system

Abstract

The system according to this embodiment aims to detect abnormalities in a patient's lifestyle and vital data at an early stage and to respond quickly. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a notification unit, an emergency response unit, and a recording unit. The collection unit collects the patient's lifestyle and vital data. The analysis unit analyzes the data collected by the collection unit and detects abnormalities. The notification unit issues a warning based on the abnormalities detected by the analysis unit and notifies the doctor. The emergency response unit responds in an emergency based on the abnormalities notified by the notification unit. The recording unit records the response taken by the emergency response unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to detect abnormalities in patients' living habits and vital data at an early stage and respond promptly.

[0005] The system according to the embodiment aims to detect abnormalities in patients' living habits and vital data at an early stage and respond promptly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a notification unit, an emergency response unit, and a recording unit. The data collection unit collects the patient's lifestyle and vital data. The analysis unit analyzes the data collected by the data collection unit and detects abnormalities. The notification unit issues a warning based on the abnormalities detected by the analysis unit and notifies the physician. The emergency response unit responds to emergencies based on the abnormalities notified by the notification unit. The recording unit records the actions taken by the emergency response unit. [Effects of the Invention]

[0007] The system according to this embodiment can detect abnormalities in a patient's lifestyle and vital data at an early stage and respond quickly. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that constantly monitors a patient's lifestyle and vital data, issues an early warning if an abnormality is detected, and notifies a physician. This AI agent system constantly monitors a patient's lifestyle and vital data, issues an early warning if an abnormality is detected, and notifies a physician. Furthermore, in an emergency, the AI ​​agent autonomously notifies emergency contacts and medical institutions, and provides real-time support for appropriate action. The response record is saved and used for future medical care. For example, the AI ​​agent system constantly monitors a patient's lifestyle and vital data. In this process, it collects data such as the patient's heart rate, blood pressure, body temperature, and activity level. For example, this data can be collected in real time using a wearable device. This allows for constant monitoring of the patient's health status. Next, the AI ​​agent system analyzes the collected data. The AI ​​agent detects abnormalities based on the collected data. For example, it can detect abnormal patterns such as a heart rate that is higher than normal or a sudden increase in blood pressure. This allows for early detection of abnormalities. If an abnormality is detected, the AI ​​agent system issues an early warning and notifies a physician. For example, when an abnormality is detected, a warning can be displayed on the patient's smartphone, and a notification can be sent to the doctor simultaneously. This allows the doctor to respond quickly. Furthermore, in emergencies, the AI ​​agent system autonomously notifies emergency contacts and medical institutions, providing real-time support for appropriate treatment. For example, in the event of cardiac arrest or a severe seizure, the AI ​​agent system can automatically call emergency contacts and send a notification to medical institutions. This enables a rapid response. Records of these responses are saved and used for future medical care. For example, emergency response records can be saved in the electronic medical record and used for future treatment. This allows doctors to provide more appropriate treatment by referring to past responses. In this way, the AI ​​agent system constantly monitors the patient's health status, issues early warnings if there are abnormalities, and notifies doctors. Furthermore, it can autonomously respond in emergencies, save the records, and use them for future medical care.This will streamline patient health management and improve the quality of medical care. The AI ​​agent system will continuously monitor the patient's health, issue early warnings if abnormalities are detected, and notify doctors. Furthermore, it can autonomously respond in emergencies, saving records for future medical use.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a notification unit, an emergency response unit, and a recording unit. The data collection unit collects the patient's lifestyle and vital data. The data collection unit collects data such as the patient's heart rate, blood pressure, body temperature, and activity level. The data collection unit can collect this data in real time, for example, using a wearable device. The data collection unit can measure heart rate using a heart rate sensor and collect data. The data collection unit can measure blood pressure using a blood pressure monitor and collect data. The data collection unit can measure body temperature using a thermometer and collect data. The data collection unit can measure activity level using an activity tracker and collect data. The analysis unit analyzes the data collected by the data collection unit and detects abnormalities. The analysis unit can analyze collected heart rate data and detect abnormalities, for example. The analysis unit can analyze collected blood pressure data and detect abnormalities, for example. The analysis unit can analyze collected body temperature data and detect abnormalities, for example. The analysis unit can, for example, analyze collected activity data and detect anomalies. The analysis unit can, for example, analyze collected data using AI and detect anomalies. The analysis unit can, for example, analyze collected data using machine learning algorithms and detect anomalies. The analysis unit can, for example, analyze collected data using anomaly detection algorithms and detect anomalies. The notification unit issues warnings based on the anomalies detected by the analysis unit and notifies the doctor. The notification unit can, for example, display a warning on the patient's smartphone when an anomaly is detected. The notification unit can, for example, send a notification to the doctor when an anomaly is detected. The notification unit can, for example, issue an audio alert when an anomaly is detected. The notification unit can, for example, send a text message when an anomaly is detected. The notification unit can, for example, send an email when an anomaly is detected. The notification unit can, for example, send an app notification when an anomaly is detected. The emergency response unit responds in emergencies based on the anomalies notified by the notification unit.The emergency response unit can, for example, automatically call emergency contacts in the event of cardiac arrest or a severe seizure. The emergency response unit can, for example, automatically send notifications to medical institutions in the event of cardiac arrest or a severe seizure. The emergency response unit can, for example, automatically arrange for an ambulance in the event of cardiac arrest or a severe seizure. The emergency response unit can, for example, automatically send notifications to family members in the event of cardiac arrest or a severe seizure. The emergency response unit can, for example, automatically contact nearby medical institutions in the event of cardiac arrest or a severe seizure. The records unit records the actions taken by the emergency response unit. The records unit can, for example, save emergency response records to the electronic medical record. The records unit can, for example, save emergency response records to a database. The records unit can, for example, save emergency response records to cloud storage. The records unit can, for example, save emergency response records to local storage. The records unit can, for example, save emergency response records to paper media. The records unit can, for example, send emergency response records via email. The recording unit can, for example, print emergency response records using a printer. This allows the AI ​​agent system according to the embodiment to constantly monitor the patient's lifestyle and vital data, issue early warnings if abnormalities are detected, and notify a doctor. Furthermore, it can autonomously respond in emergencies, save the records, and utilize them for future medical care.

[0030] The data collection unit collects patient lifestyle data and vital signs. For example, it collects data such as the patient's heart rate, blood pressure, body temperature, and activity level. Specifically, the unit can collect this data in real time using wearable devices. These wearable devices include heart rate sensors, blood pressure monitors, thermometers, and activity trackers. The heart rate sensor continuously measures the patient's heart rate and collects the data. The blood pressure monitor periodically measures the patient's blood pressure and collects the data. The thermometer measures the patient's body temperature and collects the data. The activity tracker measures the patient's steps and activity level and collects the data. This data is centrally managed by the data collection unit and transmitted to a central database in real time. The data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. For example, if the patient's condition suddenly changes, the collection frequency can be increased to collect more detailed data. Furthermore, the data collection unit is equipped with a filtering function to remove outliers and noise to ensure data quality. This allows the data collection unit to efficiently and accurately collect data, improving the overall system performance. Furthermore, the data collection unit can integrate with other medical devices and systems, providing data to understand the patient's overall health status. For example, it can integrate with a hospital's electronic medical record system, making the collected data accessible to doctors. This allows the data collection unit to support patient health management and improve the quality of medical care.

