system

Abstract

The system according to this embodiment aims to determine dispensing risks and operate an unmanned pharmacy. [Solution] The system according to the embodiment comprises a collection unit, a determination unit, a warning unit, and an operation unit. The collection unit collects data such as prescription drugs, dispensing, and patient medication history. The determination unit learns from the data collected by the collection unit and determines the dispensing risk. The warning unit issues a warning based on the risk determined by the determination unit. The operation unit operates the unmanned pharmacy based on the risk determined by the determination unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 medical errors due to prescription mistakes by doctors or dispensing mistakes by pharmacists, and increased burden due to a shortage of pharmacists.

[0005] The system according to the embodiment aims to determine dispensing risks and operate an unmanned pharmacy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a judgment unit, a warning unit, and an operation unit. The data collection unit collects data such as prescription drugs, dispensing, and patient medication history. The judgment unit learns from the data collected by the data collection unit and determines the dispensing risk. The warning unit issues a warning based on the risk determined by the judgment unit. The operation unit operates the unmanned pharmacy based on the risk determined by the judgment unit. [Effects of the Invention]

[0007] The system according to this embodiment can determine dispensing risks 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple 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 receiving 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 receiving 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 pharmacy support system according to an embodiment of the present invention is a system that solves various challenges faced by pharmacies and pharmacists by utilizing generative AI. This pharmacy support system trains the generative AI with data such as prescription drugs, dispensing information, and patient medication history to determine dispensing risks and prevent medical errors. It also enables the establishment of unmanned pharmacies using generative AI to alleviate the increased burden caused by pharmacist shortages. Furthermore, it provides medication support services using generative AI to advise patients on drug interactions and dosage methods. For example, the pharmacy support system trains the generative AI with data such as prescription drugs, dispensing information, and patient medication history. Based on this data, the generative AI determines dispensing risks and prevents medical errors. The generative AI analyzes drug interactions and patient allergy information and issues warnings if there are risks. Next, an unmanned pharmacy using generative AI is established. The generative AI has learned data equivalent to that of a pharmacist, analyzes the contents of prescriptions, and dispenses appropriate medications. This reduces the increased burden caused by pharmacist shortages and realizes 24-hour unmanned pharmacies. Furthermore, we will provide a medication support service using AI generation. Based on the patient's medication history and prescribed drug data, the AI ​​will advise on drug interactions and administration methods. When a patient takes multiple medications, the AI ​​will analyze the risk of interactions and suggest appropriate administration methods. In addition, we will open unmanned pharmacies using AI generation in sparsely populated areas and on weekends and at night. The AI ​​generation possesses knowledge equivalent to that of a pharmacist, analyzes the contents of prescriptions, and dispenses the appropriate medications. This will address the demand for dispensing in sparsely populated areas and on weekends and at night, and eliminate disparities in regional healthcare. In this way, the pharmacy support system will solve various challenges faced by pharmacies and pharmacists, preventing medical errors, alleviating pharmacist shortages, reducing the risks to patients taking medications, and addressing the demand for dispensing in sparsely populated areas and on weekends and at night.

[0029] The pharmacy support system according to this embodiment comprises a data collection unit, a judgment unit, a warning unit, and an operation unit. The data collection unit collects data such as prescription drugs, dispensing, and patient medication history. The data collection unit collects, for example, the type of prescription drug, dispensing details, and the contents of the patient's medication history. The data collection unit acquires data from, for example, an electronic medical record system. The data collection unit can also collect patient self-reported data. For example, the data collection unit stores patient-entered medication history information in a database. The judgment unit learns from the data collected by the data collection unit and determines the dispensing risk. The judgment unit determines the dispensing risk based on the collected data, for example, using generative AI. The judgment unit evaluates, for example, the risk of drug interactions and side effects. The judgment unit analyzes, for example, the patient's allergy information and issues a warning if there is a risk. The warning unit issues a warning based on the risk determined by the judgment unit. The warning unit sets, for example, the format of the alert and the notification method. The warning unit sends a warning, for example, via email or SMS. The warning unit displays a warning, for example, through an application. The operations department operates the unmanned pharmacy based on the risks determined by the assessment department. The operations department, for example, learns data equivalent to that of a pharmacist, analyzes the contents of prescriptions, and dispenses appropriate medications. The operations department, for example, sets up methods for storing medications and automated dispensing processes. The operations department operates an unmanned pharmacy that is available 24 hours a day. The operations department opens unmanned pharmacies in sparsely populated areas or on holidays and at night. As a result, the pharmacy support system according to this embodiment can solve various problems faced by pharmacies and pharmacists, prevent medical errors, alleviate pharmacist shortages, reduce patient medication risks, and meet dispensing demand in sparsely populated areas and on holidays and at night.

[0030] The data collection unit collects data on prescription drugs, dispensing, and patient medication history. Specifically, the unit obtains data from the electronic medical record system, collecting information such as the type of prescription drug, dispensing details, and patient medication history. For example, the type of prescription drug includes the drug name, dosage, administration method, and duration of administration. Dispensing details include the date and time of dispensing, pharmacist information, dispensing procedure, and information on the equipment and materials used. The patient's medication history includes a history of previously prescribed medications, allergy information, side effect history, and self-reported health status and lifestyle information. The data collection unit centrally manages this data and stores it in a database. The data collection unit can also store patient-entered medication history information in the database. For example, it can collect medication history information and health status data self-reported by patients via a smartphone app and integrate this into the database. This allows the data collection unit to grasp the patient's latest health status and medication history information in real time and use it to assess dispensing risks. Furthermore, the data collection unit can collect a wider range of data by linking data with external medical institutions and pharmacies. For example, by collecting referral letters and test results from other medical institutions, as well as dispensing history from other pharmacies, a comprehensive database can be built. This allows the data collection department to understand the patient's overall medical history and perform more accurate risk assessments.

[0031] The judgment unit learns from the data collected by the collection unit and determines the dispensing risk. Specifically, the judgment unit uses a generative AI to determine the dispensing risk based on the collected data. The generative AI learns from a large amount of medical data and analyzes drug interactions, the risk of side effects, and patient allergy information. For example, the judgment unit analyzes the collected prescription drug data and evaluates the risk due to drug interactions. It detects side effects and interactions that may occur when specific drugs are administered simultaneously and issues a warning if the risk is high. The judgment unit also analyzes the patient's medication history and evaluates the risk to the current prescription drug based on the history of past side effects and allergic reactions. For example, if a patient has shown an allergic reaction to a specific drug in the past, it will issue a warning when the same drug or a similar drug is prescribed. Furthermore, the judgment unit analyzes the patient's self-reported data and performs a risk assessment based on lifestyle and health status. For example, it considers the patient's self-reported dietary and exercise habits, medical history, etc., to evaluate whether a specific drug is appropriate. As a result, the judgment unit can comprehensively analyze the collected data and determine the dispensing risk with high accuracy.

[0032] The warning unit issues warnings based on the risks determined by the assessment unit. Specifically, the warning unit sets the alert format and notification method, and sends warnings via email or SMS. For example, if a risk due to drug interaction is determined, the warning unit immediately sends a notification to the pharmacist or patient via email or SMS. The warning unit can also display warnings through an application. For example, it can display real-time warnings to patients via a smartphone app to encourage appropriate action. Furthermore, the warning unit can customize the priority and content of warnings. For example, high-risk warnings can be displayed as emergency alerts, while low-risk warnings can be displayed in the notification center, allowing for flexible responses depending on the situation. As a result, the warning unit can provide prompt and appropriate warnings based on the risks determined by the assessment unit, preventing medical errors and ensuring patient safety.

