System

Abstract

An object of a system according to an embodiment is to improve the efficiency of dispensing work for a child at a pharmacy.SOLUTION: A system includes a data management part, a prescription creation support part, a dispensing recipe generation part, a dispensing automation part, and a double check part. The data management part unitarily manages the personal data, the medical pocketbook and the anamnesis of the child. The prescription creation support unit supports a doctor to create a prescription based on the information managed by the data management unit. The dispensing recipe creating section creates a dispensing recipe from the prescription data created by the prescription creation support section. The dispensing automating section automates actual dispensing work based on the recipe generated by the dispensing recipe generating section. The double check unit allows a pharmacist to double-check the medicine dispensed by the dispensing automation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, dispensing work at children's pharmacies takes time and has the disadvantage of being difficult to carry out efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of dispensing work at children's pharmacies. [Means for solving the problem]

[0006] The system according to the embodiment comprises a data management unit, a prescription creation support unit, a dispensing recipe generation unit, a dispensing automation unit, and a double-check unit. The data management unit centrally manages a child's personal data, medicine notebook, and medical history. The prescription creation support unit supports doctors in creating prescriptions based on the information managed by the data management unit. The dispensing recipe generation unit generates dispensing recipes from the prescription data created by the prescription creation support unit. The dispensing automation unit automates actual dispensing work based on the recipes created by the dispensing recipe generation unit. The double-check unit allows a pharmacist to double-check the medicines dispensed by the dispensing automation unit. [Effects of the Invention]

[0007] The system according to the embodiment can streamline dispensing operations at children's pharmacies. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The dispensing efficiency improvement system according to an embodiment of the present invention is a system that manages children's personal data, medicine notebooks, and medical histories in an integrated manner, supports doctors in writing prescriptions, generates dispensing recipes from prescription data, automates the actual dispensing work, and has pharmacists perform double checks. As a result, the dispensing efficiency system can improve the efficiency of dispensing work at children's pharmacies and significantly reduce the time required.

[0029] A dispensing efficiency improvement system according to an embodiment includes a data management unit, a prescription creation support unit, a dispensing recipe generation unit, a dispensing automation unit, and a double check unit. The data management unit centrally manages a child's personal data, medicine notebook, and medical history. For example, the data management unit stores a child's name, age, weight, past prescription history, past medical history, and allergy information in a database and promptly provides necessary information. The prescription creation support unit supports doctors in creating prescriptions based on the information managed by the data management unit. For example, the prescription creation support unit calculates the appropriate dosage based on the child's weight and presents it to the doctor. The prescription creation support unit can also suggest safe medications by referring to past allergy information. The dispensing recipe generation unit generates a dispensing recipe from the prescription data created by the prescription creation support unit. For example, the dispensing recipe generation unit generates a recipe that details the type and amount of medication, mixing method, and administration method. The dispensing recipe generation unit can also check drug interactions and generate an optimal dispensing recipe. The dispensing automation unit automates the actual dispensing process based on the recipe generated by the dispensing recipe generation unit. For example, the dispensing automation unit automatically performs tasks such as measuring, mixing, and packaging the medicine. The dispensing automation unit can also monitor the quality of the medicine in real time to ensure thorough quality control. The double-check unit allows a pharmacist to perform a double check of the medicine dispensed by the dispensing automation unit. For example, the double-check unit automatically analyzes the ingredients of the medicine to confirm the accuracy of the ingredients. The double-check unit can also automatically inspect the appearance and packaging of the medicine to ensure quality. As a result, the dispensing efficiency system according to the embodiment can improve the efficiency of dispensing work at children's pharmacies and significantly reduce the time required. For example, the system can quickly provide the information necessary for doctors to write prescriptions and suggest appropriate drug types and dosages, thereby improving the accuracy of prescription writing. Furthermore, automating the detailed dispensing process previously performed by pharmacists shortens the dispensing process and reduces the burden on pharmacists. Furthermore, the double-check performed by pharmacists can prevent dispensing errors and provide safe medicines to patients.

[0030] The data management unit can predict seasonal health risks based on the child's personal data and suggest preventive measures. The data management unit, for example, builds a system that predicts seasonal health risks based on the child's personal data. For example, it analyzes past medical history and allergy information to suggest preventive measures for hay fever season. The data management unit can also recommend vaccinations for influenza season. This makes it possible to predict seasonal health risks and suggest appropriate preventive measures.

[0031] The data management unit can predict future health risks based on past medical history and allergy information, and automatically generate vaccination and health checkup schedules. The data management unit, for example, analyzes past medical history and allergy information to build a system that predicts future health risks. For example, it automatically generates regular health checkup schedules for children who are at high risk of allergies. The data management unit can also automatically generate vaccination schedules and suggest that children receive vaccinations at appropriate times. This makes it possible to predict future health risks and automatically generate appropriate vaccination and health checkup schedules.

