System

Abstract

An object of a system according to an embodiment is to comprehensively evaluate a health condition of a user and provide appropriate medical support.SOLUTION: A system includes a biological data measurement part, a respiration analysis part, a visual analysis part, an inquiry part, an inspection part, and an overall determination part. The biological data measurement unit measures basic biological data such as heart rate, blood pressure, weight, and body temperature. The respiratory analyzer uses audio analysis to look for respiratory related issues of cough or shortness of breath. The visual analyzer uses machine vision to look for skin tone or eye color, a visual indicator of facial swelling. The inquiry unit performs a simple inquiry to the user. The test unit performs a saliva test or a blood test. The comprehensive determination unit performs comprehensive determination based on the collected data, and issues a prescription as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to comprehensively evaluate a user's health condition and provide appropriate medical support.

[0005] The system according to the embodiment aims to comprehensively evaluate the health condition of a user and provide appropriate medical support. [Means for solving the problem]

[0006] The system according to the embodiment includes a biometric data measurement unit, a respiration analysis unit, a visual analysis unit, an interview unit, an examination unit, and a comprehensive assessment unit. The biometric data measurement unit measures basic biometric data such as heart rate, blood pressure, weight, and body temperature. The respiration analysis unit uses voice analysis to detect respiratory problems such as coughing or shortness of breath. The visual analysis unit uses machine vision to detect visual indicators such as skin tone, eye color, and facial swelling. The interview unit conducts a simple interview with the user. The examination unit conducts a saliva test or blood test. The comprehensive assessment unit makes a comprehensive assessment based on the collected data and issues a prescription if necessary. [Effects of the Invention]

[0007] The system according to the embodiment can comprehensively evaluate the health condition of the user and provide appropriate medical support. [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 robot according to the embodiment of the present invention is a system that measures biometric data, analyzes it with a generating AI, and comprehensively evaluates the user's health condition. This allows the medical support robot to comprehensively evaluate the user's health condition and provide appropriate medical support.

[0029] The medical support robot according to the embodiment includes a biometric data measurement unit, a respiration analysis unit, a visual analysis unit, a medical interview unit, an examination unit, and a comprehensive assessment unit. The biometric data measurement unit measures basic biometric data such as heart rate, blood pressure, weight, and body temperature. For example, when a user holds their hand over the robot, the built-in sensors measure their heart rate and blood pressure. Additionally, weight can be measured by stepping on a scale-like feature. Body temperature is measured using a non-contact thermometer. The respiration analysis unit uses audio analysis to detect respiratory issues such as coughing or shortness of breath. For example, when a user coughs, the audio is analyzed to determine the type and frequency of the cough. The unit also monitors the user's breathing sounds to analyze the level of shortness of breath. The visual analysis unit uses machine vision to detect visual indicators of skin tone, eye color, and facial swelling. For example, the user's face is photographed with a camera, and image recognition AI analyzes skin tone and eye color. Furthermore, the unit analyzes facial contours and features to detect facial swelling. The medical interview unit asks the user a simple medical interview. For example, the AI ​​generation system asks questions such as, "Have you noticed any changes in your health recently?" or "What symptoms do you have?" and analyzes the user's answers. This allows the user's health condition to be understood. The testing unit performs saliva or blood tests. For example, when the user provides saliva to the robot, the saliva is analyzed and the health condition is evaluated. The robot is also equipped with a simple device for blood tests, which can collect and analyze the user's blood. The comprehensive judgment unit makes a comprehensive judgment based on the collected data and issues a prescription as necessary. For example, if the user's symptoms are mild, the robot suggests an appropriate medication. On the other hand, if a comprehensive judgment is difficult or if serious symptoms are observed, the robot has a function to escalate the case to a doctor. This allows the medical support robot according to the embodiment to comprehensively evaluate the user's health condition and provide appropriate medical support. For example, the user can easily perform health checks at home and consult a doctor as needed. This also reduces the burden on medical institutions and enables efficient medical care.

[0030] The biometric data measurement unit can incorporate an algorithm that compares data with the user's past data and automatically detects abnormal values. For example, the biometric data measurement unit compares the user's past heart rate data with current data to detect abnormal fluctuations. For example, it compares the current heart rate with the average heart rate over the past month and automatically notifies the user of any abnormal increases or decreases. The biometric data measurement unit also compares blood pressure measurement results with past data to detect abnormal values. For example, it determines whether the current measurement value is within the normal range based on past blood pressure data and issues an alert if an abnormality is detected. The biometric data measurement unit also compares weight fluctuations with past data to detect sudden increases or decreases. For example, it automatically detects sudden weight gain or loss based on weight data from the past week and notifies the user. This automatic detection of abnormal values ​​allows for early detection of health problems.

[0031] The biometric data measurement unit can upload measurement data to the cloud in real time, allowing doctors to check the data immediately. For example, the biometric data measurement unit can upload measurement data to the cloud in real time, building a system that allows doctors to check the data immediately through a dedicated dashboard. For example, heart rate and blood pressure data can be stored in the cloud and monitored remotely by doctors. The biometric data measurement unit can also upload the user's body temperature data to the cloud in real time, allowing doctors to respond immediately if they detect an abnormality. For example, an alert can be sent to the doctor if the body temperature is high. The biometric data measurement unit can also upload weight data to the cloud, allowing doctors to remotely monitor the user's health condition. For example, if there is a sudden change in weight, the doctor can respond immediately. This allows doctors in remote locations to check the data in real time.

