System

Abstract

An object of a system according to an embodiment is to provide various medical services in an integrated manner.SOLUTION: A system includes a provision part, a monitoring part, a support part, a management part, and a medical examination part. The providing unit provides health consultation or health-related information. The monitoring unit monitors the medical device based on the information provided by the providing unit. The support unit supports home medical care based on the information collected by the monitoring unit. The management unit performs recipe management based on the information provided by the support unit. The medical examination section performs online medical examination on the basis of the information provided by the management section.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] Conventional technology has the drawback of making it difficult to provide a variety of medical services, such as health consultations, home medical support, medical equipment monitoring, recipe management, and online medical consultations, in a unified manner.

[0005] The system according to the embodiment aims to provide various medical services in an integrated manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a providing unit, a monitoring unit, a support unit, a management unit, and a medical examination unit. The providing unit provides health consultations or health-related information. The monitoring unit monitors medical equipment based on the information provided by the providing unit. The support unit supports home medical care based on the information collected by the monitoring unit. The management unit manages recipes based on the information provided by the support unit. The medical examination unit provides online medical care based on the information provided by the management unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide various medical services in an integrated manner. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A medical support system according to an embodiment of the present invention uses generative AI to provide a comprehensive range of services, including health consultations, medical device monitoring, home medical support, recipe management, and online consultations. The system uses generative AI to provide a health chatbot that provides health consultations and general health information. This reduces waiting times at hospitals and makes it easier to collect necessary information. Next, generative AI is used to monitor medical devices and collect medical information. This enables patients with serious illnesses and elderly care to make calm decisions by educating them on the proper use of installed medical devices. Furthermore, generative AI is used to collect information about patients during home medical care, allowing medical staff to contact them directly as needed. This enhances support for home medical care. Generative AI can also be used for recipe management. Recipes can be provided to patients via phone or text message, allowing patients to manage their own recipes from the comfort of their own home without having to call medical staff. Finally, generative AI can be used for online consultations. Even if patients are unable to visit a hospital, they can easily receive diagnosis and treatment through communication with a doctor. After the consultation, the generative AI can provide various services, such as arranging for medication pickup and, if necessary, arranging a hospital appointment. This allows the medical support system to consistently handle health consultations, medical equipment monitoring, home medical support, recipe management, and online consultations. For example, it can shorten waiting times at crowded hospitals and make it easier to collect necessary information. It can also help patients with serious illnesses or elderly care by teaching them how to properly use installed medical equipment, allowing them to make calm decisions. Furthermore, it strengthens support for home medical care, allowing patients to manage their own recipes from the comfort of their own home without having to call medical staff. This allows patients to receive appropriate diagnosis and treatment even if they are unable to go to the hospital.

[0029] A medical support system according to an embodiment includes a providing unit, a monitoring unit, a support unit, a management unit, and a medical examination unit. The providing unit provides health consultations or health-related information. For example, the providing unit generates and provides appropriate answers to questions from users. The providing unit can also provide general health information using a generating AI. For example, the generating AI can suggest appropriate measures to take in response to questions about cold symptoms. The monitoring unit monitors medical equipment. The monitoring unit collects and analyzes data such as heart rate and blood pressure in real time. The monitoring unit can also analyze the collected data and detect abnormalities using the generating AI. For example, the monitoring unit analyzes heart rate and blood pressure data in real time to detect abnormalities. The support unit supports home medical care. The support unit collects data such as a patient's body temperature and blood pressure, detects abnormalities, and notifies medical staff. The support unit can also analyze the collected data and detect abnormalities using the generating AI. For example, the monitoring unit collects data such as a patient's body temperature and blood pressure, and the generating AI analyzes the data to detect abnormalities and notify medical staff. The management unit manages recipes. For example, when a patient requests a recipe by phone or text message, the management unit provides an appropriate recipe. The management unit can also use a generating AI to provide recipes according to the patient's request. For example, when a patient requests a recipe by phone or text message, the generating AI provides an appropriate recipe. The medical department performs online medical consultations. For example, even if a patient is unable to visit a hospital, the medical department can provide diagnosis and treatment through communication with a doctor, arrange for medication pickup, and arrange for hospital appointments. The medical department can also use a generating AI to perform diagnosis and treatment. For example, even if a patient is unable to visit a hospital, the generating AI can allow the patient to easily receive diagnosis and treatment through communication with a doctor. As a result, the medical support system according to the embodiment can consistently provide health consultations, medical device monitoring, home medical support, recipe management, and online medical consultations.

[0030] The providing unit can generate and provide appropriate answers to questions from users. The providing unit generates and provides appropriate answers to questions from users, for example. For example, the generation AI can suggest appropriate countermeasures for questions about cold symptoms. The providing unit can also use the generation AI to generate appropriate answers to user questions. For example, the generation AI receives a prompt such as "Please tell me how to deal with cold symptoms" and suggests appropriate countermeasures. The providing unit can also use the generation AI to provide detailed information to user questions. For example, the generation AI receives a prompt such as "Please tell me detailed countermeasures for cold symptoms" and suggests detailed countermeasures. In this way, the user's doubts can be resolved by providing an appropriate answer to the user's question.

[0031] The monitoring unit can collect and analyze heart rate or blood pressure data in real time. The monitoring unit collects and analyzes, for example, data such as heart rate and blood pressure in real time. For example, heart rate and blood pressure data can be collected in real time and analyzed by a generation AI to detect abnormalities. The monitoring unit can also use a generation AI to analyze the collected data and detect abnormalities. For example, heart rate and blood pressure data can be analyzed in real time to detect abnormalities. The monitoring unit can also use a generation AI to detect abnormalities based on the collected data. For example, heart rate and blood pressure data can be collected and analyzed by a generation AI to detect abnormalities. In this way, by collecting and analyzing data in real time, abnormalities can be detected early.

[0032] The support unit can collect data on a patient's body temperature or blood pressure, detect abnormalities, and notify medical staff. The support unit can collect data such as a patient's body temperature and blood pressure, detect abnormalities, and notify medical staff. For example, the support unit can collect data on a patient's body temperature and blood pressure, analyze it using a generating AI, detect abnormalities, and notify medical staff. The support unit can also use a generating AI to analyze the collected data and detect abnormalities. For example, the support unit can collect data on a patient's body temperature and blood pressure, analyze it using a generating AI, detect abnormalities, and notify medical staff. The support unit can also use a generating AI to detect abnormalities based on the collected data. For example, the support unit can collect data on a patient's body temperature and blood pressure, analyze it using a generating AI, detect abnormalities, and notify medical staff. This allows for a rapid response by detecting abnormalities and notifying medical staff.

[0033] The management unit can provide an appropriate recipe when a patient requests a recipe by phone or text message. For example, when a patient requests a recipe by phone or text message, the management unit can provide an appropriate recipe. For example, when a patient requests a recipe by phone or text message, the generation AI can provide an appropriate recipe. The management unit can also use the generation AI to provide a recipe in accordance with the patient's request. For example, when a patient requests a recipe by phone or text message, the generation AI can provide an appropriate recipe. The management unit can also use the generation AI to provide a detailed recipe in accordance with the patient's request. For example, when a patient requests a detailed recipe by phone or text message, the generation AI can provide a detailed recipe. This allows patients to obtain appropriate recipes from the comfort of their own homes.

[0034] The medical department can provide diagnosis and treatment through communication with doctors, even if the patient is unable to go to the hospital, and can arrange for the patient to receive medication and make a hospital appointment. For example, even if the patient is unable to go to the hospital, the medical department can provide diagnosis and treatment through communication with doctors, and can arrange for the patient to receive medication and make a hospital appointment. For example, even if the patient is unable to go to the hospital, the medical department can provide diagnosis and treatment through communication with doctors using generative AI, and can easily receive diagnosis and treatment. The medical department can also use generative AI to provide diagnosis and treatment. For example, even if the patient is unable to go to the hospital, the medical department can provide diagnosis and treatment through communication with doctors using generative AI, and can easily receive diagnosis and treatment. The medical department can also use generative AI to provide diagnosis and treatment. For example, after the examination, the generative AI can arrange for the patient to receive medication and make a hospital appointment if necessary. This allows the patient to receive appropriate diagnosis and treatment even if they are unable to go to the hospital.

[0035] The providing unit can analyze the user's past question history and generate an optimal answer. The providing unit, for example, analyzes the user's past question history and generates an optimal answer. For example, the providing unit provides related new information based on the content of questions the user has previously asked. The providing unit can also preferentially display answers to frequently asked questions from the user's past question history. For example, the providing unit can analyze the user's past question history and preferentially display answers to frequently asked questions. The providing unit can also analyze the user's past question history and provide personalized health advice. For example, the providing unit can analyze the user's past question history and provide personalized health advice. In this way, the user's questions can be efficiently resolved by providing an optimal answer based on the user's past question history.