[0032] The data management unit can provide dietary and exercise advice based on the child's personal data, and provide comprehensive support for health management. The data management unit, for example, builds a system that provides dietary and exercise advice based on the child's personal data. For example, it can propose appropriate dietary and exercise plans based on the child's weight and height. The data management unit can also suggest nutritionally balanced meals and allergy-friendly meals. This allows for the provision of dietary and exercise advice, and comprehensive support for health management.

[0033] The data management unit can link data with other medical institutions and schools and share children's health information, thereby realizing comprehensive health management. The data management unit, for example, links data with other medical institutions and schools and builds a system for sharing children's health information. For example, it links with the school's health room to share the results of health checkups. The data management unit can also realize comprehensive health management through data linkage with medical institutions. This allows data to be linked with other medical institutions and schools, thereby realizing comprehensive health management.

[0034] The prescription creation support unit can suggest the optimal drug combination based on past prescription history when a doctor creates a prescription. For example, when a doctor creates a prescription, the prescription creation support unit analyzes past prescription history and builds a system that suggests the optimal drug combination. For example, it can suggest drug combinations that have been effective in the past. The prescription creation support unit can also suggest the optimal drug combination taking drug interactions into consideration. This makes it possible to suggest the optimal drug combination based on past prescription history.

[0035] The prescription creation support unit can refer to the latest medical research data when a doctor creates a prescription and suggest the most appropriate treatment. For example, the prescription creation support unit will build a system that automatically collects the latest medical research data when a doctor creates a prescription and suggests the most appropriate treatment. For example, it will suggest a treatment based on the latest research results. The prescription creation support unit can also suggest evidence-based treatments. This allows the doctor to refer to the latest medical research data and suggest the most appropriate treatment.

[0036] The prescription writing support unit can provide online consultation with other medical experts when a doctor writes a prescription, and determine the optimal treatment method. The prescription writing support unit, for example, builds a system that provides online consultation with other medical experts when a doctor writes a prescription. For example, it allows a doctor to refer to expert opinions in real time. The prescription writing support unit can also determine the optimal treatment method based on the opinions of multiple experts. This allows online consultation with other medical experts to be provided, and the optimal treatment method to be determined.

[0037] The prescription creation support unit can refer to the patient's lifestyle data when a doctor creates a prescription and propose a more personalized treatment. The prescription creation support unit, for example, builds a system that refers to the patient's lifestyle data when a doctor creates a prescription and proposes a personalized treatment. For example, it proposes a treatment that takes into account dietary and exercise habits. The prescription creation support unit can also propose a treatment based on genetic information. This makes it possible to refer to the patient's lifestyle data and propose a personalized treatment.

[0038] The dispensing recipe generation unit can automatically check drug interactions based on prescription data and generate optimal dispensing recipes. The dispensing recipe generation unit, for example, builds a system that automatically checks drug interactions based on prescription data. For example, it analyzes drug ingredients and evaluates the risk of interactions. The dispensing recipe generation unit can also predict interactions based on pharmacokinetics and pharmacodynamics and generate optimal dispensing recipes. This makes it possible to automatically check drug interactions and generate optimal dispensing recipes.

[0039] The dispensing recipe generation unit can automatically calculate the storage method and expiration date of a medicine based on prescription data and reflect this in the dispensing recipe. The dispensing recipe generation unit, for example, builds a system that automatically calculates the storage method and expiration date of a medicine based on prescription data. For example, it analyzes the ingredients and storage conditions of a medicine and proposes the optimal storage method. The dispensing recipe generation unit can also set an expiration date based on the time elapsed from the date of manufacture and storage conditions and reflect this in the dispensing recipe. This allows the storage method and expiration date of a medicine to be automatically calculated and reflected in the dispensing recipe.

[0040] The dispensing recipe generation unit can predict drug side effects based on prescription data and reflect them in the dispensing recipe. The dispensing recipe generation unit, for example, builds a system that predicts drug side effects based on prescription data. For example, it analyzes drug ingredients and past data to evaluate the risk of side effects. The dispensing recipe generation unit can also predict side effects based on clinical trial data and pharmacokinetics and reflect them in the dispensing recipe. This makes it possible to predict drug side effects and reflect them in the dispensing recipe.

[0041] The dispensing recipe generation unit can optimize drug costs based on prescription data and generate economical dispensing recipes. The dispensing recipe generation unit, for example, builds a system that optimizes drug costs based on prescription data. For example, it analyzes drug prices and insurance coverage information to suggest drugs with optimal costs. The dispensing recipe generation unit can also generate economical dispensing recipes taking manufacturing costs and distribution costs into consideration. This makes it possible to optimize drug costs and generate economical dispensing recipes.

[0042] The dispensing automation department can monitor the quality of medicines in real time during the automation of dispensing work, enabling thorough quality control. For example, the dispensing automation department can build a system to monitor the quality of medicines in real time during the automation of dispensing work. For example, it can analyze the ingredients and mixing state of medicines and evaluate the quality. The dispensing automation department can also manage the purity of ingredients and the manufacturing process to ensure thorough quality control. This allows the quality of medicines to be monitored in real time and ensure thorough quality control.