[0033] The operations department will operate the unmanned pharmacies based on the risks determined by the assessment department. Specifically, the operations department will learn data equivalent to that of a pharmacist, analyze the contents of prescriptions, and dispense appropriate medications. For example, the operations department will analyze the contents of prescriptions, identify the necessary types and dosages of medications, and dispense them accurately using automated dispensing equipment. The operations department will also set up drug storage methods and automated dispensing processes to operate 24-hour unmanned pharmacies. For example, by appropriately managing the storage temperature and humidity of medications and automating the dispensing process, they will always provide high-quality dispensing. Furthermore, the operations department will open unmanned pharmacies in sparsely populated areas and on holidays and at night to meet local dispensing needs. For example, unmanned pharmacies installed in sparsely populated areas will provide prompt and appropriate dispensing services to local residents while receiving remote monitoring and support from remote locations. In this way, the operations department can contribute to alleviating the shortage of pharmacists, improving access to medical care in sparsely populated areas, and reducing the risks to patients taking medication.

[0034] The data collection unit can collect data such as prescription drugs, dispensing information, and patient medication history. For example, the data collection unit collects information such as the type of prescription drug, dispensing details, and the contents of the patient's medication history. The data collection unit can acquire data from, for example, an electronic medical record system. The data collection unit can also collect patient self-reported data. For example, the data collection unit stores patient-entered medication history information in a database. This allows for the collection of necessary data and improves the accuracy of the system. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input data acquired from the electronic medical record system into a generating AI and have the generating AI perform data collection.

[0035] The judgment unit can determine the dispensing risk based on the collected data. The judgment unit uses, for example, a generative AI to determine the dispensing risk based on the collected data. The judgment unit evaluates, for example, the risk of drug interactions and side effects. The judgment unit analyzes, for example, the patient's allergy information and issues a warning if there is a risk. This makes it possible to determine the risk based on the collected data and prevent medical errors. Some or all of the above processing in the judgment unit is performed using a generative AI. For example, the judgment unit inputs the collected data into the generative AI, and the generative AI analyzes the data to determine the dispensing risk.

[0036] The warning unit can issue warnings based on the determined risk. The warning unit can, for example, set the format of the alert and the notification method. The warning unit can, for example, send warnings via email or SMS. The warning unit can, for example, display warnings through an application. This allows warnings to be issued based on risk and medical errors to be prevented. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit inputs risk information from the determination unit into a generating AI, the generating AI generates warning content and issues a warning.

[0037] The operations department can operate unmanned pharmacies based on the assessed risk. For example, the operations department can learn data equivalent to that of a pharmacist, analyze prescription contents, and dispense appropriate medications. For example, the operations department can set up drug storage methods and automated dispensing processes. For example, the operations department can operate unmanned pharmacies that are available 24 hours a day. For example, the operations department can open unmanned pharmacies in sparsely populated areas or on holidays and at night. This allows for the operation of unmanned pharmacies based on risk and helps alleviate the shortage of pharmacists. Some or all of the above processes in the operations department are performed using a generating AI. For example, the operations department inputs risk information from the assessment department into the generating AI, which then operates the unmanned pharmacy.

[0038] The operations department can learn data equivalent to that of a pharmacist, analyze prescriptions, and dispense appropriate medications. For example, the operations department can learn the knowledge base of pharmacists and acquire methods for analyzing prescriptions. For example, the operations department can analyze prescriptions and select appropriate medications. For example, the operations department can automate the medication dispensing process and dispense medications efficiently. This will help alleviate the pharmacist shortage by learning data equivalent to that of a pharmacist and dispensing appropriate medications. Some or all of the above processes in the operations department are performed using generative AI. For example, the operations department can train the generative AI with the knowledge base of pharmacists, and the generative AI will analyze prescriptions and dispense appropriate medications.

[0039] The operations department can advise on drug interactions and administration methods based on the patient's medication history and prescription data. For example, the operations department analyzes the patient's medication history data and evaluates drug interactions. For example, the operations department proposes administration methods and advises the patient on appropriate administration methods. For example, when a patient is taking multiple medications, the operations department analyzes the risk of interaction and proposes appropriate administration methods. In this way, the risks to the patient's medication can be reduced by advising on drug interactions and administration methods. Some or all of the above processes in the operations department are performed using generative AI. For example, the operations department inputs the patient's medication history data into the generative AI, and the generative AI provides advice on drug interactions and administration methods.

[0040] The operations department can open unmanned pharmacies in sparsely populated areas and on weekends and at night. For example, the operations department can analyze the medical needs of sparsely populated areas and open unmanned pharmacies. For example, the operations department can operate unmanned pharmacies to meet the demand for dispensing medication on weekends and at night. For example, the operations department can open unmanned pharmacies to eliminate disparities in regional medical care. This will allow them to meet the demand for dispensing medication in sparsely populated areas and on weekends and at night, and eliminate disparities in regional medical care. Some or all of the above processes in the operations department are performed using generative AI. For example, the operations department inputs the medical needs of sparsely populated areas into the generative AI, and the generative AI proposes the opening of unmanned pharmacies.

[0041] The data collection unit can collect patients' lifestyle data and use it to predict the effects and side effects of medications. For example, the data collection unit can collect patients' dietary information and analyze the effect of specific foods on medication effects. For example, the data collection unit can collect patients' exercise habits and analyze the effect of exercise on medication effects. For example, the data collection unit can collect patients' sleep patterns and analyze the effect of sleep on medication effects. In this way, collecting patients' lifestyle data can be used to predict the effects and side effects of medications. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs patients' lifestyle data into a generating AI, which analyzes the data to predict the effects and side effects of medications.

[0042] The data collection unit can collect real-time data from medical institutions and reflect the latest prescription information. For example, the data collection unit can acquire prescription information in real time from the medical institution's electronic medical record system. For example, the data collection unit can collect the latest patient information from the medical institution's medical records. For example, the data collection unit can collect test result data from medical institutions in real time and reflect it in prescriptions. In this way, by collecting real-time data from medical institutions, the latest prescription information can be reflected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs real-time data from medical institutions into a generating AI, and the generating AI analyzes the data to reflect the latest prescription information.

[0043] The data collection unit can collect patient dietary and exercise data and provide advice to maximize the effectiveness of medication. For example, the data collection unit can collect patient dietary data and advise against certain foods. For example, the data collection unit can collect patient exercise data and advise on appropriate exercise amounts. For example, the data collection unit can comprehensively analyze patient lifestyle data and provide lifestyle improvement advice to maximize the effectiveness of medication. Thus, by collecting patient dietary and exercise data, it is possible to provide advice to maximize the effectiveness of medication. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs patient dietary and exercise data into a generating AI, which analyzes the data and provides advice.

[0044] The data collection unit can analyze a patient's social media activity and collect data related to their health status. For example, the data collection unit can extract keywords related to health status from a patient's social media posts. For example, the data collection unit can estimate stress levels from a patient's social media activity. For example, the data collection unit can analyze a patient's social media activity and detect changes in their health status. In this way, data related to health status can be collected by analyzing a patient's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the patient's social media data into a generating AI, which analyzes the data and collects information related to their health status.

[0045] The assessment unit can analyze a patient's genetic information and perform individualized risk assessments. For example, the assessment unit can determine the risk of allergies to a specific drug based on the patient's genetic information. For example, the assessment unit can analyze a patient's genetic information and predict the metabolic rate of a drug. For example, the assessment unit can individually determine the risk of drug effects and side effects based on the patient's genetic information. This makes individualized risk assessment possible by analyzing the patient's genetic information. Some or all of the above processes in the assessment unit are performed using a generating AI. For example, the assessment unit inputs the patient's genetic information into the generating AI, which analyzes the data and performs individualized risk assessments.