[0036] The providing unit can customize the health information based on the user's current health condition when providing the health information. For example, the providing unit customizes the information based on the user's current health condition when providing the health information. For example, appropriate exercise and diet advice is provided based on the user's current health condition. The providing unit can also provide information on a specific disease preferentially depending on the user's current health condition. For example, information on a specific disease is provided preferentially depending on the user's current health condition. The providing unit can also provide information on preventive measures and treatments taking into account the user's current health condition. For example, information on preventive measures and treatments is provided taking into account the user's current health condition. This makes it possible to provide information based on the user's current health condition.

[0037] The providing unit can adjust the content of the health information according to the age and gender of the user when providing the health information. For example, the providing unit adjusts the content of the information according to the age and gender of the user when providing the health information. For example, appropriate health advice and preventive measures are provided according to the user's age. The providing unit can also provide information on a specific health problem based on the user's gender. For example, information on a specific health problem is provided based on the user's gender. The providing unit can also provide personalized health information taking into account the user's age and gender. For example, personalized health information is provided taking into account the user's age and gender. This makes it possible to provide information according to the user's age and gender.

[0038] The providing unit can provide highly relevant information by taking into account the user's geographical location information when providing health information. For example, the providing unit can provide highly relevant information by taking into account the user's geographical location information when providing health information. For example, the providing unit can provide information on nearby medical institutions and pharmacies based on the user's current location. The providing unit can also provide information on region-specific health issues based on the user's geographical location information. For example, the providing unit can provide information on region-specific health issues based on the user's geographical location information. The providing unit can also provide appropriate preventive measures and health advice according to the user's current location. For example, the providing unit can provide appropriate preventive measures and health advice according to the user's current location. This makes it possible to provide information based on the user's geographical location information.

[0039] The providing unit can analyze the user's social media activity and provide related information when providing health information. For example, the providing unit can analyze the user's social media activity and provide related information when providing health information. For example, related advice can be provided based on health information shared by the user on social media. The providing unit can also analyze the user's social media activity and provide health information that is likely to be of interest to the user. For example, the providing unit can analyze the user's social media activity and provide health information that is likely to be of interest to the user. The providing unit can also provide related health information by referring to the activity of the user's friends on social media. For example, related health information can be provided by referring to the activity of the user's friends on social media. This makes it possible to provide information based on the user's social media activity.

[0040] The providing unit can customize the method of providing health information by reflecting the user's past feedback when providing the health information. For example, the providing unit customizes the method of providing health information by reflecting the user's past feedback when providing the health information. For example, if a user provides feedback on information previously provided, the providing unit improves the method of providing the health information based on that feedback. The providing unit can also analyze the user's past feedback and provide personalized information. For example, the providing unit can analyze the user's past feedback and provide personalized information. The providing unit can also adjust the content or format of the information by reflecting the user's feedback. For example, the content or format of the information by reflecting the user's feedback. This makes it possible to provide information based on the user's past feedback.

[0041] The monitoring unit can analyze medical device data in real time during monitoring and detect abnormalities. The monitoring unit, for example, analyzes medical device data in real time during monitoring and detects abnormalities. For example, it analyzes heart rate and blood pressure data in real time and detects abnormalities. The monitoring unit can also use a generation AI to analyze collected data in real time and detect abnormalities. For example, it analyzes heart rate and blood pressure data in real time and detects abnormalities. The monitoring unit can also use a generation AI to detect abnormalities based on collected data. For example, heart rate and blood pressure data is collected and analyzed by a generation AI to detect abnormalities. This allows data to be analyzed in real time and abnormalities to be detected early.

[0042] The monitoring unit can predict abnormalities by referring to the user's past health data during monitoring. The monitoring unit, for example, can predict abnormalities by referring to the user's past health data during monitoring. For example, it can predict abnormalities by referring to the user's past heart rate data. The monitoring unit can also predict abnormalities by referring to the user's past blood pressure data. For example, it can predict abnormalities by referring to the user's past blood pressure data. The monitoring unit can also predict abnormalities by referring to the user's past blood glucose level data. For example, it can predict abnormalities by referring to the user's past blood glucose level data. This makes it possible to predict abnormalities based on past health data.

[0043] The monitoring unit can provide appropriate guidance to the user on how to use the medical device during monitoring. The monitoring unit, for example, provides appropriate guidance to the user on how to use the medical device during monitoring. For example, the monitoring unit provides appropriate guidance to the user by explaining how to use the medical device using a video. The monitoring unit can also provide appropriate guidance to the user by explaining how to use the medical device using text. For example, the monitoring unit can provide appropriate guidance to the user by explaining how to use the medical device using text. The monitoring unit can also provide appropriate guidance to the user by explaining how to use the medical device using audio. For example, the monitoring unit can provide appropriate guidance to the user by explaining how to use the medical device using audio. This allows the user to deepen their understanding by providing guidance on how to properly use the medical device.

[0044] The monitoring unit can select an optimal monitoring method during monitoring by taking into consideration the geographical location information of the user. For example, the monitoring unit selects an optimal monitoring method by taking into consideration the geographical location information of the user during monitoring. For example, the monitoring unit selects an optimal monitoring method based on the user's current location. The monitoring unit can also select a monitoring method that addresses a region-specific health problem based on the user's geographical location information. For example, the monitoring unit selects a monitoring method that addresses a region-specific health problem based on the user's geographical location information. The monitoring unit can also provide guidance on how to use appropriate medical equipment depending on the user's current location. For example, the monitoring unit provides guidance on how to use appropriate medical equipment depending on the user's current location. This makes it possible to select an optimal monitoring method based on the user's geographical location information.

[0045] The monitoring unit can analyze the user's social media activities during monitoring and provide relevant health information. For example, the monitoring unit can analyze the user's social media activities during monitoring and provide relevant health information. For example, relevant advice can be provided based on health information shared by the user on social media. The monitoring unit can also analyze the user's social media activities and provide health information that may be of interest to the user. For example, the monitoring unit can analyze the user's social media activities and provide health information that may be of interest to the user. The monitoring unit can also provide relevant health information by referring to the activities of the user's friends on social media. For example, relevant health information can be provided by referring to the activities of the user's friends on social media. This makes it possible to provide health information based on the user's social media activities.

[0046] The monitoring unit can customize the monitoring method by reflecting the user's past feedback during monitoring. The monitoring unit, for example, customizes the monitoring method by reflecting the user's past feedback during monitoring. For example, if a user provides feedback on a monitoring method previously provided, the monitoring unit improves the monitoring method based on that feedback. The monitoring unit can also analyze the user's past feedback and provide a personalized monitoring method. For example, the monitoring unit can analyze the user's past feedback and provide a personalized monitoring method. The monitoring unit can also adjust the frequency and content of monitoring by reflecting the user's feedback. For example, the monitoring unit adjusts the frequency and content of monitoring by reflecting the user's feedback. This makes it possible to customize the monitoring method based on the user's past feedback.

[0047] The support unit can select the optimal support method by referring to the user's past health data when providing support. For example, the support unit can select the optimal support method by referring to the user's past health data when providing support. For example, an appropriate support method is selected based on the user's past health data. The support unit can also provide a support method for a specific health problem by referring to the user's past health data. For example, the support unit can provide a support method for a specific health problem by referring to the user's past health data. The support unit can also analyze the user's past health data and provide a personalized support method. For example, the support unit can analyze the user's past health data and provide a personalized support method. This makes it possible to select the optimal support method based on the user's past health data.

[0048] The support unit can customize the support content based on the user's current living situation when providing support. For example, the support unit customizes the support content based on the user's current living situation when providing support. For example, appropriate support content is provided based on the user's current living situation. The support unit can also customize support content for a specific health problem according to the user's current living situation. For example, support content for a specific health problem is customized according to the user's current living situation. The support unit can also provide personalized support content based on the user's lifestyle rhythm and daily activities. For example, personalized support content is provided based on the user's lifestyle rhythm and daily activities. This makes it possible to customize the support content according to the user's current living situation.

[0049] The support unit can improve the support method by reflecting user feedback when providing support. For example, when providing support, the support unit improves the support method by reflecting user feedback. For example, if a user provides feedback on a support method provided in the past, the support unit improves the support method based on that feedback. The support unit can also analyze the user's past feedback and provide a personalized support method. For example, the support unit can analyze the user's past feedback and provide a personalized support method. The support unit can also adjust the support content and format by reflecting user feedback. For example, the support content and format can be adjusted by reflecting user feedback. This makes it possible to improve the support method based on user feedback.

[0050] The support unit can select the optimal support method by taking into consideration the user's geographical location information when providing support. For example, the support unit selects the optimal support method by taking into consideration the user's geographical location information when providing support. For example, the optimal support method is selected based on the user's current location. The support unit can also select a support method that addresses health issues specific to a region based on the user's geographical location information. For example, the support unit selects a support method that addresses health issues specific to a region based on the user's geographical location information. The support unit can also introduce appropriate medical institutions and services based on the user's current location. For example, the support unit introduces appropriate medical institutions and services based on the user's current location. This makes it possible to select the optimal support method based on the user's geographical location information.