[0032] The respiratory analysis unit can automatically identify sound patterns associated with specific diseases in analyzing respiratory sounds. For example, the respiratory analysis unit develops an algorithm that automatically identifies sound patterns associated with specific diseases in analyzing respiratory sounds. For example, it detects specific sound patterns associated with asthma or pneumonia. The respiratory analysis unit also adds a function that analyzes coughing sounds and automatically identifies sound patterns associated with specific diseases. For example, it determines the possibility of bronchitis or pulmonary tuberculosis from the sound of coughing. The respiratory analysis unit also adds a function that analyzes shortness of breath sounds and automatically identifies sound patterns associated with specific diseases. For example, it determines the possibility of heart failure or pulmonary hypertension from the sound of shortness of breath. This makes it possible to automatically identify sound patterns associated with specific diseases.

[0033] The breathing analysis unit can assess the user's risk of sleep apnea syndrome based on the breathing analysis data and suggest necessary countermeasures. For example, the breathing analysis unit develops an algorithm to assess the user's risk of sleep apnea syndrome based on the breathing analysis data. For example, it analyzes the frequency of breathing cessation and hypopnea to assess the risk. The breathing analysis unit also monitors breathing sounds during sleep and builds a system to assess the risk of sleep apnea syndrome. For example, it analyzes the number and duration of breathing cessation during sleep. The breathing analysis unit also adds a function to suggest appropriate countermeasures if the risk of sleep apnea syndrome is high. For example, it recommends changing the sleeping position or using CPAP (continuous positive airway pressure). This makes it possible to assess the risk of sleep apnea syndrome and suggest appropriate countermeasures.

[0034] The visual analysis unit can automatically identify visual changes associated with specific diseases in analyzing skin tone and eye color. The visual analysis unit, for example, analyzes skin tone and develops an algorithm that automatically identifies changes associated with specific diseases. For example, it detects skin tone changes associated with jaundice and anemia. The visual analysis unit also adds a function that analyzes eye color and automatically identifies changes associated with specific diseases. For example, it detects eye color changes associated with liver disease and diabetes. The visual analysis unit also adds a function that analyzes facial swelling and automatically identifies changes associated with specific diseases. For example, it detects facial swelling associated with kidney disease and heart failure. This makes it possible to automatically identify visual changes associated with specific diseases.

[0035] The visual analysis unit can evaluate the user's nutritional status and need for hydration based on visual indicators and provide appropriate advice. The visual analysis unit, for example, analyzes skin tone and develops an algorithm to evaluate the user's nutritional status. For example, it detects changes in skin tone associated with vitamin or iron deficiency and suggests appropriate nutritional supplementation. The visual analysis unit also adds a function to analyze eye color and evaluate the need for hydration. For example, it detects dryness or redness of the eyes and recommends hydration. The visual analysis unit also adds a function to analyze facial swelling and evaluate the user's nutritional status and need for hydration. For example, if swelling is present, it suggests limiting salt intake and providing more water. This makes it possible to evaluate the user's nutritional status and need for hydration and provide appropriate advice.

[0036] The medical interview unit can refer to the user's past health data during the medical interview and generate more specific questions. The medical interview unit, for example, develops an algorithm that refers to the user's past health data and generates specific questions during the medical interview. For example, a user who has previously been diagnosed with high blood pressure is asked about recent blood pressure fluctuations. The medical interview unit also adds a function that generates specific questions related to the user's symptoms based on the past health data. For example, a user who has previously been diagnosed with asthma is asked about the frequency of recent shortness of breath. The medical interview unit also adds a function that generates appropriate follow-up questions during the medical interview based on the user's past health data. For example, a user who has previously been diagnosed with diabetes is asked about recent blood sugar management status. This makes it possible to refer to the user's past health data and generate more specific questions.

[0037] The medical interview unit can assess the user's health risk based on the medical interview data and suggest preventive measures. The medical interview unit, for example, develops an algorithm for assessing the user's health risk based on the medical interview data. For example, it assesses the risk of cardiovascular disease based on smoking history and family history and suggests appropriate preventive measures. The medical interview unit also analyzes the user's answers and adds a function for assessing health risk. For example, it assesses the risk of obesity and diabetes based on lack of exercise and poor eating habits and suggests preventive measures. The medical interview unit also builds a system for assessing the user's health risk based on the medical interview data and suggesting appropriate preventive measures. For example, it assesses mental health risks based on stress levels and lack of sleep and suggests relaxation methods. This makes it possible to assess the user's health risk and suggest appropriate preventive measures.

[0038] The testing unit can automatically identify biomarkers associated with specific diseases in saliva and blood analysis. For example, the testing unit develops an algorithm that automatically identifies biomarkers associated with specific diseases in saliva analysis. For example, it detects biomarkers associated with oral cancer and diabetes. The testing unit also adds a function to automatically identify biomarkers associated with specific diseases in blood analysis. For example, it detects biomarkers associated with cardiovascular disease and liver disease. The testing unit also adds a function to assess the risk of specific diseases based on saliva and blood analysis data. For example, it assesses the risk of disease based on the concentration of biomarkers and suggests appropriate countermeasures. This makes it possible to automatically identify biomarkers associated with specific diseases.

[0039] The testing unit can evaluate the user's nutritional state and immune strength based on the test data and provide appropriate advice. The testing unit, for example, develops an algorithm to evaluate the user's nutritional state based on saliva analysis data. For example, it detects vitamin and mineral deficiencies and suggests appropriate nutritional supplementation. The testing unit also adds a function to evaluate the user's immune strength based on blood analysis data. For example, it analyzes white blood cell and antibody levels to evaluate the state of the immune strength. The testing unit also builds a system to evaluate the user's nutritional state and immune strength based on saliva and blood analysis data and provide appropriate advice. For example, it suggests meals and supplements for nutritional supplementation and immune strength improvement. This makes it possible to evaluate the user's nutritional state and immune strength and provide appropriate advice.

