based on the relevance of the solutions. For example, the service provider may prioritize the delivery of highly relevant solutions. For example, it may postpone the delivery of less relevant solutions. For example, it may deliver solutions of moderate relevance in an appropriate order. In this way, the service provider can adjust the order of delivery based on the relevance of the solutions. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of the solutions into the AI, and the AI ​​can analyze the data and adjust the order.

[0095] The service provider may, at the time of delivery, provide additional relevant data to broaden the scope of application of the solution. For example, the service provider may provide additional relevant data to broaden the scope of application of the solution. For example, the service provider may provide additional relevant case studies to broaden the scope of application of the solution. For example, the service provider may provide additional relevant reference materials to broaden the scope of application of the solution. In this way, the service provider can provide additional relevant data to broaden the scope of application of the solution. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input relevant data into AI, and the AI ​​may analyze the data and provide additional information.

[0096] The operations department can estimate the user's emotions and adjust the operation of the unmanned pharmacy based on the estimated emotions. For example, if the user is nervous, the operations department can provide a simple and easy-to-understand operation method. For example, if the user is relaxed, the operations department can provide a detailed operation method. For example, if the user is in a hurry, the operations department can provide a concise operation method. In this way, the operations department can adjust the operation of the unmanned pharmacy based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the operations department may be performed using AI or not using AI. For example, the operations department can input user emotion data into a generative AI, which can estimate the emotion and adjust the operation method.

[0097] The operations department can analyze past operational data during operations to select the optimal operating method. For example, the operations department can analyze past operational data to identify the most efficient operating method. For example, the operations department can find areas for improvement in operating methods based on past operational data and optimize them. For example, the operations department can learn patterns in operating methods by referring to past operational data and propose the optimal operating method. This allows the operations department to analyze past operational data and select the optimal operating method. Some or all of the above processes in the operations department may be performed using AI, for example, or without AI. For example, the operations department can input past operational data into AI, and the AI ​​can analyze the data to select the optimal operating method.

[0098] The operations department can customize its operational methods based on regional characteristics during operation. For example, the operations department can customize operational methods according to the population density of the region. For example, the operations department can customize operational methods according to the medical needs of the region. For example, the operations department can customize operational methods according to the culture and customs of the region. In this way, the operations department can customize operational methods based on regional characteristics. Some or all of the above processes in the operations department may be performed using AI, for example, or without AI. For example, the operations department can input regional characteristic data into AI, and the AI ​​can analyze the data to customize operational methods.

[0099] The operations department can estimate the user's emotions and determine operational priorities based on those estimated emotions. For example, if the user is stressed, the operations department can prioritize only essential operational measures. If the user is relaxed, the operations department can prioritize detailed operational measures. If the user is in a hurry, the operations department can prioritize operational measures that can be implemented quickly. This allows the operations department to determine operational priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the operations department may be performed using AI or not. For example, the operations department can input user emotion data into a generative AI, which can estimate the emotion and determine operational priorities.

[0100] The operations department can select the optimal operating method while considering geographical location information during operations. For example, the operations department can select the optimal operating method based on geographical location information. For example, the operations department can customize operating methods while considering geographical location information. For example, the operations department can identify areas for improvement in operating methods and optimize them based on geographical location information. In this way, the operations department can select the optimal operating method while considering geographical location information. Some or all of the above processes in the operations department may be performed using AI, for example, or without AI. For example, the operations department can input geographical location information into AI, and the AI ​​can analyze the data to select the optimal operating method.

[0101] The operations department can analyze social media activity and propose operational methods during operations. For example, the operations department can analyze trends on social media and propose operational methods. For example, the operations department can analyze user posts on social media and propose operational methods. For example, the operations department can analyze hashtags on social media and propose operational methods. In this way, the operations department can analyze social media activity and propose operational methods. Some or all of the above processing by the operations department may be performed using AI, for example, or not using AI. For example, the operations department can input social media data into AI, and the AI ​​can analyze the data and propose operational methods.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The data collection unit can evaluate the reliability of data and prioritize the collection of highly reliable data when collecting data related to pharmacists' work. For example, the data collection unit can verify the source of the data and select reliable data. Furthermore, the data collection unit can check the consistency of the data and prioritize the collection of consistent data. In addition, the data collection unit can evaluate the timeliness of the data and prioritize the collection of the most up-to-date information. As a result, the data collection unit can more accurately support pharmacists' work by collecting highly reliable data.