[0051] The support unit can analyze the user's social media activity and provide relevant support information when providing support. For example, the support unit can analyze the user's social media activity and provide relevant support information when providing support. For example, the support unit can provide relevant support information based on health information shared by the user on social media. The support unit can also analyze the user's social media activity and provide support information that may be of interest to the user. For example, the support unit can analyze the user's social media activity and provide support information that may be of interest to the user. The support unit can also provide relevant support information by referring to the activity of the user's friends on social media. For example, the support unit can provide relevant support information by referring to the activity of the user's friends on social media. This makes it possible to provide support information based on the user's social media activity.

[0052] The support unit can customize the support method by reflecting the user's past feedback when providing support. For example, the support unit customizes the support method by reflecting the user's past feedback when providing support. For example, if a user provides feedback on a support method provided in the past, the support unit improves the support method based on that feedback. The support unit can also analyze the user's past feedback and provide a personalized support method. For example, the support unit can analyze the user's past feedback and provide a personalized support method. The support unit can also adjust the support content and format by reflecting the user's feedback. For example, the support content and format are adjusted by reflecting the user's feedback. This makes it possible to customize the support method based on the user's past feedback.

[0053] When providing a recipe, the management unit can provide an optimal recipe by referring to the user's past meal history. When providing a recipe, the management unit can, for example, provide an optimal recipe by referring to the user's past meal history. For example, an appropriate recipe is provided based on the user's past meal history. The management unit can also provide a recipe that takes into consideration a specific nutritional balance by referring to the user's past meal history. For example, a recipe that takes into consideration a specific nutritional balance by referring to the user's past meal history. The management unit can also analyze the user's past meal history and provide a personalized recipe. For example, a personalized recipe is provided by analyzing the user's past meal history. This makes it possible to provide an optimal recipe based on the user's past meal history.

[0054] The management unit can customize a recipe based on the user's current health condition when providing the recipe. For example, the management unit customizes a recipe based on the user's current health condition when providing the recipe. For example, a recipe that takes into consideration an appropriate nutritional balance based on the user's current health condition is provided. The management unit can also provide recipes that address specific health issues based on the user's current health condition. For example, a recipe that addresses specific health issues based on the user's current health condition is provided. The management unit can also provide personalized recipes taking into consideration the user's current health condition. For example, a personalized recipe taking into consideration the user's current health condition is provided. This makes it possible to customize recipes according to the user's current health condition.

[0055] The management unit can adjust the content of a recipe according to the age and gender of the user when providing the recipe. For example, the management unit adjusts the content of a recipe according to the age and gender of the user when providing the recipe. For example, a recipe that takes into consideration an appropriate nutritional balance according to the user's age is provided. The management unit can also provide a recipe that emphasizes specific nutrients based on the user's gender. For example, a recipe that emphasizes specific nutrients is provided based on the user's gender. The management unit can also provide a personalized recipe taking into consideration the user's age and gender. For example, a personalized recipe taking into consideration the user's age and gender is provided. This makes it possible to provide recipes that are appropriate for the user's age and gender.

[0056] The management unit can provide highly relevant recipes by taking into account the user's geographical location information when providing a recipe. The management unit can provide highly relevant recipes by taking into account the user's geographical location information, for example, when providing a recipe. For example, the management unit can provide recipes that use ingredients specific to a region based on the user's current location. The management unit can also provide recipes that are suitable for the climate and season of a region based on the user's geographical location information. For example, the management unit can provide recipes that are suitable for the climate and season of a region based on the user's geographical location information. The management unit can also provide recipes based on the local food culture according to the user's current location. For example, the management unit can provide recipes based on the local food culture according to the user's current location. This makes it possible to provide recipes based on the user's geographical location information.

[0057] The management unit can analyze the user's social media activity when providing a recipe and provide related recipes. For example, the management unit can analyze the user's social media activity when providing a recipe and provide related recipes. For example, related recipes can be provided based on meal information shared by the user on social media. The management unit can also analyze the user's social media activity and provide recipes that are likely to interest the user. For example, the management unit can analyze the user's social media activity and provide recipes that are likely to interest the user. The management unit can also provide related recipes by taking into account the activity of the user's friends on social media. For example, related recipes can be provided by taking into account the activity of the user's friends on social media. This makes it possible to provide recipes based on the user's social media activity.

[0058] The management unit can customize the recipe provision method by reflecting the user's past feedback when providing a recipe. For example, when providing a recipe, the management unit customizes the provision method by reflecting the user's past feedback. For example, if a user provides feedback on a recipe provided in the past, the management unit improves the provision method based on that feedback. The management unit can also analyze the user's past feedback and provide a personalized recipe. For example, the management unit can analyze the user's past feedback and provide a personalized recipe. The management unit can also adjust the content and format of the recipe by reflecting the user's feedback. For example, the management unit adjusts the content and format of the recipe by reflecting the user's feedback. This makes it possible to customize the recipe provision method based on the user's past feedback.

[0059] The medical department can select the optimal medical method by referring to the user's past health data during medical treatment. The medical department, for example, selects the optimal medical method by referring to the user's past health data during medical treatment. For example, an appropriate medical method is selected based on the user's past health data. The medical department can also provide a medical method for a specific health problem by referring to the user's past health data. For example, a medical method for a specific health problem is provided by referring to the user's past health data. The medical department can also analyze the user's past health data and provide a personalized medical method. For example, a personalized medical method is provided by analyzing the user's past health data. This makes it possible to select the optimal medical method based on the user's past health data.

[0060] The medical department can customize medical treatment contents based on the user's current health condition during medical treatment. The medical department, for example, customizes medical treatment contents based on the user's current health condition during medical treatment. For example, appropriate medical treatment contents are provided based on the user's current health condition. The medical department can also provide medical treatment contents that address specific health problems according to the user's current health condition. For example, medical treatment contents that address specific health problems according to the user's current health condition. The medical department can also provide personalized medical treatment contents taking into account the user's current health condition. For example, personalized medical treatment contents are provided taking into account the user's current health condition. This makes it possible to customize medical treatment contents according to the user's current health condition.

[0061] The medical department can improve the medical treatment method by reflecting the user's feedback during medical treatment. The medical department, for example, improves the medical treatment method by reflecting the user's feedback during medical treatment. For example, if a user provides feedback on a medical treatment method provided in the past, the medical treatment method is improved based on that feedback. The medical department can also analyze the user's past feedback and provide a personalized medical treatment method. For example, the medical department can analyze the user's past feedback and provide a personalized medical treatment method. The medical department can also adjust the content and format of medical treatment by reflecting the user's feedback. For example, the medical treatment content and format are adjusted by reflecting the user's feedback. This makes it possible to improve the medical treatment method based on the user's feedback.

[0062] The medical department can select the optimal medical method by taking into account the user's geographical location information during medical treatment. For example, the medical department selects the optimal medical method by taking into account the user's geographical location information during medical treatment. For example, the optimal medical method is selected based on the user's current location. The medical department can also select a medical method that addresses health issues specific to the region based on the user's geographical location information. For example, the medical department can select a medical method that addresses health issues specific to the region based on the user's geographical location information. The medical department can also introduce appropriate medical institutions and services based on the user's current location. For example, the medical department can introduce appropriate medical institutions and services based on the user's current location. This makes it possible to select the optimal medical method based on the user's geographical location information.

[0063] The medical department can analyze the user's social media activity during medical treatment and provide relevant medical information. The medical department, for example, analyzes the user's social media activity during medical treatment and provides relevant medical information. For example, relevant medical information is provided based on health information shared by the user on social media. The medical department can also analyze the user's social media activity and provide medical information that may be of interest to the user. For example, it analyzes the user's social media activity and provides medical information that may be of interest to the user. The medical department can also provide relevant medical information by referring to the activity of the user's friends on social media. For example, it provides relevant medical information by referring to the activity of the user's friends on social media. This makes it possible to provide medical information based on the user's social media activity.

[0064] The medical department can customize the medical treatment method by reflecting the user's past feedback during medical treatment. The medical department, for example, customizes the medical treatment method by reflecting the user's past feedback during medical treatment. For example, if the user provides feedback on a medical treatment method provided in the past, the medical treatment method is improved based on that feedback. The medical department can also analyze the user's past feedback and provide a personalized medical treatment method. For example, the medical department can analyze the user's past feedback and provide a personalized medical treatment method. The medical department can also adjust the content and format of the medical treatment by reflecting the user's feedback. For example, the medical treatment content and format are adjusted by reflecting the user's feedback. This makes it possible to customize the medical treatment method based on the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The providing unit can analyze the user's past question history and generate an optimal answer. For example, it can provide new related information based on the content of questions the user has asked in the past. The providing unit can also prioritize displaying answers to frequently asked questions from the user's past question history. For example, it can analyze the user's past question history and prioritize displaying answers to frequently asked questions. The providing unit can also analyze the user's past question history and provide personalized health advice. For example, it can analyze the user's past question history and provide personalized health advice. In this way, the user's questions can be efficiently resolved by providing optimal answers based on the user's past question history.

