System

Abstract

An object of a system according to an embodiment is to enable a depopulated area, a busy person, and an elderly person to easily receive initial medical care.SOLUTION: A system includes an information collection unit, an analysis unit, and an advice provision unit. The information collection unit collects information on a health condition from a user. The analysis unit analyzes the information collected by the information collection unit. The advice providing unit provides appropriate advice of the initial medical care based on the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for people in depopulated areas, busy people, and the elderly to receive initial medical care, and the shortage of medical professionals is a problem.

[0005] The system according to the embodiment aims to make it easier for people in depopulated areas, busy people, and the elderly to receive initial medical care. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collecting unit, an analysis unit, and an advice providing unit. The information collecting unit collects information on the health condition from a user. The analysis unit analyzes the information collected by the information collecting unit. The advice providing unit provides appropriate advice on initial medical treatment based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can make it easier for people in depopulated areas, busy people, and the elderly to receive primary care. [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) The medical support system according to an embodiment of the present invention uses generative AI to enable people who have difficulty receiving medical treatment to easily receive initial medical care. This allows the medical support system to provide convenient services for people in depopulated areas, the elderly, and busy people, and to prevent a shortage of medical personnel. It can also respond to epidemics such as coronavirus.

[0029] The medical support system according to the embodiment includes an information collecting unit, an analysis unit, and an advice providing unit. The information collecting unit collects information about a user's health condition. For example, the information collecting unit collects data such as body temperature, blood pressure, and heart rate input by the user. The information collecting unit can also collect text data about symptoms and health conditions input by the user. The information collecting unit can also refer to the user's past health data and analyze changes and patterns in symptoms. The analysis unit analyzes the information collected by the information collecting unit. For example, the analysis unit can analyze the data using statistical analysis to identify trends in the user's health condition. The analysis unit can also analyze the data using a machine learning algorithm to detect abnormalities. The analysis unit can also analyze the user's emotional state using an emotion estimation function and consider the impact of stress and anxiety on symptoms. The advice providing unit provides appropriate advice on initial medical treatment based on the results of the analysis by the analysis unit. For example, the advice providing unit can provide advice on whether the user should see a doctor or take medication. The advice providing unit can also identify the cause of symptoms based on lifestyle and environmental data and suggest preventive measures. Furthermore, the advice providing unit can provide medical advice that takes into account the emotional state of the user using the emotion estimation function. As a result, the medical assistance system according to the embodiment can enable people who have difficulty receiving medical treatment to easily receive initial medical treatment.

[0030] The information collection unit can refer to the user's past health data and analyze changes and patterns of symptoms. In the information collection unit, for example, the generation AI collects the user's past health data and analyzes changes and patterns of symptoms. For example, it evaluates the relevance to current symptoms based on past medical records and health check data. The information collection unit also refers to the user's past health data, and the generation AI analyzes changes in symptoms chronologically. For example, it identifies the cause of current symptoms based on past blood pressure and body temperature data. In the information collection unit, the generation AI also analyzes the user's past health data and identifies symptom patterns. For example, it diagnoses current symptoms based on past allergic reactions and medical history. This allows for more accurate medical advice to be provided based on past health data.

[0031] The information collection unit collects data on the user's lifestyle and environment, and based on that, can identify the cause of symptoms and suggest preventive measures. For example, the information collection unit uses a generation AI to collect the user's lifestyle data and identify the cause of symptoms. For example, it suggests measures to improve health based on diet and exercise data. The information collection unit also collects the user's environmental data, and the generation AI analyzes the cause of symptoms. For example, it identifies the cause of allergies and stress based on data on living and work environments. The information collection unit also uses a generation AI to analyze the user's lifestyle and environmental data and suggest preventive measures. For example, it provides health management advice based on sleep patterns and stress levels. This makes it possible to identify the cause of symptoms based on lifestyle and environmental data and suggest preventive measures.

[0032] The information collection unit collects the user's dietary and exercise data and can provide health management advice based on that data. For example, the information collection unit uses a generation AI to collect the user's dietary data and analyze nutritional balance. For example, it proposes a healthy meal plan based on the content of meals and calorie intake. The information collection unit also collects the user's exercise data and the generation AI analyzes their exercise habits. For example, it proposes an appropriate exercise program based on the frequency and intensity of exercise. The information collection unit also uses a generation AI to integrate the user's dietary and exercise data and provide comprehensive health management advice. For example, it proposes a health plan that takes into account the balance between diet and exercise. This makes it possible to provide health management advice based on the dietary and exercise data.