[0043] The dispensing automation unit can optimize the medicine packaging method in the automation of dispensing work and improve ease of use. The dispensing automation unit, for example, builds a system that optimizes the medicine packaging method in the automation of dispensing work. For example, it proposes the optimal packaging method depending on the shape of the medicine and how it is used. The dispensing automation unit can also propose a packaging method that takes into account sealability and ease of removal. This optimizes the medicine packaging method and improves ease of use.

[0044] The dispensing automation unit can maximize efficiency by simultaneously dispensing different medications. For example, the dispensing automation unit can build a system that simultaneously dispensing different medications, thereby maximizing efficiency. For example, multiple medications can be measured and mixed at the same time. The dispensing automation unit can also reduce work time and improve efficiency by performing multiple dispensing tasks in parallel. This allows the dispensing tasks of different medications to be performed simultaneously, thereby maximizing efficiency.

[0045] The dispensing automation department automates drug inventory management, ensuring that necessary drugs are always available. The dispensing automation department, for example, builds a system that automates drug inventory management, ensuring that necessary drugs are always available. For example, when stock runs low, it automatically places an order. The dispensing automation department can also monitor inventory in real time and quickly replenish necessary drugs. This automates drug inventory management, ensuring that necessary drugs are always available.

[0046] The double check unit can automatically analyze the ingredients of medicines during double checks to confirm the accuracy of the ingredients. The double check unit can, for example, build a system that automatically analyzes the ingredients of medicines during double checks. For example, it can analyze the ingredients of medicines and confirm that they match the prescription. The double check unit can also measure the purity and content of ingredients using techniques such as chromatography and spectroscopy. This allows the automatic analysis of medicine ingredients to confirm the accuracy of the ingredients.

[0047] The double check unit automatically inspects the appearance and packaging condition of the medicine during the double check, making it possible to guarantee quality. The double check unit, for example, builds a system that automatically inspects the appearance and packaging condition of the medicine during the double check. For example, it analyzes the shape and color of the medicine and the condition of the packaging to evaluate the quality. The double check unit can also inspect the seal and whether there is any damage, making it possible to guarantee quality. This makes it possible to automatically inspect the appearance and packaging condition of the medicine.

[0048] The Double Check Department provides online consultation with other pharmacists during double checks, making the confirmation process more efficient. The Double Check Department will, for example, build a system that provides online consultation with other pharmacists during double checks. For example, it will allow pharmacists to discuss any questions or matters to be confirmed in real time. The Double Check Department can also improve the accuracy of confirmations by having multiple pharmacists perform the confirmation process simultaneously. This will provide online consultation with other pharmacists, making the confirmation process more efficient.

[0049] The double check unit can refer to past check history when performing a double check to improve the accuracy of the check. For example, the double check unit can build a system that refers to past check history when performing a double check. For example, the accuracy of the current check can be improved based on past check results. The double check unit can also refer to past error records and take measures to prevent similar errors. This allows the accuracy of the check to be improved by referring to past check history.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The data management unit can provide an individualized health education program based on the child's personal data. For example, it can provide health education videos tailored to the child's age and health condition. The data management unit can also create health management guidelines for parents to support health management at home. Furthermore, the data management unit can provide information on health events at school or in the community, allowing children and parents to participate. This makes it possible to provide an individualized health education program and increase health awareness among children and parents.

[0052] The prescription support unit can refer to a patient's genetic information when a doctor writes a prescription and suggest a personalized treatment. For example, it can evaluate the effectiveness of a drug and the risk of side effects based on the genetic information and suggest the most appropriate drug. The prescription support unit can also suggest preventive medicine based on the genetic information. Furthermore, it can use the genetic information to predict future health risks and suggest early intervention. This makes it possible to refer to genetic information and suggest a personalized treatment.

[0053] The dispensing recipe generation unit can generate dispensing recipes that minimize the environmental impact of medicines based on prescription data. For example, it can evaluate the environmental impact of the medicine manufacturing process and packaging materials and suggest environmentally friendly options. The dispensing recipe generation unit can also recommend the use of recyclable packaging materials. Furthermore, it can also make environmentally friendly suggestions for medicine disposal methods. This makes it possible to generate dispensing recipes that minimize the environmental impact of medicines.

[0054] The pharmacy automation department can build a system that ensures drug traceability when automating dispensing work. For example, it can track the entire process from drug manufacturing to dispensing and delivery, ensuring thorough quality control. The pharmacy automation department can also record drug lot numbers and manufacturing dates, allowing for quick response when problems occur. Furthermore, it can provide drug traceability information to patients and medical institutions, improving reliability. This ensures drug traceability and thorough quality control.