[0040] The comprehensive judgment unit can make more accurate judgments by referring to the user's past health data. The comprehensive judgment unit, for example, refers to the user's past health data and develops an algorithm that makes more accurate judgments when making a comprehensive judgment. For example, it evaluates current symptoms based on past diagnostic results and treatment history. The comprehensive judgment unit also adds a function that makes specific judgments related to the user's symptoms based on past health data. For example, it evaluates current blood pressure abnormalities based on past blood pressure data. The comprehensive judgment unit also adds a function that makes appropriate follow-up when making a comprehensive judgment based on the user's past health data. For example, it evaluates the current blood sugar management status based on past diabetes data. This allows the user's past health data to be referenced and more accurate judgments to be made.

[0041] The comprehensive judgment unit can evaluate the user's health risk based on the comprehensive judgment data and suggest preventive measures. The comprehensive judgment unit, for example, develops an algorithm for evaluating the user's health risk based on the comprehensive judgment data. For example, it evaluates the risk of cardiovascular disease based on smoking history and family history and suggests appropriate preventive measures. The comprehensive judgment unit also analyzes the user's comprehensive judgment data and adds a function for evaluating health risk. For example, it evaluates the risk of obesity and diabetes based on lack of exercise and poor eating habits and suggests preventive measures. The comprehensive judgment unit also builds a system that evaluates the user's health risk based on the comprehensive judgment data and suggests appropriate preventive measures. For example, it evaluates mental health risks based on stress levels and lack of sleep and suggests relaxation methods. This makes it possible to evaluate the user's health risk and suggest appropriate preventive measures.

[0042] The comprehensive judgment unit can also apply the comprehensive judgment function to remote medical support, enabling remote health consultations. For example, the comprehensive judgment unit applies the comprehensive judgment function to remote medical support and builds a system that enables remote health consultations. For example, a doctor makes a comprehensive judgment of a user via video call. The comprehensive judgment unit also develops an online comprehensive judgment system that allows remote users to receive a comprehensive judgment. For example, a user answers a web form, and a doctor analyzes the data. The comprehensive judgment unit also builds a system that uploads comprehensive judgment data to the cloud, allowing a remote doctor to check the data in real time. For example, a user receives a comprehensive judgment through a smartphone app, and a doctor checks the data. This can also be applied to remote medical support, enabling remote health consultations.

[0043] The comprehensive assessment unit can evaluate the user's health condition based on the comprehensive assessment data and automatically generate a fitness plan and a meal plan. The comprehensive assessment unit, for example, develops an algorithm that evaluates the user's health condition based on the comprehensive assessment data and automatically generates an individual fitness plan. For example, it proposes an appropriate exercise plan based on exercise habits and physical fitness level. The comprehensive assessment unit also adds a function that analyzes the user's comprehensive assessment data and evaluates the health condition. For example, it automatically generates an individual meal plan based on dietary habits and allergy information. The comprehensive assessment unit also builds a system that evaluates the user's health condition based on the comprehensive assessment data and automatically generates a fitness plan and a meal plan. For example, it proposes a plan that takes weight management and nutritional balance into consideration. This makes it possible to evaluate the user's health condition and automatically generate a fitness plan and a meal plan.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The medical support robot may further include an audio guide unit. The audio guide unit provides the user with audio information and advice about their health. For example, when the user undergoes a health check, the audio guide unit explains the procedure and provides appropriate advice based on the measurement results. The audio guide unit may also provide audio guidance on how to help the user relax and how to stretch. Furthermore, the audio guide unit may provide appropriate answers when the user asks a health-related question. This allows the user to easily obtain health-related information through the audio guide.

[0046] The medical support robot may further include a motion analysis unit. The motion analysis unit analyzes the user's motion patterns and proposes an appropriate exercise plan. For example, when the user walks, the motion analysis unit measures the number of steps and walking speed and proposes an appropriate amount of exercise. The motion analysis unit can also analyze the user's posture and movements and provide guidance on the correct exercise form. Furthermore, the motion analysis unit can record the user's exercise history and monitor their progress. This allows the user to carry out an appropriate exercise plan and maintain their health.

[0047] The medical support robot can further include a nutritional analysis unit. The nutritional analysis unit analyzes the user's dietary content and evaluates the nutritional balance. For example, when the user takes a photo of their meal, the nutritional analysis unit analyzes the photo and evaluates the nutrients ingested. The nutritional analysis unit can also record the user's dietary history and suggest areas for improving nutritional balance. Furthermore, the nutritional analysis unit can also suggest a meal plan based on the user's health condition. This allows the user to maintain an appropriate nutritional balance and stay healthy.

[0048] The medical support robot may further include an environmental monitoring unit. The environmental monitoring unit monitors the user's surrounding environment and evaluates factors that affect health. For example, it measures the indoor temperature, humidity, and air quality and suggests appropriate environmental conditions. The environmental monitoring unit may also evaluate noise levels and lighting brightness to support a comfortable living environment for the user. Furthermore, the environmental monitoring unit may analyze the user's sleeping environment and provide advice to promote comfortable sleep. This allows the user to maintain a healthy living environment.