[0031] The analysis unit analyzes the data collected by the collection unit and detects anomalies. For example, the analysis unit can analyze collected heart rate data and detect anomalies. Specifically, the analysis unit uses AI to analyze the collected data in real time and detect anomalies. The AI ​​uses machine learning algorithms to distinguish between normal and abnormal data patterns. For example, in heart rate data, it can detect abnormal heart rates that exceed the normal range. In blood pressure data, it can detect hypertension or hypotension that exceeds the normal range. In body temperature data, it can detect fever or hypothermia. In activity level data, it can detect abnormal exercise patterns or decreased activity. The analysis unit comprehensively analyzes this data and can evaluate the causes and risks of anomalies. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical heart rate data, it can predict fluctuations in risk during specific time periods or activity levels and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The notification unit issues warnings based on anomalies detected by the analysis unit and notifies the physician. For example, when an anomaly is detected, the notification unit can display a warning on the patient's smartphone. Specifically, when an anomaly is detected, the notification unit can use voice alerts or vibration notifications to draw the patient's attention. The notification unit can also send notifications to physicians when an anomaly is detected. Physicians are notified of the details of the anomaly via text message, email, or app notification. This allows physicians to respond quickly. Furthermore, the notification unit can issue voice alerts when an anomaly is detected. For example, a voice alert can be emitted from the patient's smartphone or wearable device to inform the patient of the anomaly. The notification unit can send text messages when an anomaly is detected. For example, a text message describing the nature of the anomaly and how to respond is sent to the patient and physician. The notification unit can send emails when an anomaly is detected. For example, an email describing the details of the anomaly and how to respond is sent to the physician. The notification unit can send app notifications when an anomaly is detected. For example, notifications are displayed on a dedicated app used by patients and doctors, allowing them to check details of the abnormality and how to respond. This enables the notification unit to quickly and reliably issue warnings and notify doctors when an abnormality is detected.

[0033] The Emergency Response Unit responds to emergencies based on abnormalities notified by the Notification Unit. For example, in the event of cardiac arrest or a severe seizure, the Emergency Response Unit can automatically call emergency contacts. Specifically, when an abnormality is detected, the Emergency Response Unit can automatically call pre-registered emergency contacts and inform them of the situation. The Emergency Response Unit can also automatically send notifications to medical institutions in the event of cardiac arrest or a severe seizure. For example, a notification including the patient's condition and location information is sent to the nearest hospital or clinic, enabling a rapid response. Furthermore, the Emergency Response Unit can automatically arrange for an ambulance in the event of cardiac arrest or a severe seizure. For example, the Emergency Response Unit contacts local emergency services, provides the patient's condition and location information, and arranges for an ambulance. The Emergency Response Unit can also automatically send notifications to family members in the event of cardiac arrest or a severe seizure. For example, a notification describing the nature of the abnormality and how to respond is sent to the patient's family, enabling them to respond quickly. The Emergency Response Unit can also automatically contact nearby medical institutions in the event of cardiac arrest or a severe seizure. For example, notifications containing the patient's condition and location information are sent to hospitals and clinics near the patient, enabling a rapid response. This allows the emergency response unit to respond quickly and appropriately when an abnormality is detected, ensuring the patient's safety.

[0034] The Records Department records the actions taken by the Emergency Response Department. For example, the Records Department can save emergency response records to the electronic medical record system. Specifically, the Records Department meticulously records details such as the content and timing of the emergency response, and information about the responders, and saves this information to the electronic medical record system. This allows doctors and medical staff to review past response history and use it to inform future treatments and responses. The Records Department can save emergency response records to a database. For example, it can save response records to a cloud-based database and make them accessible as needed. The Records Department can save emergency response records to cloud storage. For example, it can use a cloud storage service to securely store and back up response records. The Records Department can save emergency response records to local storage. For example, it can save response records to a server or storage device within the hospital for quick access. The Records Department can save emergency response records on paper. For example, printing response records and storing them on paper can serve as a backup of the electronic data. The Records Department can send emergency response records via email. For example, response records can be sent to doctors and medical staff via email for quick sharing. The record-keeping unit can print emergency response records using a printer. For instance, printing response records and storing them as paper documents can be used as a backup of the electronic data. This allows the record-keeping unit to record emergency responses in detail, which can then be used for future medical care.

[0035] The data collection unit can collect data such as the patient's heart rate, blood pressure, body temperature, and activity level. For example, the data collection unit can measure heart rate using a heart rate sensor and collect data. The data collection unit can also measure blood pressure using a blood pressure monitor and collect data. The data collection unit can also measure body temperature using a thermometer and collect data. The data collection unit can also measure activity level using an activity tracker and collect data. By collecting data such as the patient's heart rate, blood pressure, body temperature, and activity level, the patient's health status can be constantly monitored. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the heart rate sensor into a generating AI and have the generating AI perform analysis of the heart rate data.

[0036] The analysis unit can detect anomalies based on the collected data. For example, the analysis unit can analyze collected heart rate data and detect anomalies. The analysis unit can also analyze collected blood pressure data and detect anomalies. The analysis unit can also analyze collected body temperature data and detect anomalies. The analysis unit can also analyze collected activity level data and detect anomalies. By detecting anomalies based on the collected data, anomalies can be detected early. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform anomaly detection.

[0037] The notification unit can display a warning on the patient's smartphone and simultaneously send a notification to the doctor when an abnormality is detected. For example, the notification unit can display a warning on the patient's smartphone when an abnormality is detected. The notification unit can also send a notification to the doctor when an abnormality is detected. The notification unit can also issue an audio alert when an abnormality is detected. The notification unit can also send a text message when an abnormality is detected. The notification unit can also send an email when an abnormality is detected. The notification unit can also send an app notification when an abnormality is detected. This allows the doctor to respond quickly by displaying a warning on the patient's smartphone and simultaneously sending a notification to the doctor when an abnormality is detected. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can generate a warning message using generative AI when an abnormality is detected and display it on the patient's smartphone.

[0038] The emergency response unit can automatically call emergency contacts and send notifications to medical institutions in the event of cardiac arrest or a severe seizure. For example, the emergency response unit can automatically call emergency contacts in the event of cardiac arrest or a severe seizure. The emergency response unit can also automatically send notifications to medical institutions in the event of cardiac arrest or a severe seizure. For example, the emergency response unit can automatically arrange for an ambulance in the event of cardiac arrest or a severe seizure. The emergency response unit can also automatically send notifications to family members in the event of cardiac arrest or a severe seizure. For example, the emergency response unit can automatically contact nearby medical institutions in the event of cardiac arrest or a severe seizure. This enables a rapid response by automatically calling emergency contacts and sending notifications to medical institutions in the event of cardiac arrest or a severe seizure. Some or all of the above processes in the emergency response unit may be performed using AI, or not. For example, the emergency response unit can use generated AI to send notifications to emergency contacts and instruct them on appropriate actions when cardiac arrest or a severe seizure occurs.

[0039] The recording unit can save emergency response records to the electronic medical record for use in subsequent medical treatment. For example, the recording unit can save emergency response records to the electronic medical record. The recording unit can also save emergency response records to a database. For example, the recording unit can save emergency response records to cloud storage. For example, the recording unit can save emergency response records to local storage. For example, the recording unit can save emergency response records to paper. For example, the recording unit can send emergency response records by email. For example, the recording unit can print emergency response records using a printer. By saving emergency response records to the electronic medical record for use in subsequent medical treatment, doctors can refer to past responses and provide more appropriate treatment. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input emergency response records into a generating AI and have the generating AI organize and save the records.

[0040] The data collection unit can analyze the patient's past health data and select the optimal data collection method. For example, the data collection unit can analyze the patient's past heart rate data and set the optimal collection interval. The data collection unit can also adjust the collection timing based on the patient's past blood pressure data. The data collection unit can also customize the collection method based on the patient's past activity level data. This enables efficient data collection by analyzing the patient's past health data and selecting the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter data based on the patient's current activity level and environment during data collection. For example, if the patient is exercising, the data collection unit will prioritize collecting exercise data. For example, if the patient is resting, the data collection unit can focus on collecting heart rate and blood pressure data. For example, if the patient is out, the data collection unit can also collect data including location information. This allows for the collection of more relevant data by filtering the data based on the patient's current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's current activity level and environment data into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data, taking into account the patient's geographical location during data collection. For example, if the patient is at high altitude, the data collection unit will prioritize the collection of oxygen concentration and heart rate data. For example, if the patient is in an urban area, the data collection unit can also collect data on ambient noise and air quality. For example, if the patient is at home, the data collection unit can also collect indoor environment data. This allows for the collection of more appropriate data by prioritizing the collection of highly relevant data, taking into account the patient's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's geographical location information into a generating AI and have the generating AI prioritize highly relevant data.