[0033] The information collection unit can analyze the user's sleep data and provide advice to improve sleep quality. In the information collection unit, for example, the generation AI collects the user's sleep data and analyzes sleep patterns. For example, the quality of sleep is evaluated based on the sleep time and the percentage of deep sleep. The information collection unit also allows the generation AI to provide advice to improve sleep quality based on the user's sleep data. For example, the generation AI suggests ways to relax before bed or how to choose appropriate bedding. The information collection unit also allows the generation AI to analyze the user's sleep data and identify the cause of sleep disorders. For example, the generation AI suggests improvement measures that take stress and environmental factors into account. This makes it possible to provide advice to improve sleep quality based on the sleep data.

[0034] The information collection unit can analyze the user's geographical information and provide information on the nearest medical institutions and pharmacies. For example, the generation AI collects the user's geographical information and provides information on the nearest medical institutions and pharmacies. For example, the location of the nearest hospital or pharmacy is displayed based on GPS data. The information collection unit also analyzes the user's geographical information and the generation AI provides the opening hours and contact information of medical institutions and pharmacies. For example, information on medical institutions that can be used in emergencies is displayed. The information collection unit also supports the generation AI in selecting the most suitable medical institution or pharmacy based on the user's geographical information. For example, it suggests hospitals with specialists or pharmacies that stock specific medications. This makes it possible to provide information on the nearest medical institutions and pharmacies based on geographical information.

[0035] The information collection unit can analyze the user's schedule and suggest the optimal timing for medical treatment. For example, the information collection unit uses a generation AI to collect the user's schedule data and suggest the optimal timing for medical treatment. For example, it may suggest an available time slot based on data from a calendar app. The information collection unit also analyzes the user's schedule and the generation AI automatically makes a medical appointment. For example, it may make an appointment at a medical institution according to the user's convenience. The information collection unit also uses a generation AI to optimize the timing of medical treatment based on the user's schedule data. For example, it may suggest the timing of medical treatment taking work and home plans into consideration. This makes it possible to suggest the optimal timing for medical treatment based on the schedule.

[0036] The information collection unit can analyze the user's means of transportation and propose the optimal travel route. For example, the generation AI of the information collection unit collects the user's transportation data and proposes the optimal travel route. For example, it displays the shortest route based on public transportation data. The information collection unit also analyzes the user's transportation method, and the generation AI proposes a route that takes travel time and cost into consideration. For example, it selects the optimal route taking traffic congestion and fares into consideration. The information collection unit also allows the generation AI to provide advice to improve the convenience of travel based on the user's transportation data. For example, it proposes routes with fewer transfers or comfortable means of travel. This makes it possible to propose the optimal travel route based on the transportation method.

[0037] The information collection unit can analyze the user's means of communication and suggest the optimal tools for remote medical consultations. For example, the generation AI collects data on the user's means of communication and suggests the optimal tools for remote medical consultations. For example, it suggests video calling apps and messaging apps. The information collection unit also analyzes the user's means of communication and the generation AI selects tools suitable for remote medical consultations. For example, it suggests apps with voice calling and chat functions. The information collection unit also provides advice to improve the convenience of remote medical consultations based on the user's communication method data. For example, it suggests tools with easy-to-use interfaces and security features. This makes it possible to suggest the optimal tools for remote medical consultations based on the communication method.

[0038] The information collection unit can analyze the schedules of medical personnel and propose optimal task allocation. For example, the generation AI in the information collection unit collects schedule data of medical personnel and proposes optimal task allocation. For example, efficient task allocation is performed based on the work shifts of doctors and nurses. The information collection unit also analyzes the schedules of medical personnel and the generation AI sets priorities for tasks. For example, tasks with high urgency are assigned first. The information collection unit also makes suggestions to eliminate overlapping and wasteful tasks based on the schedule data of medical personnel through the generation AI. For example, adjustments are made to prevent multiple doctors from performing the same tasks during the same time period. This makes it possible to propose optimal task allocation based on schedules.