[0046] The judgment unit can learn from past medical malpractice data to improve the accuracy of risk assessment. For example, the judgment unit determines the risk of a specific drug based on past medical malpractice data. For example, the judgment unit learns from past medical malpractice data to improve the risk assessment algorithm. For example, the judgment unit analyzes past medical malpractice data to improve the accuracy of risk assessment. As a result, the accuracy of risk assessment is improved by learning from past medical malpractice data. Some or all of the above processes in the judgment unit are performed using a generative AI. For example, the judgment unit inputs past medical malpractice data into the generative AI, and the generative AI learns from the data to improve the accuracy of risk assessment.

[0047] The assessment unit can make risk assessments by considering the patient's living environment data. For example, the assessment unit can determine allergy risk based on the patient's living environment data. For example, the assessment unit can determine stress risk by considering the patient's work environment data. For example, the assessment unit can make risk assessments by comprehensively analyzing the patient's living environment data. This makes it possible to make more accurate risk assessments by considering the patient's living environment data. Some or all of the above processing in the assessment unit is performed using a generating AI. For example, the assessment unit inputs the patient's living environment data into the generating AI, and the generating AI analyzes the data to make a risk assessment.

[0048] The assessment unit can improve the accuracy of risk assessment by linking with databases of other medical institutions. For example, the assessment unit can obtain past medical records of patients from databases of other medical institutions and reflect them in the risk assessment. For example, the assessment unit can perform risk assessment based on the latest medical information by linking with databases of other medical institutions. For example, the assessment unit can improve the accuracy of risk assessment by utilizing databases of other medical institutions. In this way, the accuracy of risk assessment is improved by linking with databases of other medical institutions. Some or all of the above processes in the assessment unit are performed using a generating AI. For example, the assessment unit inputs data obtained from databases of other medical institutions into the generating AI, and the generating AI analyzes the data to improve the accuracy of risk assessment.

[0049] The warning unit can select the optimal warning method by referring to the patient's past response data when issuing a warning. For example, the warning unit may select the optimal warning method based on the warning method the patient has preferred in the past. For example, the warning unit may analyze the patient's past response data to select an effective warning method. For example, the warning unit may adjust the timing of the warning based on the patient's past response data. In this way, the optimal warning method can be selected by referring to the patient's past response data. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit may input the patient's past response data into a generating AI, which will analyze the data and select the optimal warning method.

[0050] The warning unit can set different warning levels depending on the severity of the risk when a warning is issued. For example, the warning unit may issue a mild warning for low risk, provide detailed warning information for moderate risk, and issue a highly urgent warning for high risk. By setting different warning levels according to the severity of the risk, appropriate warnings can be provided. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit inputs the severity of the risk into a generating AI, and the generating AI sets the warning level.

[0051] The warning unit can select the optimal warning method when an alert is issued, taking into account the patient's device information. For example, if the patient is using a smartphone, the warning unit will display a warning on the screen. If the patient is using a tablet, the warning unit will display a warning optimized for a larger screen. If the patient is using a smartwatch, the warning unit will provide a warning via vibration or sound. This allows the system to select the optimal warning method by considering the patient's device information. Some or all of the above processing in the warning unit may be performed using AI, or not. For example, the warning unit may input the patient's device information into a generating AI, which will analyze the data and select the optimal warning method.

[0052] The warning unit can be enhanced with a function to send notifications to the patient's family and caregivers when an alert is issued. For example, the warning unit can send an alert notification to the patient's family via email. For example, the warning unit can send an alert notification to the patient's caregiver via SMS. For example, the warning unit can send an alert notification to the patient's family and caregiver via an app. This allows for broader support by notifying the patient's family and caregivers as well. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit inputs the contact information of the patient's family and caregivers into a generating AI, and the generating AI sends the notification.

[0053] The operations department can select the optimal operating method when running an unmanned pharmacy by referring to the patient's past usage history. For example, the operations department can quickly provide medication based on data of medications the patient has used in the past. For example, the operations department can prioritize securing inventory of specific medications based on the patient's past usage history. For example, the operations department can analyze the patient's past usage history and select the optimal service delivery method. In this way, the optimal operating method can be selected by referring to the patient's past usage history. Some or all of the above processes in the operations department are performed using a generating AI. For example, the operations department inputs the patient's past usage history into the generating AI, and the generating AI analyzes the data to select the optimal operating method.

[0054] The operations department can customize the operation of the unmanned pharmacy according to the local healthcare needs. For example, the operations department can analyze local healthcare needs and prioritize securing inventory of specific medications. For example, the operations department can provide services at specific times according to local healthcare needs. For example, the operations department can provide customized services to specific patient groups based on local healthcare needs. By customizing the operation method according to local healthcare needs, more appropriate services can be provided. Some or all of the above processes in the operations department are performed using generative AI. For example, the operations department inputs local healthcare needs into the generative AI, which analyzes the data and customizes the operation method.

[0055] The operations department can select the optimal operating method when running an unmanned pharmacy, taking into account the patient's geographical location information. For example, the operations department can guide patients to the nearest unmanned pharmacy based on their geographical location information. For example, the operations department can provide services tailored to specific regions, taking into account the patient's geographical location information. For example, the operations department can select the optimal delivery method based on the patient's geographical location information. In this way, the optimal operating method can be selected by considering the patient's geographical location information. Some or all of the above processes in the operations department are performed using a generative AI. For example, the operations department inputs the patient's geographical location information into the generative AI, which analyzes the data and selects the optimal operating method.

[0056] The operations department can analyze patients' social media activity and propose operational methods when operating an unmanned pharmacy. For example, the operations department can predict the demand for specific medications based on patients' social media activity. For example, the operations department can analyze patients' social media activity and provide services at specific times. For example, the operations department can provide customized services to specific patient groups based on patients' social media activity. In this way, by analyzing patients' social media activity, the operations department can propose the optimal operational method. Some or all of the above processes in the operations department are performed using generative AI. For example, the operations department inputs patients' social media data into the generative AI, which analyzes the data and proposes operational methods.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can collect patients' lifestyle data and use it to predict the effects and side effects of medications. For example, the data collection unit can collect patients' dietary information and analyze the effect of specific foods on medication effects. For example, the data collection unit can collect patients' exercise habits and analyze the effect of exercise on medication effects. For example, the data collection unit can collect patients' sleep patterns and analyze the effect of sleep on medication effects. In this way, collecting patients' lifestyle data can be used to predict the effects and side effects of medications. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs patients' lifestyle data into a generating AI, which analyzes the data to predict the effects and side effects of medications.

[0059] The assessment unit can analyze a patient's genetic information and perform individualized risk assessments. For example, the assessment unit can determine the risk of allergies to a specific drug based on the patient's genetic information. For example, the assessment unit can analyze a patient's genetic information and predict the metabolic rate of a drug. For example, the assessment unit can individually determine the risk of drug effects and side effects based on the patient's genetic information. This makes individualized risk assessment possible by analyzing the patient's genetic information. Some or all of the above processes in the assessment unit are performed using a generating AI. For example, the assessment unit inputs the patient's genetic information into the generating AI, which analyzes the data and performs individualized risk assessments.

[0060] The warning unit can select the optimal warning method by referring to the patient's past response data when issuing a warning. For example, the warning unit may select the optimal warning method based on the warning method the patient has preferred in the past. For example, the warning unit may analyze the patient's past response data to select an effective warning method. For example, the warning unit may adjust the timing of the warning based on the patient's past response data. In this way, the optimal warning method can be selected by referring to the patient's past response data. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit may input the patient's past response data into a generating AI, which will analyze the data and select the optimal warning method.