[0055] During the double check, the double check department can not only analyze the ingredients of the drug, but also build a system that monitors the drug's effectiveness in real time. For example, it can use sensors to measure the drug's effects and analyze the patient's reaction in real time. The double check department can also suggest alternative drugs if the drug's effectiveness is not as expected. Furthermore, it can accumulate data on the drug's effectiveness and use it to improve future treatment methods. This makes it possible to monitor the drug's effectiveness in real time and provide optimal treatment.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The data management department centrally manages the child's personal data, medicine notebook, and medical history. For example, the child's name, age, weight, past prescription history, past medical history, and allergy information are stored in a database, and the necessary information is provided promptly. Step 2: The prescription support unit supports doctors in writing prescriptions based on the information managed by the data management unit. For example, it calculates the appropriate dosage based on the child's weight and presents it to the doctor. It can also suggest safe medications by referring to past allergy information. Step 3: The dispensing recipe generation unit generates a dispensing recipe from the prescription data created by the prescription creation support unit. For example, it generates a recipe that details the type and amount of medication, how to mix it, how to take it, etc. It can also check for drug interactions and generate the optimal dispensing recipe. Step 4: The dispensing automation unit automates the actual dispensing process based on the recipes generated by the dispensing recipe generation unit. For example, it automatically performs tasks such as measuring, mixing, and packaging the medicine. It can also monitor the quality of the medicine in real time to ensure thorough quality control. Step 5: In the double-checking department, pharmacists double-check the medicines dispensed by the automated dispensing department. For example, they automatically analyze the ingredients of the medicines to confirm their accuracy. They can also automatically inspect the appearance and packaging of the medicines to ensure quality.

[0058] (Example 2) The dispensing efficiency improvement system according to an embodiment of the present invention is a system that manages children's personal data, medicine notebooks, and medical histories in an integrated manner, supports doctors in writing prescriptions, generates dispensing recipes from prescription data, automates the actual dispensing work, and has pharmacists perform double checks. As a result, the dispensing efficiency system can improve the efficiency of dispensing work at children's pharmacies and significantly reduce the time required.

[0059] A dispensing efficiency improvement system according to an embodiment includes a data management unit, a prescription creation support unit, a dispensing recipe generation unit, a dispensing automation unit, and a double check unit. The data management unit centrally manages a child's personal data, medicine notebook, and medical history. For example, the data management unit stores a child's name, age, weight, past prescription history, past medical history, and allergy information in a database and promptly provides necessary information. The prescription creation support unit supports doctors in creating prescriptions based on the information managed by the data management unit. For example, the prescription creation support unit calculates the appropriate dosage based on the child's weight and presents it to the doctor. The prescription creation support unit can also suggest safe medications by referring to past allergy information. The dispensing recipe generation unit generates a dispensing recipe from the prescription data created by the prescription creation support unit. For example, the dispensing recipe generation unit generates a recipe that details the type and amount of medication, mixing method, and administration method. The dispensing recipe generation unit can also check drug interactions and generate an optimal dispensing recipe. The dispensing automation unit automates the actual dispensing process based on the recipe generated by the dispensing recipe generation unit. For example, the dispensing automation unit automatically performs tasks such as measuring, mixing, and packaging the medicine. The dispensing automation unit can also monitor the quality of the medicine in real time to ensure thorough quality control. The double-check unit allows a pharmacist to perform a double check of the medicine dispensed by the dispensing automation unit. For example, the double-check unit automatically analyzes the ingredients of the medicine to confirm the accuracy of the ingredients. The double-check unit can also automatically inspect the appearance and packaging of the medicine to ensure quality. As a result, the dispensing efficiency system according to the embodiment can improve the efficiency of dispensing work at children's pharmacies and significantly reduce the time required. For example, the system can quickly provide the information necessary for doctors to write prescriptions and suggest appropriate drug types and dosages, thereby improving the accuracy of prescription writing. Furthermore, automating the detailed dispensing process previously performed by pharmacists shortens the dispensing process and reduces the burden on pharmacists. Furthermore, the double-check performed by pharmacists can prevent dispensing errors and provide safe medicines to patients.

[0060] The data management unit can predict seasonal health risks based on the child's personal data and suggest preventive measures. The data management unit, for example, builds a system that predicts seasonal health risks based on the child's personal data. For example, it analyzes past medical history and allergy information to suggest preventive measures for hay fever season. The data management unit can also recommend vaccinations for influenza season. This makes it possible to predict seasonal health risks and suggest appropriate preventive measures.

[0061] The data management unit can predict future health risks based on past medical history and allergy information, and automatically generate vaccination and health checkup schedules. The data management unit, for example, analyzes past medical history and allergy information to build a system that predicts future health risks. For example, it automatically generates regular health checkup schedules for children who are at high risk of allergies. The data management unit can also automatically generate vaccination schedules and suggest that children receive vaccinations at appropriate times. This makes it possible to predict future health risks and automatically generate appropriate vaccination and health checkup schedules.