[0049] The medical support robot can further include a rehabilitation support unit. The rehabilitation support unit has the function of supporting the user's rehabilitation. For example, when the user performs rehabilitation exercises, the rehabilitation support unit instructs the user on the correct exercise method and monitors the user's progress. The rehabilitation support unit can also create a rehabilitation plan for the user and suggest appropriate exercise amounts and frequency. Furthermore, the rehabilitation support unit can record the user's rehabilitation history and share it with doctors and physical therapists. This allows the user to undergo effective rehabilitation and aim for a rapid recovery.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The biometric data measurement unit measures basic biometric data such as heart rate, blood pressure, weight, and body temperature. For example, when a user holds their hand over the robot, the built-in sensors measure their heart rate and blood pressure. Additionally, weight can be measured by stepping on a part that functions like a weighing scale. Body temperature is measured using a non-contact thermometer. Step 2: The respiration analyzer uses audio analysis to detect breathing-related issues such as coughing or shortness of breath. For example, if the user coughs, the audio is analyzed to determine the type and frequency of the cough. The respiration analyzer also monitors the user's breathing sounds to analyze the degree of shortness of breath. Step 3: The visual analysis unit uses machine vision to look for visual indicators of skin tone, eye color, and facial swelling. For example, a camera takes a picture of the user's face, and image recognition AI analyzes skin tone and eye color. It also analyzes facial contours and features to detect facial swelling. Step 4: The medical interview section asks the user a simple medical interview. For example, the AI ​​generation section asks questions such as "Have you noticed any changes in your health recently?" or "What symptoms do you have?" and analyzes the user's answers. This allows the system to understand the user's health condition. Step 5: The testing unit performs a saliva or blood test. For example, if the user provides saliva to the robot, the saliva is analyzed and the robot's health status is evaluated. The robot is also equipped with a simple device for blood tests, which can collect and analyze the user's blood. Step 6: The comprehensive judgment unit makes a comprehensive judgment based on the collected data and issues a prescription if necessary. For example, if the user's symptoms are mild, it will suggest an appropriate medication. On the other hand, if a comprehensive judgment is difficult or if serious symptoms are observed, it has a function to escalate the situation to a doctor.

[0052] (Example 2) The medical support robot according to the embodiment of the present invention is a system that measures biometric data, analyzes it with a generating AI, and comprehensively evaluates the user's health condition. This allows the medical support robot to comprehensively evaluate the user's health condition and provide appropriate medical support.

[0053] The medical support robot according to the embodiment includes a biometric data measurement unit, a respiration analysis unit, a visual analysis unit, a medical interview unit, an examination unit, and a comprehensive assessment unit. The biometric data measurement unit measures basic biometric data such as heart rate, blood pressure, weight, and body temperature. For example, when a user holds their hand over the robot, the built-in sensors measure their heart rate and blood pressure. Additionally, weight can be measured by stepping on a scale-like feature. Body temperature is measured using a non-contact thermometer. The respiration analysis unit uses audio analysis to detect respiratory issues such as coughing or shortness of breath. For example, when a user coughs, the audio is analyzed to determine the type and frequency of the cough. The unit also monitors the user's breathing sounds to analyze the level of shortness of breath. The visual analysis unit uses machine vision to detect visual indicators of skin tone, eye color, and facial swelling. For example, the user's face is photographed with a camera, and image recognition AI analyzes skin tone and eye color. Furthermore, the unit analyzes facial contours and features to detect facial swelling. The medical interview unit asks the user a simple medical interview. For example, the AI ​​generation system asks questions such as, "Have you noticed any changes in your health recently?" or "What symptoms do you have?" and analyzes the user's answers. This allows the user's health condition to be understood. The testing unit performs saliva or blood tests. For example, when the user provides saliva to the robot, the saliva is analyzed and the health condition is evaluated. The robot is also equipped with a simple device for blood tests, which can collect and analyze the user's blood. The comprehensive judgment unit makes a comprehensive judgment based on the collected data and issues a prescription as necessary. For example, if the user's symptoms are mild, the robot suggests an appropriate medication. On the other hand, if a comprehensive judgment is difficult or if serious symptoms are observed, the robot has a function to escalate the case to a doctor. This allows the medical support robot according to the embodiment to comprehensively evaluate the user's health condition and provide appropriate medical support. For example, the user can easily perform health checks at home and consult a doctor as needed. This also reduces the burden on medical institutions and enables efficient medical care.

[0054] The biometric data measurement unit can incorporate an algorithm that compares data with the user's past data and automatically detects abnormal values. For example, the biometric data measurement unit compares the user's past heart rate data with current data to detect abnormal fluctuations. For example, it compares the current heart rate with the average heart rate over the past month and automatically notifies the user of any abnormal increases or decreases. The biometric data measurement unit also compares blood pressure measurement results with past data to detect abnormal values. For example, it determines whether the current measurement value is within the normal range based on past blood pressure data and issues an alert if an abnormality is detected. The biometric data measurement unit also compares weight fluctuations with past data to detect sudden increases or decreases. For example, it automatically detects sudden weight gain or loss based on weight data from the past week and notifies the user. This automatic detection of abnormal values ​​allows for early detection of health problems.

[0055] The biometric data measurement unit can upload measurement data to the cloud in real time, allowing doctors to check the data immediately. For example, the biometric data measurement unit can upload measurement data to the cloud in real time, building a system that allows doctors to check the data immediately through a dedicated dashboard. For example, heart rate and blood pressure data can be stored in the cloud and monitored remotely by doctors. The biometric data measurement unit can also upload the user's body temperature data to the cloud in real time, allowing doctors to respond immediately if they detect an abnormality. For example, an alert can be sent to the doctor if the body temperature is high. The biometric data measurement unit can also upload weight data to the cloud, allowing doctors to remotely monitor the user's health condition. For example, if there is a sudden change in weight, the doctor can respond immediately. This allows doctors in remote locations to check the data in real time.