[0061] The operations department can select the optimal operating method when running an unmanned pharmacy by referring to the patient's past usage history. For example, the operations department can quickly provide medication based on data of medications the patient has used in the past. For example, the operations department can prioritize securing inventory of specific medications based on the patient's past usage history. For example, the operations department can analyze the patient's past usage history and select the optimal service delivery method. In this way, the optimal operating method can be selected by referring to the patient's past usage history. Some or all of the above processes in the operations department are performed using a generating AI. For example, the operations department inputs the patient's past usage history into the generating AI, and the generating AI analyzes the data to select the optimal operating method.

[0062] The judgment unit can learn from past medical malpractice data to improve the accuracy of risk assessment. For example, the judgment unit determines the risk of a specific drug based on past medical malpractice data. For example, the judgment unit learns from past medical malpractice data to improve the risk assessment algorithm. For example, the judgment unit analyzes past medical malpractice data to improve the accuracy of risk assessment. As a result, the accuracy of risk assessment is improved by learning from past medical malpractice data. Some or all of the above processes in the judgment unit are performed using a generative AI. For example, the judgment unit inputs past medical malpractice data into the generative AI, and the generative AI learns from the data to improve the accuracy of risk assessment.

[0063] The operations department can customize the operation of the unmanned pharmacy according to the local healthcare needs. For example, the operations department can analyze local healthcare needs and prioritize securing inventory of specific medications. For example, the operations department can provide services at specific times according to local healthcare needs. For example, the operations department can provide customized services to specific patient groups based on local healthcare needs. By customizing the operation method according to local healthcare needs, more appropriate services can be provided. Some or all of the above processes in the operations department are performed using generative AI. For example, the operations department inputs local healthcare needs into the generative AI, which analyzes the data and customizes the operation method.

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

[0065] Step 1: The data collection unit collects data such as prescription drugs, dispensing information, and patient medication history. For example, the data collection unit collects information such as the type of prescription drug, dispensing details, and the contents of the patient's medication history. The data collection unit obtains data from, for example, an electronic medical record system. The data collection unit can also collect patient self-reported data. For example, the data collection unit stores patient-entered medication history information in a database. Step 2: The judgment unit learns from the data collected by the collection unit and determines the dispensing risk. The judgment unit determines the dispensing risk based on the collected data, for example, using generative AI. The judgment unit evaluates, for example, the risk of drug interactions and side effects. The judgment unit analyzes, for example, the patient's allergy information and issues a warning if there is a risk. Step 3: The warning unit issues a warning based on the risk determined by the judgment unit. The warning unit sets, for example, the format of the alert and the notification method. The warning unit sends the warning, for example, via email or SMS. The warning unit displays the warning, for example, through an application. Step 4: The operations department operates the unmanned pharmacy based on the risks determined by the assessment department. The operations department, for example, learns data equivalent to that of a pharmacist, analyzes prescription contents, and dispenses appropriate medications. The operations department, for example, sets up drug storage methods and automated dispensing processes. The operations department, for example, operates an unmanned pharmacy that is available 24 hours a day. The operations department, for example, opens an unmanned pharmacy in a sparsely populated area or on holidays and at night.

[0066] (Example of form 2) The pharmacy support system according to an embodiment of the present invention is a system that solves various challenges faced by pharmacies and pharmacists by utilizing generative AI. This pharmacy support system trains the generative AI with data such as prescription drugs, dispensing information, and patient medication history to determine dispensing risks and prevent medical errors. It also enables the establishment of unmanned pharmacies using generative AI to alleviate the increased burden caused by pharmacist shortages. Furthermore, it provides medication support services using generative AI to advise patients on drug interactions and dosage methods. For example, the pharmacy support system trains the generative AI with data such as prescription drugs, dispensing information, and patient medication history. Based on this data, the generative AI determines dispensing risks and prevents medical errors. The generative AI analyzes drug interactions and patient allergy information and issues warnings if there are risks. Next, an unmanned pharmacy using generative AI is established. The generative AI has learned data equivalent to that of a pharmacist, analyzes the contents of prescriptions, and dispenses appropriate medications. This reduces the increased burden caused by pharmacist shortages and realizes 24-hour unmanned pharmacies. Furthermore, we will provide a medication support service using AI generation. Based on the patient's medication history and prescribed drug data, the AI ​​will advise on drug interactions and administration methods. When a patient takes multiple medications, the AI ​​will analyze the risk of interactions and suggest appropriate administration methods. In addition, we will open unmanned pharmacies using AI generation in sparsely populated areas and on weekends and at night. The AI ​​generation possesses knowledge equivalent to that of a pharmacist, analyzes the contents of prescriptions, and dispenses the appropriate medications. This will address the demand for dispensing in sparsely populated areas and on weekends and at night, and eliminate disparities in regional healthcare. In this way, the pharmacy support system will solve various challenges faced by pharmacies and pharmacists, preventing medical errors, alleviating pharmacist shortages, reducing the risks to patients taking medications, and addressing the demand for dispensing in sparsely populated areas and on weekends and at night.

[0067] The pharmacy support system according to this embodiment comprises a data collection unit, a judgment unit, a warning unit, and an operation unit. The data collection unit collects data such as prescription drugs, dispensing, and patient medication history. The data collection unit collects, for example, the type of prescription drug, dispensing details, and the contents of the patient's medication history. The data collection unit acquires data from, for example, an electronic medical record system. The data collection unit can also collect patient self-reported data. For example, the data collection unit stores patient-entered medication history information in a database. The judgment unit learns from the data collected by the data collection unit and determines the dispensing risk. The judgment unit determines the dispensing risk based on the collected data, for example, using generative AI. The judgment unit evaluates, for example, the risk of drug interactions and side effects. The judgment unit analyzes, for example, the patient's allergy information and issues a warning if there is a risk. The warning unit issues a warning based on the risk determined by the judgment unit. The warning unit sets, for example, the format of the alert and the notification method. The warning unit sends a warning, for example, via email or SMS. The warning unit displays a warning, for example, through an application. The operations department operates the unmanned pharmacy based on the risks determined by the assessment department. The operations department, for example, learns data equivalent to that of a pharmacist, analyzes the contents of prescriptions, and dispenses appropriate medications. The operations department, for example, sets up methods for storing medications and automated dispensing processes. The operations department operates an unmanned pharmacy that is available 24 hours a day. The operations department opens unmanned pharmacies in sparsely populated areas or on holidays and at night. As a result, the pharmacy support system according to this embodiment can solve various problems faced by pharmacies and pharmacists, prevent medical errors, alleviate pharmacist shortages, reduce patient medication risks, and meet dispensing demand in sparsely populated areas and on holidays and at night.

[0068] The data collection unit collects data on prescription drugs, dispensing, and patient medication history. Specifically, the unit obtains data from the electronic medical record system, collecting information such as the type of prescription drug, dispensing details, and patient medication history. For example, the type of prescription drug includes the drug name, dosage, administration method, and duration of administration. Dispensing details include the date and time of dispensing, pharmacist information, dispensing procedure, and information on the equipment and materials used. The patient's medication history includes a history of previously prescribed medications, allergy information, side effect history, and self-reported health status and lifestyle information. The data collection unit centrally manages this data and stores it in a database. The data collection unit can also store patient-entered medication history information in the database. For example, it can collect medication history information and health status data self-reported by patients via a smartphone app and integrate this into the database. This allows the data collection unit to grasp the patient's latest health status and medication history information in real time and use it to assess dispensing risks. Furthermore, the data collection unit can collect a wider range of data by linking data with external medical institutions and pharmacies. For example, by collecting referral letters and test results from other medical institutions, as well as dispensing history from other pharmacies, a comprehensive database can be built. This allows the data collection department to understand the patient's overall medical history and perform more accurate risk assessments.