[0104] The analysis unit can adjust the level of detail in the analysis based on the importance of the collected data. For example, it can perform a detailed analysis on highly important data and a simplified analysis on less important data. It can also perform an analysis with an appropriate level of detail on data of moderate importance. As a result, the analysis unit can provide solutions to efficiently reduce the risk of medical errors by performing analyses according to the importance of the data.

[0105] The service provider can estimate the user's emotions when building a generator AI model specifically for pharmacists, and adjust the output of the generator AI model based on the estimated emotions. For example, if the user is nervous, it can provide simple and easy-to-understand output; if the user is relaxed, it can provide detailed output. Also, if the user is in a hurry, it can provide output that gets straight to the point. In this way, the service provider can effectively support the work of pharmacists by providing output content that matches the user's emotions.

[0106] The operations department can customize the operational methods of an unmanned pharmacy based on the characteristics of the region. For example, they can adjust the operational methods according to the population density of the region, enabling efficient operation in sparsely populated areas. They can also customize the services provided according to the local medical needs. Furthermore, they can adjust the operational methods according to the local culture and customs. In this way, the operations department can provide operational methods tailored to the characteristics of the region, thereby achieving effective operation of the unmanned pharmacy.

[0107] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, it can analyze trends on social media and collect relevant data. It can also analyze user posts on social media and collect relevant data. Furthermore, it can analyze hashtags on social media and collect relevant data. In this way, the data collection unit can support the work of pharmacists by analyzing social media activity and collecting relevant data.

[0108] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is tense, it can provide simple and easy-to-understand analysis results; if the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. This allows the analysis unit to effectively provide solutions to reduce the risk of medical errors by offering analysis results tailored to the user's emotions.

[0109] The service provider can adjust the level of detail provided based on the importance of the solution being offered. For example, detailed explanations can be provided for high-importance solutions, while simplified explanations can be provided for low-importance solutions. Furthermore, explanations of moderate importance can be provided. This allows the service provider to effectively support pharmacists' work by providing solutions according to their importance.

[0110] The operations department can estimate the user's emotions and adjust the operation of the unmanned pharmacy based on those estimates. For example, if the user is nervous, a simple and highly visible operation method can be provided; if the user is relaxed, a detailed operation method can be provided. Furthermore, if the user is in a hurry, a concise operation method can be provided. In this way, the operations department can achieve effective operation of the unmanned pharmacy by providing an operation method that responds to the user's emotions.

[0111] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, it can prioritize the collection of highly relevant data based on the user's current location. It can also prioritize the collection of data related to a specific region. Furthermore, it can select the optimal data collection points based on geographical location information. As a result, the data collection unit can effectively support the work of pharmacists by collecting highly relevant data while considering geographical location information.

[0112] The analysis unit can determine the priority of analysis based on the data collection date. For example, it can prioritize the analysis of the most recent data and postpone the analysis of older data. It can also analyze data of moderate recency with an appropriate priority. By determining the priority of analysis based on the data collection date, the analysis unit can efficiently provide solutions to reduce the risk of medical errors.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects data related to pharmacists. The data collection unit collects data such as relevant laws, information on drugs, patient medication history information, and medical accident cases that occurred at pharmacies, etc. The data collection unit can collect medication history information and understand the patient's past prescription history and allergy information. In addition, the data collection unit can collect medical accident cases and accumulate past cases of dispensing errors and prescription errors. Furthermore, the data collection unit can collect information on drugs and understand information such as drug efficacy, dosage and administration, and side effect information. Step 2: The analysis unit analyzes the data collected by the collection unit and provides solutions for each problem. For example, the analysis unit can analyze the collected data using data mining techniques to provide solutions to reduce the risk of medical errors caused by pharmacist dispensing errors or physician prescription errors. The analysis unit can use machine learning algorithms to extract patterns from the collected data and predict the risk of medical errors. In addition, the analysis unit can use statistical analysis to analyze trends in the collected data and take measures to reduce the risk of medical errors. Step 3: The service provider supports pharmacists' work based on the analysis results obtained by the analysis unit. For example, the service provider builds a generative AI model specifically for pharmacists and uses AI to supplement knowledge specific to pharmacist work. The service provider can use the generative AI model to support pharmacists in determining the correctness of prescription contents when reviewing prescriptions. The service provider can also use the generative AI model to provide appropriate advice to pharmacists when providing medication guidance to patients. Step 4: The operations department operates the unmanned pharmacy based on the solutions provided by the supply department. For example, the operations department may open an unmanned pharmacy with a license equivalent to that of a pharmacist to alleviate the shortage of pharmacies in sparsely populated areas. The operations department can use an AI agent to dispense and provide appropriate medications to patients based on their medication history information. The operations department can also use the AI ​​agent to provide appropriate advice to patients when they consult about drug interactions.