[0056] The biometric data measurement unit can use the emotion estimation function to evaluate the user's stress level and provide advice to relax if the stress level is high. The biometric data measurement unit can use the emotion estimation function to evaluate the user's stress level, for example, while measuring heart rate or blood pressure. For example, the biometric data measurement unit can calculate a stress score based on heart rate fluctuations or blood pressure increases, and provide advice to relax if the stress level is high. The biometric data measurement unit can also use the emotion estimation function to evaluate the user's stress level while measuring body temperature. For example, the biometric data measurement unit can calculate a stress score based on body temperature fluctuations, and provide advice to take deep breaths or relax if the stress level is high. The biometric data measurement unit can also use the emotion estimation function to evaluate the user's stress level while measuring weight. For example, the biometric data measurement unit can calculate a stress score based on weight fluctuations, and provide advice to relax if the stress level is high. This makes it possible to evaluate the user's stress level and provide appropriate advice.

[0057] The respiratory analysis unit can automatically identify sound patterns associated with specific diseases in analyzing respiratory sounds. For example, the respiratory analysis unit develops an algorithm that automatically identifies sound patterns associated with specific diseases in analyzing respiratory sounds. For example, it detects specific sound patterns associated with asthma or pneumonia. The respiratory analysis unit also adds a function that analyzes coughing sounds and automatically identifies sound patterns associated with specific diseases. For example, it determines the possibility of bronchitis or pulmonary tuberculosis from the sound of coughing. The respiratory analysis unit also adds a function that analyzes shortness of breath sounds and automatically identifies sound patterns associated with specific diseases. For example, it determines the possibility of heart failure or pulmonary hypertension from the sound of shortness of breath. This makes it possible to automatically identify sound patterns associated with specific diseases.

[0058] The breathing analysis unit can assess the user's risk of sleep apnea syndrome based on the breathing analysis data and suggest necessary countermeasures. For example, the breathing analysis unit develops an algorithm to assess the user's risk of sleep apnea syndrome based on the breathing analysis data. For example, it analyzes the frequency of breathing cessation and hypopnea to assess the risk. The breathing analysis unit also monitors breathing sounds during sleep and builds a system to assess the risk of sleep apnea syndrome. For example, it analyzes the number and duration of breathing cessation during sleep. The breathing analysis unit also adds a function to suggest appropriate countermeasures if the risk of sleep apnea syndrome is high. For example, it recommends changing the sleeping position or using CPAP (continuous positive airway pressure). This makes it possible to assess the risk of sleep apnea syndrome and suggest appropriate countermeasures.

[0059] The breathing analysis unit can use the emotion estimation function to evaluate the user's tension or relaxation state from breathing sounds and provide stress management advice. The breathing analysis unit, for example, incorporates the emotion estimation function into the analysis of breathing sounds to evaluate the user's tension or relaxation state. For example, it calculates a stress level based on the speed and depth of breathing and provides advice for relaxation. The breathing analysis unit also adds a function to analyze coughing sounds and evaluate the user's tension state. For example, it calculates a stress level based on the frequency and strength of coughing and provides advice for relaxation. The breathing analysis unit also adds a function to analyze shortness of breath sounds and evaluate the user's relaxation state. For example, it calculates a stress level based on the frequency and duration of shortness of breath and provides advice for relaxation. This makes it possible to evaluate the user's tension or relaxation state and provide appropriate stress management advice.

[0060] The visual analysis unit can automatically identify visual changes associated with specific diseases in analyzing skin tone and eye color. The visual analysis unit, for example, analyzes skin tone and develops an algorithm that automatically identifies changes associated with specific diseases. For example, it detects skin tone changes associated with jaundice and anemia. The visual analysis unit also adds a function that analyzes eye color and automatically identifies changes associated with specific diseases. For example, it detects eye color changes associated with liver disease and diabetes. The visual analysis unit also adds a function that analyzes facial swelling and automatically identifies changes associated with specific diseases. For example, it detects facial swelling associated with kidney disease and heart failure. This makes it possible to automatically identify visual changes associated with specific diseases.

[0061] The visual analysis unit can evaluate the user's nutritional status and need for hydration based on visual indicators and provide appropriate advice. The visual analysis unit, for example, analyzes skin tone and develops an algorithm to evaluate the user's nutritional status. For example, it detects changes in skin tone associated with vitamin or iron deficiency and suggests appropriate nutritional supplementation. The visual analysis unit also adds a function to analyze eye color and evaluate the need for hydration. For example, it detects dryness or redness of the eyes and recommends hydration. The visual analysis unit also adds a function to analyze facial swelling and evaluate the user's nutritional status and need for hydration. For example, if swelling is present, it suggests limiting salt intake and providing more water. This makes it possible to evaluate the user's nutritional status and need for hydration and provide appropriate advice.

[0062] The visual analysis unit uses the emotion estimation function to evaluate the user's emotional state from visual indicators and analyze the impact of emotional fluctuations on health. The visual analysis unit, for example, analyzes skin tone and evaluates the user's emotional state using the emotion estimation function. For example, it analyzes the tendency for skin tone to change during times of high stress. The visual analysis unit also adds a function to analyze eye color and evaluate the user's emotional state using the emotion estimation function. For example, it analyzes the tendency for eye color to change during times of high stress. The visual analysis unit also adds a function to analyze facial swelling and evaluate the user's emotional state using the emotion estimation function. For example, it analyzes the tendency for facial swelling to increase during times of high stress. This makes it possible to evaluate the user's emotional state and analyze the impact of emotional fluctuations on health.

[0063] The medical interview unit can refer to the user's past health data during the medical interview and generate more specific questions. The medical interview unit, for example, develops an algorithm that refers to the user's past health data and generates specific questions during the medical interview. For example, a user who has previously been diagnosed with high blood pressure is asked about recent blood pressure fluctuations. The medical interview unit also adds a function that generates specific questions related to the user's symptoms based on the past health data. For example, a user who has previously been diagnosed with asthma is asked about the frequency of recent shortness of breath. The medical interview unit also adds a function that generates appropriate follow-up questions during the medical interview based on the user's past health data. For example, a user who has previously been diagnosed with diabetes is asked about recent blood sugar management status. This makes it possible to refer to the user's past health data and generate more specific questions.

