System

Abstract

To provide a system for notifying a medical staff and a family at appropriate timing by monitoring a state of a patient in real time.SOLUTION: A system includes a monitoring section, a notification section, and a support section. The monitoring section monitors a state of a patient. The notification section notifies a medical staff and a family on the basis of data collected by the monitoring section. The support section psychologically supports a patient on the basis of information notified by the notification section.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately monitor a patient's condition in real time and notify medical staff and family members in a timely manner, and there is room for improvement.

[0005] The system according to the embodiment aims to monitor the condition of a patient in real time and notify medical staff and family members at an appropriate time. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a notification unit, and a support unit. The monitoring unit monitors the condition of a patient. The notification unit notifies medical staff and family members based on data collected by the monitoring unit. The support unit provides psychological support to the patient based on information notified by the notification unit. Effect of the Invention

[0007] The system according to the embodiment can monitor the condition of a patient in real time and notify medical staff and family members at an appropriate time. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The attendant robot system according to the embodiment of the present invention is a system that aims to transfer and reduce the main pain caused by attending hospitalization. This attendant robot system monitors the patient's condition and provides the necessary care. Next, the attendant robot system notifies medical staff and family members according to the patient's condition. Furthermore, the attendant robot system provides psychological support to the patient and reduces stress and anxiety. For example, the attendant robot system monitors the patient's condition. At this time, the robot uses sensors to monitor the patient's vital signs, such as body temperature, heart rate, and respiratory rate, in real time. For example, if the patient's body temperature rises, the robot activates the cooling system to lower the body temperature. Also, if an abnormality is observed in the heart rate or respiratory rate, the robot notifies the medical staff. Next, the attendant robot system notifies the medical staff and family members according to the patient's condition. For example, if the patient's condition suddenly changes, the robot notifies the medical staff and encourages a quick response. Also, if the patient's condition is stable, the robot can send a reassuring message to the family. This allows the medical staff and family members to always be aware of the patient's condition. Furthermore, the attendant robot system provides psychological support to patients. For example, the robot can talk to the patient or play music to reduce the patient's stress and anxiety. The robot can also analyze the patient's facial expressions and tone of voice to grasp the patient's psychological state. This allows the robot to provide appropriate psychological support to the patient. This allows the robot to transfer and reduce the main pain of attending hospitalization. By receiving care and support from the robot, patients can reduce their physical burden and psychological stress. Furthermore, medical staff and family members can always grasp the patient's condition, allowing them to attend hospitalization with peace of mind. This allows the attendant robot system to transfer and reduce the main pain of attending hospitalization.

[0029] The attendant robot system according to the embodiment includes a monitoring unit, a notification unit, and a support unit. The monitoring unit monitors the condition of the patient. The condition of the patient includes, for example, body temperature, heart rate, respiratory rate, and the like, but is not limited to such examples. The monitoring unit monitors the body temperature of the patient in real time, for example, using a body temperature sensor. The monitoring unit can also monitor the heart rate using a heart rate sensor. The monitoring unit can also monitor the respiratory rate using a respiratory sensor. For example, the monitoring unit measures the body temperature of the patient using the body temperature sensor and notifies if there is an abnormality. The heart rate sensor measures the heart rate and notifies if there is an abnormality. The respiratory sensor measures the respiratory rate and notifies if there is an abnormality. The notification unit notifies medical staff and family members based on the data collected by the monitoring unit. The notification is performed, for example, using a smartphone app or a dedicated device, but is not limited to such examples. For example, the notification unit notifies medical staff using a smartphone app. The notification unit can also notify family members using a dedicated device. The notification unit can also perform an emergency notification when the condition of the patient suddenly changes. For example, the notification unit uses a smartphone app to notify medical staff of an emergency. The dedicated device issues an emergency notification to a family member. The support unit provides psychological support to the patient based on the information notified by the notification unit. The support is provided, for example, using voice recognition or emotion analysis, but is not limited to such examples. For example, the support unit uses voice recognition to analyze the patient's voice and grasp the patient's psychological state. The support unit can also use emotion analysis to analyze the patient's facial expression and grasp the patient's psychological state. The support unit can also reduce stress and anxiety by talking to the patient or playing music. For example, the support unit speaks to the patient in gentle words. The support unit plays music to relax the patient. As a result, the attendant robot system according to the embodiment can transfer and reduce the main pain of attending hospitalization by monitoring and notifying the patient's condition and providing psychological support.

[0030] The monitoring unit monitors the condition of the patient. The condition of the patient includes, for example, body temperature, heart rate, respiratory rate, etc., but is not limited to such examples. The monitoring unit monitors the body temperature of the patient in real time, for example, using a body temperature sensor. Specifically, the body temperature sensor may be a type that contacts the skin or a non-contact type infrared sensor, and the body temperature of the patient can be continuously measured using these. Fluctuations in body temperature are an important indicator of the health condition of the patient, and if an abnormality is detected, an immediate response is required. The monitoring unit can also monitor the heart rate using a heart rate sensor. The heart rate sensor detects changes in blood flow using, for example, photoelectric volume plethysmography (PPG) to measure the heart rate. This enables early detection of abnormalities in the heart rate, arrhythmia, and the like. Furthermore, the monitoring unit can also monitor the respiratory rate using a respiratory sensor. For example, a strain gauge that detects chest movement and capnography that measures the carbon dioxide concentration in exhaled air are used as the respiratory sensor. This makes it possible to detect abnormalities in the respiratory rate and respiratory pattern and respond to them early. The monitoring unit analyzes the data obtained from these sensors in real time, and if an abnormality is detected, it immediately sends the information to the notification unit. This makes it possible to continuously monitor the patient's condition and quickly respond to any abnormalities that occur.

[0031] The notification unit notifies medical staff and family members based on the data collected by the monitoring unit. The notification is performed, for example, using a smartphone app or a dedicated device, but is not limited to such examples. Specifically, the notification unit receives the abnormal data transmitted from the monitoring unit and immediately notifies medical staff and family members. When a smartphone app is used, the app has a push notification function, and a notification is sent immediately when an abnormality is detected. The notification content includes the type of abnormality, specific numerical values, recommended countermeasures, and the like. When a dedicated device is used, the device has a function to notify abnormalities by voice, vibration, light, etc., so that family members and medical staff can respond quickly. Furthermore, the notification unit can also issue an emergency notification when the patient's condition suddenly changes. An emergency notification has a higher priority than normal notifications and is used in situations where an immediate response is required. For example, an emergency notification is issued in a life-threatening situation, such as when the heart rate suddenly drops or breathing stops. The notification unit can use multiple communication means in combination to ensure that these notifications are sent. For example, in addition to notifications via the Internet, notifications using SMS and voice calls are also performed to ensure that information is transmitted. This enables the notification unit to quickly and reliably provide appropriate notifications according to the patient's condition, helping medical staff and family members to respond quickly.

