System

Abstract

An object of a system according to an embodiment is to appropriately manage an immunization schedule for a child and provide a protector with necessary information and reminders.SOLUTION: A system according to an embodiment includes a health condition acquisition unit, a genetic information analysis unit, a schedule generation unit, and a reminder providing unit. The health condition acquisition unit acquires a health condition of the child. The genetic information analysis unit analyzes genetic information of the child. The schedule generation unit generates a vaccination schedule based on the information acquired and analyzed by the health condition acquisition unit and the genetic information analysis unit. The reminder providing unit provides a reminder based on the immunization schedule generated by the schedule generating 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] Previous technologies have had issues in that they have not adequately managed children's vaccination schedules and provided parents with the necessary information and reminders.

[0005] The system according to the embodiment aims to properly manage a child's vaccination schedule and provide necessary information and reminders to parents. [Means for solving the problem]

[0006] The system according to the embodiment includes a health condition acquisition unit, a genetic information analysis unit, a schedule generation unit, and a reminder provision unit. The health condition acquisition unit acquires the child's health condition. The genetic information analysis unit analyzes the child's genetic information. The schedule generation unit generates a vaccination schedule based on the information acquired and analyzed by the health condition acquisition unit and the genetic information analysis unit. The reminder provision unit provides reminders based on the vaccination schedule generated by the schedule generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can properly manage a child's vaccination schedule and provide necessary information and reminders to parents. [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 mobile app according to the embodiment of the present invention is a system for easily managing children's vaccinations and providing necessary information and reminders to parents, allowing them to track their children's health and vaccination schedules, avoid missing vaccinations, and support a healthier lifestyle.

[0029] A mobile app according to an embodiment includes a health condition acquisition unit, a genetic information analysis unit, a schedule generation unit, and a reminder provision unit. The health condition acquisition unit acquires the child's health condition. For example, the health condition acquisition unit collects data such as body temperature, heart rate, and blood pressure. The health condition acquisition unit can also acquire health information manually entered by a guardian. The genetic information analysis unit analyzes the child's genetic information. For example, the genetic information analysis unit analyzes specific genetic markers and gene sequences. The genetic information analysis unit can also perform analysis based on genetic test results. The schedule generation unit generates a vaccination schedule based on the information acquired and analyzed by the health condition acquisition unit and the genetic information analysis unit. For example, the schedule generation unit generates an optimal vaccination schedule using a generation AI. The schedule generation unit can also prioritize vaccinations that correspond to the child's specific risks in the schedule. The reminder provision unit provides reminders based on the vaccination schedule generated by the schedule generation unit. For example, the reminder provision unit provides reminders via email or app notifications. The reminder provider can also provide reminders in conjunction with a calendar app, allowing the mobile app to provide an optimal vaccination schedule based on a child's health status and genetic information, and provide reminders to help the child manage vaccinations without missing them.

[0030] The schedule generation unit can evaluate the risk of a specific disease from the genetic information and prioritize vaccinations corresponding to the risk in the schedule. The schedule generation unit, for example, evaluates the risk of a specific disease from the genetic information. For example, it calculates a risk score for a specific disease from the genetic information. The schedule generation unit also prioritizes vaccinations in the schedule based on the risk score. For example, it prioritizes vaccinations against diseases with high risks in the schedule. In this way, by evaluating the risk of a specific disease and prioritizing vaccinations corresponding to the risk in the schedule, the effectiveness of vaccinations can be maximized.

[0031] The schedule generation unit can link the vaccination schedule with other health management apps based on health status and genetic information to achieve comprehensive health management. The schedule generation unit, for example, shares data with other health management apps, and the generation AI evaluates the overall health status. For example, it imports data from a fitness app and adjusts the vaccination schedule based on the amount of exercise. The schedule generation unit also works with other health management apps to build a system that achieves comprehensive health management. For example, it integrates multiple health data sets and evaluates the overall health status. This allows comprehensive health management to be achieved by linking with other health management apps.