[0039] The information gathering unit can analyze the expertise of medical professionals and assemble the optimal medical team. For example, the information gathering unit uses a generation AI to collect expertise data from medical professionals and assemble the optimal medical team. For example, the team is assembled based on each doctor's area of ​​expertise and experience. The information gathering unit also analyzes the expertise of medical professionals and the generation AI proposes a division of roles in the medical team. For example, the most suitable specialist is assigned to a specific case. The information gathering unit also uses the generation AI to make proposals to improve the efficiency of the medical team based on the expertise data of medical professionals. For example, roles are divided to facilitate communication within the team. This makes it possible to assemble the optimal medical team based on expertise.

[0040] The information collection unit can analyze the training data of medical personnel and propose optimal training programs. In the information collection unit, for example, the generation AI collects the training data of medical personnel and proposes optimal training programs. For example, it provides an individualized program based on past training history and skill level. The information collection unit also analyzes the training data of medical personnel and the generation AI proposes a training plan for skill improvement. For example, it proposes training to strengthen specific techniques and knowledge. The information collection unit also evaluates the effectiveness of training based on the training data of medical personnel and proposes areas for improvement. For example, it monitors the progress of training and makes necessary adjustments. This makes it possible to propose optimal training programs based on the training data.

[0041] The information collection unit can analyze communication data of medical professionals and provide advice to improve teamwork. For example, the generation AI collects communication data of medical professionals and provides advice to improve teamwork. For example, it evaluates the frequency and quality of communication and suggests areas for improvement. The information collection unit also analyzes communication data of medical professionals and the generation AI suggests effective communication methods. For example, it recommends regular meetings and feedback sessions. The information collection unit also uses communication data of medical professionals to make suggestions to optimize the division of roles and cooperation within the team. For example, it suggests methods and tools for sharing information. This makes it possible to provide advice to improve teamwork based on communication data.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The medical support system can also provide health management advice based on the user's hobbies and interests. For example, the information collection unit collects data on the user's hobbies, and the analysis unit analyzes the data to suggest health maintenance methods through hobbies. For example, for a user whose hobby is gardening, it can suggest moderate exercise and stress relief methods through gardening. Also, for a user whose hobby is reading, it can provide advice on relaxation methods and improving concentration through reading. Furthermore, for a user whose hobby is music, it can suggest the relaxation effects and stress reduction methods that can be obtained by listening to music. In this way, it is possible to provide personalized health management advice based on the user's hobbies and interests.

[0044] The medical support system can also analyze the user's social network to provide support for health management. For example, the information collection unit collects the user's social media data, and the analysis unit analyzes the data to understand the user's social support situation. For example, the system can analyze how frequently the user communicates with friends and family, and if social support is lacking, provide advice on how to increase communication. The system can also analyze the activities of online communities and groups in which the user participates to provide health-related information and support. Furthermore, the system can utilize the user's social network to suggest joint activities and support groups for health management. This makes it possible to provide health management support that utilizes the user's social network.

[0045] The medical support system can also analyze the user's occupational data and provide health management advice based on the work environment. For example, the information collection unit collects data on the user's occupation, and the analysis unit analyzes that data to suggest health management methods suited to the work environment. For example, a user who does a lot of desk work can be provided with advice on regular stretching and posture improvement. Also, a user who does a lot of physical labor can be suggested with appropriate rest and nutritional methods. Furthermore, a user who frequently works shifts can be provided with advice on sleep management and meal timings tailored to the shifts. This makes it possible to provide personalized health management advice tailored to the user's occupation.

[0046] The medical support system can also analyze the user's travel data and provide health management advice during travel. For example, the information collection unit collects the user's travel plans and past travel history, and the analysis unit analyzes that data to suggest health management methods during travel. For example, in the case of a long flight, advice on stretching and hydration to prevent economy class syndrome can be provided. Health management methods that adapt to different climates and food cultures can also be suggested. Furthermore, information on medical institutions and pharmacies at the travel destination can be provided to prepare for emergencies. This can support the user's health management during travel.