[0062] The data management unit can use the emotion estimation function to analyze parents' emotions regarding their child's health condition and provide information to provide a sense of security. For example, the data management unit uses the emotion estimation function to build a system that analyzes parents' emotions regarding their child's health condition in real time. For example, if it detects anxiety in a parent, it provides information to provide a sense of security. The data management unit can also provide advice from a doctor and health condition monitoring results. This makes it possible to analyze parents' emotions and provide information to provide a sense of security.

[0063] The data management unit can provide dietary and exercise advice based on the child's personal data, and provide comprehensive support for health management. The data management unit, for example, builds a system that provides dietary and exercise advice based on the child's personal data. For example, it can propose appropriate dietary and exercise plans based on the child's weight and height. The data management unit can also suggest nutritionally balanced meals and allergy-friendly meals. This allows for the provision of dietary and exercise advice, and comprehensive support for health management.

[0064] The data management unit can link data with other medical institutions and schools and share children's health information, thereby realizing comprehensive health management. The data management unit, for example, links data with other medical institutions and schools and builds a system for sharing children's health information. For example, it links with the school's health room to share the results of health checkups. The data management unit can also realize comprehensive health management through data linkage with medical institutions. This allows data to be linked with other medical institutions and schools, thereby realizing comprehensive health management.

[0065] The data management unit can use the emotion estimation function to monitor parents' emotions regarding their child's health condition in real time and provide appropriate support. For example, the data management unit uses the emotion estimation function to build a system that monitors parents' emotions regarding their child's health condition in real time. For example, if anxiety in a parent is detected, advice from a medical professional is provided. The data management unit can also provide psychological support according to the parent's emotional state. This makes it possible to monitor parents' emotions in real time and provide appropriate support.

[0066] The prescription creation support unit can suggest the optimal drug combination based on past prescription history when a doctor creates a prescription. For example, when a doctor creates a prescription, the prescription creation support unit analyzes past prescription history and builds a system that suggests the optimal drug combination. For example, it can suggest drug combinations that have been effective in the past. The prescription creation support unit can also suggest the optimal drug combination taking drug interactions into consideration. This makes it possible to suggest the optimal drug combination based on past prescription history.

[0067] The prescription creation support unit can refer to the latest medical research data when a doctor creates a prescription and suggest the most appropriate treatment. For example, the prescription creation support unit will build a system that automatically collects the latest medical research data when a doctor creates a prescription and suggests the most appropriate treatment. For example, it will suggest a treatment based on the latest research results. The prescription creation support unit can also suggest evidence-based treatments. This allows the doctor to refer to the latest medical research data and suggest the most appropriate treatment.

[0068] The prescription creation support unit can use the emotion estimation function to analyze the doctor's stress level and provide support to reduce stress. The prescription creation support unit, for example, uses the emotion estimation function to build a system that analyzes the doctor's stress level in real time. For example, when the doctor feels stressed, the system suggests relaxation methods. The prescription creation support unit can also provide psychological support according to the doctor's emotional state. This makes it possible to analyze the doctor's stress level and provide support to reduce stress.

[0069] The prescription writing support unit can provide online consultation with other medical experts when a doctor writes a prescription, and determine the optimal treatment method. The prescription writing support unit, for example, builds a system that provides online consultation with other medical experts when a doctor writes a prescription. For example, it allows a doctor to refer to expert opinions in real time. The prescription writing support unit can also determine the optimal treatment method based on the opinions of multiple experts. This allows online consultation with other medical experts to be provided, and the optimal treatment method to be determined.

[0070] The prescription creation support unit can refer to the patient's lifestyle data when a doctor creates a prescription and propose a more personalized treatment. The prescription creation support unit, for example, builds a system that refers to the patient's lifestyle data when a doctor creates a prescription and proposes a personalized treatment. For example, it proposes a treatment that takes into account dietary and exercise habits. The prescription creation support unit can also propose a treatment based on genetic information. This makes it possible to refer to the patient's lifestyle data and propose a personalized treatment.

[0071] The prescription creation support unit can use the emotion estimation function to monitor the doctor's emotional state in real time and provide appropriate support. The prescription creation support unit, for example, uses the emotion estimation function to build a system that monitors the doctor's emotional state in real time. For example, when the doctor feels stressed, the system can suggest relaxation methods. The prescription creation support unit can also provide psychological support according to the doctor's emotional state. This makes it possible to monitor the doctor's emotional state in real time and provide appropriate support.

[0072] The dispensing recipe generation unit can automatically check drug interactions based on prescription data and generate optimal dispensing recipes. The dispensing recipe generation unit, for example, builds a system that automatically checks drug interactions based on prescription data. For example, it analyzes drug ingredients and evaluates the risk of interactions. The dispensing recipe generation unit can also predict interactions based on pharmacokinetics and pharmacodynamics and generate optimal dispensing recipes. This makes it possible to automatically check drug interactions and generate optimal dispensing recipes.