[0032] The schedule generation unit can add a function that allows parents to customize the vaccination schedule provided by the generation AI based on health status and genetic information, enabling flexible schedule management. The schedule generation unit, for example, provides an interface that allows parents to customize the vaccination schedule. For example, if a parent wants to avoid vaccination on a specific day, a function to exclude that day can be added. The schedule generation unit also provides a function that allows parents to readjust the schedule. For example, if a parent wants to change the schedule, an interface that allows changes to be made easily can be provided. This allows parents to customize the vaccination schedule, enabling flexible schedule management.

[0033] The schedule generation unit can analyze the local infectious disease outbreak situation in real time and dynamically adjust the vaccination schedule based on that. For example, the schedule generation unit collects local infectious disease outbreak status in real time, and the generation AI analyzes that data. For example, it prioritizes scheduling vaccinations during influenza epidemic periods. The schedule generation unit also builds a system that dynamically adjusts the vaccination schedule based on the local infectious disease outbreak situation. For example, it prioritizes vaccinations during periods when the risk of infectious diseases is high. In this way, the risk of infectious diseases can be minimized by analyzing the local infectious disease outbreak situation in real time and dynamically adjusting the vaccination schedule based on that information.

[0034] The schedule generation unit uses a child's allergy information as input data, and the generation AI can provide a vaccination schedule that takes allergy risk into account. For example, the schedule generation unit records a child's allergy information in detail, and the generation AI analyzes that data. For example, if a child has an egg allergy, vaccines that do not contain egg components are prioritized in the schedule. The schedule generation unit also builds a system that provides a vaccination schedule that takes allergy risk into account. For example, it adjusts the vaccination schedule based on the allergy information. This allows the risk of allergic reactions to be minimized by providing a vaccination schedule that takes allergy risk into account.

[0035] The schedule generation unit can link the personalized vaccination management function with schools and nursery schools to realize group vaccination management. The schedule generation unit, for example, links with schools and nursery schools to build a system for managing group vaccination schedules. For example, it manages the vaccination schedules for an entire class in one place. The schedule generation unit also shares data with schools and nursery schools to realize group vaccination management. For example, it plans vaccinations based on the school or nursery school schedule. In this way, by linking with schools and nursery schools, group vaccination management can be efficiently realized.

[0036] The schedule generation unit can integrate the vaccination schedule provided by the generation AI with the schedules of other family members to support health management for the entire household. For example, the schedule generation unit imports the schedules of other family members, and the generation AI supports health management for the entire household. For example, it manages the vaccination schedules of all family members in one place. The schedule generation unit also integrates the health data of all family members and builds a system to evaluate the overall health status. For example, it adjusts the vaccination schedule based on the health data of all family members. This makes it possible to support health management for the entire household by integrating it with the schedules of other family members.

[0037] The schedule generation unit automatically adjusts the guardian's schedule and hospital availability to ensure the optimal appointment. For example, the schedule generation unit takes in the guardian's schedule, and the generation AI automatically adjusts it with the hospital's availability. For example, it sets a vaccination appointment for a time that is convenient for the guardian. The schedule generation unit also builds a system that ensures the optimal appointment based on the guardian's schedule and hospital availability. For example, it sets an appointment by adjusting it with the hospital's availability based on data from the guardian's calendar app. This automatically adjusts the guardian's schedule and hospital availability to ensure the optimal appointment, reducing the guardian's workload.

[0038] The schedule generation unit can link the automatic reservation function established through partnerships with hospitals with pharmacies and testing institutions, thereby realizing comprehensive reservation management for medical services. The schedule generation unit, for example, can link not only with hospitals but also with pharmacies and testing institutions to realize comprehensive reservation management for medical services. For example, it can also automatically make reservations for follow-up tests after vaccinations. The schedule generation unit also shares data with pharmacies and testing institutions to build a system for comprehensive reservation management for medical services. For example, it adjusts reservations based on prescription data from pharmacies and test result data from testing institutions. In this way, by linking with pharmacies and testing institutions, comprehensive reservation management for medical services can be realized.