[0064] The medical interview unit can assess the user's health risk based on the medical interview data and suggest preventive measures. The medical interview unit, for example, develops an algorithm for assessing the user's health risk based on the medical interview data. For example, it assesses the risk of cardiovascular disease based on smoking history and family history and suggests appropriate preventive measures. The medical interview unit also analyzes the user's answers and adds a function for assessing health risk. For example, it assesses the risk of obesity and diabetes based on lack of exercise and poor eating habits and suggests preventive measures. The medical interview unit also builds a system for assessing the user's health risk based on the medical interview data and suggesting appropriate preventive measures. For example, it assesses mental health risks based on stress levels and lack of sleep and suggests relaxation methods. This makes it possible to assess the user's health risk and suggest appropriate preventive measures.

[0065] The medical interview unit can use the emotion estimation function to evaluate the emotional state of the user during the interview and provide advice to relax if stress is high. The medical interview unit, for example, uses the emotion estimation function to evaluate the emotional state of the user during the interview. For example, it analyzes the user's facial expressions and voice and calculates the stress level. The medical interview unit also adds a function to evaluate the emotional state of the user during the interview and provide advice to relax if stress is high. For example, it suggests deep breathing or meditation techniques. The medical interview unit also uses the emotion estimation function to build a system that evaluates the emotional state of the user during the interview in real time and provides advice to relax if stress is high. For example, it suggests playing relaxing music or using a relaxation app. This makes it possible to evaluate the user's emotional state and provide appropriate advice.

[0066] The testing unit can automatically identify biomarkers associated with specific diseases in saliva and blood analysis. For example, the testing unit develops an algorithm that automatically identifies biomarkers associated with specific diseases in saliva analysis. For example, it detects biomarkers associated with oral cancer and diabetes. The testing unit also adds a function to automatically identify biomarkers associated with specific diseases in blood analysis. For example, it detects biomarkers associated with cardiovascular disease and liver disease. The testing unit also adds a function to assess the risk of specific diseases based on saliva and blood analysis data. For example, it assesses the risk of disease based on the concentration of biomarkers and suggests appropriate countermeasures. This makes it possible to automatically identify biomarkers associated with specific diseases.

[0067] The testing unit can evaluate the user's nutritional state and immune strength based on the test data and provide appropriate advice. The testing unit, for example, develops an algorithm to evaluate the user's nutritional state based on saliva analysis data. For example, it detects vitamin and mineral deficiencies and suggests appropriate nutritional supplementation. The testing unit also adds a function to evaluate the user's immune strength based on blood analysis data. For example, it analyzes white blood cell and antibody levels to evaluate the state of the immune strength. The testing unit also builds a system to evaluate the user's nutritional state and immune strength based on saliva and blood analysis data and provide appropriate advice. For example, it suggests meals and supplements for nutritional supplementation and immune strength improvement. This makes it possible to evaluate the user's nutritional state and immune strength and provide appropriate advice.

[0068] The testing unit can use the emotion estimation function to evaluate the user's emotional state during the test and provide advice to relax if stress is high. The testing unit, for example, uses the emotion estimation function to evaluate the user's emotional state during a saliva test. For example, it analyzes the user's facial expressions and voice to calculate the stress level. The testing unit also adds a function to evaluate the user's emotional state during a blood test and provide advice to relax if stress is high. For example, it suggests deep breathing or meditation techniques. The testing unit also builds a system that uses the emotion estimation function to evaluate the user's emotional state during the test in real time and provides advice to relax if stress is high. For example, it suggests playing relaxing music or using a relaxation app. This makes it possible to evaluate the user's emotional state and provide appropriate advice.

[0069] The comprehensive judgment unit can make more accurate judgments by referring to the user's past health data. The comprehensive judgment unit, for example, refers to the user's past health data and develops an algorithm that makes more accurate judgments when making a comprehensive judgment. For example, it evaluates current symptoms based on past diagnostic results and treatment history. The comprehensive judgment unit also adds a function that makes specific judgments related to the user's symptoms based on past health data. For example, it evaluates current blood pressure abnormalities based on past blood pressure data. The comprehensive judgment unit also adds a function that makes appropriate follow-up when making a comprehensive judgment based on the user's past health data. For example, it evaluates the current blood sugar management status based on past diabetes data. This allows the user's past health data to be referenced and more accurate judgments to be made.

[0070] The comprehensive judgment unit can evaluate the user's health risk based on the comprehensive judgment data and suggest preventive measures. The comprehensive judgment unit, for example, develops an algorithm for evaluating the user's health risk based on the comprehensive judgment data. For example, it evaluates the risk of cardiovascular disease based on smoking history and family history and suggests appropriate preventive measures. The comprehensive judgment unit also analyzes the user's comprehensive judgment data and adds a function for evaluating health risk. For example, it evaluates the risk of obesity and diabetes based on lack of exercise and poor eating habits and suggests preventive measures. The comprehensive judgment unit also builds a system that evaluates the user's health risk based on the comprehensive judgment data and suggests appropriate preventive measures. For example, it evaluates mental health risks based on stress levels and lack of sleep and suggests relaxation methods. This makes it possible to evaluate the user's health risk and suggest appropriate preventive measures.