[0073] The dispensing recipe generation unit can automatically calculate the storage method and expiration date of a medicine based on prescription data and reflect this in the dispensing recipe. The dispensing recipe generation unit, for example, builds a system that automatically calculates the storage method and expiration date of a medicine based on prescription data. For example, it analyzes the ingredients and storage conditions of a medicine and proposes the optimal storage method. The dispensing recipe generation unit can also set an expiration date based on the time elapsed from the date of manufacture and storage conditions and reflect this in the dispensing recipe. This allows the storage method and expiration date of a medicine to be automatically calculated and reflected in the dispensing recipe.

[0074] The dispensing recipe generation unit can use the emotion estimation function to analyze the emotional state of the pharmacist and provide support to reduce stress. The dispensing recipe generation unit, for example, uses the emotion estimation function to build a system that analyzes the emotional state of the pharmacist in real time. For example, when the pharmacist feels stressed, the dispensing recipe generation unit can suggest relaxation methods. The dispensing recipe generation unit can also provide psychological support according to the pharmacist's emotional state. This makes it possible to analyze the emotional state of the pharmacist and provide support to reduce stress.

[0075] The dispensing recipe generation unit can predict drug side effects based on prescription data and reflect them in the dispensing recipe. The dispensing recipe generation unit, for example, builds a system that predicts drug side effects based on prescription data. For example, it analyzes drug ingredients and past data to evaluate the risk of side effects. The dispensing recipe generation unit can also predict side effects based on clinical trial data and pharmacokinetics and reflect them in the dispensing recipe. This makes it possible to predict drug side effects and reflect them in the dispensing recipe.

[0076] The dispensing recipe generation unit can optimize drug costs based on prescription data and generate economical dispensing recipes. The dispensing recipe generation unit, for example, builds a system that optimizes drug costs based on prescription data. For example, it analyzes drug prices and insurance coverage information to suggest drugs with optimal costs. The dispensing recipe generation unit can also generate economical dispensing recipes taking manufacturing costs and distribution costs into consideration. This makes it possible to optimize drug costs and generate economical dispensing recipes.

[0077] The dispensing recipe generation unit can use the emotion estimation function to monitor the emotional state of the pharmacist in real time and provide appropriate support. The dispensing recipe generation unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of the pharmacist in real time. For example, when the pharmacist feels stressed, the system suggests relaxation methods. The dispensing recipe generation unit can also provide psychological support according to the pharmacist's emotional state. This makes it possible to monitor the emotional state of the pharmacist in real time and provide appropriate support.

[0078] The dispensing automation department can monitor the quality of medicines in real time during the automation of dispensing work, enabling thorough quality control. For example, the dispensing automation department can build a system to monitor the quality of medicines in real time during the automation of dispensing work. For example, it can analyze the ingredients and mixing state of medicines and evaluate the quality. The dispensing automation department can also manage the purity of ingredients and the manufacturing process to ensure thorough quality control. This allows the quality of medicines to be monitored in real time and ensure thorough quality control.

[0079] The dispensing automation unit can optimize the medicine packaging method in the automation of dispensing work and improve ease of use. The dispensing automation unit, for example, builds a system that optimizes the medicine packaging method in the automation of dispensing work. For example, it proposes the optimal packaging method depending on the shape of the medicine and how it is used. The dispensing automation unit can also propose a packaging method that takes into account sealability and ease of removal. This optimizes the medicine packaging method and improves ease of use.

[0080] The dispensing automation unit can use the emotion estimation function to analyze the emotional state of a pharmacist during dispensing work and provide support to reduce stress. For example, the dispensing automation unit uses the emotion estimation function to build a system that analyzes the emotional state of a pharmacist during dispensing work in real time. For example, when a pharmacist feels stressed, the dispensing automation unit can suggest relaxation methods. The dispensing automation unit can also provide psychological support according to the pharmacist's emotional state. This makes it possible to analyze the emotional state of a pharmacist during dispensing work and provide support to reduce stress.

[0081] The dispensing automation unit can maximize efficiency by simultaneously dispensing different medications. For example, the dispensing automation unit can build a system that simultaneously dispensing different medications, thereby maximizing efficiency. For example, multiple medications can be measured and mixed at the same time. The dispensing automation unit can also reduce work time and improve efficiency by performing multiple dispensing tasks in parallel. This allows the dispensing tasks of different medications to be performed simultaneously, thereby maximizing efficiency.

[0082] The dispensing automation department automates drug inventory management, ensuring that necessary drugs are always available. The dispensing automation department, for example, builds a system that automates drug inventory management, ensuring that necessary drugs are always available. For example, when stock runs low, it automatically places an order. The dispensing automation department can also monitor inventory in real time and quickly replenish necessary drugs. This automates drug inventory management, ensuring that necessary drugs are always available.

[0083] The dispensing automation unit can use the emotion estimation function to monitor the emotional state of pharmacists during dispensing work in real time and provide appropriate support. The dispensing automation unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of pharmacists during dispensing work in real time. For example, when a pharmacist feels stressed, the system suggests relaxation methods. The dispensing automation unit can also provide psychological support according to the pharmacist's emotional state. This makes it possible to monitor the emotional state of pharmacists during dispensing work in real time and provide appropriate support.