[0039] The schedule generation unit can add a function that enables the generation AI to automatically manage appointments for regular health checkups and follow-ups in addition to vaccination appointments. For example, the schedule generation unit builds a system that automatically manages appointments for regular health checkups and follow-ups in addition to vaccination appointments. For example, it automatically sets the schedule for regular health checkups. The schedule generation unit also provides a function to automatically manage follow-up appointments. For example, it automatically sets the schedule for follow-up tests after vaccinations. This allows for the automatic management of appointments for regular health checkups and follow-ups, thereby supporting comprehensive health management.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The schedule generation unit can acquire growth data on the child and provide a vaccination schedule that corresponds to the child's progress. For example, it can periodically collect height and weight data and adjust the timing of vaccinations according to the child's growth rate. It can also prioritize vaccinations required at specific growth stages based on the growth data. This allows for more effective health management by providing an optimal vaccination schedule that corresponds to the child's growth.

[0042] The schedule generation unit can acquire lifestyle data of the guardian and adjust the vaccination schedule based on that data. For example, it can optimize the timing of vaccinations by taking into account the guardian's work schedule and travel plans. It can also provide reminders tailored to the guardian's lifestyle, helping the guardian remember to get vaccinated. This makes it possible to provide a flexible vaccination schedule tailored to the guardian's lifestyle.

[0043] The schedule generation unit can acquire a child's dietary data and adjust the vaccination schedule based on the child's nutritional status. For example, it can analyze the child's dietary content and nutritional balance and schedule vaccinations for a time when the child's nutritional status is good. It can also provide reminders to replenish certain nutrients if the child is lacking in those nutrients. This allows the system to provide an optimal vaccination schedule based on the child's nutritional status.

[0044] The schedule generation unit can acquire the guardian's health data and adjust the vaccination schedule based on the guardian's health condition. For example, if the guardian is sick or tired, the vaccination schedule can be scheduled to avoid those times. In addition, by providing reminders based on the guardian's health condition, the guardian can remember to get vaccinated. This makes it possible to provide a flexible vaccination schedule based on the guardian's health condition.

[0045] The schedule generation unit can acquire the child's sleep data and adjust the vaccination schedule based on the child's sleep patterns. For example, it can analyze the child's sleep duration and quality and schedule vaccinations for a time when the child is getting enough sleep. It can also provide reminders to encourage rest after vaccinations based on the sleep data. This allows for the provision of an optimal vaccination schedule based on the child's sleep patterns.

[0046] The schedule generation unit can link the personalized vaccination management function with schools and nursery schools to realize mass vaccination management. For example, it can link with schools and nursery schools to build a system for managing mass vaccination schedules. For example, it can manage the vaccination schedule for an entire class all at once. The schedule generation unit also shares data with schools and nursery schools to realize mass vaccination management. For example, it can plan vaccinations based on the school or nursery school schedule. In this way, by linking with schools and nursery schools, it can efficiently realize mass vaccination management.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The health status acquisition unit acquires the child's health status. For example, it collects data such as body temperature, heart rate, and blood pressure. It can also acquire health information manually entered by the parent or guardian. Step 2: The genetic information analysis unit analyzes the child's genetic information. For example, it can analyze specific genetic markers and gene sequences and perform analysis based on the genetic test results. Step 3: The schedule generation unit generates a vaccination schedule based on the information acquired and analyzed by the health status acquisition unit and the genetic information analysis unit. For example, an optimal vaccination schedule can be generated using generation AI, and vaccinations corresponding to a child's specific risks can be prioritized in the schedule. Step 4: The reminder providing unit provides a reminder based on the vaccination schedule generated by the schedule generating unit. For example, the reminder can be provided via email or app notification, or in cooperation with a calendar app.

[0049] (Example 2) The mobile app according to the embodiment of the present invention is a system for easily managing children's vaccinations and providing necessary information and reminders to parents, allowing them to track their children's health and vaccination schedules, avoid missing vaccinations, and support a healthier lifestyle.