[0067] During monitoring, the monitoring unit can refer to the user's past health data to predict abnormalities. For example, it refers to the user's past heart rate data to predict abnormalities. The monitoring unit can also refer to the user's past blood pressure data to predict abnormalities. For example, it refers to the user's past blood pressure data to predict abnormalities. The monitoring unit can also refer to the user's past blood glucose level data to predict abnormalities. For example, it refers to the user's past blood glucose level data to predict abnormalities. This makes it possible to predict abnormalities based on past health data.

[0068] When providing support, the support unit can select the optimal support method by referring to the user's past health data. For example, an appropriate support method is selected based on the user's past health data. The support unit can also refer to the user's past health data to provide a support method for a specific health problem. For example, the support unit can refer to the user's past health data to provide a support method for a specific health problem. The support unit can also analyze the user's past health data to provide a personalized support method. For example, the support unit can analyze the user's past health data to provide a personalized support method. This makes it possible to select the optimal support method based on the user's past health data.

[0069] When providing a recipe, the management unit can provide an optimal recipe by referring to the user's past meal history. For example, an appropriate recipe is provided based on the user's past meal history. The management unit can also provide a recipe that takes into consideration a specific nutritional balance by referring to the user's past meal history. For example, a recipe that takes into consideration a specific nutritional balance by referring to the user's past meal history. The management unit can also analyze the user's past meal history and provide a personalized recipe. For example, a personalized recipe is provided by analyzing the user's past meal history. This makes it possible to provide an optimal recipe based on the user's past meal history.

[0070] During medical treatment, the medical department can select the optimal medical treatment method by referring to the user's past health data. For example, an appropriate medical treatment method is selected based on the user's past health data. The medical department can also refer to the user's past health data to provide a medical treatment method for a specific health problem. For example, the medical department can refer to the user's past health data to provide a medical treatment method for a specific health problem. The medical department can also analyze the user's past health data to provide a personalized medical treatment method. For example, the medical department can analyze the user's past health data to provide a personalized medical treatment method. This makes it possible to select the optimal medical treatment method based on the user's past health data.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The provider provides health consultations or health-related information. The provider generates and provides appropriate answers to questions from users. The generator AI can also be used to provide general health information. For example, in response to a question about cold symptoms, the generator AI suggests appropriate measures to take. Step 2: The monitoring unit monitors the medical device. The monitoring unit collects and analyzes data such as heart rate and blood pressure in real time. It can also use generative AI to analyze the collected data and detect abnormalities. For example, it can analyze heart rate and blood pressure data in real time to detect abnormalities. Step 3: The support department provides support for home medical care. The support department collects data such as the patient's temperature and blood pressure, detects abnormalities, and notifies medical staff. Generative AI can also be used to analyze the collected data and detect abnormalities. For example, data on the patient's temperature and blood pressure can be collected and analyzed by generative AI to detect abnormalities and notify medical staff. Step 4: The management unit manages recipes. When a patient requests a recipe by phone or text message, the management unit provides the appropriate recipe. The management unit can also use generative AI to provide recipes based on the patient's request. For example, when a patient requests a recipe by phone or text message, the generative AI provides the appropriate recipe. Step 5: The medical department conducts online consultations. Even if the patient is unable to go to the hospital, the medical department can provide diagnosis and treatment through communication with the doctor, and arrange for the patient to receive medication and make an appointment at the hospital. Diagnosis and treatment can also be performed using generative AI. For example, even if the patient is unable to go to the hospital, they can easily receive diagnosis and treatment through communication with the doctor using generative AI.

[0073] (Example 2) A medical support system according to an embodiment of the present invention uses generative AI to provide a comprehensive range of services, including health consultations, medical device monitoring, home medical support, recipe management, and online consultations. The system uses generative AI to provide a health chatbot that provides health consultations and general health information. This reduces waiting times at hospitals and makes it easier to collect necessary information. Next, generative AI is used to monitor medical devices and collect medical information. This enables patients with serious illnesses and elderly care to make calm decisions by educating them on the proper use of installed medical devices. Furthermore, generative AI is used to collect information about patients during home medical care, allowing medical staff to contact them directly as needed. This enhances support for home medical care. Generative AI can also be used for recipe management. Recipes can be provided to patients via phone or text message, allowing patients to manage their own recipes from the comfort of their own home without having to call medical staff. Finally, generative AI can be used for online consultations. Even if patients are unable to visit a hospital, they can easily receive diagnosis and treatment through communication with a doctor. After the consultation, the generative AI can provide various services, such as arranging for medication pickup and, if necessary, arranging a hospital appointment. This allows the medical support system to consistently handle health consultations, medical equipment monitoring, home medical support, recipe management, and online consultations. For example, it can shorten waiting times at crowded hospitals and make it easier to collect necessary information. It can also help patients with serious illnesses or elderly care by teaching them how to properly use installed medical equipment, allowing them to make calm decisions. Furthermore, it strengthens support for home medical care, allowing patients to manage their own recipes from the comfort of their own home without having to call medical staff. This allows patients to receive appropriate diagnosis and treatment even if they are unable to go to the hospital.

[0074] A medical support system according to an embodiment includes a providing unit, a monitoring unit, a support unit, a management unit, and a medical examination unit. The providing unit provides health consultations or health-related information. For example, the providing unit generates and provides appropriate answers to questions from users. The providing unit can also provide general health information using a generating AI. For example, the generating AI can suggest appropriate measures to take in response to questions about cold symptoms. The monitoring unit monitors medical equipment. The monitoring unit collects and analyzes data such as heart rate and blood pressure in real time. The monitoring unit can also analyze the collected data and detect abnormalities using the generating AI. For example, the monitoring unit analyzes heart rate and blood pressure data in real time to detect abnormalities. The support unit supports home medical care. The support unit collects data such as a patient's body temperature and blood pressure, detects abnormalities, and notifies medical staff. The support unit can also analyze the collected data and detect abnormalities using the generating AI. For example, the monitoring unit collects data such as a patient's body temperature and blood pressure, and the generating AI analyzes the data to detect abnormalities and notify medical staff. The management unit manages recipes. For example, when a patient requests a recipe by phone or text message, the management unit provides an appropriate recipe. The management unit can also use a generating AI to provide recipes according to the patient's request. For example, when a patient requests a recipe by phone or text message, the generating AI provides an appropriate recipe. The medical department performs online medical consultations. For example, even if a patient is unable to visit a hospital, the medical department can provide diagnosis and treatment through communication with a doctor, arrange for medication pickup, and arrange for hospital appointments. The medical department can also use a generating AI to perform diagnosis and treatment. For example, even if a patient is unable to visit a hospital, the generating AI can allow the patient to easily receive diagnosis and treatment through communication with a doctor. As a result, the medical support system according to the embodiment can consistently provide health consultations, medical device monitoring, home medical support, recipe management, and online medical consultations.

[0075] The providing unit can generate and provide appropriate answers to questions from users. The providing unit generates and provides appropriate answers to questions from users, for example. For example, the generation AI can suggest appropriate countermeasures for questions about cold symptoms. The providing unit can also use the generation AI to generate appropriate answers to user questions. For example, the generation AI receives a prompt such as "Please tell me how to deal with cold symptoms" and suggests appropriate countermeasures. The providing unit can also use the generation AI to provide detailed information to user questions. For example, the generation AI receives a prompt such as "Please tell me detailed countermeasures for cold symptoms" and suggests detailed countermeasures. In this way, the user's doubts can be resolved by providing an appropriate answer to the user's question.

[0076] The monitoring unit can collect and analyze heart rate or blood pressure data in real time. The monitoring unit collects and analyzes, for example, data such as heart rate and blood pressure in real time. For example, heart rate and blood pressure data can be collected in real time and analyzed by a generation AI to detect abnormalities. The monitoring unit can also use a generation AI to analyze the collected data and detect abnormalities. For example, heart rate and blood pressure data can be analyzed in real time to detect abnormalities. The monitoring unit can also use a generation AI to detect abnormalities based on the collected data. For example, heart rate and blood pressure data can be collected and analyzed by a generation AI to detect abnormalities. In this way, by collecting and analyzing data in real time, abnormalities can be detected early.

[0077] The support unit can collect data on a patient's body temperature or blood pressure, detect abnormalities, and notify medical staff. The support unit can collect data such as a patient's body temperature and blood pressure, detect abnormalities, and notify medical staff. For example, the support unit can collect data on a patient's body temperature and blood pressure, analyze it using a generating AI, detect abnormalities, and notify medical staff. The support unit can also use a generating AI to analyze the collected data and detect abnormalities. For example, the support unit can collect data on a patient's body temperature and blood pressure, analyze it using a generating AI, detect abnormalities, and notify medical staff. The support unit can also use a generating AI to detect abnormalities based on the collected data. For example, the support unit can collect data on a patient's body temperature and blood pressure, analyze it using a generating AI, detect abnormalities, and notify medical staff. This allows for a rapid response by detecting abnormalities and notifying medical staff.