[0084] The double check unit can automatically analyze the ingredients of medicines during double checks to confirm the accuracy of the ingredients. The double check unit can, for example, build a system that automatically analyzes the ingredients of medicines during double checks. For example, it can analyze the ingredients of medicines and confirm that they match the prescription. The double check unit can also measure the purity and content of ingredients using techniques such as chromatography and spectroscopy. This allows the automatic analysis of medicine ingredients to confirm the accuracy of the ingredients.

[0085] The double check unit automatically inspects the appearance and packaging condition of the medicine during the double check, making it possible to guarantee quality. The double check unit, for example, builds a system that automatically inspects the appearance and packaging condition of the medicine during the double check. For example, it analyzes the shape and color of the medicine and the condition of the packaging to evaluate the quality. The double check unit can also inspect the seal and whether there is any damage, making it possible to guarantee quality. This makes it possible to automatically inspect the appearance and packaging condition of the medicine.

[0086] The W-check unit can use the emotion estimation function to analyze the emotional state of the pharmacist during the W-check and provide support to reduce stress. For example, the W-check unit can use the emotion estimation function to build a system that analyzes the emotional state of the pharmacist during the W-check in real time. For example, when a pharmacist feels stressed, the system can suggest ways to relax. The W-check unit can also provide psychological support according to the pharmacist's emotional state. This makes it possible to analyze the emotional state of the pharmacist during the W-check and provide support to reduce stress.

[0087] The Double Check Department provides online consultation with other pharmacists during double checks, making the confirmation process more efficient. The Double Check Department will, for example, build a system that provides online consultation with other pharmacists during double checks. For example, it will allow pharmacists to discuss any questions or matters to be confirmed in real time. The Double Check Department can also improve the accuracy of confirmations by having multiple pharmacists perform the confirmation process simultaneously. This will provide online consultation with other pharmacists, making the confirmation process more efficient.

[0088] The double check unit can refer to past check history when performing a double check to improve the accuracy of the check. For example, the double check unit can build a system that refers to past check history when performing a double check. For example, the accuracy of the current check can be improved based on past check results. The double check unit can also refer to past error records and take measures to prevent similar errors. This allows the accuracy of the check to be improved by referring to past check history.

[0089] The W-Check unit uses the emotion estimation function to monitor the emotional state of the pharmacist during the W-Check in real time and provide appropriate support. For example, the W-Check unit uses the emotion estimation function to build a system that monitors the emotional state of the pharmacist during the W-Check in real time. For example, when a pharmacist feels stressed, the system suggests ways to relax. The W-Check unit can also provide psychological support according to the pharmacist's emotional state. This makes it possible to monitor the emotional state of the pharmacist during the W-Check in real time and provide appropriate support.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The data management unit can provide an individualized health education program based on the child's personal data. For example, it can provide health education videos tailored to the child's age and health condition. The data management unit can also create health management guidelines for parents to support health management at home. Furthermore, the data management unit can provide information on health events at school or in the community, allowing children and parents to participate. This makes it possible to provide an individualized health education program and increase health awareness among children and parents.

[0092] The prescription support unit can refer to a patient's genetic information when a doctor writes a prescription and suggest a personalized treatment. For example, it can evaluate the effectiveness of a drug and the risk of side effects based on the genetic information and suggest the most appropriate drug. The prescription support unit can also suggest preventive medicine based on the genetic information. Furthermore, it can use the genetic information to predict future health risks and suggest early intervention. This makes it possible to refer to genetic information and suggest a personalized treatment.

[0093] The dispensing recipe generation unit can generate dispensing recipes that minimize the environmental impact of medicines based on prescription data. For example, it can evaluate the environmental impact of the medicine manufacturing process and packaging materials and suggest environmentally friendly options. The dispensing recipe generation unit can also recommend the use of recyclable packaging materials. Furthermore, it can also make environmentally friendly suggestions for medicine disposal methods. This makes it possible to generate dispensing recipes that minimize the environmental impact of medicines.

[0094] The pharmacy automation department can build a system that ensures drug traceability when automating dispensing work. For example, it can track the entire process from drug manufacturing to dispensing and delivery, ensuring thorough quality control. The pharmacy automation department can also record drug lot numbers and manufacturing dates, allowing for quick response when problems occur. Furthermore, it can provide drug traceability information to patients and medical institutions, improving reliability. This ensures drug traceability and thorough quality control.

[0095] During the double check, the double check department can not only analyze the ingredients of the drug, but also build a system that monitors the drug's effectiveness in real time. For example, it can use sensors to measure the drug's effects and analyze the patient's reaction in real time. The double check department can also suggest alternative drugs if the drug's effectiveness is not as expected. Furthermore, it can accumulate data on the drug's effectiveness and use it to improve future treatment methods. This makes it possible to monitor the drug's effectiveness in real time and provide optimal treatment.