[0050] A mobile app according to an embodiment includes a health condition acquisition unit, a genetic information analysis unit, a schedule generation unit, and a reminder provision unit. The health condition acquisition unit acquires the child's health condition. For example, the health condition acquisition unit collects data such as body temperature, heart rate, and blood pressure. The health condition acquisition unit can also acquire health information manually entered by a guardian. The genetic information analysis unit analyzes the child's genetic information. For example, the genetic information analysis unit analyzes specific genetic markers and gene sequences. The genetic information analysis unit can also perform analysis based on genetic test results. The schedule generation unit generates a vaccination schedule based on the information acquired and analyzed by the health condition acquisition unit and the genetic information analysis unit. For example, the schedule generation unit generates an optimal vaccination schedule using a generation AI. The schedule generation unit can also prioritize vaccinations that correspond to the child's specific risks in the schedule. The reminder provision unit provides reminders based on the vaccination schedule generated by the schedule generation unit. For example, the reminder provision unit provides reminders via email or app notifications. The reminder provider can also provide reminders in conjunction with a calendar app, allowing the mobile app to provide an optimal vaccination schedule based on a child's health status and genetic information, and provide reminders to help the child manage vaccinations without missing them.

[0051] The schedule generation unit can evaluate the risk of a specific disease from the genetic information and prioritize vaccinations corresponding to the risk in the schedule. The schedule generation unit, for example, evaluates the risk of a specific disease from the genetic information. For example, it calculates a risk score for a specific disease from the genetic information. The schedule generation unit also prioritizes vaccinations in the schedule based on the risk score. For example, it prioritizes vaccinations against diseases with high risks in the schedule. In this way, by evaluating the risk of a specific disease and prioritizing vaccinations corresponding to the risk in the schedule, the effectiveness of vaccinations can be maximized.

[0052] The schedule generation unit can link the vaccination schedule with other health management apps based on health status and genetic information to achieve comprehensive health management. The schedule generation unit, for example, shares data with other health management apps, and the generation AI evaluates the overall health status. For example, it imports data from a fitness app and adjusts the vaccination schedule based on the amount of exercise. The schedule generation unit also works with other health management apps to build a system that achieves comprehensive health management. For example, it integrates multiple health data sets and evaluates the overall health status. This allows comprehensive health management to be achieved by linking with other health management apps.

[0053] The schedule generation unit can add a function that allows parents to customize the vaccination schedule provided by the generation AI based on health status and genetic information, enabling flexible schedule management. The schedule generation unit, for example, provides an interface that allows parents to customize the vaccination schedule. For example, if a parent wants to avoid vaccination on a specific day, a function to exclude that day can be added. The schedule generation unit also provides a function that allows parents to readjust the schedule. For example, if a parent wants to change the schedule, an interface that allows changes to be made easily can be provided. This allows parents to customize the vaccination schedule, enabling flexible schedule management.

[0054] The schedule generation unit can use the emotion estimation function to analyze parents' anxieties and concerns and adjust the vaccination schedule based on that. For example, the schedule generation unit analyzes parents' emotions in real time and flexibly adjusts the vaccination schedule if anxiety is high. For example, if parents feel anxious, the vaccination date is changed. The schedule generation unit also builds a system that analyzes parents' anxieties and concerns and adjusts the vaccination schedule based on that. For example, it collects parents' emotional data and has the generation AI analyze that data to adjust the schedule. This allows parents to feel more at ease by analyzing their anxieties and concerns and adjusting the vaccination schedule based on that.

[0055] The schedule generation unit can analyze the local infectious disease outbreak situation in real time and dynamically adjust the vaccination schedule based on that. For example, the schedule generation unit collects local infectious disease outbreak status in real time, and the generation AI analyzes that data. For example, it prioritizes scheduling vaccinations during influenza epidemic periods. The schedule generation unit also builds a system that dynamically adjusts the vaccination schedule based on the local infectious disease outbreak situation. For example, it prioritizes vaccinations during periods when the risk of infectious diseases is high. In this way, the risk of infectious diseases can be minimized by analyzing the local infectious disease outbreak situation in real time and dynamically adjusting the vaccination schedule based on that information.