[0043] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, if the patient is experiencing stress on social media, the data collection unit can collect heart rate and blood pressure data. For example, if the patient is relaxing on social media, the data collection unit can also collect activity level and body temperature data. For example, if the patient is experiencing anxiety on social media, the data collection unit can also collect overall health data. This allows for the collection of more comprehensive health data by analyzing the patient's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, if heart rate or blood pressure data is important, the analysis unit will perform a detailed analysis. For example, if activity level or body temperature data is important, the analysis unit can also perform a detailed analysis. For example, if overall health data is important, the analysis unit can perform a balanced analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. For example, the analysis unit can also apply an activity pattern analysis algorithm to activity level data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may also prioritize the analysis of data collected during a specific time period. The analysis unit may also prioritize the analysis of data that shows anomalies when compared to past data. By determining the priority of analysis based on the data collection timing, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relationships between the data during the analysis. For example, the analysis unit can analyze data relating heart rate and blood pressure. For example, the analysis unit can analyze data relating activity level and body temperature. For example, the analysis unit can analyze data relating overall health data. By adjusting the order of analysis based on the relationships between the data, more efficient analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The notification unit can adjust the level of detail of the notification based on the severity of the anomaly. For example, if a major anomaly is detected, the notification unit will send a notification containing detailed information. For example, if a minor anomaly is detected, the notification unit may send a concise notification. For example, if a moderate anomaly is detected, the notification unit may send a notification with an appropriate level of detail. This allows for efficient notification by adjusting the level of detail of the notification based on the severity of the anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the severity of the anomaly into a generating AI and have the generating AI perform the adjustment of the level of detail of the notification.

[0049] The notification unit can apply different notification methods depending on the category of the anomaly when it issues a notification. For example, if an abnormality in heart rate is detected, the notification unit can issue an audio notification. For example, if an abnormality in blood pressure is detected, the notification unit can also issue a text notification. For example, if an abnormality in activity level is detected, the notification unit can also issue a visual notification. By applying different notification methods depending on the category of the anomaly, more appropriate notifications can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the category of the anomaly into a generating AI and have the generating AI execute the application of an appropriate notification method.

[0050] The notification unit can determine the priority of notifications based on when the anomaly occurred. For example, the notification unit may prioritize notifications for recently occurring anomalies. The notification unit may also prioritize notifications for anomalies that occurred during a specific time period. The notification unit may also prioritize notifications for anomalies that are more important than past anomalies. By determining the priority of notifications based on when the anomaly occurred, more appropriate notifications can be made. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the timing of the anomaly occurrence into a generating AI and have the generating AI perform the determination of the notification priority.

[0051] The notification unit can adjust the order of notifications based on the correlation of the abnormalities. For example, the notification unit may associate abnormalities in heart rate and blood pressure with each other and notify accordingly. For example, the notification unit may associate abnormalities in activity level and body temperature with each other and notify accordingly. For example, the notification unit may associate abnormalities in overall health data with each other and notify accordingly. This allows for more efficient notifications by adjusting the order of notifications based on the correlation of the abnormalities. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the correlation of the abnormalities into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0052] The emergency response unit can analyze a patient's past emergency history to select the optimal response method during an emergency. For example, if the patient has previously experienced cardiac arrest, the emergency response unit can perform rapid cardiopulmonary resuscitation. For example, if the patient has previously experienced a severe seizure, the emergency response unit can administer appropriate medication. For example, if the patient has previously experienced a minor emergency, the emergency response unit can provide appropriate treatment. By analyzing the patient's past emergency history and selecting the optimal response method, a more appropriate response becomes possible. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the patient's past emergency history into a generating AI and have the generating AI select the optimal response method.

[0053] The emergency response unit can customize response measures based on the patient's current situation during an emergency. For example, if the patient is at home, the emergency response unit can contact a nearby medical facility and arrange for an ambulance. If the patient is out, the emergency response unit can also contact the nearest medical facility and instruct them on appropriate action. If the patient is at work, the emergency response unit can contact the workplace emergency contact to encourage a quick response. By customizing response measures based on the patient's current situation, a more appropriate response becomes possible. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or not. For example, the emergency response unit can input data on the patient's current situation into a generating AI and have the generating AI perform the customization of response measures.

[0054] The emergency response unit can select the optimal response method in the event of an emergency, taking into account the patient's geographical location. For example, if the patient is in an urban area, the emergency response unit can contact nearby medical facilities and arrange for a rapid response. If the patient is in a suburban area, the emergency response unit can also arrange for the nearest ambulance and contact an appropriate medical facility. If the patient is traveling, the emergency response unit can also contact local medical facilities and instruct them on appropriate action. By selecting the optimal response method considering the patient's geographical location, a more appropriate response becomes possible. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the patient's geographical location information into a generating AI and have the generating AI select the optimal response method.

[0055] The emergency response unit can analyze a patient's social media activity and propose appropriate responses during an emergency. For example, if a patient is asking for help on social media, the emergency response unit can respond quickly. For example, if a patient is reporting their health status on social media, the emergency response unit can also propose appropriate responses. For example, if a patient is expressing anxiety on social media, the emergency response unit can provide reassurance. By analyzing a patient's social media activity and proposing appropriate responses, more appropriate responses become possible. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or not. For example, the emergency response unit can input the patient's social media activity data into a generating AI and have the generating AI propose appropriate responses.

[0056] The recording unit can adjust the level of detail in the recording based on the importance of the emergency response. For example, if a major emergency response occurs, the recording unit will record a detailed record. If a minor emergency response occurs, the recording unit may also record a concise record. If a moderate emergency response occurs, the recording unit may also record a record with a moderate level of detail. This allows for efficient recording by adjusting the level of detail in the recording based on the importance of the emergency response. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the importance of the emergency response into a generating AI and have the generating AI perform the adjustment of the level of detail in the recording.

[0057] The recording unit can apply different recording methods depending on the category of emergency response during recording. For example, in the case of an emergency response to an abnormal heart rate, the recording unit will record heart rate data in detail. For example, in the case of an emergency response to an abnormal blood pressure, the recording unit can also record blood pressure data in detail. For example, in the case of an emergency response to an abnormal activity level, the recording unit can also record activity level data in detail. This allows for more appropriate recording by applying different recording methods depending on the category of emergency response. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the category of emergency response into a generating AI and have the generating AI execute the application of an appropriate recording method.

[0058] The recording unit can determine the priority of recordings based on the timing of the emergency response. For example, the recording unit may prioritize recording recently occurring emergencies. The recording unit may also prioritize recording emergencies that occurred within a specific time period. The recording unit may also prioritize recording emergencies that are of higher importance compared to past emergencies. This allows for more appropriate recording by determining the priority of recordings based on the timing of the emergency response. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the timing of the emergency response into a generating AI and have the generating AI determine the priority of recordings.

[0059] The recording unit can adjust the order of recordings based on the relevance of emergency responses during recording. For example, the recording unit can associate and record emergency responses to abnormal heart rate and blood pressure. For example, the recording unit can also associate and record emergency responses to abnormal activity levels and body temperature. For example, the recording unit can also associate and record emergency responses to abnormalities in overall health data. This allows for more efficient recording by adjusting the order of recordings based on the relevance of emergency responses. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the relevance of emergency responses into a generating AI and have the generating AI perform the adjustment of the recording order.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The AI ​​agent system can not only monitor patients' lifestyles and vital data, but also collect and analyze their diet and nutritional intake. For example, the data collection unit can input meal details using an application that patients use to record their meals. The analysis unit can detect imbalances or deficiencies in nutrition based on the collected meal data. The notification unit can notify patients with dietary improvement advice if there are problems with their nutritional balance. This allows for more comprehensive health management of patients.

[0062] The AI ​​agent system can not only monitor patients' lifestyles and vital data, but also analyze their sleep patterns and evaluate their sleep quality. For example, the data collection unit can use wearable devices to record the patient's heart rate and movement during sleep. The analysis unit can analyze the depth of sleep and the frequency of interruptions based on the collected data. The notification unit can notify patients with advice on improving their sleep if their sleep quality is poor. This allows for more comprehensive health management of patients.