[0096] The data management unit uses the emotion estimation function to analyze parents' emotions regarding their child's health condition and provide information that will reassure them. For example, if a parent feels anxious, it can provide advice from a doctor or a detailed explanation of the child's health condition. The data management unit can also provide relaxation methods and advice on stress reduction according to the parent's emotions. It can also share success stories and the experiences of other parents that will reassure parents. This makes it possible to analyze parents' emotions and provide information that will reassure them.

[0097] The prescription creation support unit can use the emotion estimation function to analyze the doctor's emotional state and provide an environment in which the doctor can relax. For example, when the doctor feels stressed, it can provide relaxing music or videos. The prescription creation support unit can also suggest break times based on the doctor's emotional state and provide activities to reduce stress. It can also promote communication between doctors and provide emotional support. This makes it possible to analyze the doctor's emotional state and provide an environment in which the doctor can relax.

[0098] The dispensing recipe generation unit can analyze the emotional state of the pharmacist using the emotion estimation function and provide an environment in which the pharmacist can relax. For example, when the pharmacist feels stressed, it can provide relaxing music or images. The dispensing recipe generation unit can also suggest break times according to the pharmacist's emotional state and provide activities to reduce stress. It can also promote communication between pharmacists and provide emotional support. This makes it possible to analyze the emotional state of the pharmacist and provide an environment in which the pharmacist can relax.

[0099] The dispensing automation unit uses the emotion estimation function to monitor the emotional state of pharmacists in real time while dispensing medication and can provide appropriate support. For example, if a pharmacist feels stressed, it can suggest relaxation methods. The dispensing automation unit can also provide psychological support according to the pharmacist's emotional state. It can also make suggestions to create a work environment that allows the pharmacist to relax. This makes it possible to monitor the emotional state of pharmacists in real time while dispensing medication and provide appropriate support.

[0100] The W-Check Department uses its emotion estimation function to monitor the emotional state of pharmacists during W-Checks in real time and provide appropriate support. For example, if a pharmacist feels stressed, it can suggest ways to relax. The W-Check Department can also provide psychological support according to the pharmacist's emotional state. It can also make suggestions to create a work environment that allows the pharmacist to relax. This makes it possible to monitor the emotional state of pharmacists during W-Checks in real time and provide appropriate support.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The data management department centrally manages the child's personal data, medicine notebook, and medical history. For example, the child's name, age, weight, past prescription history, past medical history, and allergy information are stored in a database, and the necessary information is provided promptly. Step 2: The prescription support unit supports doctors in writing prescriptions based on the information managed by the data management unit. For example, it calculates the appropriate dosage based on the child's weight and presents it to the doctor. It can also suggest safe medications by referring to past allergy information. Step 3: The dispensing recipe generation unit generates a dispensing recipe from the prescription data created by the prescription creation support unit. For example, it generates a recipe that details the type and amount of medication, how to mix it, how to take it, etc. It can also check for drug interactions and generate the optimal dispensing recipe. Step 4: The dispensing automation unit automates the actual dispensing process based on the recipes generated by the dispensing recipe generation unit. For example, it automatically performs tasks such as measuring, mixing, and packaging the medicine. It can also monitor the quality of the medicine in real time to ensure thorough quality control. Step 5: In the double-checking department, pharmacists double-check the medicines dispensed by the automated dispensing department. For example, they automatically analyze the ingredients of the medicines to confirm their accuracy. They can also automatically inspect the appearance and packaging of the medicines to ensure quality.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a 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.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data management department that centrally manages children's personal data, medicine notebooks, and medical history, a prescription creation support unit that supports doctors in creating prescriptions based on the information managed by the data management unit; a prescription recipe generation unit that generates a prescription recipe from the prescription data generated by the prescription generation support unit; a dispensing automation unit that automates actual dispensing work based on the recipe generated by the dispensing recipe generation unit; a double-check unit in which a pharmacist double-checks the medicine dispensed by the automated dispensing unit. A system characterized by:

2. The data management unit Predicting seasonal health risks based on the child's personal data and suggesting preventative measures The system of claim 1 .

3. The data management unit Providing comprehensive support for health management by providing dietary and exercise advice based on the child's personal data The system of claim 1 .

4. The prescription creation support unit When the doctor writes a prescription, he or she will suggest the optimal combination of medicines based on the patient's past prescription history. The system of claim 1 .

5. The dispensing recipe generation unit Automatically check for drug interactions based on the prescription data and generate an optimal prescription recipe. The system of claim 1 .

6. The dispensing automation unit includes: To monitor the quality of the medicine in real time and ensure thorough quality control during the automation of the dispensing process. The system of claim 1 .

7. The W check unit During the double check, the drug's ingredients are automatically analyzed to confirm the accuracy of the ingredients. The system of claim 1 .

8. The data management unit To analyze the parents' feelings regarding the health of the child and provide information to provide reassurance. The system of claim 1 .

Images