[0071] The overall judgment unit can use the emotion estimation function to evaluate the user's emotional state during the overall judgment and provide advice to relax if stress is high. The overall judgment unit, for example, uses the emotion estimation function to evaluate the user's emotional state during the overall judgment. For example, it analyzes the user's facial expressions and voice to calculate the stress level. The overall judgment unit also adds a function to evaluate the user's emotional state during the overall judgment and provide advice to relax if stress is high. For example, it suggests deep breathing or meditation techniques. The overall judgment unit also uses the emotion estimation function to build a system that evaluates the user's emotional state during the overall judgment in real time and provides advice to relax if stress is high. For example, it suggests playing relaxing music or using a relaxation app. This makes it possible to evaluate the user's emotional state and provide appropriate advice.

[0072] The comprehensive judgment unit can also apply the comprehensive judgment function to remote medical support, enabling remote health consultations. For example, the comprehensive judgment unit applies the comprehensive judgment function to remote medical support and builds a system that enables remote health consultations. For example, a doctor makes a comprehensive judgment of a user via video call. The comprehensive judgment unit also develops an online comprehensive judgment system that allows remote users to receive a comprehensive judgment. For example, a user answers a web form, and a doctor analyzes the data. The comprehensive judgment unit also builds a system that uploads comprehensive judgment data to the cloud, allowing a remote doctor to check the data in real time. For example, a user receives a comprehensive judgment through a smartphone app, and a doctor checks the data. This can also be applied to remote medical support, enabling remote health consultations.

[0073] The comprehensive assessment unit can evaluate the user's health condition based on the comprehensive assessment data and automatically generate a fitness plan and a meal plan. The comprehensive assessment unit, for example, develops an algorithm that evaluates the user's health condition based on the comprehensive assessment data and automatically generates an individual fitness plan. For example, it proposes an appropriate exercise plan based on exercise habits and physical fitness level. The comprehensive assessment unit also adds a function that analyzes the user's comprehensive assessment data and evaluates the health condition. For example, it automatically generates an individual meal plan based on dietary habits and allergy information. The comprehensive assessment unit also builds a system that evaluates the user's health condition based on the comprehensive assessment data and automatically generates a fitness plan and a meal plan. For example, it proposes a plan that takes weight management and nutritional balance into consideration. This makes it possible to evaluate the user's health condition and automatically generate a fitness plan and a meal plan.

[0074] The comprehensive judgment unit can use the emotion estimation function to evaluate the user's emotional state during the comprehensive judgment and analyze the impact of emotional fluctuations on health. The comprehensive judgment unit, for example, uses the emotion estimation function to evaluate the user's emotional state during the comprehensive judgment. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The comprehensive judgment unit also adds a function to evaluate the user's emotional state during the comprehensive judgment and analyze the impact of emotional fluctuations on health. For example, it analyzes the impact of stress levels and anxiety on health. The comprehensive judgment unit also uses the emotion estimation function to evaluate the user's emotional state during the comprehensive judgment in real time and build a system that analyzes the impact of emotional fluctuations on health. For example, it evaluates health risks based on fluctuations in the emotion score. This makes it possible to evaluate the user's emotional state and analyze the impact of emotional fluctuations on health.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The medical support robot may further include an audio guide unit. The audio guide unit provides the user with audio information and advice about their health. For example, when the user undergoes a health check, the audio guide unit explains the procedure and provides appropriate advice based on the measurement results. The audio guide unit may also provide audio guidance on how to help the user relax and how to stretch. Furthermore, the audio guide unit may provide appropriate answers when the user asks a health-related question. This allows the user to easily obtain health-related information through the audio guide.

[0077] The medical support robot may further include a motion analysis unit. The motion analysis unit analyzes the user's motion patterns and proposes an appropriate exercise plan. For example, when the user walks, the motion analysis unit measures the number of steps and walking speed and proposes an appropriate amount of exercise. The motion analysis unit can also analyze the user's posture and movements and provide guidance on the correct exercise form. Furthermore, the motion analysis unit can record the user's exercise history and monitor their progress. This allows the user to carry out an appropriate exercise plan and maintain their health.

[0078] The medical support robot can further include a nutritional analysis unit. The nutritional analysis unit analyzes the user's dietary content and evaluates the nutritional balance. For example, when the user takes a photo of their meal, the nutritional analysis unit analyzes the photo and evaluates the nutrients ingested. The nutritional analysis unit can also record the user's dietary history and suggest areas for improving nutritional balance. Furthermore, the nutritional analysis unit can also suggest a meal plan based on the user's health condition. This allows the user to maintain an appropriate nutritional balance and stay healthy.

[0079] The medical support robot may further include an environmental monitoring unit. The environmental monitoring unit monitors the user's surrounding environment and evaluates factors that affect health. For example, it measures the indoor temperature, humidity, and air quality and suggests appropriate environmental conditions. The environmental monitoring unit may also evaluate noise levels and lighting brightness to support a comfortable living environment for the user. Furthermore, the environmental monitoring unit may analyze the user's sleeping environment and provide advice to promote comfortable sleep. This allows the user to maintain a healthy living environment.

[0080] The medical support robot can further include a rehabilitation support unit. The rehabilitation support unit has the function of supporting the user's rehabilitation. For example, when the user performs rehabilitation exercises, the rehabilitation support unit instructs the user on the correct exercise method and monitors the user's progress. The rehabilitation support unit can also create a rehabilitation plan for the user and suggest appropriate exercise amounts and frequency. Furthermore, the rehabilitation support unit can record the user's rehabilitation history and share it with doctors and physical therapists. This allows the user to undergo effective rehabilitation and aim for a rapid recovery.

[0081] The medical support robot can further use its emotion estimation function to evaluate the user's emotional state and provide health advice based on the emotion. For example, it can analyze the user's facial expressions and voice to evaluate the user's stress and anxiety levels. It can also use the emotion estimation function to provide relaxation techniques and stress management advice based on the user's emotional state. It can also use the emotion estimation function to monitor the user's emotional state and analyze long-term emotional fluctuations. This allows the user to receive appropriate health advice based on their emotional state.