[0056] The schedule generation unit uses a child's allergy information as input data, and the generation AI can provide a vaccination schedule that takes allergy risk into account. For example, the schedule generation unit records a child's allergy information in detail, and the generation AI analyzes that data. For example, if a child has an egg allergy, vaccines that do not contain egg components are prioritized in the schedule. The schedule generation unit also builds a system that provides a vaccination schedule that takes allergy risk into account. For example, it adjusts the vaccination schedule based on the allergy information. This allows the risk of allergic reactions to be minimized by providing a vaccination schedule that takes allergy risk into account.

[0057] The schedule generation unit can use the emotion estimation function to analyze the stress level of the parent or guardian and propose a vaccination schedule to reduce stress. The schedule generation unit, for example, analyzes the parent or guardian's stress level in real time and schedules vaccinations to avoid periods of high stress. For example, it schedules vaccinations for times when the parent or guardian is relaxed. The schedule generation unit also builds a system that analyzes the parent or guardian's stress level and proposes a vaccination schedule based on that. For example, it collects emotional data from the parent or guardian, and the generation AI analyzes that data to propose a schedule. This makes it possible to reduce the parent's stress by analyzing the parent's stress level and proposing a vaccination schedule based on that.

[0058] The schedule generation unit can link the personalized vaccination management function with schools and nursery schools to realize group vaccination management. The schedule generation unit, for example, links with schools and nursery schools to build a system for managing group vaccination schedules. For example, it manages the vaccination schedules for an entire class in one place. The schedule generation unit also shares data with schools and nursery schools to realize group vaccination management. For example, it plans vaccinations based on the school or nursery school schedule. In this way, by linking with schools and nursery schools, group vaccination management can be efficiently realized.

[0059] The schedule generation unit can integrate the vaccination schedule provided by the generation AI with the schedules of other family members to support health management for the entire household. For example, the schedule generation unit imports the schedules of other family members, and the generation AI supports health management for the entire household. For example, it manages the vaccination schedules of all family members in one place. The schedule generation unit also integrates the health data of all family members and builds a system to evaluate the overall health status. For example, it adjusts the vaccination schedule based on the health data of all family members. This makes it possible to support health management for the entire household by integrating it with the schedules of other family members.

[0060] The schedule generation unit can use the emotion estimation function to analyze a child's emotional response to vaccination and suggest vaccination in an environment where the child can relax. The schedule generation unit, for example, analyzes a child's emotional response in real time and suggests vaccination in an environment where the child can relax. For example, it recommends vaccination in a place where the child feels safe. The schedule generation unit also builds a system that adjusts the vaccination schedule based on the child's emotional response. For example, it plans vaccination in an environment where the child can relax. This allows the child to receive vaccination in a relaxing environment, thereby reducing stress.

[0061] The schedule generation unit automatically adjusts the guardian's schedule and hospital availability to ensure the optimal appointment. For example, the schedule generation unit takes in the guardian's schedule, and the generation AI automatically adjusts it with the hospital's availability. For example, it sets a vaccination appointment for a time that is convenient for the guardian. The schedule generation unit also builds a system that ensures the optimal appointment based on the guardian's schedule and hospital availability. For example, it sets an appointment by adjusting it with the hospital's availability based on data from the guardian's calendar app. This automatically adjusts the guardian's schedule and hospital availability to ensure the optimal appointment, reducing the guardian's workload.

[0062] The schedule generation unit can use the emotion estimation function to analyze parents' satisfaction with reservations and learn and suggest reservation patterns that result in high satisfaction. The schedule generation unit, for example, analyzes parents' satisfaction with reservations in real time and learns reservation patterns that result in high satisfaction. For example, it suggests optimal reservation times based on past reservation data. The schedule generation unit also builds a system that suggests reservation patterns based on parents' satisfaction. For example, it collects parents' feedback data, and the generation AI analyzes that data to suggest optimal reservation times. In this way, parent satisfaction can be improved by analyzing parents' satisfaction with reservations and learning and suggesting reservation patterns that result in high satisfaction.