[0032] The support unit provides psychological support to the patient based on the information notified by the notification unit. The support is provided, for example, using voice recognition and emotion analysis, but is not limited to such examples. Specifically, the support unit uses voice recognition to analyze the patient's voice and grasp the patient's psychological state. Voice recognition technology can detect changes in the patient's tone of voice and speaking style to identify signs of stress and anxiety. In addition, emotion analysis can analyze the patient's facial expression and grasp the patient's psychological state. Emotion analysis technology analyzes facial expressions, eye movements, and minute muscle movements to evaluate the patient's emotional state. This makes it possible to detect the stress and anxiety felt by the patient early and provide appropriate support. Furthermore, the support unit can reduce stress and anxiety by talking to the patient or playing music. For example, the support unit can give the patient a sense of security by talking to the patient in gentle words. It can also play music according to the patient's preferences to relax the patient. This reduces the patient's psychological burden and allows the patient to spend time in a more comfortable environment. The support unit combines these functions to comprehensively support the patient's psychological state. For example, by combining voice recognition and emotion analysis, it is possible to grasp the psychological state of the patient more accurately and respond appropriately. In addition, by collecting patient feedback and continuously improving the support content, it is possible to provide more effective support. This allows the support department to reduce the psychological burden on patients and transfer and reduce the main pain of hospitalization.

[0033] The monitoring unit includes a body temperature sensor, a heart rate sensor, and a breathing sensor, and can monitor the patient's vital signs in real time. The monitoring unit, for example, uses a body temperature sensor to monitor the patient's body temperature in real time. For example, the body temperature sensor measures the patient's body temperature and notifies the patient if there is an abnormality. The monitoring unit can also monitor the heart rate using a heart rate sensor. For example, the heart rate sensor measures the heart rate and notifies the patient if there is an abnormality. The monitoring unit can also monitor the respiratory rate using a breathing sensor. For example, the breathing sensor measures the respiratory rate and notifies the patient if there is an abnormality. This makes it possible to monitor the patient's vital signs in real time and respond quickly. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired from the body temperature sensor, heart rate sensor, and breathing sensor to the generation AI, and the generation AI can analyze the data.

[0034] The notification unit can notify medical staff or family members using a smartphone app or a dedicated device. The notification unit can notify medical staff using, for example, a smartphone app. For example, the smartphone app displays the patient's condition in real time and notifies if there is an abnormality. The notification unit can also notify family members using a dedicated device. For example, the dedicated device displays the patient's condition and notifies if there is an abnormality. The notification unit can also issue an emergency notification if the patient's condition suddenly changes. For example, the notification unit issues an emergency notification to medical staff using a smartphone app. The dedicated device issues an emergency notification to family members. This allows the notification to be issued quickly and reliably by using the smartphone app or the dedicated device. A part or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can cause the smartphone app or the dedicated device to generate the notification content using a generating AI.

[0035] The support unit can grasp the psychological state of the patient using voice recognition or emotion analysis and take appropriate measures. The support unit, for example, uses voice recognition to analyze the patient's voice and grasp the psychological state. For example, the voice recognition technology analyzes the tone and content of the patient's voice to estimate the psychological state. The support unit can also analyze the patient's facial expression using emotion analysis to grasp the psychological state. For example, the emotion analysis technology analyzes changes in the patient's facial expression to estimate the psychological state. The support unit can also reduce stress and anxiety by talking to the patient or playing music. For example, the support unit talks to the patient in gentle words. Music is played to relax the patient. In this way, the patient's psychological state can be appropriately grasped and responded to by using voice recognition and emotion analysis. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the results of voice recognition and emotion analysis to the generation AI and cause the generation AI to generate an appropriate response.

[0036] The monitoring unit can activate the cooling system when the patient's body temperature rises. The monitoring unit activates the cooling system when the patient's body temperature rises, for example. For example, the cooling system uses a cooling pad or cooling gel to lower the body temperature. The cooling system can also lower the body temperature using a cooling fan. For example, the cooling pad detects the patient's body temperature and starts cooling. The cooling gel is used to lower the body temperature. The cooling fan lowers the body temperature by blowing cooling air. In this way, the body temperature can be appropriately managed by activating the cooling system when the patient's body temperature rises. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input data acquired from a body temperature sensor to the generation AI, and the generation AI can control the operation of the cooling system.

[0037] The notification unit can provide an emergency notification to medical staff when the patient's condition suddenly changes. The notification unit provides an emergency notification to medical staff when the patient's condition suddenly changes, for example. For example, the notification unit provides an emergency notification to medical staff using a smartphone app. The notification unit can also provide an emergency notification to family members using a dedicated device. For example, the smartphone app displays the patient's condition in real time and provides an emergency notification if there is an abnormality. The dedicated device displays the patient's condition and provides an emergency notification if there is an abnormality. This enables a rapid response by providing an emergency notification when the patient's condition suddenly changes. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the patient's condition data to a generation AI and have the generation AI generate the content of the emergency notification.

[0038] The support unit can reduce stress and anxiety by talking to the patient or playing music. The support unit reduces stress and anxiety by talking to the patient, for example. For example, the support unit talks to the patient in gentle words. The support unit can also reduce stress and anxiety by playing music. For example, the support unit plays music that has a relaxing effect. In this way, stress and anxiety can be reduced by talking to the patient or playing music. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the patient's voice and facial expression data into the generation AI, and have the generation AI generate appropriate conversation content and music.

[0039] The monitoring unit can analyze the patient's past health data and select an appropriate monitoring method. The monitoring unit, for example, analyzes the patient's past health data and selects an appropriate monitoring method. For example, the patient's past body temperature data is analyzed, and if the body temperature fluctuates greatly, monitoring is performed frequently. In addition, the patient's past heart rate data can be analyzed, and if an abnormality is observed, the sensitivity of the heart rate sensor can be increased. In addition, the patient's past respiratory data can be analyzed, and if the respiratory rate is stable, the frequency of monitoring can be reduced. In this way, the optimal monitoring method can be selected by analyzing the patient's past health data. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit inputs the patient's past health data into the generation AI, and the generation AI can select the optimal monitoring method.

[0040] The monitoring unit can adjust the sensitivity of the sensor based on the activity level and sleep pattern of the patient during monitoring. For example, when the patient is active, the monitoring unit increases the sensitivity of the sensor to collect detailed data. For example, when the patient is active, the monitoring unit increases the sensitivity of the sensor to collect detailed data. In addition, when the patient is sleeping, the sensor sensitivity can be lowered to reduce the burden on the patient. For example, when the patient is sleeping, the sensor sensitivity can be lowered to reduce the burden on the patient. In addition, when the patient's activity level is low, the sensor sensitivity can be appropriately adjusted to collect only necessary data. As a result, the patient's burden can be reduced by adjusting the sensitivity of the sensor based on the patient's activity level and sleep pattern. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input data on the patient's activity level and sleep pattern to the generation AI, and the generation AI can adjust the sensitivity of the sensor.

[0041] The monitoring unit can collect highly relevant data preferentially based on the geographical location information of the patient during monitoring. For example, when the patient is in a hospital, the monitoring unit preferentially collects heart rate and body temperature data. For example, when the patient is in a hospital, the monitoring unit preferentially collects heart rate and body temperature data. Also, when the patient is at home, the monitoring unit can preferentially collect respiratory rate and activity level data. For example, when the patient is at home, the monitoring unit preferentially collects respiratory rate and activity level data. Also, when the patient is out, all vital sign data can be collected equally. In this way, by taking into account the geographical location information of the patient, highly relevant data can be collected preferentially. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the geographical location information of the patient to the generation AI, and cause the generation AI to preferentially collect highly relevant data.