[0078] The management unit can provide an appropriate recipe when a patient requests a recipe by phone or text message. For example, when a patient requests a recipe by phone or text message, the management unit can provide an appropriate recipe. For example, when a patient requests a recipe by phone or text message, the generation AI can provide an appropriate recipe. The management unit can also use the generation AI to provide a recipe in accordance with the patient's request. For example, when a patient requests a recipe by phone or text message, the generation AI can provide an appropriate recipe. The management unit can also use the generation AI to provide a detailed recipe in accordance with the patient's request. For example, when a patient requests a detailed recipe by phone or text message, the generation AI can provide a detailed recipe. This allows patients to obtain appropriate recipes from the comfort of their own homes.

[0079] The medical department can provide diagnosis and treatment through communication with doctors, even if the patient is unable to go to the hospital, and can arrange for the patient to receive medication and make a hospital appointment. For example, even if the patient is unable to go to the hospital, the medical department can provide diagnosis and treatment through communication with doctors, and can arrange for the patient to receive medication and make a hospital appointment. For example, even if the patient is unable to go to the hospital, the medical department can provide diagnosis and treatment through communication with doctors using generative AI, and can easily receive diagnosis and treatment. The medical department can also use generative AI to provide diagnosis and treatment. For example, even if the patient is unable to go to the hospital, the medical department can provide diagnosis and treatment through communication with doctors using generative AI, and can easily receive diagnosis and treatment. The medical department can also use generative AI to provide diagnosis and treatment. For example, after the examination, the generative AI can arrange for the patient to receive medication and make a hospital appointment if necessary. This allows the patient to receive appropriate diagnosis and treatment even if they are unable to go to the hospital.

[0080] The providing unit can estimate the user's emotions and adjust the method of providing health information based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the method of providing health information based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided to facilitate information acquisition. Furthermore, the providing unit can provide detailed information or additional health advice when the user is relaxed. For example, detailed information or additional health advice can be provided when the user is relaxed. Furthermore, the providing unit can provide concise information that focuses on the main points when the user is in a hurry. For example, concise information that focuses on the main points can be provided when the user is in a hurry. This allows for more appropriate information provision by adjusting the method of providing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The providing unit can analyze the user's past question history and generate an optimal answer. The providing unit, for example, analyzes the user's past question history and generates an optimal answer. For example, the providing unit provides related new information based on the content of questions the user has previously asked. The providing unit can also preferentially display answers to frequently asked questions from the user's past question history. For example, the providing unit can analyze the user's past question history and preferentially display answers to frequently asked questions. The providing unit can also analyze the user's past question history and provide personalized health advice. For example, the providing unit can analyze the user's past question history and provide personalized health advice. In this way, the user's questions can be efficiently resolved by providing an optimal answer based on the user's past question history.

[0082] The providing unit can customize the health information based on the user's current health condition when providing the health information. For example, the providing unit customizes the information based on the user's current health condition when providing the health information. For example, appropriate exercise and diet advice is provided based on the user's current health condition. The providing unit can also provide information on a specific disease preferentially depending on the user's current health condition. For example, information on a specific disease is provided preferentially depending on the user's current health condition. The providing unit can also provide information on preventive measures and treatments taking into account the user's current health condition. For example, information on preventive measures and treatments is provided taking into account the user's current health condition. This makes it possible to provide information based on the user's current health condition.

[0083] The providing unit can adjust the content of the health information according to the age and gender of the user when providing the health information. For example, the providing unit adjusts the content of the information according to the age and gender of the user when providing the health information. For example, appropriate health advice and preventive measures are provided according to the user's age. The providing unit can also provide information on a specific health problem based on the user's gender. For example, information on a specific health problem is provided based on the user's gender. The providing unit can also provide personalized health information taking into account the user's age and gender. For example, personalized health information is provided taking into account the user's age and gender. This makes it possible to provide information according to the user's age and gender.

[0084] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of information to be provided based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can prioritize providing information that gives a sense of security. Furthermore, if the user is excited, the providing unit can prioritize providing information to help the user regain their composure. For example, if the user is excited, the providing unit can prioritize providing information to help the user regain their composure. Furthermore, if the user is relaxed, the providing unit can provide detailed information or additional advice. For example, if the user is relaxed, the providing unit can provide detailed information or additional advice. This enables more appropriate information to be provided by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The providing unit can provide highly relevant information by taking into account the user's geographical location information when providing health information. For example, the providing unit can provide highly relevant information by taking into account the user's geographical location information when providing health information. For example, the providing unit can provide information on nearby medical institutions and pharmacies based on the user's current location. The providing unit can also provide information on region-specific health issues based on the user's geographical location information. For example, the providing unit can provide information on region-specific health issues based on the user's geographical location information. The providing unit can also provide appropriate preventive measures and health advice according to the user's current location. For example, the providing unit can provide appropriate preventive measures and health advice according to the user's current location. This makes it possible to provide information based on the user's geographical location information.

[0086] The providing unit can analyze the user's social media activity and provide related information when providing health information. For example, the providing unit can analyze the user's social media activity and provide related information when providing health information. For example, related advice can be provided based on health information shared by the user on social media. The providing unit can also analyze the user's social media activity and provide health information that is likely to be of interest to the user. For example, the providing unit can analyze the user's social media activity and provide health information that is likely to be of interest to the user. The providing unit can also provide related health information by referring to the activity of the user's friends on social media. For example, related health information can be provided by referring to the activity of the user's friends on social media. This makes it possible to provide information based on the user's social media activity.

[0087] The providing unit can customize the method of providing health information by reflecting the user's past feedback when providing the health information. For example, the providing unit customizes the method of providing health information by reflecting the user's past feedback when providing the health information. For example, if a user provides feedback on information previously provided, the providing unit improves the method of providing the health information based on that feedback. The providing unit can also analyze the user's past feedback and provide personalized information. For example, the providing unit can analyze the user's past feedback and provide personalized information. The providing unit can also adjust the content or format of the information by reflecting the user's feedback. For example, the content or format of the information by reflecting the user's feedback. This makes it possible to provide information based on the user's past feedback.

[0088] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, the monitoring unit estimates the user's emotions and adjusts the monitoring frequency based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring frequency is increased to monitor the health condition in detail. The monitoring unit can also reduce the monitoring frequency to reduce the burden on the user when the user is relaxed. For example, if the user is relaxed, the monitoring frequency is reduced to reduce the burden. The monitoring unit can also adjust the monitoring frequency to provide a sense of security when the user is feeling anxious. For example, if the user is feeling anxious, the monitoring frequency is adjusted to provide a sense of security. This makes it possible to adjust the monitoring frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The monitoring unit can analyze medical device data in real time during monitoring and detect abnormalities. The monitoring unit, for example, analyzes medical device data in real time during monitoring and detects abnormalities. For example, it analyzes heart rate and blood pressure data in real time and detects abnormalities. The monitoring unit can also use a generation AI to analyze collected data in real time and detect abnormalities. For example, it analyzes heart rate and blood pressure data in real time and detects abnormalities. The monitoring unit can also use a generation AI to detect abnormalities based on collected data. For example, heart rate and blood pressure data is collected and analyzed by a generation AI to detect abnormalities. This allows data to be analyzed in real time and abnormalities to be detected early.

[0090] The monitoring unit can predict abnormalities by referring to the user's past health data during monitoring. The monitoring unit, for example, can predict abnormalities by referring to the user's past health data during monitoring. For example, it can predict abnormalities by referring to the user's past heart rate data. The monitoring unit can also predict abnormalities by referring to the user's past blood pressure data. For example, it can predict abnormalities by referring to the user's past blood pressure data. The monitoring unit can also predict abnormalities by referring to the user's past blood glucose level data. For example, it can predict abnormalities by referring to the user's past blood glucose level data. This makes it possible to predict abnormalities based on past health data.

[0091] The monitoring unit can provide appropriate guidance to the user on how to use the medical device during monitoring. The monitoring unit, for example, provides appropriate guidance to the user on how to use the medical device during monitoring. For example, the monitoring unit provides appropriate guidance to the user by explaining how to use the medical device using a video. The monitoring unit can also provide appropriate guidance to the user by explaining how to use the medical device using text. For example, the monitoring unit can provide appropriate guidance to the user by explaining how to use the medical device using text. The monitoring unit can also provide appropriate guidance to the user by explaining how to use the medical device using audio. For example, the monitoring unit can provide appropriate guidance to the user by explaining how to use the medical device using audio. This allows the user to deepen their understanding by providing guidance on how to properly use the medical device.