[0047] The medical support system can also analyze data related to the user's pet and provide advice on health management through living with the pet. For example, the information collection unit collects data related to the user's pet, and the analysis unit analyzes the data to suggest health management methods through living with the pet. For example, a user who owns a dog can be provided with advice on moderate exercise and stress relief through dog walks. Also, a user who owns a cat can be provided with advice on relaxation methods and mental health care through interacting with their cat. Furthermore, the system can provide information and advice on pet health management and support for living a healthy life with their pet. This makes it possible to support health management through the user's life with their pet.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The information collection unit collects information about the user's health condition. For example, it collects data such as body temperature, blood pressure, and heart rate entered by the user, as well as text data about symptoms and health conditions. It can also refer to the user's past health data and analyze changes and patterns of symptoms. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, statistical analysis can be used to analyze the data and identify trends in health status. Machine learning algorithms can also be used to analyze the data and detect abnormalities. Furthermore, an emotion estimation function can be used to analyze the user's emotional state and take into account the impact of stress and anxiety on symptoms. Step 3: The advice provider provides appropriate initial medical advice based on the results of the analysis by the analysis unit. For example, it provides advice on whether to see a doctor or take medication. It can also identify the cause of symptoms based on lifestyle and environmental data and suggest preventive measures. Furthermore, it can provide medical advice that takes into account the user's emotional state using an emotion estimation function.

[0050] (Example 2) The medical support system according to an embodiment of the present invention uses generative AI to enable people who have difficulty receiving medical treatment to easily receive initial medical care. This allows the medical support system to provide convenient services for people in depopulated areas, the elderly, and busy people, and to prevent a shortage of medical personnel. It can also respond to epidemics such as coronavirus.

[0051] The medical support system according to the embodiment includes an information collecting unit, an analysis unit, and an advice providing unit. The information collecting unit collects information about a user's health condition. For example, the information collecting unit collects data such as body temperature, blood pressure, and heart rate input by the user. The information collecting unit can also collect text data about symptoms and health conditions input by the user. The information collecting unit can also refer to the user's past health data and analyze changes and patterns in symptoms. The analysis unit analyzes the information collected by the information collecting unit. For example, the analysis unit can analyze the data using statistical analysis to identify trends in the user's health condition. The analysis unit can also analyze the data using a machine learning algorithm to detect abnormalities. The analysis unit can also analyze the user's emotional state using an emotion estimation function and consider the impact of stress and anxiety on symptoms. The advice providing unit provides appropriate advice on initial medical treatment based on the results of the analysis by the analysis unit. For example, the advice providing unit can provide advice on whether the user should see a doctor or take medication. The advice providing unit can also identify the cause of symptoms based on lifestyle and environmental data and suggest preventive measures. Furthermore, the advice providing unit can provide medical advice that takes into account the emotional state of the user using the emotion estimation function. As a result, the medical assistance system according to the embodiment can enable people who have difficulty receiving medical treatment to easily receive initial medical treatment.

[0052] The information collection unit can refer to the user's past health data and analyze changes and patterns of symptoms. In the information collection unit, for example, the generation AI collects the user's past health data and analyzes changes and patterns of symptoms. For example, it evaluates the relevance to current symptoms based on past medical records and health check data. The information collection unit also refers to the user's past health data, and the generation AI analyzes changes in symptoms chronologically. For example, it identifies the cause of current symptoms based on past blood pressure and body temperature data. In the information collection unit, the generation AI also analyzes the user's past health data and identifies symptom patterns. For example, it diagnoses current symptoms based on past allergic reactions and medical history. This allows for more accurate medical advice to be provided based on past health data.

[0053] The information collection unit collects data on the user's lifestyle and environment, and based on that, can identify the cause of symptoms and suggest preventive measures. For example, the information collection unit uses a generation AI to collect the user's lifestyle data and identify the cause of symptoms. For example, it suggests measures to improve health based on diet and exercise data. The information collection unit also collects the user's environmental data, and the generation AI analyzes the cause of symptoms. For example, it identifies the cause of allergies and stress based on data on living and work environments. The information collection unit also uses a generation AI to analyze the user's lifestyle and environmental data and suggest preventive measures. For example, it provides health management advice based on sleep patterns and stress levels. This makes it possible to identify the cause of symptoms based on lifestyle and environmental data and suggest preventive measures.