[0042] The monitoring unit can analyze the patient's food and medicine intake status during monitoring and collect related data. For example, the monitoring unit preferentially collects blood glucose level data after the patient has eaten. For example, the monitoring unit preferentially collects blood glucose level data after the patient has eaten. Also, the monitoring unit can preferentially collect heart rate and body temperature data after the patient has taken medicine. For example, the monitoring unit preferentially collects heart rate and body temperature data after the patient has taken medicine. Also, when the patient has not eaten or taken medicine, normal vital sign data can be collected. In this way, the patient's food and medicine intake status can be analyzed to collect related data. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the patient's food and medicine intake status data to the generation AI and cause the generation AI to collect related data.

[0043] The notification unit can adjust the level of detail of the notification based on the importance of the patient's condition at the time of notification. For example, when the patient's condition suddenly changes, the notification unit issues an emergency notification including detailed information. For example, when the patient's condition suddenly changes, the notification unit issues an emergency notification including detailed information. In addition, when the patient's condition is stable, a brief notification can be issued. For example, when the patient's condition is stable, a brief notification can be issued. In addition, when the patient's condition shows a mild abnormality, a notification with an appropriate level of detail can be issued. For example, when the patient's condition shows a mild abnormality, a notification with an appropriate level of detail is issued. As a result, by adjusting the level of detail of the notification based on the importance of the patient's condition, appropriate information can be provided. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit inputs the patient's condition data into the generation AI, and the generation AI can adjust the level of detail of the notification.

[0044] The notification unit can apply different notification algorithms depending on the category of the patient's condition at the time of notification. For example, when an abnormality is found in the patient's vital signs, the notification unit applies an emergency notification algorithm. For example, when an abnormality is found in the patient's vital signs, the notification unit applies an emergency notification algorithm. In addition, when the patient's psychological state is unstable, the notification unit can also apply a psychological support notification algorithm. For example, when the patient's psychological state is unstable, the notification unit applies a psychological support notification algorithm. In addition, when the patient's condition is stable, the notification unit can also apply a periodic notification algorithm. For example, when the patient's condition is stable, the notification unit applies a periodic notification algorithm. In this way, by applying different notification algorithms depending on the category of the patient's condition, appropriate notification can be performed. Part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the patient's condition data to the generation AI and cause the generation AI to apply an appropriate notification algorithm.

[0045] The notification unit can select an appropriate notification method based on the geographical location information of the patient's family at the time of notification. For example, when the patient's family is nearby, the notification unit performs direct notification. For example, when the patient's family is nearby, the notification unit performs direct notification. In addition, when the patient's family is far away, the notification can be performed through a smartphone app. For example, when the patient's family is far away, the notification can be performed through a smartphone app. In addition, when the patient's family is overseas, the notification can be performed through an international call or a message app. For example, when the patient's family is overseas, the notification can be performed through an international call or a message app. In this way, by taking into account the geographical location information of the patient's family, the optimal notification method can be selected. Part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit inputs the geographical location information of the patient's family to the generation AI, and the generation AI can select an appropriate notification method.

[0046] The notification unit can customize the contents of the notification by referring to the patient's past medical history at the time of notification. For example, if the patient has had heart disease in the past, the notification unit provides a detailed notification regarding the heart rate. For example, if the patient has had heart disease in the past, the notification unit provides a detailed notification regarding the heart rate. In addition, if the patient has had a respiratory disease in the past, the notification unit can also provide a detailed notification regarding the respiratory rate. For example, if the patient has had a respiratory disease in the past, the notification unit can also provide a detailed notification regarding the blood glucose level. For example, if the patient has had diabetes in the past, the notification unit provides a detailed notification regarding the blood glucose level. This allows the contents of the notification to be customized by referring to the patient's past medical history. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit inputs the patient's past medical history data into the generation AI, and the generation AI can customize the contents of the notification.

[0047] During support, the support unit can select the optimal support method by analyzing the patient's past psychological state. For example, the support unit speaks to the patient in gentle words based on data of when the patient felt anxious in the past. For example, the support unit speaks to the patient in gentle words based on data of when the patient felt anxious in the past. In addition, the support unit can provide a detailed explanation based on data of when the patient was relaxed in the past. For example, the support unit can provide a detailed explanation based on data of when the patient was relaxed in the past. In addition, the support unit can provide concise, to-the-point support based on data of when the patient felt stress in the past. This makes it possible to select the optimal support method by analyzing the patient's past psychological state. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit inputs the patient's past psychological state data into the generation AI, and allows the generation AI to select the optimal support method.

[0048] The support unit can customize the means of support based on the patient's current living situation during support. For example, when the patient is hospitalized, the support unit provides support in the hospital room. For example, when the patient is hospitalized, the support unit provides support in the hospital room. Also, when the patient is recuperating at home, the support unit can provide support at home. For example, when the patient is recuperating at home, the support unit provides support at home. Also, when the patient is out, the support unit can provide support at the out-of-home location. For example, when the patient is out, the support unit provides support at the out-of-home location. As a result, by customizing the means of support based on the patient's current living situation, appropriate support can be provided. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the patient's current living situation data into the generation AI, and have the generation AI customize the means of support.

[0049] The support unit can select the optimal support method in consideration of the geographical location information of the patient when providing support. For example, when the patient is in a hospital, the support unit provides support in the hospital room. For example, when the patient is in a hospital, the support unit provides support in the hospital room. Also, when the patient is at home, the support unit can provide support at home. For example, when the patient is at home, the support unit provides support at home. Also, when the patient is out, the support unit can provide support at the out-of-home location. For example, when the patient is out, the support unit provides support at the out-of-home location. This allows the optimal support method to be selected by considering the geographical location information of the patient. Part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit inputs the geographical location information of the patient to the generation AI, and allows the generation AI to select the optimal support method.

[0050] During support, the support unit can analyze the social media activity of the patient and suggest a means of support. For example, if the patient expresses anxiety on social media, the support unit suggests psychological support. For example, if the patient expresses anxiety on social media, the support unit suggests psychological support. Also, if the patient is relaxing on social media, the support unit can suggest physical support. For example, if the patient is relaxing on social media, the support unit suggests physical support. Also, if the patient expresses stress on social media, the support unit can suggest emergency support. For example, if the patient expresses stress on social media, the support unit suggests emergency support. In this way, by analyzing the social media activity of the patient, it is possible to suggest an appropriate means of support. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the social media activity data of the patient into the generation AI and have the generation AI suggest a means of support.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The attendant robot system may further include a meal monitoring unit that monitors the patient's meal intake status. The meal monitoring unit, for example, records the content and amount of food when the patient eats and analyzes the nutritional balance. For example, if the patient is lacking in a certain nutrient, the meal monitoring unit can send that information to the notification unit and prompt the medical staff to provide appropriate dietary guidance. The meal monitoring unit can also issue an alert if the patient does not eat. This makes it possible to properly manage the patient's nutritional state and support health maintenance.

[0053] The monitoring unit can analyze the patient's sleep patterns. For example, it can record the patient's sleep time and quality and notify if there is an abnormality. The analysis of sleep patterns can provide a more comprehensive understanding of the patient's health condition. For example, if the patient is not getting enough sleep, it can notify medical staff and encourage them to take appropriate measures. It can also estimate the patient's stress level and psychological state based on the sleep pattern data. This allows for more accurate monitoring of the patient's overall health condition.