[0092] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the display method of the monitoring results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method is provided. Furthermore, if the user is relaxed, the monitoring unit can also provide a display method including detailed information. For example, if the user is relaxed, a display method including detailed information is provided. Furthermore, if the user is in a hurry, the monitoring unit can also provide a display method that focuses on the main points. For example, if the user is in a hurry, a display method that focuses on the main points is provided. This makes it possible to adjust the display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The monitoring unit can select an optimal monitoring method during monitoring by taking into consideration the geographical location information of the user. For example, the monitoring unit selects an optimal monitoring method by taking into consideration the geographical location information of the user during monitoring. For example, the monitoring unit selects an optimal monitoring method based on the user's current location. The monitoring unit can also select a monitoring method that addresses a region-specific health problem based on the user's geographical location information. For example, the monitoring unit selects a monitoring method that addresses a region-specific health problem based on the user's geographical location information. The monitoring unit can also provide guidance on how to use appropriate medical equipment depending on the user's current location. For example, the monitoring unit provides guidance on how to use appropriate medical equipment depending on the user's current location. This makes it possible to select an optimal monitoring method based on the user's geographical location information.

[0094] The monitoring unit can analyze the user's social media activities during monitoring and provide relevant health information. For example, the monitoring unit can analyze the user's social media activities during monitoring and provide relevant health information. For example, relevant advice can be provided based on health information shared by the user on social media. The monitoring unit can also analyze the user's social media activities and provide health information that may be of interest to the user. For example, the monitoring unit can analyze the user's social media activities and provide health information that may be of interest to the user. The monitoring unit can also provide relevant health information by referring to the activities of the user's friends on social media. For example, relevant health information can be provided by referring to the activities of the user's friends on social media. This makes it possible to provide health information based on the user's social media activities.

[0095] The monitoring unit can customize the monitoring method by reflecting the user's past feedback during monitoring. The monitoring unit, for example, customizes the monitoring method by reflecting the user's past feedback during monitoring. For example, if a user provides feedback on a monitoring method previously provided, the monitoring unit improves the monitoring method based on that feedback. The monitoring unit can also analyze the user's past feedback and provide a personalized monitoring method. For example, the monitoring unit can analyze the user's past feedback and provide a personalized monitoring method. The monitoring unit can also adjust the frequency and content of monitoring by reflecting the user's feedback. For example, the monitoring unit adjusts the frequency and content of monitoring by reflecting the user's feedback. This makes it possible to customize the monitoring method based on the user's past feedback.

[0096] The support unit can estimate the user's emotions and adjust the support method based on the estimated user emotions. For example, the support unit estimates the user's emotions and adjusts the support method based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive support method is provided. Furthermore, if the user is relaxed, the support unit can provide detailed support information or additional advice. For example, if the user is relaxed, detailed support information or additional advice is provided. Furthermore, if the user is in a hurry, the support unit can provide a concise support method that focuses on the main points. For example, if the user is in a hurry, a concise support method that focuses on the main points is provided. This makes it possible to adjust the support method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The support unit can select the optimal support method by referring to the user's past health data when providing support. For example, the support unit can select the optimal support method by referring to the user's past health data when providing support. For example, an appropriate support method is selected based on the user's past health data. The support unit can also provide a support method for a specific health problem by referring to the user's past health data. For example, the support unit can provide a support method for a specific health problem by referring to the user's past health data. The support unit can also analyze the user's past health data and provide a personalized support method. For example, the support unit can analyze the user's past health data and provide a personalized support method. This makes it possible to select the optimal support method based on the user's past health data.

[0098] The support unit can customize the support content based on the user's current living situation when providing support. For example, the support unit customizes the support content based on the user's current living situation when providing support. For example, appropriate support content is provided based on the user's current living situation. The support unit can also customize support content for a specific health problem according to the user's current living situation. For example, support content for a specific health problem is customized according to the user's current living situation. The support unit can also provide personalized support content based on the user's lifestyle rhythm and daily activities. For example, personalized support content is provided based on the user's lifestyle rhythm and daily activities. This makes it possible to customize the support content according to the user's current living situation.

[0099] The support unit can improve the support method by reflecting user feedback when providing support. For example, when providing support, the support unit improves the support method by reflecting user feedback. For example, if a user provides feedback on a support method provided in the past, the support unit improves the support method based on that feedback. The support unit can also analyze the user's past feedback and provide a personalized support method. For example, the support unit can analyze the user's past feedback and provide a personalized support method. The support unit can also adjust the support content and format by reflecting user feedback. For example, the support content and format can be adjusted by reflecting user feedback. This makes it possible to improve the support method based on user feedback.

[0100] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. The support unit, for example, estimates the user's emotions and determines the priority of support based on the estimated user emotions. For example, if the user is feeling anxious, the support unit can prioritize providing support that gives a sense of security. Furthermore, if the user is excited, the support unit can prioritize providing support to help the user regain their composure. For example, if the user is excited, the support unit can prioritize providing support to help the user regain their composure. Furthermore, if the user is relaxed, the support unit can provide detailed support information or additional advice. For example, if the user is relaxed, the support unit provides detailed support information or additional advice. This makes it possible to determine the priority of support based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The support unit can select the optimal support method by taking into consideration the user's geographical location information when providing support. For example, the support unit selects the optimal support method by taking into consideration the user's geographical location information when providing support. For example, the optimal support method is selected based on the user's current location. The support unit can also select a support method that addresses health issues specific to a region based on the user's geographical location information. For example, the support unit selects a support method that addresses health issues specific to a region based on the user's geographical location information. The support unit can also introduce appropriate medical institutions and services based on the user's current location. For example, the support unit introduces appropriate medical institutions and services based on the user's current location. This makes it possible to select the optimal support method based on the user's geographical location information.

[0102] The support unit can analyze the user's social media activity and provide relevant support information when providing support. For example, the support unit can analyze the user's social media activity and provide relevant support information when providing support. For example, the support unit can provide relevant support information based on health information shared by the user on social media. The support unit can also analyze the user's social media activity and provide support information that may be of interest to the user. For example, the support unit can analyze the user's social media activity and provide support information that may be of interest to the user. The support unit can also provide relevant support information by referring to the activity of the user's friends on social media. For example, the support unit can provide relevant support information by referring to the activity of the user's friends on social media. This makes it possible to provide support information based on the user's social media activity.

[0103] The support unit can customize the support method by reflecting the user's past feedback when providing support. For example, the support unit customizes the support method by reflecting the user's past feedback when providing support. For example, if a user provides feedback on a support method provided in the past, the support unit improves the support method based on that feedback. The support unit can also analyze the user's past feedback and provide a personalized support method. For example, the support unit can analyze the user's past feedback and provide a personalized support method. The support unit can also adjust the support content and format by reflecting the user's feedback. For example, the support content and format are adjusted by reflecting the user's feedback. This makes it possible to customize the support method based on the user's past feedback.

[0104] The management unit can estimate the user's emotions and adjust the recipe provision method based on the estimated user emotions. For example, the management unit estimates the user's emotions and adjusts the recipe provision method based on the estimated user emotions. For example, if the user is feeling stressed, the management unit can provide a simple and intuitive recipe. Furthermore, if the user is relaxed, the management unit can provide a detailed recipe or additional cooking advice. For example, if the user is relaxed, the management unit can provide a detailed recipe or additional cooking advice. Furthermore, if the user is in a hurry, the management unit can provide a concise recipe that focuses on the main points. For example, if the user is in a hurry, the management unit can provide a concise recipe that focuses on the main points. This makes it possible to adjust the recipe provision method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] When providing a recipe, the management unit can provide an optimal recipe by referring to the user's past meal history. When providing a recipe, the management unit can, for example, provide an optimal recipe by referring to the user's past meal history. For example, an appropriate recipe is provided based on the user's past meal history. The management unit can also provide a recipe that takes into consideration a specific nutritional balance by referring to the user's past meal history. For example, a recipe that takes into consideration a specific nutritional balance by referring to the user's past meal history. The management unit can also analyze the user's past meal history and provide a personalized recipe. For example, a personalized recipe is provided by analyzing the user's past meal history. This makes it possible to provide an optimal recipe based on the user's past meal history.

[0106] The management unit can customize a recipe based on the user's current health condition when providing the recipe. For example, the management unit customizes a recipe based on the user's current health condition when providing the recipe. For example, a recipe that takes into consideration an appropriate nutritional balance based on the user's current health condition is provided. The management unit can also provide recipes that address specific health issues based on the user's current health condition. For example, a recipe that addresses specific health issues based on the user's current health condition is provided. The management unit can also provide personalized recipes taking into consideration the user's current health condition. For example, a personalized recipe taking into consideration the user's current health condition is provided. This makes it possible to customize recipes according to the user's current health condition.

[0107] The management unit can adjust the content of a recipe according to the age and gender of the user when providing the recipe. For example, the management unit adjusts the content of a recipe according to the age and gender of the user when providing the recipe. For example, a recipe that takes into consideration an appropriate nutritional balance according to the user's age is provided. The management unit can also provide a recipe that emphasizes specific nutrients based on the user's gender. For example, a recipe that emphasizes specific nutrients is provided based on the user's gender. The management unit can also provide a personalized recipe taking into consideration the user's age and gender. For example, a personalized recipe taking into consideration the user's age and gender is provided. This makes it possible to provide recipes that are appropriate for the user's age and gender.