[0054] The analysis unit can use the emotion estimation function to analyze the user's emotional state and provide medical advice that takes into account the impact of stress and anxiety on symptoms. The analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time. For example, it analyzes facial expressions and vocal tone to evaluate the level of stress and anxiety. The analysis unit also allows the generation AI to provide medical advice that takes into account the impact of stress and anxiety on symptoms based on the emotion estimation data. For example, it provides advice on relaxation techniques and stress management. The analysis unit also analyzes the user's emotional state and the generation AI provides medical advice based on the emotion. For example, it suggests mental health care to elicit positive emotions. This makes it possible to provide medical advice that takes into account the emotional state.

[0055] The information collection unit collects the user's dietary and exercise data and can provide health management advice based on that data. For example, the information collection unit uses a generation AI to collect the user's dietary data and analyze nutritional balance. For example, it proposes a healthy meal plan based on the content of meals and calorie intake. The information collection unit also collects the user's exercise data and the generation AI analyzes their exercise habits. For example, it proposes an appropriate exercise program based on the frequency and intensity of exercise. The information collection unit also uses a generation AI to integrate the user's dietary and exercise data and provide comprehensive health management advice. For example, it proposes a health plan that takes into account the balance between diet and exercise. This makes it possible to provide health management advice based on the dietary and exercise data.

[0056] The information collection unit can analyze the user's sleep data and provide advice to improve sleep quality. In the information collection unit, for example, the generation AI collects the user's sleep data and analyzes sleep patterns. For example, the quality of sleep is evaluated based on the sleep time and the percentage of deep sleep. The information collection unit also allows the generation AI to provide advice to improve sleep quality based on the user's sleep data. For example, the generation AI suggests ways to relax before bed or how to choose appropriate bedding. The information collection unit also allows the generation AI to analyze the user's sleep data and identify the cause of sleep disorders. For example, the generation AI suggests improvement measures that take stress and environmental factors into account. This makes it possible to provide advice to improve sleep quality based on the sleep data.

[0057] The information collection unit can analyze the user's geographical information and provide information on the nearest medical institutions and pharmacies. For example, the generation AI collects the user's geographical information and provides information on the nearest medical institutions and pharmacies. For example, the location of the nearest hospital or pharmacy is displayed based on GPS data. The information collection unit also analyzes the user's geographical information and the generation AI provides the opening hours and contact information of medical institutions and pharmacies. For example, information on medical institutions that can be used in emergencies is displayed. The information collection unit also supports the generation AI in selecting the most suitable medical institution or pharmacy based on the user's geographical information. For example, it suggests hospitals with specialists or pharmacies that stock specific medications. This makes it possible to provide information on the nearest medical institutions and pharmacies based on geographical information.

[0058] The information collection unit can analyze the user's schedule and suggest the optimal timing for medical treatment. For example, the information collection unit uses a generation AI to collect the user's schedule data and suggest the optimal timing for medical treatment. For example, it may suggest an available time slot based on data from a calendar app. The information collection unit also analyzes the user's schedule and the generation AI automatically makes a medical appointment. For example, it may make an appointment at a medical institution according to the user's convenience. The information collection unit also uses a generation AI to optimize the timing of medical treatment based on the user's schedule data. For example, it may suggest the timing of medical treatment taking work and home plans into consideration. This makes it possible to suggest the optimal timing for medical treatment based on the schedule.

[0059] The analysis unit can use the emotion estimation function to analyze the emotional state of elderly people and busy people and provide advice to reduce stress. The analysis unit, for example, uses the emotion estimation function to analyze the emotional state of elderly people and busy people in real time. For example, it analyzes facial expressions and voice tone to evaluate stress levels. The analysis unit also allows the generation AI to provide advice to reduce stress based on the emotion estimation data. For example, it provides advice on relaxation techniques and stress management. The analysis unit also analyzes the user's emotional state, and the generation AI suggests stress reduction measures based on the emotions. For example, it suggests mental health care to bring out positive emotions. This makes it possible to analyze the emotional state and provide advice to reduce stress.

[0060] The information collection unit can analyze the user's means of transportation and propose the optimal travel route. For example, the generation AI of the information collection unit collects the user's transportation data and proposes the optimal travel route. For example, it displays the shortest route based on public transportation data. The information collection unit also analyzes the user's transportation method, and the generation AI proposes a route that takes travel time and cost into consideration. For example, it selects the optimal route taking traffic congestion and fares into consideration. The information collection unit also allows the generation AI to provide advice to improve the convenience of travel based on the user's transportation data. For example, it proposes routes with fewer transfers or comfortable means of travel. This makes it possible to propose the optimal travel route based on the transportation method.