[0054] The notification unit can provide a video call function to the patient's family. For example, if the patient's condition is stable, a video call invitation can be sent to the family. This allows the family to check on the patient's condition even from a remote location and feel reassured. In addition, if the patient needs psychological support, direct communication with the family can be promoted through a video call. Furthermore, the video call function can be used by medical staff to remotely check the patient's condition. This allows the patient's condition to be grasped more quickly and accurately.

[0055] The support unit can provide entertainment content customized based on the patient's hobbies and interests. For example, if the patient likes music, the support unit can play music of the patient's favorite genre. If the patient likes reading, the support unit can provide e-books. If the patient likes movies, the support unit can provide movie streaming services. This can enhance psychological support and reduce stress and anxiety by providing entertainment according to the patient's interests.

[0056] The monitoring unit can measure the amount of exercise the patient does and suggest an appropriate exercise program. For example, if the patient has been sitting for a long time, it can notify the patient to do some light stretching or walk. If the patient is not getting enough exercise, it can notify medical staff and provide appropriate exercise guidance. Furthermore, it can also evaluate the patient's physical strength and health condition based on the data on the amount of exercise, and create an individual exercise program. This can help maintain the patient's physical health and support their rehabilitation.

[0057] The process flow of the first embodiment will be briefly described below.

[0058] Step 1: The monitoring unit monitors the patient's condition. The patient's condition includes body temperature, heart rate, respiratory rate, etc. The monitoring unit monitors these data in real time using a body temperature sensor, heart rate sensor, and respiratory rate sensor, and notifies the patient if there is an abnormality. Step 2: The notification department notifies medical staff and family members based on the data collected by the monitoring department. Notifications are made using a smartphone app or a dedicated device, and emergency notifications are also made if the patient's condition suddenly changes. Step 3: The support unit provides psychological support to the patient based on the information notified by the notification unit. The support is provided using voice recognition and emotion analysis, and can reduce stress and anxiety by talking to the patient or playing music.

[0059] (Example 2) The attendant robot system according to the embodiment of the present invention is a system that aims to transfer and reduce the main pain caused by attending hospitalization. This attendant robot system monitors the patient's condition and provides the necessary care. Next, the attendant robot system notifies medical staff and family members according to the patient's condition. Furthermore, the attendant robot system provides psychological support to the patient and reduces stress and anxiety. For example, the attendant robot system monitors the patient's condition. At this time, the robot uses sensors to monitor the patient's vital signs, such as body temperature, heart rate, and respiratory rate, in real time. For example, if the patient's body temperature rises, the robot activates the cooling system to lower the body temperature. Also, if an abnormality is observed in the heart rate or respiratory rate, the robot notifies the medical staff. Next, the attendant robot system notifies the medical staff and family members according to the patient's condition. For example, if the patient's condition suddenly changes, the robot notifies the medical staff and encourages a quick response. Also, if the patient's condition is stable, the robot can send a reassuring message to the family. This allows the medical staff and family members to always be aware of the patient's condition. Furthermore, the attendant robot system provides psychological support to patients. For example, the robot can talk to the patient or play music to reduce the patient's stress and anxiety. The robot can also analyze the patient's facial expressions and tone of voice to grasp the patient's psychological state. This allows the robot to provide appropriate psychological support to the patient. This allows the robot to transfer and reduce the main pain of attending hospitalization. By receiving care and support from the robot, patients can reduce their physical burden and psychological stress. Furthermore, medical staff and family members can always grasp the patient's condition, allowing them to attend hospitalization with peace of mind. This allows the attendant robot system to transfer and reduce the main pain of attending hospitalization.

[0060] The attendant robot system according to the embodiment includes a monitoring unit, a notification unit, and a support unit. The monitoring unit monitors the condition of the patient. The condition of the patient includes, for example, body temperature, heart rate, respiratory rate, and the like, but is not limited to such examples. The monitoring unit monitors the body temperature of the patient in real time, for example, using a body temperature sensor. The monitoring unit can also monitor the heart rate using a heart rate sensor. The monitoring unit can also monitor the respiratory rate using a respiratory sensor. For example, the monitoring unit measures the body temperature of the patient using the body temperature sensor and notifies if there is an abnormality. The heart rate sensor measures the heart rate and notifies if there is an abnormality. The respiratory sensor measures the respiratory rate and notifies if there is an abnormality. The notification unit notifies medical staff and family members based on the data collected by the monitoring unit. The notification is performed, for example, using a smartphone app or a dedicated device, but is not limited to such examples. For example, the notification unit notifies medical staff using a smartphone app. The notification unit can also notify family members using a dedicated device. The notification unit can also perform an emergency notification when the condition of the patient suddenly changes. For example, the notification unit uses a smartphone app to notify medical staff of an emergency. The dedicated device issues an emergency notification to a family member. The support unit provides psychological support to the patient based on the information notified by the notification unit. The support is provided, for example, using voice recognition or emotion analysis, but is not limited to such examples. For example, the support unit uses voice recognition to analyze the patient's voice and grasp the patient's psychological state. The support unit can also use emotion analysis to analyze the patient's facial expression and grasp the patient's psychological state. The support unit can also reduce stress and anxiety by talking to the patient or playing music. For example, the support unit speaks to the patient in gentle words. The support unit plays music to relax the patient. As a result, the attendant robot system according to the embodiment can transfer and reduce the main pain of attending hospitalization by monitoring and notifying the patient's condition and providing psychological support.

[0061] The monitoring unit monitors the condition of the patient. The condition of the patient includes, for example, body temperature, heart rate, respiratory rate, etc., but is not limited to such examples. The monitoring unit monitors the body temperature of the patient in real time, for example, using a body temperature sensor. Specifically, the body temperature sensor may be a type that contacts the skin or a non-contact type infrared sensor, and the body temperature of the patient can be continuously measured using these. Fluctuations in body temperature are an important indicator of the health condition of the patient, and if an abnormality is detected, an immediate response is required. The monitoring unit can also monitor the heart rate using a heart rate sensor. The heart rate sensor detects changes in blood flow using, for example, photoelectric volume plethysmography (PPG) to measure the heart rate. This enables early detection of abnormalities in the heart rate, arrhythmia, and the like. Furthermore, the monitoring unit can also monitor the respiratory rate using a respiratory sensor. For example, a strain gauge that detects chest movement and capnography that measures the carbon dioxide concentration in exhaled air are used as the respiratory sensor. This makes it possible to detect abnormalities in the respiratory rate and respiratory pattern and respond to them early. The monitoring unit analyzes the data obtained from these sensors in real time, and if an abnormality is detected, it immediately sends the information to the notification unit. This makes it possible to continuously monitor the patient's condition and quickly respond to any abnormalities that occur.

[0062] The notification unit notifies medical staff and family members based on the data collected by the monitoring unit. The notification is performed, for example, using a smartphone app or a dedicated device, but is not limited to such examples. Specifically, the notification unit receives the abnormal data transmitted from the monitoring unit and immediately notifies medical staff and family members. When a smartphone app is used, the app has a push notification function, and a notification is sent immediately when an abnormality is detected. The notification content includes the type of abnormality, specific numerical values, recommended countermeasures, and the like. When a dedicated device is used, the device has a function to notify abnormalities by voice, vibration, light, etc., so that family members and medical staff can respond quickly. Furthermore, the notification unit can also issue an emergency notification when the patient's condition suddenly changes. An emergency notification has a higher priority than normal notifications and is used in situations where an immediate response is required. For example, an emergency notification is issued in a life-threatening situation, such as when the heart rate suddenly drops or breathing stops. The notification unit can use multiple communication means in combination to ensure that these notifications are sent. For example, in addition to notifications via the Internet, notifications using SMS and voice calls are also performed to ensure that information is transmitted. This enables the notification unit to quickly and reliably provide appropriate notifications according to the patient's condition, helping medical staff and family members to respond quickly.