[0063] The AI ​​agent system can not only monitor patients' lifestyles and vital data, but also analyze their exercise habits and detect insufficient or excessive exercise. For example, the data collection unit can use a wearable device to record the amount and type of exercise the patient is doing. The analysis unit can analyze the frequency and intensity of exercise based on the collected data. If insufficient or excessive exercise is detected, the notification unit can notify the patient with advice on improving their exercise habits. This allows for more comprehensive health management of patients.

[0064] The AI ​​agent system can not only monitor patients' lifestyles and vital data, but also analyze their stress levels and support stress management. For example, the data collection unit can record the patient's heart rate variability and skin electrical activity using a wearable device. The analysis unit can analyze the stress level based on the collected data. The notification unit can notify the patient of relaxation methods and stress management advice if their stress level is high. This allows for more comprehensive health management of patients.

[0065] The AI ​​agent system can not only monitor patients' lifestyles and vital data, but also analyze their social activities to help alleviate feelings of isolation. For example, the data collection unit can record patients' social media usage and frequency of going out. The analysis unit can analyze the frequency and quality of social activities based on the collected data. The notification unit can notify patients with advice to encourage them to increase their social activity if their social activity is declining. This allows for more comprehensive health management of patients.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects the patient's lifestyle and vital data. For example, wearable devices can be used to collect data such as heart rate, blood pressure, body temperature, and activity level. The data collection unit collects this data in real time using heart rate sensors, blood pressure monitors, thermometers, activity trackers, etc. Step 2: The analysis unit analyzes the data collected by the collection unit and detects anomalies. For example, AI, machine learning algorithms, and anomaly detection algorithms can be used to analyze the collected heart rate data, blood pressure data, body temperature data, and activity level data and detect anomalies. Step 3: The notification unit issues a warning based on the anomaly detected by the analysis unit and notifies the doctor. For example, when an anomaly is detected, it can display a warning on the patient's smartphone, send a notification to the doctor, issue an audio alert, or send a text message, email, or app notification. Step 4: The emergency response unit responds to emergencies based on the abnormalities notified by the notification unit. For example, in the event of cardiac arrest or a severe seizure, it can automatically call emergency contacts, send notifications to medical institutions, arrange for an ambulance, or contact family members or nearby medical institutions. Step 5: The Records Department records the actions taken by the Emergency Response Department. For example, emergency response records can be saved to electronic medical records, databases, cloud storage, local storage, paper documents, emailed, or printed.

[0068] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that constantly monitors a patient's lifestyle and vital data, issues an early warning if an abnormality is detected, and notifies a physician. This AI agent system constantly monitors a patient's lifestyle and vital data, issues an early warning if an abnormality is detected, and notifies a physician. Furthermore, in an emergency, the AI ​​agent autonomously notifies emergency contacts and medical institutions, and provides real-time support for appropriate action. The response record is saved and used for future medical care. For example, the AI ​​agent system constantly monitors a patient's lifestyle and vital data. In this process, it collects data such as the patient's heart rate, blood pressure, body temperature, and activity level. For example, this data can be collected in real time using a wearable device. This allows for constant monitoring of the patient's health status. Next, the AI ​​agent system analyzes the collected data. The AI ​​agent detects abnormalities based on the collected data. For example, it can detect abnormal patterns such as a heart rate that is higher than normal or a sudden increase in blood pressure. This allows for early detection of abnormalities. If an abnormality is detected, the AI ​​agent system issues an early warning and notifies a physician. For example, when an abnormality is detected, a warning can be displayed on the patient's smartphone, and a notification can be sent to the doctor simultaneously. This allows the doctor to respond quickly. Furthermore, in emergencies, the AI ​​agent system autonomously notifies emergency contacts and medical institutions, providing real-time support for appropriate treatment. For example, in the event of cardiac arrest or a severe seizure, the AI ​​agent system can automatically call emergency contacts and send a notification to medical institutions. This enables a rapid response. Records of these responses are saved and used for future medical care. For example, emergency response records can be saved in the electronic medical record and used for future treatment. This allows doctors to provide more appropriate treatment by referring to past responses. In this way, the AI ​​agent system constantly monitors the patient's health status, issues early warnings if there are abnormalities, and notifies doctors. Furthermore, it can autonomously respond in emergencies, save the records, and use them for future medical care.This will streamline patient health management and improve the quality of medical care. The AI ​​agent system will continuously monitor the patient's health, issue early warnings if abnormalities are detected, and notify doctors. Furthermore, it can autonomously respond in emergencies, saving records for future medical use.

[0069] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a notification unit, an emergency response unit, and a recording unit. The data collection unit collects the patient's lifestyle and vital data. The data collection unit collects data such as the patient's heart rate, blood pressure, body temperature, and activity level. The data collection unit can collect this data in real time, for example, using a wearable device. The data collection unit can measure heart rate using a heart rate sensor and collect data. The data collection unit can measure blood pressure using a blood pressure monitor and collect data. The data collection unit can measure body temperature using a thermometer and collect data. The data collection unit can measure activity level using an activity tracker and collect data. The analysis unit analyzes the data collected by the data collection unit and detects abnormalities. The analysis unit can analyze collected heart rate data and detect abnormalities, for example. The analysis unit can analyze collected blood pressure data and detect abnormalities, for example. The analysis unit can analyze collected body temperature data and detect abnormalities, for example. The analysis unit can, for example, analyze collected activity data and detect anomalies. The analysis unit can, for example, analyze collected data using AI and detect anomalies. The analysis unit can, for example, analyze collected data using machine learning algorithms and detect anomalies. The analysis unit can, for example, analyze collected data using anomaly detection algorithms and detect anomalies. The notification unit issues warnings based on the anomalies detected by the analysis unit and notifies the doctor. The notification unit can, for example, display a warning on the patient's smartphone when an anomaly is detected. The notification unit can, for example, send a notification to the doctor when an anomaly is detected. The notification unit can, for example, issue an audio alert when an anomaly is detected. The notification unit can, for example, send a text message when an anomaly is detected. The notification unit can, for example, send an email when an anomaly is detected. The notification unit can, for example, send an app notification when an anomaly is detected. The emergency response unit responds in emergencies based on the anomalies notified by the notification unit.The emergency response unit can, for example, automatically call emergency contacts in the event of cardiac arrest or a severe seizure. The emergency response unit can, for example, automatically send notifications to medical institutions in the event of cardiac arrest or a severe seizure. The emergency response unit can, for example, automatically arrange for an ambulance in the event of cardiac arrest or a severe seizure. The emergency response unit can, for example, automatically send notifications to family members in the event of cardiac arrest or a severe seizure. The emergency response unit can, for example, automatically contact nearby medical institutions in the event of cardiac arrest or a severe seizure. The records unit records the actions taken by the emergency response unit. The records unit can, for example, save emergency response records to the electronic medical record. The records unit can, for example, save emergency response records to a database. The records unit can, for example, save emergency response records to cloud storage. The records unit can, for example, save emergency response records to local storage. The records unit can, for example, save emergency response records to paper media. The records unit can, for example, send emergency response records via email. The recording unit can, for example, print emergency response records using a printer. This allows the AI ​​agent system according to the embodiment to constantly monitor the patient's lifestyle and vital data, issue early warnings if abnormalities are detected, and notify a doctor. Furthermore, it can autonomously respond in emergencies, save the records, and utilize them for future medical care.