[0108] The management unit can estimate the user's emotions and determine the priority of recipes to be provided based on the estimated user emotions. The management unit, for example, estimates the user's emotions and determines the priority of recipes to be provided based on the estimated user emotions. For example, if the user is feeling anxious, recipes that provide a sense of security can be provided preferentially. Furthermore, if the user is excited, the management unit can also provide recipes to help the user regain their composure preferentially. For example, if the user is excited, the management unit can provide recipes to help the user regain their composure preferentially. Furthermore, if the user is relaxed, the management unit can provide detailed recipes or additional cooking advice. For example, if the user is relaxed, the management unit can provide detailed recipes or additional cooking advice. This makes it possible to determine the priority of recipes according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The management unit can provide highly relevant recipes by taking into account the user's geographical location information when providing a recipe. The management unit can provide highly relevant recipes by taking into account the user's geographical location information, for example, when providing a recipe. For example, the management unit can provide recipes that use ingredients specific to a region based on the user's current location. The management unit can also provide recipes that are suitable for the climate and season of a region based on the user's geographical location information. For example, the management unit can provide recipes that are suitable for the climate and season of a region based on the user's geographical location information. The management unit can also provide recipes based on the local food culture according to the user's current location. For example, the management unit can provide recipes based on the local food culture according to the user's current location. This makes it possible to provide recipes based on the user's geographical location information.

[0110] The management unit can analyze the user's social media activity when providing a recipe and provide related recipes. For example, the management unit can analyze the user's social media activity when providing a recipe and provide related recipes. For example, related recipes can be provided based on meal information shared by the user on social media. The management unit can also analyze the user's social media activity and provide recipes that are likely to interest the user. For example, the management unit can analyze the user's social media activity and provide recipes that are likely to interest the user. The management unit can also provide related recipes by taking into account the activity of the user's friends on social media. For example, related recipes can be provided by taking into account the activity of the user's friends on social media. This makes it possible to provide recipes based on the user's social media activity.

[0111] The management unit can customize the recipe provision method by reflecting the user's past feedback when providing a recipe. For example, when providing a recipe, the management unit customizes the provision method by reflecting the user's past feedback. For example, if a user provides feedback on a recipe provided in the past, the management unit improves the provision method based on that feedback. The management unit can also analyze the user's past feedback and provide a personalized recipe. For example, the management unit can analyze the user's past feedback and provide a personalized recipe. The management unit can also adjust the content and format of the recipe by reflecting the user's feedback. For example, the management unit adjusts the content and format of the recipe by reflecting the user's feedback. This makes it possible to customize the recipe provision method based on the user's past feedback.

[0112] The medical treatment unit can estimate the user's emotions and adjust the medical treatment method based on the estimated user emotions. For example, the medical treatment unit estimates the user's emotions and adjusts the medical treatment method based on the estimated user emotions. For example, if the user is feeling stressed, the medical treatment unit provides a simple and intuitive medical treatment method. Furthermore, if the user is relaxed, the medical treatment unit can provide detailed medical information and additional advice. For example, if the user is relaxed, the medical treatment unit can provide detailed medical information and additional advice. Furthermore, if the user is in a hurry, the medical treatment unit can provide a concise medical treatment method that focuses on the main points. For example, if the user is in a hurry, the medical treatment unit provides a concise medical treatment method that focuses on the main points. This makes it possible to adjust the medical treatment method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0113] The medical department can select the optimal medical method by referring to the user's past health data during medical treatment. The medical department, for example, selects the optimal medical method by referring to the user's past health data during medical treatment. For example, an appropriate medical method is selected based on the user's past health data. The medical department can also provide a medical method for a specific health problem by referring to the user's past health data. For example, a medical method for a specific health problem is provided by referring to the user's past health data. The medical department can also analyze the user's past health data and provide a personalized medical method. For example, a personalized medical method is provided by analyzing the user's past health data. This makes it possible to select the optimal medical method based on the user's past health data.

[0114] The medical department can customize medical treatment contents based on the user's current health condition during medical treatment. The medical department, for example, customizes medical treatment contents based on the user's current health condition during medical treatment. For example, appropriate medical treatment contents are provided based on the user's current health condition. The medical department can also provide medical treatment contents that address specific health problems according to the user's current health condition. For example, medical treatment contents that address specific health problems according to the user's current health condition. The medical department can also provide personalized medical treatment contents taking into account the user's current health condition. For example, personalized medical treatment contents are provided taking into account the user's current health condition. This makes it possible to customize medical treatment contents according to the user's current health condition.

[0115] The medical department can improve the medical treatment method by reflecting the user's feedback during medical treatment. The medical department, for example, improves the medical treatment method by reflecting the user's feedback during medical treatment. For example, if a user provides feedback on a medical treatment method provided in the past, the medical treatment method is improved based on that feedback. The medical department can also analyze the user's past feedback and provide a personalized medical treatment method. For example, the medical department can analyze the user's past feedback and provide a personalized medical treatment method. The medical department can also adjust the content and format of medical treatment by reflecting the user's feedback. For example, the medical treatment content and format are adjusted by reflecting the user's feedback. This makes it possible to improve the medical treatment method based on the user's feedback.

[0116] The medical unit can estimate the user's emotions and determine the priority of medical treatment based on the estimated user emotions. For example, the medical unit estimates the user's emotions and determines the priority of medical treatment based on the estimated user emotions. For example, if the user is feeling anxious, the medical unit can prioritize medical treatment that provides a sense of security. Furthermore, if the user is excited, the medical unit can prioritize medical treatment to help the user regain their composure. For example, if the user is excited, the medical unit can prioritize medical treatment to help the user regain their composure. Furthermore, if the user is relaxed, the medical unit can provide detailed medical information and additional advice. For example, if the user is relaxed, the medical unit provides detailed medical information and additional advice. This makes it possible to determine the priority of medical treatment based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The medical department can select the optimal medical method by taking into account the user's geographical location information during medical treatment. For example, the medical department selects the optimal medical method by taking into account the user's geographical location information during medical treatment. For example, the optimal medical method is selected based on the user's current location. The medical department can also select a medical method that addresses health issues specific to the region based on the user's geographical location information. For example, the medical department can select a medical method that addresses health issues specific to the region based on the user's geographical location information. The medical department can also introduce appropriate medical institutions and services based on the user's current location. For example, the medical department can introduce appropriate medical institutions and services based on the user's current location. This makes it possible to select the optimal medical method based on the user's geographical location information.

[0118] The medical department can analyze the user's social media activity during medical treatment and provide relevant medical information. The medical department, for example, analyzes the user's social media activity during medical treatment and provides relevant medical information. For example, relevant medical information is provided based on health information shared by the user on social media. The medical department can also analyze the user's social media activity and provide medical information that may be of interest to the user. For example, it analyzes the user's social media activity and provides medical information that may be of interest to the user. The medical department can also provide relevant medical information by referring to the activity of the user's friends on social media. For example, it provides relevant medical information by referring to the activity of the user's friends on social media. This makes it possible to provide medical information based on the user's social media activity.