[0063] The support unit provides psychological support to the patient based on the information notified by the notification unit. The support is provided, for example, using voice recognition and emotion analysis, but is not limited to such examples. Specifically, the support unit uses voice recognition to analyze the patient's voice and grasp the patient's psychological state. Voice recognition technology can detect changes in the patient's tone of voice and speaking style to identify signs of stress and anxiety. In addition, emotion analysis can analyze the patient's facial expression and grasp the patient's psychological state. Emotion analysis technology analyzes facial expressions, eye movements, and minute muscle movements to evaluate the patient's emotional state. This makes it possible to detect the stress and anxiety felt by the patient early and provide appropriate support. Furthermore, the support unit can reduce stress and anxiety by talking to the patient or playing music. For example, the support unit can give the patient a sense of security by talking to the patient in gentle words. It can also play music according to the patient's preferences to relax the patient. This reduces the patient's psychological burden and allows the patient to spend time in a more comfortable environment. The support unit combines these functions to comprehensively support the patient's psychological state. For example, by combining voice recognition and emotion analysis, it is possible to grasp the psychological state of the patient more accurately and respond appropriately. In addition, by collecting patient feedback and continuously improving the support content, it is possible to provide more effective support. This allows the support department to reduce the psychological burden on patients and transfer and reduce the main pain of hospitalization.

[0064] The monitoring unit includes a body temperature sensor, a heart rate sensor, and a breathing sensor, and can monitor the patient's vital signs in real time. The monitoring unit, for example, uses a body temperature sensor to monitor the patient's body temperature in real time. For example, the body temperature sensor measures the patient's body temperature and notifies the patient if there is an abnormality. The monitoring unit can also monitor the heart rate using a heart rate sensor. For example, the heart rate sensor measures the heart rate and notifies the patient if there is an abnormality. The monitoring unit can also monitor the respiratory rate using a breathing sensor. For example, the breathing sensor measures the respiratory rate and notifies the patient if there is an abnormality. This makes it possible to monitor the patient's vital signs in real time and respond quickly. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data acquired from the body temperature sensor, heart rate sensor, and breathing sensor to the generation AI, and the generation AI can analyze the data.

[0065] The notification unit can notify medical staff or family members using a smartphone app or a dedicated device. The notification unit can notify medical staff using, for example, a smartphone app. For example, the smartphone app displays the patient's condition in real time and notifies if there is an abnormality. The notification unit can also notify family members using a dedicated device. For example, the dedicated device displays the patient's condition and notifies if there is an abnormality. The notification unit can also issue an emergency notification if the patient's condition suddenly changes. For example, the notification unit issues an emergency notification to medical staff using a smartphone app. The dedicated device issues an emergency notification to family members. This allows the notification to be issued quickly and reliably by using the smartphone app or the dedicated device. A part or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can cause the smartphone app or the dedicated device to generate the notification content using a generating AI.

[0066] The support unit can grasp the psychological state of the patient using voice recognition or emotion analysis and take appropriate measures. The support unit, for example, uses voice recognition to analyze the patient's voice and grasp the psychological state. For example, the voice recognition technology analyzes the tone and content of the patient's voice to estimate the psychological state. The support unit can also analyze the patient's facial expression using emotion analysis to grasp the psychological state. For example, the emotion analysis technology analyzes changes in the patient's facial expression to estimate the psychological state. The support unit can also reduce stress and anxiety by talking to the patient or playing music. For example, the support unit talks to the patient in gentle words. Music is played to relax the patient. In this way, the patient's psychological state can be appropriately grasped and responded to by using voice recognition and emotion analysis. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the results of voice recognition and emotion analysis to the generation AI and cause the generation AI to generate an appropriate response.

[0067] The monitoring unit can activate the cooling system when the patient's body temperature rises. The monitoring unit activates the cooling system when the patient's body temperature rises, for example. For example, the cooling system uses a cooling pad or cooling gel to lower the body temperature. The cooling system can also lower the body temperature using a cooling fan. For example, the cooling pad detects the patient's body temperature and starts cooling. The cooling gel is used to lower the body temperature. The cooling fan lowers the body temperature by blowing cooling air. In this way, the body temperature can be appropriately managed by activating the cooling system when the patient's body temperature rises. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input data acquired from a body temperature sensor to the generation AI, and the generation AI can control the operation of the cooling system.

[0068] The notification unit can provide an emergency notification to medical staff when the patient's condition suddenly changes. The notification unit provides an emergency notification to medical staff when the patient's condition suddenly changes, for example. For example, the notification unit provides an emergency notification to medical staff using a smartphone app. The notification unit can also provide an emergency notification to family members using a dedicated device. For example, the smartphone app displays the patient's condition in real time and provides an emergency notification if there is an abnormality. The dedicated device displays the patient's condition and provides an emergency notification if there is an abnormality. This enables a rapid response by providing an emergency notification when the patient's condition suddenly changes. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the patient's condition data to a generation AI and have the generation AI generate the content of the emergency notification.

[0069] The support unit can reduce stress and anxiety by talking to the patient or playing music. The support unit reduces stress and anxiety by talking to the patient, for example. For example, the support unit talks to the patient in gentle words. The support unit can also reduce stress and anxiety by playing music. For example, the support unit plays music that has a relaxing effect. In this way, stress and anxiety can be reduced by talking to the patient or playing music. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the patient's voice and facial expression data into the generation AI, and have the generation AI generate appropriate conversation content and music.

[0070] The monitoring unit can estimate the patient's emotion and adjust the monitoring frequency based on the estimated patient's emotion. The monitoring unit, for example, estimates the patient's emotion and adjusts the monitoring frequency based on the estimated patient's emotion. For example, when the patient feels anxious, the monitoring frequency is increased to collect data in real time. Also, when the patient is relaxed, the monitoring frequency can be reduced to reduce the burden on the patient. For example, when the patient feels stressed, the monitoring frequency is moderately adjusted to collect only necessary data. In this way, the burden on the patient can be reduced by adjusting the monitoring frequency based on the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the patient's emotion data to the generation AI and cause the generation AI to adjust the monitoring frequency.

[0071] The monitoring unit can analyze the patient's past health data and select an appropriate monitoring method. The monitoring unit, for example, analyzes the patient's past health data and selects an appropriate monitoring method. For example, the patient's past body temperature data is analyzed, and if the body temperature fluctuates greatly, monitoring is performed frequently. In addition, the patient's past heart rate data can be analyzed, and if an abnormality is observed, the sensitivity of the heart rate sensor can be increased. In addition, the patient's past respiratory data can be analyzed, and if the respiratory rate is stable, the frequency of monitoring can be reduced. In this way, the optimal monitoring method can be selected by analyzing the patient's past health data. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit inputs the patient's past health data into the generation AI, and the generation AI can select the optimal monitoring method.