[0070] The data collection unit collects patient lifestyle data and vital signs. For example, it collects data such as the patient's heart rate, blood pressure, body temperature, and activity level. Specifically, the unit can collect this data in real time using wearable devices. These wearable devices include heart rate sensors, blood pressure monitors, thermometers, and activity trackers. The heart rate sensor continuously measures the patient's heart rate and collects the data. The blood pressure monitor periodically measures the patient's blood pressure and collects the data. The thermometer measures the patient's body temperature and collects the data. The activity tracker measures the patient's steps and activity level and collects the data. This data is centrally managed by the data collection unit and transmitted to a central database in real time. The data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. For example, if the patient's condition suddenly changes, the collection frequency can be increased to collect more detailed data. Furthermore, the data collection unit is equipped with a filtering function to remove outliers and noise to ensure data quality. This allows the data collection unit to efficiently and accurately collect data, improving the overall system performance. Furthermore, the data collection unit can integrate with other medical devices and systems, providing data to understand the patient's overall health status. For example, it can integrate with a hospital's electronic medical record system, making the collected data accessible to doctors. This allows the data collection unit to support patient health management and improve the quality of medical care.

[0071] The analysis unit analyzes the data collected by the collection unit and detects anomalies. For example, the analysis unit can analyze collected heart rate data and detect anomalies. Specifically, the analysis unit uses AI to analyze the collected data in real time and detect anomalies. The AI ​​uses machine learning algorithms to distinguish between normal and abnormal data patterns. For example, in heart rate data, it can detect abnormal heart rates that exceed the normal range. In blood pressure data, it can detect hypertension or hypotension that exceeds the normal range. In body temperature data, it can detect fever or hypothermia. In activity level data, it can detect abnormal exercise patterns or decreased activity. The analysis unit comprehensively analyzes this data and can evaluate the causes and risks of anomalies. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on historical heart rate data, it can predict fluctuations in risk during specific time periods or activity levels and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0072] The notification unit issues warnings based on anomalies detected by the analysis unit and notifies the physician. For example, when an anomaly is detected, the notification unit can display a warning on the patient's smartphone. Specifically, when an anomaly is detected, the notification unit can use voice alerts or vibration notifications to draw the patient's attention. The notification unit can also send notifications to physicians when an anomaly is detected. Physicians are notified of the details of the anomaly via text message, email, or app notification. This allows physicians to respond quickly. Furthermore, the notification unit can issue voice alerts when an anomaly is detected. For example, a voice alert can be emitted from the patient's smartphone or wearable device to inform the patient of the anomaly. The notification unit can send text messages when an anomaly is detected. For example, a text message describing the nature of the anomaly and how to respond is sent to the patient and physician. The notification unit can send emails when an anomaly is detected. For example, an email describing the details of the anomaly and how to respond is sent to the physician. The notification unit can send app notifications when an anomaly is detected. For example, notifications are displayed on a dedicated app used by patients and doctors, allowing them to check details of the abnormality and how to respond. This enables the notification unit to quickly and reliably issue warnings and notify doctors when an abnormality is detected.

[0073] The Emergency Response Unit responds to emergencies based on abnormalities notified by the Notification Unit. For example, in the event of cardiac arrest or a severe seizure, the Emergency Response Unit can automatically call emergency contacts. Specifically, when an abnormality is detected, the Emergency Response Unit can automatically call pre-registered emergency contacts and inform them of the situation. The Emergency Response Unit can also automatically send notifications to medical institutions in the event of cardiac arrest or a severe seizure. For example, a notification including the patient's condition and location information is sent to the nearest hospital or clinic, enabling a rapid response. Furthermore, the Emergency Response Unit can automatically arrange for an ambulance in the event of cardiac arrest or a severe seizure. For example, the Emergency Response Unit contacts local emergency services, provides the patient's condition and location information, and arranges for an ambulance. The Emergency Response Unit can also automatically send notifications to family members in the event of cardiac arrest or a severe seizure. For example, a notification describing the nature of the abnormality and how to respond is sent to the patient's family, enabling them to respond quickly. The Emergency Response Unit can also automatically contact nearby medical institutions in the event of cardiac arrest or a severe seizure. For example, notifications containing the patient's condition and location information are sent to hospitals and clinics near the patient, enabling a rapid response. This allows the emergency response unit to respond quickly and appropriately when an abnormality is detected, ensuring the patient's safety.

[0074] The Records Department records the actions taken by the Emergency Response Department. For example, the Records Department can save emergency response records to the electronic medical record system. Specifically, the Records Department meticulously records details such as the content and timing of the emergency response, and information about the responders, and saves this information to the electronic medical record system. This allows doctors and medical staff to review past response history and use it to inform future treatments and responses. The Records Department can save emergency response records to a database. For example, it can save response records to a cloud-based database and make them accessible as needed. The Records Department can save emergency response records to cloud storage. For example, it can use a cloud storage service to securely store and back up response records. The Records Department can save emergency response records to local storage. For example, it can save response records to a server or storage device within the hospital for quick access. The Records Department can save emergency response records on paper. For example, printing response records and storing them on paper can serve as a backup of the electronic data. The Records Department can send emergency response records via email. For example, response records can be sent to doctors and medical staff via email for quick sharing. The record-keeping unit can print emergency response records using a printer. For instance, printing response records and storing them as paper documents can be used as a backup of the electronic data. This allows the record-keeping unit to record emergency responses in detail, which can then be used for future medical care.

[0075] The data collection unit can collect data such as the patient's heart rate, blood pressure, body temperature, and activity level. For example, the data collection unit can measure heart rate using a heart rate sensor and collect data. The data collection unit can also measure blood pressure using a blood pressure monitor and collect data. The data collection unit can also measure body temperature using a thermometer and collect data. The data collection unit can also measure activity level using an activity tracker and collect data. By collecting data such as the patient's heart rate, blood pressure, body temperature, and activity level, the patient's health status can be constantly monitored. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired by the heart rate sensor into a generating AI and have the generating AI perform analysis of the heart rate data.

[0076] The analysis unit can detect anomalies based on the collected data. For example, the analysis unit can analyze collected heart rate data and detect anomalies. The analysis unit can also analyze collected blood pressure data and detect anomalies. The analysis unit can also analyze collected body temperature data and detect anomalies. The analysis unit can also analyze collected activity level data and detect anomalies. By detecting anomalies based on the collected data, anomalies can be detected early. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform anomaly detection.

[0077] The notification unit can display a warning on the patient's smartphone and simultaneously send a notification to the doctor when an abnormality is detected. For example, the notification unit can display a warning on the patient's smartphone when an abnormality is detected. The notification unit can also send a notification to the doctor when an abnormality is detected. The notification unit can also issue an audio alert when an abnormality is detected. The notification unit can also send a text message when an abnormality is detected. The notification unit can also send an email when an abnormality is detected. The notification unit can also send an app notification when an abnormality is detected. This allows the doctor to respond quickly by displaying a warning on the patient's smartphone and simultaneously sending a notification to the doctor when an abnormality is detected. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can generate a warning message using generative AI when an abnormality is detected and display it on the patient's smartphone.

[0078] The emergency response unit can automatically call emergency contacts and send notifications to medical institutions in the event of cardiac arrest or a severe seizure. For example, the emergency response unit can automatically call emergency contacts in the event of cardiac arrest or a severe seizure. The emergency response unit can also automatically send notifications to medical institutions in the event of cardiac arrest or a severe seizure. For example, the emergency response unit can automatically arrange for an ambulance in the event of cardiac arrest or a severe seizure. The emergency response unit can also automatically send notifications to family members in the event of cardiac arrest or a severe seizure. For example, the emergency response unit can automatically contact nearby medical institutions in the event of cardiac arrest or a severe seizure. This enables a rapid response by automatically calling emergency contacts and sending notifications to medical institutions in the event of cardiac arrest or a severe seizure. Some or all of the above processes in the emergency response unit may be performed using AI, or not. For example, the emergency response unit can use generated AI to send notifications to emergency contacts and instruct them on appropriate actions when cardiac arrest or a severe seizure occurs.

[0079] The recording unit can save emergency response records to the electronic medical record for use in subsequent medical treatment. For example, the recording unit can save emergency response records to the electronic medical record. The recording unit can also save emergency response records to a database. For example, the recording unit can save emergency response records to cloud storage. For example, the recording unit can save emergency response records to local storage. For example, the recording unit can save emergency response records to paper. For example, the recording unit can send emergency response records by email. For example, the recording unit can print emergency response records using a printer. By saving emergency response records to the electronic medical record for use in subsequent medical treatment, doctors can refer to past responses and provide more appropriate treatment. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input emergency response records into a generating AI and have the generating AI organize and save the records.