[0061] The information collection unit can analyze the user's means of communication and suggest the optimal tools for remote medical consultations. For example, the generation AI collects data on the user's means of communication and suggests the optimal tools for remote medical consultations. For example, it suggests video calling apps and messaging apps. The information collection unit also analyzes the user's means of communication and the generation AI selects tools suitable for remote medical consultations. For example, it suggests apps with voice calling and chat functions. The information collection unit also provides advice to improve the convenience of remote medical consultations based on the user's communication method data. For example, it suggests tools with easy-to-use interfaces and security features. This makes it possible to suggest the optimal tools for remote medical consultations based on the communication method.

[0062] The analysis unit uses the emotion estimation function to analyze the emotional reaction of the user when receiving medical advice, thereby improving the ease with which the advice is accepted. The analysis unit, for example, uses the emotion estimation function to analyze the emotional reaction of the user when receiving medical advice in real time. For example, it analyzes facial expressions and tone of voice to calculate an emotion score. The analysis unit also enables the generation AI to make adjustments to improve the ease with which the advice is accepted based on the emotional reaction data. For example, it proposes an approach to elicit positive emotions. The analysis unit also analyzes the user's emotional reaction, and the generation AI optimizes the content and method of advice. For example, it proposes a communication method that takes emotions into consideration. This makes it possible to analyze emotional reactions and improve the ease with which the advice is accepted.

[0063] The information collection unit can analyze the schedules of medical personnel and propose optimal task allocation. For example, the generation AI in the information collection unit collects schedule data of medical personnel and proposes optimal task allocation. For example, efficient task allocation is performed based on the work shifts of doctors and nurses. The information collection unit also analyzes the schedules of medical personnel and the generation AI sets priorities for tasks. For example, tasks with high urgency are assigned first. The information collection unit also makes suggestions to eliminate overlapping and wasteful tasks based on the schedule data of medical personnel through the generation AI. For example, adjustments are made to prevent multiple doctors from performing the same tasks during the same time period. This makes it possible to propose optimal task allocation based on schedules.

[0064] The information gathering unit can analyze the expertise of medical professionals and assemble the optimal medical team. For example, the information gathering unit uses a generation AI to collect expertise data from medical professionals and assemble the optimal medical team. For example, the team is assembled based on each doctor's area of ​​expertise and experience. The information gathering unit also analyzes the expertise of medical professionals and the generation AI proposes a division of roles in the medical team. For example, the most suitable specialist is assigned to a specific case. The information gathering unit also uses the generation AI to make proposals to improve the efficiency of the medical team based on the expertise data of medical professionals. For example, roles are divided to facilitate communication within the team. This makes it possible to assemble the optimal medical team based on expertise.

[0065] The analysis unit can use the emotion estimation function to analyze the emotional state of medical professionals and provide advice to reduce stress. The analysis unit, for example, uses the emotion estimation function to analyze the emotional state of medical professionals in real time. For example, it analyzes facial expressions and voice tone to evaluate stress levels. The analysis unit also allows the generation AI to provide advice to reduce stress based on the emotion estimation data. For example, it provides advice on relaxation techniques and stress management. The analysis unit also analyzes the emotional state of medical professionals and the generation AI suggests stress reduction measures based on emotions. For example, it suggests mental health care to elicit positive emotions. This makes it possible to analyze the emotional state and provide advice to reduce stress.

[0066] The information collection unit can analyze the training data of medical personnel and propose optimal training programs. In the information collection unit, for example, the generation AI collects the training data of medical personnel and proposes optimal training programs. For example, it provides an individualized program based on past training history and skill level. The information collection unit also analyzes the training data of medical personnel and the generation AI proposes a training plan for skill improvement. For example, it proposes training to strengthen specific techniques and knowledge. The information collection unit also evaluates the effectiveness of training based on the training data of medical personnel and proposes areas for improvement. For example, it monitors the progress of training and makes necessary adjustments. This makes it possible to propose optimal training programs based on the training data.