[0072] The monitoring unit can adjust the sensitivity of the sensor based on the activity level and sleep pattern of the patient during monitoring. For example, when the patient is active, the monitoring unit increases the sensitivity of the sensor to collect detailed data. For example, when the patient is active, the monitoring unit increases the sensitivity of the sensor to collect detailed data. In addition, when the patient is sleeping, the sensor sensitivity can be lowered to reduce the burden on the patient. For example, when the patient is sleeping, the sensor sensitivity can be lowered to reduce the burden on the patient. In addition, when the patient's activity level is low, the sensor sensitivity can be appropriately adjusted to collect only necessary data. As a result, the patient's burden can be reduced by adjusting the sensitivity of the sensor based on the patient's activity level and sleep pattern. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input data on the patient's activity level and sleep pattern to the generation AI, and the generation AI can adjust the sensitivity of the sensor.

[0073] The monitoring unit can estimate the patient's emotion and determine the priority of the monitoring data based on the estimated patient's emotion. The monitoring unit, for example, estimates the patient's emotion and determines the priority of the monitoring data based on the estimated patient's emotion. For example, if the patient is feeling anxious, the heart rate data can be collected preferentially. Also, if the patient is relaxed, the body temperature data can be collected preferentially. Also, if the patient is feeling stressed, the respiratory rate data can be collected preferentially. In this way, important data can be collected preferentially by determining the priority of the monitoring data based on the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the patient's emotion data to the generation AI and have the generation AI determine the priority of the monitoring data.

[0074] The monitoring unit can collect highly relevant data preferentially based on the geographical location information of the patient during monitoring. For example, when the patient is in a hospital, the monitoring unit preferentially collects heart rate and body temperature data. For example, when the patient is in a hospital, the monitoring unit preferentially collects heart rate and body temperature data. Also, when the patient is at home, the monitoring unit can preferentially collect respiratory rate and activity level data. For example, when the patient is at home, the monitoring unit preferentially collects respiratory rate and activity level data. Also, when the patient is out, all vital sign data can be collected equally. In this way, by taking into account the geographical location information of the patient, highly relevant data can be collected preferentially. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the geographical location information of the patient to the generation AI, and cause the generation AI to preferentially collect highly relevant data.

[0075] The monitoring unit can analyze the patient's food and medicine intake status during monitoring and collect related data. For example, the monitoring unit preferentially collects blood glucose level data after the patient has eaten. For example, the monitoring unit preferentially collects blood glucose level data after the patient has eaten. Also, the monitoring unit can preferentially collect heart rate and body temperature data after the patient has taken medicine. For example, the monitoring unit preferentially collects heart rate and body temperature data after the patient has taken medicine. Also, when the patient has not eaten or taken medicine, normal vital sign data can be collected. In this way, the patient's food and medicine intake status can be analyzed to collect related data. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the patient's food and medicine intake status data to the generation AI and cause the generation AI to collect related data.

[0076] The notification unit can estimate the patient's emotion and adjust the notification expression method based on the estimated patient's emotion. The notification unit, for example, estimates the patient's emotion and adjusts the notification expression method based on the estimated patient's emotion. For example, if the patient feels anxious, the notification is made in gentle words. Also, if the patient is relaxed, a notification including detailed information can be made. Also, if the patient feels stressed, a notification that is concise and to the point can be made. In this way, by adjusting the notification expression method based on the patient's emotion, it is possible to provide an appropriate notification to the patient. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the patient's emotion data to the generation AI and have the generation AI adjust the notification expression method.

[0077] The notification unit can adjust the level of detail of the notification based on the importance of the patient's condition at the time of notification. For example, when the patient's condition suddenly changes, the notification unit issues an emergency notification including detailed information. For example, when the patient's condition suddenly changes, the notification unit issues an emergency notification including detailed information. In addition, when the patient's condition is stable, a brief notification can be issued. For example, when the patient's condition is stable, a brief notification can be issued. In addition, when the patient's condition shows a mild abnormality, a notification with an appropriate level of detail can be issued. For example, when the patient's condition shows a mild abnormality, a notification with an appropriate level of detail is issued. As a result, by adjusting the level of detail of the notification based on the importance of the patient's condition, appropriate information can be provided. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit inputs the patient's condition data into the generation AI, and the generation AI can adjust the level of detail of the notification.

[0078] The notification unit can apply different notification algorithms depending on the category of the patient's condition at the time of notification. For example, when an abnormality is found in the patient's vital signs, the notification unit applies an emergency notification algorithm. For example, when an abnormality is found in the patient's vital signs, the notification unit applies an emergency notification algorithm. In addition, when the patient's psychological state is unstable, the notification unit can also apply a psychological support notification algorithm. For example, when the patient's psychological state is unstable, the notification unit applies a psychological support notification algorithm. In addition, when the patient's condition is stable, the notification unit can also apply a periodic notification algorithm. For example, when the patient's condition is stable, the notification unit applies a periodic notification algorithm. In this way, by applying different notification algorithms depending on the category of the patient's condition, appropriate notification can be performed. Part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the patient's condition data to the generation AI and cause the generation AI to apply an appropriate notification algorithm.

[0079] The notification unit can estimate the patient's emotion and determine the priority of notifications based on the estimated patient's emotion. The notification unit, for example, estimates the patient's emotion and determines the priority of notifications based on the estimated patient's emotion. For example, if the patient is feeling anxious, important notifications are given priority. Also, if the patient is relaxed, normal notifications can be given priority. Also, if the patient is feeling stressed, emergency notifications can be given priority. In this way, important notifications can be given priority by determining the priority of notifications based on the patient's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the patient's emotion data to the generation AI and have the generation AI determine the priority of notifications.

[0080] The notification unit can select an appropriate notification method based on the geographical location information of the patient's family at the time of notification. For example, when the patient's family is nearby, the notification unit performs direct notification. For example, when the patient's family is nearby, the notification unit performs direct notification. In addition, when the patient's family is far away, the notification can be performed through a smartphone app. For example, when the patient's family is far away, the notification can be performed through a smartphone app. In addition, when the patient's family is overseas, the notification can be performed through an international call or a message app. For example, when the patient's family is overseas, the notification can be performed through an international call or a message app. In this way, by taking into account the geographical location information of the patient's family, the optimal notification method can be selected. Part or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit inputs the geographical location information of the patient's family to the generation AI, and the generation AI can select an appropriate notification method.

[0081] The notification unit can customize the contents of the notification by referring to the patient's past medical history at the time of notification. For example, if the patient has had heart disease in the past, the notification unit provides a detailed notification regarding the heart rate. For example, if the patient has had heart disease in the past, the notification unit provides a detailed notification regarding the heart rate. In addition, if the patient has had a respiratory disease in the past, the notification unit can also provide a detailed notification regarding the respiratory rate. For example, if the patient has had a respiratory disease in the past, the notification unit can also provide a detailed notification regarding the blood glucose level. For example, if the patient has had diabetes in the past, the notification unit provides a detailed notification regarding the blood glucose level. This allows the contents of the notification to be customized by referring to the patient's past medical history. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit inputs the patient's past medical history data into the generation AI, and the generation AI can customize the contents of the notification.

[0082] The support unit can estimate the patient's emotions and adjust the support method based on the estimated patient's emotions. The support unit, for example, estimates the patient's emotions and adjusts the support method based on the estimated patient's emotions. For example, if the patient feels anxious, the support unit speaks to the patient in gentle words. If the patient feels relaxed, the support unit can provide a detailed explanation. If the patient feels stressed, the support unit can provide concise, to-the-point support. This allows appropriate support to be provided by adjusting the support method based on the patient's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the patient's emotion data into the generation AI and have the generation AI adjust the support method.