[0080] The data collection unit can estimate the patient's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the patient is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the patient is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the patient is anxious, the data collection unit can appropriately adjust the frequency of data collection to provide reassurance. In this way, by adjusting the frequency of data collection based on the patient's emotions, the burden on the patient can be reduced and more detailed data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the patient's emotion data into the generative AI and have the generative AI perform emotion estimation and adjustment of the data collection frequency.

[0081] The data collection unit can analyze the patient's past health data and select the optimal data collection method. For example, the data collection unit can analyze the patient's past heart rate data and set the optimal collection interval. The data collection unit can also adjust the collection timing based on the patient's past blood pressure data. The data collection unit can also customize the collection method based on the patient's past activity level data. This enables efficient data collection by analyzing the patient's past health data and selecting the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0082] The data collection unit can filter data based on the patient's current activity level and environment during data collection. For example, if the patient is exercising, the data collection unit will prioritize collecting exercise data. For example, if the patient is resting, the data collection unit can focus on collecting heart rate and blood pressure data. For example, if the patient is out, the data collection unit can also collect data including location information. This allows for the collection of more relevant data by filtering the data based on the patient's current activity level and environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's current activity level and environment data into a generating AI and have the generating AI perform data filtering.

[0083] The data collection unit can estimate the patient's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the patient is stressed, the data collection unit may prioritize collecting heart rate and blood pressure data. For example, if the patient is relaxed, the data collection unit may prioritize collecting activity level and body temperature data. For example, if the patient is anxious, the data collection unit may collect overall health data in a balanced manner. This allows for the priority collection of more important data by determining the priority of data to collect based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation and data prioritization.

[0084] The data collection unit can prioritize the collection of highly relevant data, taking into account the patient's geographical location during data collection. For example, if the patient is at high altitude, the data collection unit will prioritize the collection of oxygen concentration and heart rate data. For example, if the patient is in an urban area, the data collection unit can also collect data on ambient noise and air quality. For example, if the patient is at home, the data collection unit can also collect indoor environment data. This allows for the collection of more appropriate data by prioritizing the collection of highly relevant data, taking into account the patient's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's geographical location information into a generating AI and have the generating AI prioritize highly relevant data.

[0085] The data collection unit can analyze the patient's social media activity and collect relevant data during data collection. For example, if the patient is experiencing stress on social media, the data collection unit can collect heart rate and blood pressure data. For example, if the patient is relaxing on social media, the data collection unit can also collect activity level and body temperature data. For example, if the patient is experiencing anxiety on social media, the data collection unit can also collect overall health data. This allows for the collection of more comprehensive health data by analyzing the patient's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0086] The analysis unit can estimate the patient's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the patient is stressed, the analysis unit will prioritize stress-related data in its analysis. For example, if the patient is relaxed, the analysis unit can also analyze overall health data in a balanced manner. For example, if the patient is anxious, the analysis unit can also prioritize anxiety-related data in its analysis. This allows for more accurate analysis by adjusting the analysis algorithm based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation and algorithm adjustment.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, if heart rate or blood pressure data is important, the analysis unit will perform a detailed analysis. For example, if activity level or body temperature data is important, the analysis unit can also perform a detailed analysis. For example, if overall health data is important, the analysis unit can perform a balanced analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. For example, the analysis unit can also apply an activity pattern analysis algorithm to activity level data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0089] The analysis unit can estimate the patient's emotions and determine the priority of analysis based on the estimated emotions. For example, if the patient is stressed, the analysis unit will prioritize the analysis of stress-related data. For example, if the patient is relaxed, the analysis unit can also analyze overall health data in a balanced manner. For example, if the patient is anxious, the analysis unit can also prioritize the analysis of anxiety-related data. This allows for prioritizing the analysis of more important data by determining the priority of analysis based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the patient's emotion data into a generative AI and have the generative AI perform emotion estimation and determination of analysis priorities.

[0090] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may also prioritize the analysis of data collected during a specific time period. The analysis unit may also prioritize the analysis of data that shows anomalies when compared to past data. By determining the priority of analysis based on the data collection timing, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relationships between the data during the analysis. For example, the analysis unit can analyze data relating heart rate and blood pressure. For example, the analysis unit can analyze data relating activity level and body temperature. For example, the analysis unit can analyze data relating overall health data. By adjusting the order of analysis based on the relationships between the data, more efficient analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relationships between the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0092] The notification unit can estimate the patient's emotions and adjust the way notifications are presented based on the estimated emotions. For example, if the patient is stressed, the notification unit can provide a simple and easily visible notification. If the patient is relaxed, the notification unit can also provide a notification containing detailed information. If the patient is anxious, the notification unit can also provide a reassuring notification. By adjusting the way notifications are presented based on the patient's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the way notifications are presented.

[0093] The notification unit can adjust the level of detail of the notification based on the severity of the anomaly. For example, if a major anomaly is detected, the notification unit will send a notification containing detailed information. For example, if a minor anomaly is detected, the notification unit may send a concise notification. For example, if a moderate anomaly is detected, the notification unit may send a notification with an appropriate level of detail. This allows for efficient notification by adjusting the level of detail of the notification based on the severity of the anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the severity of the anomaly into a generating AI and have the generating AI perform the adjustment of the level of detail of the notification.

[0094] The notification unit can apply different notification methods depending on the category of the anomaly when it issues a notification. For example, if an abnormality in heart rate is detected, the notification unit can issue an audio notification. For example, if an abnormality in blood pressure is detected, the notification unit can also issue a text notification. For example, if an abnormality in activity level is detected, the notification unit can also issue a visual notification. By applying different notification methods depending on the category of the anomaly, more appropriate notifications can be provided. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the category of the anomaly into a generating AI and have the generating AI execute the application of an appropriate notification method.

[0095] The notification unit can estimate the patient's emotions and determine the priority of notifications based on the estimated emotions. For example, if the patient is stressed, the notification unit will prioritize important notifications. For example, if the patient is relaxed, the notification unit can also distribute notifications in a balanced manner. For example, if the patient is anxious, the notification unit can also prioritize reassuring notifications. This allows for prioritizing more important notifications by determining the priority of notifications based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation and notification priority determination.

[0096] The notification unit can determine the priority of notifications based on when the anomaly occurred. For example, the notification unit may prioritize notifications for recently occurring anomalies. The notification unit may also prioritize notifications for anomalies that occurred during a specific time period. The notification unit may also prioritize notifications for anomalies that are more important than past anomalies. By determining the priority of notifications based on when the anomaly occurred, more appropriate notifications can be made. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the timing of the anomaly occurrence into a generating AI and have the generating AI perform the determination of the notification priority.

[0097] The notification unit can adjust the order of notifications based on the correlation of the abnormalities. For example, the notification unit may associate abnormalities in heart rate and blood pressure with each other and notify accordingly. For example, the notification unit may associate abnormalities in activity level and body temperature with each other and notify accordingly. For example, the notification unit may associate abnormalities in overall health data with each other and notify accordingly. This allows for more efficient notifications by adjusting the order of notifications based on the correlation of the abnormalities. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit may input the correlation of the abnormalities into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0098] The emergency response unit can estimate the patient's emotions and adjust its emergency response method based on the estimated emotions. For example, if the patient is stressed, the emergency response unit will provide a quick and concise response. For example, if the patient is relaxed, the emergency response unit may provide a response that includes detailed explanations. For example, if the patient is anxious, the emergency response unit may provide a reassuring response. This allows for a more appropriate response by adjusting the emergency response method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency response unit may be performed using AI or not using AI. For example, the emergency response unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the emergency response method.