[0067] The information collection unit can analyze communication data of medical professionals and provide advice to improve teamwork. For example, the generation AI collects communication data of medical professionals and provides advice to improve teamwork. For example, it evaluates the frequency and quality of communication and suggests areas for improvement. The information collection unit also analyzes communication data of medical professionals and the generation AI suggests effective communication methods. For example, it recommends regular meetings and feedback sessions. The information collection unit also uses communication data of medical professionals to make suggestions to optimize the division of roles and cooperation within the team. For example, it suggests methods and tools for sharing information. This makes it possible to provide advice to improve teamwork based on communication data.

[0068] The analysis unit uses the emotion estimation function to analyze the emotional reactions of medical professionals when they receive medical advice, thereby improving the ease with which the advice is accepted. The analysis unit, for example, uses the emotion estimation function to analyze the emotional reactions of medical professionals in real time when they receive medical advice. For example, it analyzes facial expressions and tone of voice to calculate an emotion score. The analysis unit also enables the generation AI to make adjustments to improve the ease with which the advice is accepted based on the emotional reaction data. For example, it proposes an approach to elicit positive emotions. The analysis unit also analyzes the emotional reactions of medical professionals, and the generation AI optimizes the content and method of advice. For example, it proposes a communication method that takes emotions into consideration. This makes it possible to analyze emotional reactions and improve the ease with which the advice is accepted.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The medical support system can also provide health management advice based on the user's hobbies and interests. For example, the information collection unit collects data on the user's hobbies, and the analysis unit analyzes the data to suggest health maintenance methods through hobbies. For example, for a user whose hobby is gardening, it can suggest moderate exercise and stress relief methods through gardening. Also, for a user whose hobby is reading, it can provide advice on relaxation methods and improving concentration through reading. Furthermore, for a user whose hobby is music, it can suggest the relaxation effects and stress reduction methods that can be obtained by listening to music. In this way, it is possible to provide personalized health management advice based on the user's hobbies and interests.

[0071] The medical support system can also analyze the user's social network to provide support for health management. For example, the information collection unit collects the user's social media data, and the analysis unit analyzes the data to understand the user's social support situation. For example, the system can analyze how frequently the user communicates with friends and family, and if social support is lacking, provide advice on how to increase communication. The system can also analyze the activities of online communities and groups in which the user participates to provide health-related information and support. Furthermore, the system can utilize the user's social network to suggest joint activities and support groups for health management. This makes it possible to provide health management support that utilizes the user's social network.

[0072] The medical support system can also analyze the user's occupational data and provide health management advice based on the work environment. For example, the information collection unit collects data on the user's occupation, and the analysis unit analyzes that data to suggest health management methods suited to the work environment. For example, a user who does a lot of desk work can be provided with advice on regular stretching and posture improvement. Also, a user who does a lot of physical labor can be suggested with appropriate rest and nutritional methods. Furthermore, a user who frequently works shifts can be provided with advice on sleep management and meal timings tailored to the shifts. This makes it possible to provide personalized health management advice tailored to the user's occupation.

[0073] The medical support system can also analyze the user's travel data and provide health management advice during travel. For example, the information collection unit collects the user's travel plans and past travel history, and the analysis unit analyzes that data to suggest health management methods during travel. For example, in the case of a long flight, advice on stretching and hydration to prevent economy class syndrome can be provided. Health management methods that adapt to different climates and food cultures can also be suggested. Furthermore, information on medical institutions and pharmacies at the travel destination can be provided to prepare for emergencies. This can support the user's health management during travel.

[0074] The medical support system can also analyze data related to the user's pet and provide advice on health management through living with the pet. For example, the information collection unit collects data related to the user's pet, and the analysis unit analyzes the data to suggest health management methods through living with the pet. For example, a user who owns a dog can be provided with advice on moderate exercise and stress relief through dog walks. Also, a user who owns a cat can be provided with advice on relaxation methods and mental health care through interacting with their cat. Furthermore, the system can provide information and advice on pet health management and support for living a healthy life with their pet. This makes it possible to support health management through the user's life with their pet.