[0119] The medical department can customize the medical treatment method by reflecting the user's past feedback during medical treatment. The medical department, for example, customizes the medical treatment method by reflecting the user's past feedback during medical treatment. For example, if the user provides feedback on a medical treatment method provided in the past, the medical treatment method is improved based on that feedback. The medical department can also analyze the user's past feedback and provide a personalized medical treatment method. For example, the medical department can analyze the user's past feedback and provide a personalized medical treatment method. The medical department can also adjust the content and format of the medical treatment by reflecting the user's feedback. For example, the medical treatment content and format are adjusted by reflecting the user's feedback. This makes it possible to customize the medical treatment method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the providing unit, monitoring unit, support unit, management unit, and medical treatment unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart device 14 and generates and provides appropriate answers to questions from the user. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects and analyzes data such as heart rate and blood pressure in real time. The support unit is realized, for example, by the control unit 46A of the smart device 14 and collects data such as the patient's body temperature and blood pressure, detects abnormalities, and notifies medical staff. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate recipes when the patient requests a recipe by phone or text message. The medical treatment unit is realized, for example, by the control unit 46A of the smart device 14 and communicates with doctors to diagnose and treat patients even when they are unable to visit a hospital, and arranges for them to receive medication and make hospital appointments. === Hard Collateral 1-2 === Each of the multiple elements, including the providing unit, monitoring unit, support unit, management unit, and medical treatment unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the smart glasses 214 and generates and provides appropriate answers to questions from the user. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects and analyzes data such as heart rate and blood pressure in real time. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 and collects data such as the patient's temperature and blood pressure, detects abnormalities, and notifies medical staff. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides an appropriate recipe when the patient requests a recipe by phone or text message. The medical treatment unit is realized, for example, by the control unit 46A of the smart glasses 214 and communicates with a doctor to diagnose and treat the patient even if the patient is unable to visit a hospital, and arranges for the patient to receive medication and make a hospital appointment. === Hard Collateral 1-3 === Each of the multiple elements, including the providing unit, monitoring unit, support unit, management unit, and medical treatment unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the providing unit is implemented by the control unit 46A of the headset terminal 314 and generates and provides appropriate answers to questions from the user. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects and analyzes data such as heart rate and blood pressure in real time. The support unit is implemented, for example, by the control unit 46A of the headset terminal 314 and collects data such as the patient's body temperature and blood pressure, detects abnormalities, and notifies medical staff. The management unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate recipes when the patient requests a recipe by phone or text message. The medical treatment unit is implemented, for example, by the control unit 46A of the headset terminal 314 and communicates with doctors to diagnose and treat patients even when they are unable to visit a hospital, and arranges for them to receive medication and make hospital appointments. === Hard Collateral 1-4 === Each of the multiple elements, including the providing unit, monitoring unit, support unit, management unit, and medical treatment unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the providing unit is realized by the control unit 46A of the robot 414 and generates and provides appropriate answers to questions from the user. The monitoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects and analyzes data such as heart rate and blood pressure in real time. The support unit is realized, for example, by the control unit 46A of the robot 414 and collects data such as the patient's body temperature and blood pressure, detects abnormalities, and notifies medical staff. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate recipes when the patient requests a recipe by phone or text message. The medical treatment unit is realized, for example, by the control unit 46A of the robot 414 and communicates with doctors to diagnose and treat patients even when they are unable to visit a hospital, and arranges for them to receive medication and make hospital appointments.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The providing unit can estimate the user's emotions and adjust the method of providing health information based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface is provided, making it easy to obtain information. Furthermore, the providing unit can provide detailed information or additional health advice if the user is relaxed. For example, if the user is relaxed, detailed information or additional health advice is provided. Furthermore, if the user is in a hurry, the providing unit can provide concise information that focuses on the main points. For example, if the user is in a hurry, concise information that focuses on the main points is provided. This allows for adjusting the information provision method according to the user's emotions, thereby enabling more appropriate information to be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0122] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user's emotions. For example, if the user is feeling stressed, the monitoring frequency is increased to monitor the user's health condition in detail. The monitoring unit can also reduce the monitoring frequency to reduce the burden on the user when the user is relaxed. For example, if the user is relaxed, the monitoring frequency is reduced to reduce the burden. The monitoring unit can also adjust the monitoring frequency to provide a sense of security when the user is feeling anxious. For example, if the user is feeling anxious, the monitoring frequency is adjusted to provide a sense of security. This makes it possible to adjust the monitoring frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0123] The support unit can estimate the user's emotions and adjust the support method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and intuitive support method is provided. The support unit can also provide detailed support information or additional advice if the user is relaxed. For example, if the user is relaxed, detailed support information or additional advice is provided. The support unit can also provide a concise support method that focuses on the main points if the user is in a hurry. For example, if the user is in a hurry, a concise support method that focuses on the main points is provided. This makes it possible to adjust the support method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0124] The management unit can estimate the user's emotions and adjust the recipe provision method based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive recipe is provided. The management unit can also provide detailed recipes and additional cooking advice if the user is relaxed. For example, if the user is relaxed, a detailed recipe and additional cooking advice is provided. The management unit can also provide concise recipes that focus on the main points if the user is in a hurry. For example, if the user is in a hurry, a concise recipe that focuses on the main points is provided. This makes it possible to adjust the recipe provision method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0125] The medical treatment unit can estimate the user's emotions and adjust the medical treatment method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and intuitive medical treatment method is provided. The medical treatment unit can also provide detailed medical information and additional advice if the user is relaxed. For example, if the user is relaxed, detailed medical information and additional advice is provided. The medical treatment unit can also provide a concise medical treatment method that focuses on the main points if the user is in a hurry. For example, if the user is in a hurry, a concise medical treatment method that focuses on the main points is provided. This makes it possible to adjust the medical treatment method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0126] The providing unit can analyze the user's past question history and generate an optimal answer. For example, it can provide new related information based on the content of questions the user has asked in the past. The providing unit can also prioritize displaying answers to frequently asked questions from the user's past question history. For example, it can analyze the user's past question history and prioritize displaying answers to frequently asked questions. The providing unit can also analyze the user's past question history and provide personalized health advice. For example, it can analyze the user's past question history and provide personalized health advice. In this way, the user's questions can be efficiently resolved by providing optimal answers based on the user's past question history.

[0127] During monitoring, the monitoring unit can refer to the user's past health data to predict abnormalities. For example, it refers to the user's past heart rate data to predict abnormalities. The monitoring unit can also refer to the user's past blood pressure data to predict abnormalities. For example, it refers to the user's past blood pressure data to predict abnormalities. The monitoring unit can also refer to the user's past blood glucose level data to predict abnormalities. For example, it refers to the user's past blood glucose level data to predict abnormalities. This makes it possible to predict abnormalities based on past health data.

[0128] When providing support, the support unit can select the optimal support method by referring to the user's past health data. For example, an appropriate support method is selected based on the user's past health data. The support unit can also refer to the user's past health data to provide a support method for a specific health problem. For example, the support unit can refer to the user's past health data to provide a support method for a specific health problem. The support unit can also analyze the user's past health data to provide a personalized support method. For example, the support unit can analyze the user's past health data to provide a personalized support method. This makes it possible to select the optimal support method based on the user's past health data.

[0129] When providing a recipe, the management unit can provide an optimal recipe by referring to the user's past meal history. For example, an appropriate recipe is provided based on the user's past meal history. The management unit can also provide a recipe that takes into consideration a specific nutritional balance by referring to the user's past meal history. For example, a recipe that takes into consideration a specific nutritional balance by referring to the user's past meal history. The management unit can also analyze the user's past meal history and provide a personalized recipe. For example, a personalized recipe is provided by analyzing the user's past meal history. This makes it possible to provide an optimal recipe based on the user's past meal history.

[0130] During medical treatment, the medical department can select the optimal medical treatment method by referring to the user's past health data. For example, an appropriate medical treatment method is selected based on the user's past health data. The medical department can also refer to the user's past health data to provide a medical treatment method for a specific health problem. For example, the medical department can refer to the user's past health data to provide a medical treatment method for a specific health problem. The medical department can also analyze the user's past health data to provide a personalized medical treatment method. For example, the medical department can analyze the user's past health data to provide a personalized medical treatment method. This makes it possible to select the optimal medical treatment method based on the user's past health data.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The provider provides health consultations or health-related information. The provider generates and provides appropriate answers to questions from users. The generator AI can also be used to provide general health information. For example, in response to a question about cold symptoms, the generator AI suggests appropriate measures to take. Step 2: The monitoring unit monitors the medical device. The monitoring unit collects and analyzes data such as heart rate and blood pressure in real time. It can also use generative AI to analyze the collected data and detect abnormalities. For example, it can analyze heart rate and blood pressure data in real time to detect abnormalities. Step 3: The support department provides support for home medical care. The support department collects data such as the patient's temperature and blood pressure, detects abnormalities, and notifies medical staff. Generative AI can also be used to analyze the collected data and detect abnormalities. For example, data on the patient's temperature and blood pressure can be collected and analyzed by generative AI to detect abnormalities and notify medical staff. Step 4: The management unit manages recipes. When a patient requests a recipe by phone or text message, the management unit provides the appropriate recipe. The management unit can also use generative AI to provide recipes based on the patient's request. For example, when a patient requests a recipe by phone or text message, the generative AI provides the appropriate recipe. Step 5: The medical department conducts online consultations. Even if the patient is unable to go to the hospital, the medical department can provide diagnosis and treatment through communication with the doctor, and arrange for the patient to receive medication and make an appointment at the hospital. Diagnosis and treatment can also be performed using generative AI. For example, even if the patient is unable to go to the hospital, they can easily receive diagnosis and treatment through communication with the doctor using generative AI.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 AI 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 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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 AI 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] 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.

[0182] 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.

[0183] 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 AI 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 provision department that provides health consultations or health information; a monitoring unit that monitors the medical device based on the information provided by the providing unit; a support unit that provides support for home medical care based on the information collected by the monitoring unit; a management unit that performs recipe management based on the information provided by the support unit; a medical examination unit that performs online medical examinations based on the information provided by the management unit; A system characterized by:

2. The providing unit Generate and provide appropriate answers to user questions 2. The system of claim 1.

3. The monitoring unit Collect and analyze heart rate or blood pressure data in real time 2. The system of claim 1.

4. The support portion is Collecting patient temperature or blood pressure data, detecting abnormalities, and notifying medical staff 2. The system of claim 1.

5. The management unit Provide appropriate recipes when patients call or text you for recipe requests 2. The system of claim 1.

6. The medical department Communicating with doctors to diagnose and treat patients when they are unable to go to the hospital, and arranging for medication pickup and hospital appointments 2. The system of claim 1.

7. The providing unit Estimating a user's emotions and adjusting the method of providing health information based on the estimated user's emotions 2. The system of claim 1.

8. The providing unit Analyze the user's past question history and generate the best answer 2. The system of claim 1.

Images