[0099] The emergency response unit can analyze a patient's past emergency history to select the optimal response method during an emergency. For example, if the patient has previously experienced cardiac arrest, the emergency response unit can perform rapid cardiopulmonary resuscitation. For example, if the patient has previously experienced a severe seizure, the emergency response unit can administer appropriate medication. For example, if the patient has previously experienced a minor emergency, the emergency response unit can provide appropriate treatment. By analyzing the patient's past emergency history and selecting the optimal response method, a more appropriate response becomes possible. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the patient's past emergency history into a generating AI and have the generating AI select the optimal response method.

[0100] The emergency response unit can customize response measures based on the patient's current situation during an emergency. For example, if the patient is at home, the emergency response unit can contact a nearby medical facility and arrange for an ambulance. If the patient is out, the emergency response unit can also contact the nearest medical facility and instruct them on appropriate action. If the patient is at work, the emergency response unit can contact the workplace emergency contact to encourage a quick response. By customizing response measures based on the patient's current situation, a more appropriate response becomes possible. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or not. For example, the emergency response unit can input data on the patient's current situation into a generating AI and have the generating AI perform the customization of response measures.

[0101] The emergency response unit can estimate the patient's emotions and determine the priority of emergency responses based on the estimated emotions. For example, if the patient is stressed, the emergency response unit will prioritize a rapid response. If the patient is relaxed, the emergency response unit can also provide a balanced overall response. If the patient is anxious, the emergency response unit can also prioritize reassuring responses. By determining the priority of emergency responses based on the patient's emotions, more important responses can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emergency response unit may be performed using AI or not. For example, the emergency response unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation and determination of emergency response priorities.

[0102] The emergency response unit can select the optimal response method in the event of an emergency, taking into account the patient's geographical location. For example, if the patient is in an urban area, the emergency response unit can contact nearby medical facilities and arrange for a rapid response. If the patient is in a suburban area, the emergency response unit can also arrange for the nearest ambulance and contact an appropriate medical facility. If the patient is traveling, the emergency response unit can also contact local medical facilities and instruct them on appropriate action. By selecting the optimal response method considering the patient's geographical location, a more appropriate response becomes possible. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or without AI. For example, the emergency response unit can input the patient's geographical location information into a generating AI and have the generating AI select the optimal response method.

[0103] The emergency response unit can analyze a patient's social media activity and propose appropriate responses during an emergency. For example, if a patient is asking for help on social media, the emergency response unit can respond quickly. For example, if a patient is reporting their health status on social media, the emergency response unit can also propose appropriate responses. For example, if a patient is expressing anxiety on social media, the emergency response unit can provide reassurance. By analyzing a patient's social media activity and proposing appropriate responses, more appropriate responses become possible. Some or all of the above processes in the emergency response unit may be performed using AI, for example, or not. For example, the emergency response unit can input the patient's social media activity data into a generating AI and have the generating AI propose appropriate responses.

[0104] The recording unit can estimate the patient's emotions and adjust the recording method based on the estimated emotions. For example, if the patient is stressed, the recording unit can make a concise record. For example, if the patient is relaxed, the recording unit can also make a detailed record. For example, if the patient is anxious, the recording unit can also make a reassuring record. This allows for more appropriate recording by adjusting the recording method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or not using AI. For example, the recording unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the recording method.

[0105] The recording unit can adjust the level of detail in the recording based on the importance of the emergency response. For example, if a major emergency response occurs, the recording unit will record a detailed record. If a minor emergency response occurs, the recording unit may also record a concise record. If a moderate emergency response occurs, the recording unit may also record a record with a moderate level of detail. This allows for efficient recording by adjusting the level of detail in the recording based on the importance of the emergency response. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the importance of the emergency response into a generating AI and have the generating AI perform the adjustment of the level of detail in the recording.

[0106] The recording unit can apply different recording methods depending on the category of emergency response during recording. For example, in the case of an emergency response to an abnormal heart rate, the recording unit will record heart rate data in detail. For example, in the case of an emergency response to an abnormal blood pressure, the recording unit can also record blood pressure data in detail. For example, in the case of an emergency response to an abnormal activity level, the recording unit can also record activity level data in detail. This allows for more appropriate recording by applying different recording methods depending on the category of emergency response. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the category of emergency response into a generating AI and have the generating AI execute the application of an appropriate recording method.

[0107] The recording unit can estimate the patient's emotions and determine the priority of recordings based on the estimated emotions. For example, if the patient is stressed, the recording unit will prioritize important recordings. If the patient is relaxed, the recording unit can also balance the overall recordings. If the patient is anxious, the recording unit can also prioritize recordings that provide a sense of security. This allows for prioritizing more important recordings by determining the priority of recordings based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input patient emotion data into a generative AI and have the generative AI perform emotion estimation and determination of recording priorities.

[0108] The recording unit can determine the priority of recordings based on the timing of the emergency response. For example, the recording unit may prioritize recording recently occurring emergencies. The recording unit may also prioritize recording emergencies that occurred within a specific time period. The recording unit may also prioritize recording emergencies that are of higher importance compared to past emergencies. This allows for more appropriate recording by determining the priority of recordings based on the timing of the emergency response. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the timing of the emergency response into a generating AI and have the generating AI determine the priority of recordings.

[0109] The recording unit can adjust the order of recordings based on the relevance of emergency responses during recording. For example, the recording unit can associate and record emergency responses to abnormal heart rate and blood pressure. For example, the recording unit can also associate and record emergency responses to abnormal activity levels and body temperature. For example, the recording unit can also associate and record emergency responses to abnormalities in overall health data. This allows for more efficient recording by adjusting the order of recordings based on the relevance of emergency responses. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the relevance of emergency responses into a generating AI and have the generating AI perform the adjustment of the recording order.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The AI ​​agent system can not only monitor patients' lifestyles and vital data, but also collect and analyze their diet and nutritional intake. For example, the data collection unit can input meal details using an application that patients use to record their meals. The analysis unit can detect imbalances or deficiencies in nutrition based on the collected meal data. The notification unit can notify patients with dietary improvement advice if there are problems with their nutritional balance. This allows for more comprehensive health management of patients.

[0112] The AI ​​agent system can not only monitor patients' lifestyles and vital data, but also analyze their sleep patterns and evaluate their sleep quality. For example, the data collection unit can use wearable devices to record the patient's heart rate and movement during sleep. The analysis unit can analyze the depth of sleep and the frequency of interruptions based on the collected data. The notification unit can notify patients with advice on improving their sleep if their sleep quality is poor. This allows for more comprehensive health management of patients.

[0113] The AI ​​agent system can not only monitor patients' lifestyles and vital data, but also analyze their exercise habits and detect insufficient or excessive exercise. For example, the data collection unit can use a wearable device to record the amount and type of exercise the patient is doing. The analysis unit can analyze the frequency and intensity of exercise based on the collected data. If insufficient or excessive exercise is detected, the notification unit can notify the patient with advice on improving their exercise habits. This allows for more comprehensive health management of patients.

[0114] The AI ​​agent system can not only monitor patients' lifestyles and vital data, but also analyze their stress levels and support stress management. For example, the data collection unit can record the patient's heart rate variability and skin electrical activity using a wearable device. The analysis unit can analyze the stress level based on the collected data. The notification unit can notify the patient of relaxation methods and stress management advice if their stress level is high. This allows for more comprehensive health management of patients.

[0115] The AI ​​agent system can not only monitor patients' lifestyles and vital data, but also analyze their social activities to help alleviate feelings of isolation. For example, the data collection unit can record patients' social media usage and frequency of going out. The analysis unit can analyze the frequency and quality of social activities based on the collected data. The notification unit can notify patients with advice to encourage them to increase their social activity if their social activity is declining. This allows for more comprehensive health management of patients.

[0116] The AI ​​agent system can estimate a patient's emotions and adjust the content of notifications sent to doctors based on those estimated emotions. For example, if the analysis unit is experiencing stress, it can prioritize and analyze stress-related data. If the stress level is high, the notification unit can send a notification to the doctor that includes detailed stress management advice. This allows doctors to take appropriate action that takes the patient's emotional state into consideration.