[0063] The schedule generation unit can link the automatic reservation function established through partnerships with hospitals with pharmacies and testing institutions, thereby realizing comprehensive reservation management for medical services. The schedule generation unit, for example, can link not only with hospitals but also with pharmacies and testing institutions to realize comprehensive reservation management for medical services. For example, it can also automatically make reservations for follow-up tests after vaccinations. The schedule generation unit also shares data with pharmacies and testing institutions to build a system for comprehensive reservation management for medical services. For example, it adjusts reservations based on prescription data from pharmacies and test result data from testing institutions. In this way, by linking with pharmacies and testing institutions, comprehensive reservation management for medical services can be realized.

[0064] The schedule generation unit can add a function that enables the generation AI to automatically manage appointments for regular health checkups and follow-ups in addition to vaccination appointments. For example, the schedule generation unit builds a system that automatically manages appointments for regular health checkups and follow-ups in addition to vaccination appointments. For example, it automatically sets the schedule for regular health checkups. The schedule generation unit also provides a function to automatically manage follow-up appointments. For example, it automatically sets the schedule for follow-up tests after vaccinations. This allows for the automatic management of appointments for regular health checkups and follow-ups, thereby supporting comprehensive health management.

[0065] The schedule generation unit can use the emotion estimation function to monitor the parent's emotional response to the reservation in real time and make suggestions to elicit positive emotions. The schedule generation unit, for example, monitors the parent's emotional response to the reservation in real time and makes suggestions to elicit positive emotions. For example, it displays an encouraging message when confirming the reservation. The schedule generation unit also builds a system that adjusts reservations based on the parent's emotional response. For example, it sets reservations for times when the parent can relax. In this way, the parent's emotional response can be monitored in real time and suggestions to elicit positive emotions can be made, thereby improving parent satisfaction.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The schedule generation unit can acquire growth data on the child and provide a vaccination schedule that corresponds to the child's progress. For example, it can periodically collect height and weight data and adjust the timing of vaccinations according to the child's growth rate. It can also prioritize vaccinations required at specific growth stages based on the growth data. This allows for more effective health management by providing an optimal vaccination schedule that corresponds to the child's growth.

[0068] The schedule generation unit can acquire lifestyle data of the guardian and adjust the vaccination schedule based on that data. For example, it can optimize the timing of vaccinations by taking into account the guardian's work schedule and travel plans. It can also provide reminders tailored to the guardian's lifestyle, helping the guardian remember to get vaccinated. This makes it possible to provide a flexible vaccination schedule tailored to the guardian's lifestyle.

[0069] The schedule generation unit can use the emotion estimation function to analyze parents' emotions regarding their children's vaccinations and provide information to elicit positive emotions. For example, by introducing the benefits of vaccinations and success stories, it can reduce parents' anxiety. It can also provide advice from doctors and experts to reassure parents. In this way, by providing information that takes parents' emotions into consideration, it is possible to elicit positive emotions regarding vaccinations.

[0070] The schedule generation unit can acquire a child's dietary data and adjust the vaccination schedule based on the child's nutritional status. For example, it can analyze the child's dietary content and nutritional balance and schedule vaccinations for a time when the child's nutritional status is good. It can also provide reminders to replenish certain nutrients if the child is lacking in those nutrients. This allows the system to provide an optimal vaccination schedule based on the child's nutritional status.

[0071] The schedule generation unit can use the emotion estimation function to analyze a child's emotions regarding vaccinations and suggest vaccinations in an environment where the child can relax. For example, it can analyze a child's emotional reactions in real time and suggest vaccinations in an environment where the child can relax. For example, it can recommend vaccinations in a place where the child feels safe. The schedule generation unit also builds a system that adjusts vaccination schedules based on the child's emotional reactions. For example, it can plan vaccinations in an environment where the child can relax. This allows children to receive vaccinations in a relaxing environment, thereby reducing stress.

[0072] The schedule generation unit can acquire the guardian's health data and adjust the vaccination schedule based on the guardian's health condition. For example, if the guardian is sick or tired, the vaccination schedule can be scheduled to avoid those times. In addition, by providing reminders based on the guardian's health condition, the guardian can remember to get vaccinated. This makes it possible to provide a flexible vaccination schedule based on the guardian's health condition.