[0069] The judgment unit learns from the data collected by the collection unit and determines the dispensing risk. Specifically, the judgment unit uses a generative AI to determine the dispensing risk based on the collected data. The generative AI learns from a large amount of medical data and analyzes drug interactions, the risk of side effects, and patient allergy information. For example, the judgment unit analyzes the collected prescription drug data and evaluates the risk due to drug interactions. It detects side effects and interactions that may occur when specific drugs are administered simultaneously and issues a warning if the risk is high. The judgment unit also analyzes the patient's medication history and evaluates the risk to the current prescription drug based on the history of past side effects and allergic reactions. For example, if a patient has shown an allergic reaction to a specific drug in the past, it will issue a warning when the same drug or a similar drug is prescribed. Furthermore, the judgment unit analyzes the patient's self-reported data and performs a risk assessment based on lifestyle and health status. For example, it considers the patient's self-reported dietary and exercise habits, medical history, etc., to evaluate whether a specific drug is appropriate. As a result, the judgment unit can comprehensively analyze the collected data and determine the dispensing risk with high accuracy.

[0070] The warning unit issues warnings based on the risks determined by the assessment unit. Specifically, the warning unit sets the alert format and notification method, and sends warnings via email or SMS. For example, if a risk due to drug interaction is determined, the warning unit immediately sends a notification to the pharmacist or patient via email or SMS. The warning unit can also display warnings through an application. For example, it can display real-time warnings to patients via a smartphone app to encourage appropriate action. Furthermore, the warning unit can customize the priority and content of warnings. For example, high-risk warnings can be displayed as emergency alerts, while low-risk warnings can be displayed in the notification center, allowing for flexible responses depending on the situation. As a result, the warning unit can provide prompt and appropriate warnings based on the risks determined by the assessment unit, preventing medical errors and ensuring patient safety.

[0071] The operations department will operate the unmanned pharmacies based on the risks determined by the assessment department. Specifically, the operations department will learn data equivalent to that of a pharmacist, analyze the contents of prescriptions, and dispense appropriate medications. For example, the operations department will analyze the contents of prescriptions, identify the necessary types and dosages of medications, and dispense them accurately using automated dispensing equipment. The operations department will also set up drug storage methods and automated dispensing processes to operate 24-hour unmanned pharmacies. For example, by appropriately managing the storage temperature and humidity of medications and automating the dispensing process, they will always provide high-quality dispensing. Furthermore, the operations department will open unmanned pharmacies in sparsely populated areas and on holidays and at night to meet local dispensing needs. For example, unmanned pharmacies installed in sparsely populated areas will provide prompt and appropriate dispensing services to local residents while receiving remote monitoring and support from remote locations. In this way, the operations department can contribute to alleviating the shortage of pharmacists, improving access to medical care in sparsely populated areas, and reducing the risks to patients taking medication.

[0072] The data collection unit can collect data such as prescription drugs, dispensing information, and patient medication history. For example, the data collection unit collects information such as the type of prescription drug, dispensing details, and the contents of the patient's medication history. The data collection unit can acquire data from, for example, an electronic medical record system. The data collection unit can also collect patient self-reported data. For example, the data collection unit stores patient-entered medication history information in a database. This allows for the collection of necessary data and improves the accuracy of the system. Some or all of the above-described processes in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input data acquired from the electronic medical record system into a generating AI and have the generating AI perform data collection.

[0073] The judgment unit can determine the dispensing risk based on the collected data. The judgment unit uses, for example, a generative AI to determine the dispensing risk based on the collected data. The judgment unit evaluates, for example, the risk of drug interactions and side effects. The judgment unit analyzes, for example, the patient's allergy information and issues a warning if there is a risk. This makes it possible to determine the risk based on the collected data and prevent medical errors. Some or all of the above processing in the judgment unit is performed using a generative AI. For example, the judgment unit inputs the collected data into the generative AI, and the generative AI analyzes the data to determine the dispensing risk.

[0074] The warning unit can issue warnings based on the determined risk. The warning unit can, for example, set the format of the alert and the notification method. The warning unit can, for example, send warnings via email or SMS. The warning unit can, for example, display warnings through an application. This allows warnings to be issued based on risk and medical errors to be prevented. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit inputs risk information from the determination unit into a generating AI, the generating AI generates warning content and issues a warning.

[0075] The operations department can operate unmanned pharmacies based on the assessed risk. For example, the operations department can learn data equivalent to that of a pharmacist, analyze prescription contents, and dispense appropriate medications. For example, the operations department can set up drug storage methods and automated dispensing processes. For example, the operations department can operate unmanned pharmacies that are available 24 hours a day. For example, the operations department can open unmanned pharmacies in sparsely populated areas or on holidays and at night. This allows for the operation of unmanned pharmacies based on risk and helps alleviate the shortage of pharmacists. Some or all of the above processes in the operations department are performed using a generating AI. For example, the operations department inputs risk information from the assessment department into the generating AI, which then operates the unmanned pharmacy.

[0076] The operations department can learn data equivalent to that of a pharmacist, analyze prescriptions, and dispense appropriate medications. For example, the operations department can learn the knowledge base of pharmacists and acquire methods for analyzing prescriptions. For example, the operations department can analyze prescriptions and select appropriate medications. For example, the operations department can automate the medication dispensing process and dispense medications efficiently. This will help alleviate the pharmacist shortage by learning data equivalent to that of a pharmacist and dispensing appropriate medications. Some or all of the above processes in the operations department are performed using generative AI. For example, the operations department can train the generative AI with the knowledge base of pharmacists, and the generative AI will analyze prescriptions and dispense appropriate medications.

[0077] The operations department can advise on drug interactions and administration methods based on the patient's medication history and prescription data. For example, the operations department analyzes the patient's medication history data and evaluates drug interactions. For example, the operations department proposes administration methods and advises the patient on appropriate administration methods. For example, when a patient is taking multiple medications, the operations department analyzes the risk of interaction and proposes appropriate administration methods. In this way, the risks to the patient's medication can be reduced by advising on drug interactions and administration methods. Some or all of the above processes in the operations department are performed using generative AI. For example, the operations department inputs the patient's medication history data into the generative AI, and the generative AI provides advice on drug interactions and administration methods.

[0078] The operations department can open unmanned pharmacies in sparsely populated areas and on weekends and at night. For example, the operations department can analyze the medical needs of sparsely populated areas and open unmanned pharmacies. For example, the operations department can operate unmanned pharmacies to meet the demand for dispensing medication on weekends and at night. For example, the operations department can open unmanned pharmacies to eliminate disparities in regional medical care. This will allow them to meet the demand for dispensing medication in sparsely populated areas and on weekends and at night, and eliminate disparities in regional medical care. Some or all of the above processes in the operations department are performed using generative AI. For example, the operations department inputs the medical needs of sparsely populated areas into the generative AI, and the generative AI proposes the opening of unmanned pharmacies.

[0079] 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 alleviate the user's burden. For example, if the user is relaxed, the data collection unit can collect detailed data to obtain more accurate information. For example, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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. For example, the data collection unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the timing of data collection.

[0080] The data collection unit can collect patients' lifestyle data and use it to predict the effects and side effects of medications. For example, the data collection unit can collect patients' dietary information and analyze the effect of specific foods on medication effects. For example, the data collection unit can collect patients' exercise habits and analyze the effect of exercise on medication effects. For example, the data collection unit can collect patients' sleep patterns and analyze the effect of sleep on medication effects. In this way, collecting patients' lifestyle data can be used to predict the effects and side effects of medications. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs patients' lifestyle data into a generating AI, which analyzes the data to predict the effects and side effects of medications.