[0082] The medical support robot can further use its emotion estimation function to evaluate the user's emotional state and propose an exercise plan based on the emotion. For example, it can analyze the user's facial expressions and voice to evaluate the user's stress and anxiety levels. The emotion estimation function can also be used to propose an exercise plan based on the user's emotional state. For example, if stress is high, the robot can suggest yoga or stretching to relax, and if energy is high, the robot can suggest aerobic exercise. The emotion estimation function can also be used to monitor the user's emotional state and evaluate the effectiveness of the exercise plan. This allows the user to implement an appropriate exercise plan based on their emotional state.

[0083] The medical support robot can also use its emotion estimation function to evaluate the user's emotional state and provide nutritional advice based on the emotion. For example, it can analyze the user's facial expressions and voice to evaluate their stress and anxiety levels. The emotion estimation function can also be used to provide nutritional advice based on the user's emotional state. For example, if stress is high, it can suggest foods with a relaxing effect, and if energy is low, it can suggest foods with high nutritional value. Furthermore, the emotion estimation function can be used to monitor the user's emotional state and evaluate their long-term nutritional balance. This allows the user to receive appropriate nutritional advice based on their emotional state.

[0084] The medical support robot can also use its emotion estimation function to evaluate the user's emotional state and provide sleep advice based on the emotion. For example, it can analyze the user's facial expressions and voice to evaluate their stress and anxiety levels. It can also use the emotion estimation function to provide sleep advice based on the user's emotional state. For example, if the user is highly stressed, it can suggest a relaxing sleep environment, and if the user is highly anxious, it can suggest relaxation techniques. Furthermore, it can use the emotion estimation function to monitor the user's emotional state and evaluate the quality of their sleep. This allows the user to receive appropriate sleep advice based on their emotional state.

[0085] The medical support robot can further use its emotion estimation function to evaluate the user's emotional state and propose a rehabilitation plan based on the user's emotions. For example, it can analyze the user's facial expressions and voice to evaluate the user's stress and anxiety levels. It can also use the emotion estimation function to propose a rehabilitation plan based on the user's emotional state. For example, if the user is highly stressed, it can propose rehabilitation exercises to relax, and if the user is highly energetic, it can propose active rehabilitation exercises. Furthermore, it can use the emotion estimation function to monitor the user's emotional state and evaluate the effectiveness of the rehabilitation plan. This allows the user to implement an appropriate rehabilitation plan based on their emotional state.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The biometric data measurement unit measures basic biometric data such as heart rate, blood pressure, weight, and body temperature. For example, when a user holds their hand over the robot, the built-in sensors measure their heart rate and blood pressure. Additionally, weight can be measured by stepping on a part that functions like a weighing scale. Body temperature is measured using a non-contact thermometer. Step 2: The respiration analyzer uses audio analysis to detect breathing-related issues such as coughing or shortness of breath. For example, if the user coughs, the audio is analyzed to determine the type and frequency of the cough. The respiration analyzer also monitors the user's breathing sounds to analyze the degree of shortness of breath. Step 3: The visual analysis unit uses machine vision to look for visual indicators of skin tone, eye color, and facial swelling. For example, a camera takes a picture of the user's face, and image recognition AI analyzes skin tone and eye color. It also analyzes facial contours and features to detect facial swelling. Step 4: The medical interview section asks the user a simple medical interview. For example, the AI ​​generation section asks questions such as "Have you noticed any changes in your health recently?" or "What symptoms do you have?" and analyzes the user's answers. This allows the system to understand the user's health condition. Step 5: The testing unit performs a saliva or blood test. For example, if the user provides saliva to the robot, the saliva is analyzed and the robot's health status is evaluated. The robot is also equipped with a simple device for blood tests, which can collect and analyze the user's blood. Step 6: The comprehensive judgment unit makes a comprehensive judgment based on the collected data and issues a prescription if necessary. For example, if the user's symptoms are mild, it will suggest an appropriate medication. On the other hand, if a comprehensive judgment is difficult or if serious symptoms are observed, it has a function to escalate the situation to a doctor.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The 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.

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

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

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

[0113] Fig. 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.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0116] 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.

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

[0118] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0120] The data processing system 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

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

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a biometric data measurement unit that measures basic biometric data such as heart rate, blood pressure, weight, and body temperature; a respiratory analysis component that uses audio analysis to look for breathing-related issues such as coughing or shortness of breath; a visual analytics unit that uses machine vision to look for visual indicators of skin tone, eye color, and facial puffiness; an interview unit that asks a simple question to a user; A testing department that conducts saliva or blood tests; and a comprehensive judgment unit that makes a comprehensive judgment based on the collected data and issues a prescription as necessary. A system characterized by:

2. The biological data measurement unit Implement an algorithm that compares the data with the user's past data and automatically detects outliers.

2. The system of claim 1.

3. The biological data measurement unit Measurement data is uploaded to the cloud in real time, allowing doctors to view it immediately.

2. The system of claim 1.

4. The biological data measurement unit Evaluate the user's stress level and provide advice on how to relax if stress levels are high 2. The system of claim 1.

5. The respiration analysis unit Automatically identify disease-related sound patterns in respiratory sound analysis 2. The system of claim 1.

6. The respiration analysis unit Based on the respiratory analysis data, the risk of sleep apnea syndrome for the user is assessed and necessary measures are proposed.

2. The system of claim 1.

7. The respiration analysis unit Evaluate the user's state of tension or relaxation from breathing sounds and provide advice for stress management 2. The system of claim 1.

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