[0073] The schedule generation unit can use the emotion estimation function to analyze the stress level of parents and propose a vaccination schedule to reduce stress. For example, the stress level of parents can be analyzed in real time and vaccinations can be scheduled to avoid periods of high stress. For example, vaccinations can be scheduled for times when parents are relaxed. The schedule generation unit can also build a system that analyzes the stress level of parents and proposes a vaccination schedule based on that. For example, emotional data of parents can be collected and the generation AI can analyze that data to propose a schedule. This makes it possible to reduce stress for parents by analyzing their stress level and proposing a vaccination schedule based on that.

[0074] The schedule generation unit can acquire the child's sleep data and adjust the vaccination schedule based on the child's sleep patterns. For example, it can analyze the child's sleep duration and quality and schedule vaccinations for a time when the child is getting enough sleep. It can also provide reminders to encourage rest after vaccinations based on the sleep data. This allows for the provision of an optimal vaccination schedule based on the child's sleep patterns.

[0075] The schedule generation unit uses the emotion estimation function to analyze parents' satisfaction with reservations, and can learn and suggest reservation patterns that result in high satisfaction. For example, it can analyze parents' satisfaction with reservations in real time and learn reservation patterns that result in high satisfaction. For example, it can suggest optimal reservation times based on past reservation data. The schedule generation unit also builds a system that suggests reservation patterns based on parents' satisfaction. For example, it can collect parent feedback data, and the generation AI can analyze that data to suggest optimal reservation times. In this way, it can analyze parents' satisfaction with reservations, learn and suggest reservation patterns that result in high satisfaction, and improve parent satisfaction.

[0076] The schedule generation unit can link the personalized vaccination management function with schools and nursery schools to realize mass vaccination management. For example, it can link with schools and nursery schools to build a system for managing mass vaccination schedules. For example, it can manage the vaccination schedule for an entire class all at once. The schedule generation unit also shares data with schools and nursery schools to realize mass vaccination management. For example, it can plan vaccinations based on the school or nursery school schedule. In this way, by linking with schools and nursery schools, it can efficiently realize mass vaccination management.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The health status acquisition unit acquires the child's health status. For example, it collects data such as body temperature, heart rate, and blood pressure. It can also acquire health information manually entered by the parent or guardian. Step 2: The genetic information analysis unit analyzes the child's genetic information. For example, it can analyze specific genetic markers and gene sequences and perform analysis based on the genetic test results. Step 3: The schedule generation unit generates a vaccination schedule based on the information acquired and analyzed by the health status acquisition unit and the genetic information analysis unit. For example, an optimal vaccination schedule can be generated using generation AI, and vaccinations corresponding to a child's specific risks can be prioritized in the schedule. Step 4: The reminder providing unit provides a reminder based on the vaccination schedule generated by the schedule generating unit. For example, the reminder can be provided via email or app notification, or in cooperation with a calendar app.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 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 health status acquisition unit for acquiring the child's health status; A genetic information analysis unit that analyzes the child's genetic information; a schedule generation unit that generates a vaccination schedule based on the information acquired and analyzed by the health status acquisition unit and the genetic information analysis unit; a reminder providing unit that provides a reminder based on the vaccination schedule generated by the schedule generating unit. A system characterized by:

2. The schedule generation unit The risk of a particular disease is assessed based on the genetic information, and vaccinations corresponding to that risk are prioritized in the schedule.

2. The system of claim 1.

3. The schedule generation unit Analyzing the local infectious disease outbreak situation in real time and dynamically adjusting the vaccination schedule based on that.

2. The system of claim 1.

4. The schedule generation unit Analyzing hospital congestion in real time and suggesting optimal appointment times 2. The system of claim 1.

5. The schedule generation unit Analyze parental fears and concerns and adjust the vaccination schedule accordingly 2. The system of claim 1.

6. The schedule generation unit Analyze the stress level of parents and suggest the vaccination schedule to reduce stress 2. The system of claim 1.

7. The schedule generation unit Analyzing children's emotional responses to vaccinations and suggesting vaccinations in a relaxed environment 2. The system of claim 1.

8. The schedule generation unit Analyzes parental satisfaction with reservations, learns reservation patterns that generate high satisfaction, and proposes them 2. The system of claim 1.

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