[0081] The data collection unit can collect real-time data from medical institutions and reflect the latest prescription information. For example, the data collection unit can acquire prescription information in real time from the medical institution's electronic medical record system. For example, the data collection unit can collect the latest patient information from the medical institution's medical records. For example, the data collection unit can collect test result data from medical institutions in real time and reflect it in prescriptions. In this way, by collecting real-time data from medical institutions, the latest prescription information can be reflected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs real-time data from medical institutions into a generating AI, and the generating AI analyzes the data to reflect the latest prescription information.

[0082] 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 will prioritize collecting only high-priority data. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit will prioritize data that can be collected quickly. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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. For example, the data collection unit inputs the user's emotion data into the generative AI, and the generative AI determines the priority of the data.

[0083] The data collection unit can collect patient dietary and exercise data and provide advice to maximize the effectiveness of medication. For example, the data collection unit can collect patient dietary data and advise against certain foods. For example, the data collection unit can collect patient exercise data and advise on appropriate exercise amounts. For example, the data collection unit can comprehensively analyze patient lifestyle data and provide lifestyle improvement advice to maximize the effectiveness of medication. Thus, by collecting patient dietary and exercise data, it is possible to provide advice to maximize the effectiveness of medication. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs patient dietary and exercise data into a generating AI, which analyzes the data and provides advice.

[0084] The data collection unit can analyze a patient's social media activity and collect data related to their health status. For example, the data collection unit can extract keywords related to health status from a patient's social media posts. For example, the data collection unit can estimate stress levels from a patient's social media activity. For example, the data collection unit can analyze a patient's social media activity and detect changes in their health status. In this way, data related to health status can be collected by analyzing a patient's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs the patient's social media data into a generating AI, which analyzes the data and collects information related to their health status.

[0085] The judgment unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated emotions. For example, if the user is stressed, the judgment unit will tighten the risk assessment criteria. For example, if the user is relaxed, the judgment unit will relax the risk assessment criteria. For example, if the user is in a hurry, the judgment unit will set criteria for making a quick risk assessment. This allows for more appropriate risk assessment by adjusting the risk assessment criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit is performed using generative AI. For example, the judgment unit inputs user emotion data into the generative AI, and the generative AI adjusts the risk assessment criteria.

[0086] The assessment unit can analyze a patient's genetic information and perform individualized risk assessments. For example, the assessment unit can determine the risk of allergies to a specific drug based on the patient's genetic information. For example, the assessment unit can analyze a patient's genetic information and predict the metabolic rate of a drug. For example, the assessment unit can individually determine the risk of drug effects and side effects based on the patient's genetic information. This makes individualized risk assessment possible by analyzing the patient's genetic information. Some or all of the above processes in the assessment unit are performed using a generating AI. For example, the assessment unit inputs the patient's genetic information into the generating AI, which analyzes the data and performs individualized risk assessments.

[0087] The judgment unit can learn from past medical malpractice data to improve the accuracy of risk assessment. For example, the judgment unit determines the risk of a specific drug based on past medical malpractice data. For example, the judgment unit learns from past medical malpractice data to improve the risk assessment algorithm. For example, the judgment unit analyzes past medical malpractice data to improve the accuracy of risk assessment. As a result, the accuracy of risk assessment is improved by learning from past medical malpractice data. Some or all of the above processes in the judgment unit are performed using a generative AI. For example, the judgment unit inputs past medical malpractice data into the generative AI, and the generative AI learns from the data to improve the accuracy of risk assessment.

[0088] The judgment unit can estimate the user's emotions and adjust the order in which the risk assessment results are displayed based on the estimated emotions. For example, if the user is stressed, the judgment unit will display important risk information first. If the user is relaxed, the judgment unit will display detailed risk information sequentially. If the user is in a hurry, the judgment unit will prioritize displaying concise risk information. In this way, by adjusting the order in which the risk assessment results are displayed according to the user's emotions, important information can be displayed preferentially. 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 judgment unit is performed using generative AI. For example, the judgment unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the order in which the risk assessment results are displayed.

[0089] The assessment unit can make risk assessments by considering the patient's living environment data. For example, the assessment unit can determine allergy risk based on the patient's living environment data. For example, the assessment unit can determine stress risk by considering the patient's work environment data. For example, the assessment unit can make risk assessments by comprehensively analyzing the patient's living environment data. This makes it possible to make more accurate risk assessments by considering the patient's living environment data. Some or all of the above processing in the assessment unit is performed using a generating AI. For example, the assessment unit inputs the patient's living environment data into the generating AI, and the generating AI analyzes the data to make a risk assessment.

[0090] The assessment unit can improve the accuracy of risk assessment by linking with databases of other medical institutions. For example, the assessment unit can obtain past medical records of patients from databases of other medical institutions and reflect them in the risk assessment. For example, the assessment unit can perform risk assessment based on the latest medical information by linking with databases of other medical institutions. For example, the assessment unit can improve the accuracy of risk assessment by utilizing databases of other medical institutions. In this way, the accuracy of risk assessment is improved by linking with databases of other medical institutions. Some or all of the above processes in the assessment unit are performed using a generating AI. For example, the assessment unit inputs data obtained from databases of other medical institutions into the generating AI, and the generating AI analyzes the data to improve the accuracy of risk assessment.

[0091] The warning unit can estimate the user's emotions and adjust the way the warning is expressed based on the estimated emotions. For example, if the user is stressed, the warning unit will issue a warning in a gentle tone. If the user is relaxed, the warning unit will provide detailed warning information. If the user is in a hurry, the warning unit will issue a concise and quick warning. This allows for more effective warnings by adjusting the way the warning is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit inputs user emotion data into the generative AI, and the generative AI adjusts the way the warning is expressed.

[0092] The warning unit can select the optimal warning method by referring to the patient's past response data when issuing a warning. For example, the warning unit may select the optimal warning method based on the warning method the patient has preferred in the past. For example, the warning unit may analyze the patient's past response data to select an effective warning method. For example, the warning unit may adjust the timing of the warning based on the patient's past response data. In this way, the optimal warning method can be selected by referring to the patient's past response data. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit may input the patient's past response data into a generating AI, which will analyze the data and select the optimal warning method.

[0093] The warning unit can set different warning levels depending on the severity of the risk when a warning is issued. For example, the warning unit may issue a mild warning for low risk, provide detailed warning information for moderate risk, and issue a highly urgent warning for high risk. By setting different warning levels according to the severity of the risk, appropriate warnings can be provided. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit inputs the severity of the risk into a generating AI, and the generating AI sets the warning level.

[0094] The warning unit can estimate the user's emotions and determine the priority of warnings based on the estimated emotions. For example, if the user is stressed, the warning unit will prioritize displaying important warnings. For example, if the user is relaxed, the warning unit will sequentially display detailed warning information. For example, if the user is in a hurry, the warning unit will prioritize displaying concise warnings. In this way, by determining the priority of warnings according to the user's emotions, important warnings can be displayed preferentially. 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 warning unit may be performed using AI or not. For example, the warning unit inputs user emotion data into the generative AI, and the generative AI determines the priority of warnings.

[0095] The warning unit can select the optimal warning method when an alert is issued, taking into account the patient's device information. For example, if the patient is using a smartphone, the warning unit will display a warning on the screen. If the patient is using a tablet, the warning unit will display a warning optimized for a larger screen. If the patient is using a smartwatch, the warning unit will provide a warning via vibration or sound. This allows the system to select the optimal warning method by considering the patient's device information. Some or all of the above processing in the warning unit may be performed using AI, or not. For example, the warning unit may input the patient's device information into a generating AI, which will analyze the data and select the optimal warning method.