[0083] During support, the support unit can select the optimal support method by analyzing the patient's past psychological state. For example, the support unit speaks to the patient in gentle words based on data of when the patient felt anxious in the past. For example, the support unit speaks to the patient in gentle words based on data of when the patient felt anxious in the past. In addition, the support unit can provide a detailed explanation based on data of when the patient was relaxed in the past. For example, the support unit can provide a detailed explanation based on data of when the patient was relaxed in the past. In addition, the support unit can provide concise, to-the-point support based on data of when the patient felt stress in the past. This makes it possible to select the optimal support method by analyzing the patient's past psychological state. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit inputs the patient's past psychological state data into the generation AI, and allows the generation AI to select the optimal support method.

[0084] The support unit can customize the means of support based on the patient's current living situation during support. For example, when the patient is hospitalized, the support unit provides support in the hospital room. For example, when the patient is hospitalized, the support unit provides support in the hospital room. Also, when the patient is recuperating at home, the support unit can provide support at home. For example, when the patient is recuperating at home, the support unit provides support at home. Also, when the patient is out, the support unit can provide support at the out-of-home location. For example, when the patient is out, the support unit provides support at the out-of-home location. As a result, by customizing the means of support based on the patient's current living situation, appropriate support can be provided. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the patient's current living situation data into the generation AI, and have the generation AI customize the means of support.

[0085] The support unit can estimate the patient's emotion and determine the priority of support based on the estimated patient's emotion. The support unit, for example, estimates the patient's emotion and determines the priority of support based on the estimated patient's emotion. For example, if the patient feels anxious, psychological support can be given priority. Also, if the patient is relaxed, physical support can be given priority. Also, if the patient feels stressed, emergency support can be given priority. In this way, important support can be given priority by determining the priority of support based on the patient's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input the patient's emotion data to the generation AI and have the generation AI determine the priority of support.

[0086] The support unit can select the optimal support method in consideration of the geographical location information of the patient when providing support. For example, when the patient is in a hospital, the support unit provides support in the hospital room. For example, when the patient is in a hospital, the support unit provides support in the hospital room. Also, when the patient is at home, the support unit can provide support at home. For example, when the patient is at home, the support unit provides support at home. Also, when the patient is out, the support unit can provide support at the out-of-home location. For example, when the patient is out, the support unit provides support at the out-of-home location. This allows the optimal support method to be selected by considering the geographical location information of the patient. Part or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit inputs the geographical location information of the patient to the generation AI, and allows the generation AI to select the optimal support method.

[0087] During support, the support unit can analyze the social media activity of the patient and suggest a means of support. For example, if the patient expresses anxiety on social media, the support unit suggests psychological support. For example, if the patient expresses anxiety on social media, the support unit suggests psychological support. Also, if the patient is relaxing on social media, the support unit can suggest physical support. For example, if the patient is relaxing on social media, the support unit suggests physical support. Also, if the patient expresses stress on social media, the support unit can suggest emergency support. For example, if the patient expresses stress on social media, the support unit suggests emergency support. In this way, by analyzing the social media activity of the patient, it is possible to suggest an appropriate means of support. Part or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the social media activity data of the patient into the generation AI and have the generation AI suggest a means of support.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The attendant robot system may further include a meal monitoring unit that monitors the patient's meal intake status. The meal monitoring unit, for example, records the content and amount of food when the patient eats and analyzes the nutritional balance. For example, if the patient is lacking in a certain nutrient, the meal monitoring unit can send that information to the notification unit and prompt the medical staff to provide appropriate dietary guidance. The meal monitoring unit can also issue an alert if the patient does not eat. This makes it possible to properly manage the patient's nutritional state and support health maintenance.

[0090] The monitoring unit can analyze the patient's sleep patterns. For example, it can record the patient's sleep time and quality and notify if there is an abnormality. The analysis of sleep patterns can provide a more comprehensive understanding of the patient's health condition. For example, if the patient is not getting enough sleep, it can notify medical staff and encourage them to take appropriate measures. It can also estimate the patient's stress level and psychological state based on the sleep pattern data. This allows for more accurate monitoring of the patient's overall health condition.

[0091] The notification unit can provide a video call function to the patient's family. For example, if the patient's condition is stable, a video call invitation can be sent to the family. This allows the family to check on the patient's condition even from a remote location and feel reassured. In addition, if the patient needs psychological support, direct communication with the family can be promoted through a video call. Furthermore, the video call function can be used by medical staff to remotely check the patient's condition. This allows the patient's condition to be grasped more quickly and accurately.

[0092] The support unit can provide entertainment content customized based on the patient's hobbies and interests. For example, if the patient likes music, the support unit can play music of the patient's favorite genre. If the patient likes reading, the support unit can provide e-books. If the patient likes movies, the support unit can provide movie streaming services. This can enhance psychological support and reduce stress and anxiety by providing entertainment according to the patient's interests.

[0093] The monitoring unit can measure the amount of exercise the patient does and suggest an appropriate exercise program. For example, if the patient has been sitting for a long time, it can notify the patient to do some light stretching or walk. If the patient is not getting enough exercise, it can notify medical staff and provide appropriate exercise guidance. Furthermore, it can also evaluate the patient's physical strength and health condition based on the data on the amount of exercise, and create an individual exercise program. This can help maintain the patient's physical health and support their rehabilitation.

[0094] The monitoring unit can estimate the patient's emotions and select music based on the estimated patient's emotions. For example, if the patient is feeling anxious, music with a relaxing effect can be played. Also, if the patient is relaxed, lively music can be played. Furthermore, if the patient is feeling stressed, music with a stress-reducing effect can be selected. In this way, by providing music according to the patient's emotions, psychological support can be strengthened and stress and anxiety can be reduced.

[0095] The notification unit can estimate the patient's emotions and adjust the timing of notifications based on the estimated patient's emotions. For example, if the patient is relaxed, the notification can be delayed to reduce stress. Also, if the patient is feeling anxious, a sense of security can be provided by quickly notifying the patient. Furthermore, if the patient is feeling stressed, the frequency of notifications can be adjusted to provide only the necessary information. In this way, by adjusting the timing of notifications based on the patient's emotions, appropriate information can be provided and the psychological burden can be reduced.

[0096] The support unit can estimate the patient's emotions and provide relaxation techniques based on the estimated patient's emotions. For example, if the patient is feeling anxious, it can provide deep breathing or meditation guidance. If the patient is feeling stressed, it can also suggest massage or aromatherapy. Furthermore, if the patient is relaxed, it can play relaxation music or natural sounds. In this way, by providing relaxation techniques according to the patient's emotions, it is possible to strengthen psychological support and reduce stress and anxiety.

[0097] The monitoring unit can estimate the patient's emotions and adjust the display method of the monitoring data based on the estimated patient's emotions. For example, if the patient is feeling anxious, concise and easy-to-understand data is displayed. If the patient is relaxed, detailed data can be displayed. Furthermore, if the patient is feeling stressed, only important data can be highlighted and displayed. In this way, by adjusting the display method of the monitoring data based on the patient's emotions, appropriate information can be provided and psychological burden can be reduced.