[0115] 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.

[0116] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] 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.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and operation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data related to pharmacists using the camera 42 and microphone 38B of the smart device 14 and manages the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and provides solutions to each problem. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, which supports the work of pharmacists. The operation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which operates the unmanned pharmacy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and operation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data related to pharmacists using the camera 42 and microphone 238 of the smart glasses 214 and manages the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and provides solutions to each problem. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which supports the work of pharmacists. The operation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which operates the unmanned pharmacy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and operation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data related to pharmacists using the camera 42 and microphone 238 of the headset terminal 314 and manages the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and provides solutions to each problem. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, which supports the work of pharmacists. The operation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which operates the unmanned pharmacy. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and operation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data related to pharmacists using the camera 42 and microphone 238 of the robot 414 and manages the data with the control unit 46A. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and provides solutions to each problem. The provision unit is implemented by, for example, the control unit 46A of the robot 414, which supports the work of pharmacists. The operation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which operates the unmanned pharmacy. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) The data collection department collects data related to pharmacists, An analysis unit analyzes the data collected by the aforementioned collection unit and provides solutions for each problem, Based on the analysis results obtained by the aforementioned analysis unit, a provision unit is provided to support the pharmacist's work, Based on the solution provided by the aforementioned provisioning unit, the operation unit operates an unmanned pharmacy. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as relevant laws, information on medications, patient medication history information, and medical accident cases that occurred at pharmacies, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected data and provide solutions to reduce the risk of medical accidents caused by pharmacist dispensing errors and physician prescription errors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We will build a generative AI model specifically for pharmacists and use AI to support the supplementation of knowledge specific to pharmacist duties. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned operations department, We will establish unmanned dispensing pharmacies that have received accreditation equivalent to that of a pharmacist, thereby alleviating the shortage of dispensing pharmacies in sparsely populated areas. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide medication support services based on the patient's medication history information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Analyzing vast amounts of research data to support new drug research. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the reliability of the data to be collected is evaluated, and reliable data is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the solutions provided are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the solution, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing a solution, different delivery algorithms are applied depending on the solution category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of solutions to provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing solutions, adjust the order of delivery based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the solution, we will also provide additional relevant data to broaden its scope. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned operations department, The system estimates user emotions and adjusts the operation of the unmanned pharmacy based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned operations department, During operation, past operational data is analyzed to select the optimal operating method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned operations department, During operation, the operational methods are customized based on the characteristics of the region. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned operations department, We estimate user sentiment and determine operational priorities based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned operations department, During operation, the optimal operating method will be selected considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned operations department, During operation, we analyze social media activity and propose operational methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 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. The data collection department collects data related to pharmacists, An analysis unit analyzes the data collected by the aforementioned collection unit and provides solutions for each problem, Based on the analysis results obtained by the aforementioned analysis unit, a provision unit is provided to support the pharmacist's work, Based on the solution provided by the aforementioned provisioning unit, the operation unit operates an unmanned pharmacy. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as relevant laws, information on medications, patient medication history information, and medical accident cases that occurred at pharmacies, etc. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the collected data and provide solutions to reduce the risk of medical accidents caused by pharmacist dispensing errors and physician prescription errors. The system according to feature 1.

4. The aforementioned supply unit is, We will build a generative AI model specifically for pharmacists and use AI to support the supplementation of knowledge specific to pharmacist duties. The system according to feature 1.

5. The aforementioned operations department, We will establish unmanned dispensing pharmacies that have received accreditation equivalent to that of a pharmacist, thereby alleviating the shortage of dispensing pharmacies in sparsely populated areas. The system according to feature 1.

6. The aforementioned supply unit is, We provide medication support services based on the patient's medication history information. The system according to feature 1.

7. The aforementioned analysis unit, Analyzing vast amounts of research data to support new drug research. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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