[0075] The medical support system can further analyze the user's emotional state and provide health management advice based on the emotion. For example, the analysis unit analyzes the user's emotional state in real time and evaluates the level of stress and anxiety. For example, if the user is feeling stressed, the analysis unit can provide advice on relaxation techniques and stress management. Also, if the user is feeling positive, the system can provide advice on how to maintain those emotions. Furthermore, the system can provide advice on appropriate exercise and diet based on the emotional state. This makes it possible to provide personalized health management advice that takes the user's emotional state into consideration.

[0076] The medical support system can further analyze the user's emotional state and provide sleep management advice based on the emotion. For example, the analysis unit can analyze the user's emotional state in real time and evaluate the impact of stress and anxiety on sleep. For example, if the user is feeling stressed, the analysis unit can suggest relaxation techniques and measures to improve the sleep environment. Also, if the user is feeling anxious, the system can provide mental health care advice to reduce anxiety. Furthermore, the system can provide advice on appropriate sleep duration and sleep patterns based on the emotional state. This makes it possible to provide personalized sleep management advice that takes the user's emotional state into consideration.

[0077] The medical support system can further analyze the user's emotional state and provide dietary management advice based on the emotion. For example, the analysis unit analyzes the user's emotional state in real time and evaluates the impact of stress and anxiety on diet. For example, if the user is feeling stressed, dietary advice to reduce stress can be provided. Also, if the user is feeling anxious, a nutritionally balanced meal to reduce anxiety can be suggested. Furthermore, advice on meal timing and content can be provided based on the emotional state. This makes it possible to provide personalized dietary management advice that takes the user's emotional state into consideration.

[0078] The medical support system can further analyze the user's emotional state and provide exercise management advice based on the emotion. For example, the analysis unit analyzes the user's emotional state in real time and evaluates the impact of stress and anxiety on exercise. For example, if the user is feeling stressed, the analysis unit can provide exercise advice to reduce stress. Also, if the user is feeling anxious, the system can suggest exercise that has a relaxing effect to reduce anxiety. Furthermore, the system can provide advice on the frequency and intensity of exercise based on the emotional state. This makes it possible to provide personalized exercise management advice that takes the user's emotional state into consideration.

[0079] The medical support system can further analyze the user's emotional state and provide emotionally based mental health care advice. For example, the analysis unit can analyze the user's emotional state in real time and evaluate the user's stress and anxiety levels. For example, if the user is feeling stressed, the analysis unit can provide relaxation techniques and stress management advice. If the user is feeling anxious, the system can provide mental health care advice to reduce anxiety. Furthermore, based on the emotional state, the system can suggest activities and relaxation techniques to elicit positive emotions. This makes it possible to provide personalized mental health care advice that takes the user's emotional state into consideration.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The information collection unit collects information about the user's health condition. For example, it collects data such as body temperature, blood pressure, and heart rate entered by the user, as well as text data about symptoms and health conditions. It can also refer to the user's past health data and analyze changes and patterns of symptoms. Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, statistical analysis can be used to analyze the data and identify trends in health status. Machine learning algorithms can also be used to analyze the data and detect abnormalities. Furthermore, an emotion estimation function can be used to analyze the user's emotional state and take into account the impact of stress and anxiety on symptoms. Step 3: The advice provider provides appropriate initial medical advice based on the results of the analysis by the analysis unit. For example, it provides advice on whether to see a doctor or take medication. It can also identify the cause of symptoms based on lifestyle and environmental data and suggest preventive measures. Furthermore, it can provide medical advice that takes into account the user's emotional state using an emotion estimation function.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, 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 robot 414 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.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 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. an information collection unit that collects information on a user's health condition; an analysis unit that analyzes the information collected by the information collection unit; an advice providing unit that provides appropriate advice on initial medical treatment based on the results of the analysis by the analysis unit; A system characterized by:

2. The information collecting unit Refer to the user's past health data and analyze changes and patterns of symptoms 2. The system of claim 1.

3. The information collecting unit Collects data on the user's lifestyle and environment, identifies the cause of symptoms, and suggests preventative measures 2. The system of claim 1.

4. The analysis unit Analyzing the user's emotional state and providing medical advice that takes into account the impact of stress and anxiety on symptoms 2. The system of claim 1.

5. The information collecting unit Collects user's diet and exercise data and provides health management advice based on that data 2. The system of claim 1.

6. The information collecting unit Analyzing the user's sleep data and providing advice to improve sleep quality 2. The system of claim 1.

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