[0098] The support unit can estimate the patient's emotions and adjust the method of communication based on the estimated patient's emotions. For example, if the patient feels anxious, the support unit can speak to the patient in gentle words. If the patient feels relaxed, the support unit can provide a detailed explanation. If the patient feels stressed, the support unit can communicate concisely and to the point. In this way, by adjusting the method of communication based on the patient's emotions, appropriate support can be provided and the psychological burden can be reduced.

[0099] The process flow of the second embodiment will be briefly described below.

[0100] Step 1: The monitoring unit monitors the patient's condition. The patient's condition includes body temperature, heart rate, respiratory rate, etc. The monitoring unit monitors this data in real time using a body temperature sensor, heart rate sensor, and respiratory rate sensor, and notifies the patient if there is an abnormality. Step 2: The notification department notifies medical staff and family members based on the data collected by the monitoring department. Notifications are made using a smartphone app or a dedicated device, and emergency notifications are also made if the patient's condition suddenly changes. Step 3: The support unit provides psychological support to the patient based on the information notified by the notification unit. The support is provided using voice recognition and emotion analysis, and can reduce stress and anxiety by talking to the patient or playing music.

[0101] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0102] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0103] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements including the monitoring unit, notification unit, and support unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit monitors the patient's body temperature, heart rate, respiratory rate, etc. in real time using a sensor of the smart device 14. The notification unit notifies medical staff and family members via the communication I / F 44 of the smart device 14. The support unit provides psychological support to the patient through the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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, the RAM 30, and the 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.

[0108] 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.

[0109] 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 the voice according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0111] 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 performed in a secure state.

[0112] Fig. 4 shows an example of 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0116] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0117] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0118] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0119] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements including the above-mentioned monitoring unit, notification unit, and support unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit monitors the patient's body temperature, heart rate, respiratory rate, etc. in real time using a sensor of the smart glasses 214. In addition, the notification unit notifies medical staff and family members via the communication I / F 44 of the smart glasses 214. The support unit provides psychological support to the patient through the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average 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 performed in a secure state.

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

[0129] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements including the monitoring unit, notification unit, and support unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the monitoring unit monitors the patient's body temperature, heart rate, respiratory rate, and the like in real time using a sensor of the headset type terminal 314. The notification unit notifies medical staff and family members via the communication I / F 44 of the headset type terminal 314. The support unit provides psychological support to the patient through the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0148] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0149] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0150] 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 a voice indicating a user input for 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.

[0151] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0152] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0153] Each of the multiple elements including the monitoring unit, notification unit, and support unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit monitors the patient's body temperature, heart rate, respiratory rate, and the like in real time using a sensor of the robot 414. The notification unit notifies medical staff and family members via the communication I / F 44 of the robot 414. The support unit provides psychological support to the patient through the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0155] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0157] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0159] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0161] 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.

[0162] 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 Universal Serial Bus (USB) 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.

[0163] In addition, 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 upon request from the data processing device 12.

[0164] 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.

[0165] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0166] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0167] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0168] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0170] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0172] (Appendix 1) A monitoring unit that monitors a patient's condition; A notification unit that notifies medical staff and family members based on the data collected by the monitoring unit; A support unit that provides psychological support to the patient based on the information notified by the notification unit. A system characterized by: (Appendix 2) The monitoring unit is Equipped with temperature, heart rate, and respiratory sensors, it monitors the patient's vital signs in real time. 2. The system of claim 1, (Appendix 3) The notification unit is Notify medical staff or family members using a smartphone app or dedicated device 2. The system of claim 1, (Appendix 4) The support portion is Using voice recognition or emotion analysis to understand the psychological state of the patient and respond appropriately 2. The system of claim 1, (Appendix 5) The monitoring unit is Activate the cooling system if the patient's temperature rises 2. The system of claim 1, (Appendix 6) The notification unit is If a patient's condition suddenly changes, medical staff will be notified immediately 2. The system of claim 1, (Appendix 7) The support portion is Talking to the patient or playing music to reduce stress and anxiety 2. The system of claim 1, (Appendix 8) The monitoring unit is Estimating a patient's emotions and adjusting the frequency of monitoring based on the estimated patient's emotions 2. The system of claim 1, (Appendix 9) The monitoring unit is Analyze the patient's past health data and choose the appropriate monitoring method 2. The system of claim 1, (Appendix 10) The monitoring unit is During monitoring, the sensor sensitivity is adjusted based on the patient's activity level and sleep patterns 2. The system of claim 1, (Appendix 11) The monitoring unit is To estimate a patient's emotion and prioritize monitoring data based on the estimated patient's emotion. 2. The system of claim 1, (Appendix 12) The monitoring unit is Prioritize collection of relevant data during monitoring based on the patient's geographic location 2. The system of claim 1, (Appendix 13) The monitoring unit is During monitoring, analyze the patient's diet and drug intake and collect relevant data. 2. The system of claim 1, (Appendix 14) The notification unit is Inferring patient emotion and adjusting notification language based on the inferred patient emotion 2. The system of claim 1, (Appendix 15) The notification unit is When notifying, adjust notification detail based on the severity of the patient's condition 2. The system of claim 1, (Appendix 16) The notification unit is At the time of notification, apply different notification algorithms depending on the category of the patient's condition 2. The system of claim 1, (Appendix 17) The notification unit is Estimate the patient's emotions and prioritize notifications based on the estimated patient emotions 2. The system of claim 1, (Appendix 18) The notification unit is At the time of notification, the appropriate notification method is selected based on the geographic location of the patient family member. 2. The system of claim 1, (Appendix 19) The notification unit is When notifying patients, the content of the notification can be customized by referencing their past medical history 2. The system of claim 1, (Appendix 20) The support portion is Estimate the patient's emotions and adjust support methods based on the estimated patient emotions 2. The system of claim 1, (Appendix 21) The support portion is When providing support, analyze the patient's past psychological state and select the most appropriate support method. 2. The system of claim 1, (Appendix 22) The support portion is When providing support, tailor support measures based on the patient's current life situation 2. The system of claim 1, (Appendix 23) The support portion is Estimate the patient's emotions and prioritize support based on the estimated patient's emotions 2. The system of claim 1, (Appendix 24) The support portion is When providing support, the system selects the most appropriate support method based on the patient's geographic location. 2. The system of claim 1, (Appendix 25) The support portion is When providing support, analyze the patient's social media activity to suggest ways to support them 2. The system of claim 1, [Explanation of symbols]

[0173] 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 monitoring unit that monitors a patient's condition; A notification unit that notifies medical staff and family members based on the data collected by the monitoring unit; A support unit that provides psychological support to the patient based on the information notified by the notification unit. A system characterized by:

2. The monitoring unit is Equipped with temperature, heart rate, and respiratory sensors, it monitors the patient's vital signs in real time.

2. The system of claim 1 .

3. The notification unit is Notify medical staff or family members using a smartphone app or dedicated device 2. The system of claim 1 .

4. The support portion is Using voice recognition or emotion analysis to understand the psychological state of the patient and respond appropriately 2. The system of claim 1 .

5. The monitoring unit is Activate the cooling system if the patient's temperature rises 2. The system of claim 1 .

6. The notification unit is If a patient's condition suddenly changes, medical staff will be notified immediately 2. The system of claim 1 .

7. The support portion is Talking to the patient or playing music to reduce stress and anxiety 2. The system of claim 1 .

8. The monitoring unit is Estimating a patient's emotions and adjusting the frequency of monitoring based on the estimated patient's emotions 2. The system of claim 1 .

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