[0117] The AI ​​agent system can estimate a patient's emotions and adjust its emergency response based on those emotions. For example, if the emergency response team perceives a patient as anxious, they can prioritize reassuring responses. If the patient is relaxed, they can provide responses that include detailed explanations. This allows for more appropriate responses by adjusting emergency response methods based on the patient's emotions.

[0118] The AI ​​agent system can estimate the patient's emotions and adjust the frequency of data collection based on those emotions. For example, if the patient is stressed, the data collection unit can reduce the frequency of data collection to lessen the burden. If the patient is relaxed, the frequency of data collection can be increased to collect more detailed data. In this way, by adjusting the frequency of data collection based on the patient's emotions, the burden on the patient can be reduced and more detailed data can be collected.

[0119] The AI ​​agent system can estimate the patient's emotions and adjust the way notifications are presented based on those emotions. For example, if the patient is stressed, the notification unit can provide simple, highly visible notifications. If the patient is relaxed, it can provide notifications with more detailed information. This allows for more appropriate notifications by adjusting the way notifications are presented based on the patient's emotions.

[0120] The AI ​​agent system can estimate a patient's emotions and adjust the recording method based on those estimates. For example, the recording unit can make a concise record if the patient is stressed, or a detailed record if the patient is relaxed. This allows for more appropriate recording by adjusting the recording method based on the patient's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects the patient's lifestyle and vital data. For example, wearable devices can be used to collect data such as heart rate, blood pressure, body temperature, and activity level. The data collection unit collects this data in real time using heart rate sensors, blood pressure monitors, thermometers, activity trackers, etc. Step 2: The analysis unit analyzes the data collected by the collection unit and detects anomalies. For example, AI, machine learning algorithms, and anomaly detection algorithms can be used to analyze the collected heart rate data, blood pressure data, body temperature data, and activity level data and detect anomalies. Step 3: The notification unit issues a warning based on the anomaly detected by the analysis unit and notifies the doctor. For example, when an anomaly is detected, it can display a warning on the patient's smartphone, send a notification to the doctor, issue an audio alert, or send a text message, email, or app notification. Step 4: The emergency response unit responds to emergencies based on the abnormalities notified by the notification unit. For example, in the event of cardiac arrest or a severe seizure, it can automatically call emergency contacts, send notifications to medical institutions, arrange for an ambulance, or contact family members or nearby medical institutions. Step 5: The Records Department records the actions taken by the Emergency Response Department. For example, emergency response records can be saved to electronic medical records, databases, cloud storage, local storage, paper documents, emailed, or printed.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, notification unit, emergency response unit, and recording unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit can collect data such as the patient's heart rate, blood pressure, body temperature, and activity level in real time using the sensors of the smart device 14. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The notification unit issues a warning when an abnormality is detected by the control unit 46A of the smart device 14 and sends a notification to the doctor. The emergency response unit automatically sends notifications to emergency contacts and medical institutions in the event of an emergency using the identification processing unit 290 of the data processing unit 12. The recording unit can save emergency response records to an electronic medical record or database using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, notification unit, emergency response unit, and recording unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit can collect data such as the patient's heart rate, blood pressure, body temperature, and activity level in real time using the sensors of the smart glasses 214. The analysis unit analyzes the collected data using, for example, the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The notification unit issues a warning when an abnormality is detected by, for example, the control unit 46A of the smart glasses 214 and sends a notification to the doctor. The emergency response unit automatically sends notifications to emergency contacts and medical institutions in the event of an emergency using, for example, the identification processing unit 290 of the data processing unit 12. The recording unit can save emergency response records to an electronic medical record or database using, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, notification unit, emergency response unit, and recording unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit can collect data such as the patient's heart rate, blood pressure, body temperature, and activity level in real time using the sensors of the headset terminal 314. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The notification unit issues a warning when an abnormality is detected by the control unit 46A of the headset terminal 314 and sends a notification to the doctor. The emergency response unit automatically sends notifications to emergency contacts and medical institutions in the event of an emergency using the identification processing unit 290 of the data processing unit 12. The recording unit can save emergency response records to an electronic medical record or database using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, notification unit, emergency response unit, and recording unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit can collect data such as the patient's heart rate, blood pressure, body temperature, and activity level in real time using the sensors of the robot 414. The analysis unit analyzes the collected data by, for example, the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The notification unit issues a warning when an abnormality is detected by, for example, the control unit 46A of the robot 414 and sends a notification to the doctor. The emergency response unit automatically sends a notification to emergency contacts and medical institutions in an emergency by, for example, the identification processing unit 290 of the data processing unit 12. The recording unit can save emergency response records in an electronic medical record or database by, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The collection unit collects patients' lifestyle habits and vital data, An analysis unit analyzes the data collected by the aforementioned collection unit and detects anomalies, A notification unit issues a warning and notifies a doctor based on the abnormality detected by the aforementioned analysis unit, An emergency response unit that responds to an emergency based on an abnormality notified by the aforementioned notification unit, The system includes a recording unit that records the response performed by the emergency response unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as the patient's heart rate, blood pressure, body temperature, and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Anomalies are detected based on the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, When an abnormality is detected, a warning is displayed on the patient's smartphone, and a notification is simultaneously sent to the doctor. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned emergency response unit, In the event of cardiac arrest or a severe seizure, the system will automatically call emergency contacts and notify medical institutions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The recording unit is, Emergency response records are saved in the electronic medical record to be used for future treatment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the patient's emotions and adjusts the frequency of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the patient's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current activity level and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the patient's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, The system estimates the patient's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When a notification is sent, adjust the level of detail in the notification based on the severity of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, When a notification is sent, different notification methods will be applied depending on the category of the anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, The system estimates the patient's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When a notification is sent, the notification priority is determined based on when the anomaly occurred. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, When sending notifications, adjust the order of notifications based on the relevance of the anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned emergency response unit, The system estimates the patient's emotions and adjusts emergency response methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned emergency response unit, During an emergency, the patient's past emergency history is analyzed to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned emergency response unit, During emergency situations, customize response methods based on the patient's current condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned emergency response unit, The system estimates the patient's emotions and determines the priority of emergency response based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned emergency response unit, During emergency response, the optimal response method is selected considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned emergency response unit, During emergency situations, we analyze patients' social media activity and propose appropriate response strategies. The system described in Appendix 1, characterized by the features described herein. (Note 31) The recording unit is, Estimate the patient's emotions and adjust the recording method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The recording unit is, When recording, adjust the level of detail in the recording based on the importance of the emergency response. The system described in Appendix 1, characterized by the features described herein. (Note 33) The recording unit is, When recording, apply different recording methods depending on the category of emergency response. The system described in Appendix 1, characterized by the features described herein. (Note 34) The recording unit is, The system estimates the patient's emotions and prioritizes recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The recording unit is, When recording, prioritize the recording based on when the emergency response occurred. The system described in Appendix 1, characterized by the features described herein. (Note 36) The recording unit is, When recording, adjust the order of recordings based on the relevance of the emergency response. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit collects patients' lifestyle habits and vital data, An analysis unit analyzes the data collected by the aforementioned collection unit and detects anomalies, A notification unit issues a warning and notifies a doctor based on the abnormality detected by the aforementioned analysis unit, An emergency response unit that responds to an emergency based on an abnormality notified by the aforementioned notification unit, The system includes a recording unit that records the response performed by the emergency response unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as the patient's heart rate, blood pressure, body temperature, and activity level. The system according to feature 1.

3. The aforementioned analysis unit, Anomalies are detected based on the collected data. The system according to feature 1.

4. The aforementioned notification unit, When an abnormality is detected, a warning is displayed on the patient's smartphone, and a notification is simultaneously sent to the doctor. The system according to feature 1.

5. The aforementioned emergency response unit, In the event of cardiac arrest or a severe seizure, the system will automatically call emergency contacts and notify medical institutions. The system according to feature 1.

6. The aforementioned recording unit is Emergency response records are saved in the electronic medical record to be used for future treatment. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the patient's emotions and adjusts the frequency of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the patient's past health data and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the patient's current activity level and environment. The system according to feature 1.

10. The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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