[0096] The warning unit can be enhanced with a function to send notifications to the patient's family and caregivers when an alert is issued. For example, the warning unit can send an alert notification to the patient's family via email. For example, the warning unit can send an alert notification to the patient's caregiver via SMS. For example, the warning unit can send an alert notification to the patient's family and caregiver via an app. This allows for broader support by notifying the patient's family and caregivers as well. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit inputs the contact information of the patient's family and caregivers into a generating AI, and the generating AI sends the notification.

[0097] 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 stressed, the operations department will provide a simple and quick service. If the user is relaxed, the operations department will provide a service that includes detailed explanations. If the user is in a hurry, the operations department will adjust the operation to ensure that the user can receive their medication quickly. In this way, by adjusting the operation according to the user's emotions, a more appropriate service can be provided. 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 is performed using generative AI. For example, the operations department inputs user emotion data into the generative AI, and the generative AI adjusts the operation.

[0098] The operations department can select the optimal operating method when running an unmanned pharmacy by referring to the patient's past usage history. For example, the operations department can quickly provide medication based on data of medications the patient has used in the past. For example, the operations department can prioritize securing inventory of specific medications based on the patient's past usage history. For example, the operations department can analyze the patient's past usage history and select the optimal service delivery method. In this way, the optimal operating method can be selected by referring to the patient's past usage history. Some or all of the above processes in the operations department are performed using a generating AI. For example, the operations department inputs the patient's past usage history into the generating AI, and the generating AI analyzes the data to select the optimal operating method.

[0099] The operations department can customize the operation of the unmanned pharmacy according to the local healthcare needs. For example, the operations department can analyze local healthcare needs and prioritize securing inventory of specific medications. For example, the operations department can provide services at specific times according to local healthcare needs. For example, the operations department can provide customized services to specific patient groups based on local healthcare needs. By customizing the operation method according to local healthcare needs, more appropriate services can be provided. Some or all of the above processes in the operations department are performed using generative AI. For example, the operations department inputs local healthcare needs into the generative AI, which analyzes the data and customizes the operation method.

[0100] The operations department can estimate the user's emotions and determine the operational priorities of the unmanned pharmacy based on the estimated emotions. For example, if the user is stressed, the operations department will provide medication quickly. If the user is relaxed, the operations department will provide services including detailed explanations. If the user is in a hurry, the operations department will adjust operational priorities to ensure they receive their medication quickly. This allows for the priority provision of important services by determining operational priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, 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 operations department are performed using generative AI. For example, the operations department inputs user emotion data into the generative AI, which then determines the operational priorities.

[0101] The operations department can select the optimal operating method when running an unmanned pharmacy, taking into account the patient's geographical location information. For example, the operations department can guide patients to the nearest unmanned pharmacy based on their geographical location information. For example, the operations department can provide services tailored to specific regions, taking into account the patient's geographical location information. For example, the operations department can select the optimal delivery method based on the patient's geographical location information. In this way, the optimal operating method can be selected by considering the patient's geographical location information. Some or all of the above processes in the operations department are performed using a generative AI. For example, the operations department inputs the patient's geographical location information into the generative AI, which analyzes the data and selects the optimal operating method.

[0102] The operations department can analyze patients' social media activity and propose operational methods when operating an unmanned pharmacy. For example, the operations department can predict the demand for specific medications based on patients' social media activity. For example, the operations department can analyze patients' social media activity and provide services at specific times. For example, the operations department can provide customized services to specific patient groups based on patients' social media activity. In this way, by analyzing patients' social media activity, the operations department can propose the optimal operational method. Some or all of the above processes in the operations department are performed using generative AI. For example, the operations department inputs patients' social media data into the generative AI, which analyzes the data and proposes operational methods.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The data collection unit can collect patients' lifestyle data and use it to predict the effects and side effects of medications. For example, the data collection unit can collect patients' dietary information and analyze the effect of specific foods on medication effects. For example, the data collection unit can collect patients' exercise habits and analyze the effect of exercise on medication effects. For example, the data collection unit can collect patients' sleep patterns and analyze the effect of sleep on medication effects. In this way, collecting patients' lifestyle data can be used to predict the effects and side effects of medications. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit inputs patients' lifestyle data into a generating AI, which analyzes the data to predict the effects and side effects of medications.

[0105] The assessment unit can analyze a patient's genetic information and perform individualized risk assessments. For example, the assessment unit can determine the risk of allergies to a specific drug based on the patient's genetic information. For example, the assessment unit can analyze a patient's genetic information and predict the metabolic rate of a drug. For example, the assessment unit can individually determine the risk of drug effects and side effects based on the patient's genetic information. This makes individualized risk assessment possible by analyzing the patient's genetic information. Some or all of the above processes in the assessment unit are performed using a generating AI. For example, the assessment unit inputs the patient's genetic information into the generating AI, which analyzes the data and performs individualized risk assessments.

[0106] The warning unit can select the optimal warning method by referring to the patient's past response data when issuing a warning. For example, the warning unit may select the optimal warning method based on the warning method the patient has preferred in the past. For example, the warning unit may analyze the patient's past response data to select an effective warning method. For example, the warning unit may adjust the timing of the warning based on the patient's past response data. In this way, the optimal warning method can be selected by referring to the patient's past response data. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit may input the patient's past response data into a generating AI, which will analyze the data and select the optimal warning method.

[0107] The operations department can select the optimal operating method when running an unmanned pharmacy by referring to the patient's past usage history. For example, the operations department can quickly provide medication based on data of medications the patient has used in the past. For example, the operations department can prioritize securing inventory of specific medications based on the patient's past usage history. For example, the operations department can analyze the patient's past usage history and select the optimal service delivery method. In this way, the optimal operating method can be selected by referring to the patient's past usage history. Some or all of the above processes in the operations department are performed using a generating AI. For example, the operations department inputs the patient's past usage history into the generating AI, and the generating AI analyzes the data to select the optimal operating method.

[0108] The judgment unit can learn from past medical malpractice data to improve the accuracy of risk assessment. For example, the judgment unit determines the risk of a specific drug based on past medical malpractice data. For example, the judgment unit learns from past medical malpractice data to improve the risk assessment algorithm. For example, the judgment unit analyzes past medical malpractice data to improve the accuracy of risk assessment. As a result, the accuracy of risk assessment is improved by learning from past medical malpractice data. Some or all of the above processes in the judgment unit are performed using a generative AI. For example, the judgment unit inputs past medical malpractice data into the generative AI, and the generative AI learns from the data to improve the accuracy of risk assessment.

[0109] 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 alleviate the user's burden. For example, if the user is relaxed, the data collection unit can collect detailed data to obtain more accurate information. For example, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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. For example, the data collection unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the timing of data collection.

[0110] The judgment unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated emotions. For example, if the user is stressed, the judgment unit will tighten the risk assessment criteria. For example, if the user is relaxed, the judgment unit will relax the risk assessment criteria. For example, if the user is in a hurry, the judgment unit will set criteria for making a quick risk assessment. This allows for more appropriate risk assessment by adjusting the risk assessment criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit is performed using generative AI. For example, the judgment unit inputs user emotion data into the generative AI, and the generative AI adjusts the risk assessment criteria.

[0111] The warning unit can estimate the user's emotions and adjust the way the warning is expressed based on the estimated emotions. For example, if the user is stressed, the warning unit will issue a warning in a gentle tone. If the user is relaxed, the warning unit will provide detailed warning information. If the user is in a hurry, the warning unit will issue a concise and quick warning. This allows for more effective warnings by adjusting the way the warning is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit inputs user emotion data into the generative AI, and the generative AI adjusts the way the warning is expressed.

[0112] 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 stressed, the operations department will provide a simple and quick service. If the user is relaxed, the operations department will provide a service that includes detailed explanations. If the user is in a hurry, the operations department will adjust the operation to ensure that the user can receive their medication quickly. In this way, by adjusting the operation according to the user's emotions, a more appropriate service can be provided. 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 is performed using generative AI. For example, the operations department inputs user emotion data into the generative AI, and the generative AI adjusts the operation.

[0113] The operations department can customize the operation of the unmanned pharmacy according to the local healthcare needs. For example, the operations department can analyze local healthcare needs and prioritize securing inventory of specific medications. For example, the operations department can provide services at specific times according to local healthcare needs. For example, the operations department can provide customized services to specific patient groups based on local healthcare needs. By customizing the operation method according to local healthcare needs, more appropriate services can be provided. Some or all of the above processes in the operations department are performed using generative AI. For example, the operations department inputs local healthcare needs into the generative AI, which analyzes the data and customizes the operation method.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The data collection unit collects data such as prescription drugs, dispensing information, and patient medication history. For example, the data collection unit collects information such as the type of prescription drug, dispensing details, and the contents of the patient's medication history. The data collection unit obtains data from, for example, an electronic medical record system. The data collection unit can also collect patient self-reported data. For example, the data collection unit stores patient-entered medication history information in a database. Step 2: The judgment unit learns from the data collected by the collection unit and determines the dispensing risk. The judgment unit determines the dispensing risk based on the collected data, for example, using generative AI. The judgment unit evaluates, for example, the risk of drug interactions and side effects. The judgment unit analyzes, for example, the patient's allergy information and issues a warning if there is a risk. Step 3: The warning unit issues a warning based on the risk determined by the judgment unit. The warning unit sets, for example, the format of the alert and the notification method. The warning unit sends the warning, for example, via email or SMS. The warning unit displays the warning, for example, through an application. Step 4: The operations department operates the unmanned pharmacy based on the risks determined by the assessment department. The operations department, for example, learns data equivalent to that of a pharmacist, analyzes prescription contents, and dispenses appropriate medications. The operations department, for example, sets up drug storage methods and automated dispensing processes. The operations department, for example, operates an unmanned pharmacy that is available 24 hours a day. The operations department, for example, opens an unmanned pharmacy in a sparsely populated area or on holidays and at night.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the data collection unit, judgment unit, warning unit, and operation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and processes it with the control unit 46A. The judgment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines the dispensing risk based on the collected data. The warning unit displays a warning using, for example, the output device 40 of the smart device 14 and adjusts the way the warning is expressed with the control unit 46A. The operation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and adjusts the operation method of 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.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the data collection unit, judgment unit, warning unit, and operation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and processes it with the control unit 46A. The judgment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and determines the dispensing risk based on the collected data. The warning unit displays a warning using the speaker 240 of the smart glasses 214 and adjusts the way the warning is expressed with the control unit 46A. The operation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and adjusts the operation method of 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.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the data collection unit, judgment unit, warning unit, and operation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and processes it with the control unit 46A. The judgment unit is implemented in the identification processing unit 290 of the data processing unit 12 and determines the dispensing risk based on the collected data. The warning unit displays a warning using the speaker 240 of the headset terminal 314 and adjusts the way the warning is expressed with the control unit 46A. The operation unit is implemented in the identification processing unit 290 of the data processing unit 12 and adjusts the operation method of 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.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the data collection unit, judgment unit, warning unit, and operation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the robot 414 and processes it with the control unit 46A. The judgment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and determines the dispensing risk based on the collected data. The warning unit displays a warning using the speaker 240 of the robot 414 and adjusts the way the warning is expressed with the control unit 46A. The operation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and adjusts the operation method of 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A data collection unit that collects data on prescription drugs, dispensing, patient medication history, etc. A determination unit learns from the data collected by the aforementioned collection unit and determines the dispensing risk, A warning unit that issues a warning based on the risk determined by the aforementioned determination unit, The system includes an operations unit that operates an unmanned pharmacy based on the risk determined by the aforementioned determination unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on prescription drugs, dispensing, and patient medication history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The determination unit, Based on the collected data, we determine the dispensing risk. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned warning unit is Issue a warning based on the assessed risk. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned operations department, Operate an unmanned pharmacy based on the assessed risk. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned operations department, It learns data equivalent to that of a pharmacist, analyzes prescription contents, and dispenses appropriate medications. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned operations department, Based on the patient's medication history and prescription data, we provide advice on drug interactions and administration methods. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned operations department, Open unmanned pharmacies in sparsely populated areas and on weekends and at night. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is Collecting patients' lifestyle data helps predict the effectiveness and side effects of medications. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We collect real-time data from medical institutions and reflect the latest prescription information. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned collection unit is We collect patients' diet and exercise data and provide advice to maximize the effectiveness of their medications. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Analyze patients' social media activity and collect data related to their health status. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, The system estimates user sentiment and adjusts risk assessment criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, Analyze the patient's genetic information to perform individualized risk assessment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, By learning from past medical malpractice data, we can improve the accuracy of risk assessment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, The system estimates the user's emotions and adjusts the order in which the risk assessment results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, Risk assessment is performed by considering data on the patient's living environment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, We will improve the accuracy of risk assessment by collaborating with databases from other medical institutions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is The system estimates the user's emotions and adjusts the way warnings are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is When a warning is issued, the system selects the most appropriate warning method by referring to the patient's past response data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is When a warning is issued, different warning levels can be set according to the severity of the risk. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned warning unit is The system estimates the user's emotions and prioritizes warnings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned warning unit is When issuing a warning, the optimal warning method is selected considering the patient's device information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned warning unit is Add a feature to send notifications to the patient's family and caregivers when an alert is issued. The system described in Appendix 1, characterized by the features described herein. (Note 27) 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 28) The aforementioned operations department, When operating an unmanned pharmacy, the optimal operating method is selected by referring to the patient's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned operations department, When operating an unmanned pharmacy, customize the operating method according to the local healthcare needs. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned operations department, The system estimates user sentiment and determines operational priorities for the unmanned pharmacy based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned operations department, When operating an unmanned pharmacy, the optimal operating method is selected by considering the patient's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned operations department, When operating an unmanned pharmacy, we analyze patients' social media activity and propose operational methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0188] 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. A data collection unit that collects data on prescription drugs, dispensing, patient medication history, etc. A determination unit learns from the data collected by the aforementioned collection unit and determines the dispensing risk, A warning unit that issues a warning based on the risk determined by the aforementioned determination unit, The system includes an operations unit that operates an unmanned pharmacy based on the risk determined by the aforementioned determination unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on prescription drugs, dispensing, and patient medication history. The system according to feature 1.

3. The determination unit, Based on the collected data, we determine the dispensing risk. The system according to feature 1.

4. The aforementioned warning unit is Issue a warning based on the assessed risk. The system according to feature 1.

5. The aforementioned operations department, Operate an unmanned pharmacy based on the assessed risk. The system according to feature 1.

6. The aforementioned operations department, It learns data equivalent to that of a pharmacist, analyzes prescription contents, and dispenses appropriate medications. The system according to feature 1.

7. The aforementioned operations department, Based on the patient's medication history and prescription data, we provide advice on drug interactions and administration methods. The system according to feature 1.

8. The aforementioned operations department, Open unmanned pharmacies in sparsely populated areas and on weekends and at night. The system according to feature 1.

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