system

Abstract

The system according to this embodiment aims to comprehensively analyze an individual's health data and suggest appropriate countermeasures and medical institutions to visit in emergency situations. [Solution] The system according to the embodiment comprises a data collection unit, a data analysis unit, a response suggestion unit, and a monitoring unit. The data collection unit collects personal biometric data, health checkup results, and medication record information. The data analysis unit analyzes the information collected by the data collection unit and comprehensively diagnoses the patient's symptoms in emergencies. The response suggestion unit proposes appropriate response methods and medical institutions to visit based on the diagnosis results obtained by the data analysis unit. The monitoring unit continuously monitors the user's health data and automatically implements countermeasures when an abnormality is detected.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes 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 in 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, comprehensive analysis of personal health data and proposal of appropriate countermeasures and medical institutions to visit in case of emergency have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively analyze personal health data and propose appropriate countermeasures and medical institutions to visit in case of emergency.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a data analysis unit, a response suggestion unit, and a monitoring unit. The data collection unit collects personal biometric data, health checkup results, and medication record information. The data analysis unit analyzes the information collected by the data collection unit and makes a comprehensive diagnosis of the patient's symptoms in an emergency. The response suggestion unit proposes appropriate response methods and medical institutions to visit based on the diagnosis results obtained by the data analysis unit. The monitoring unit continuously monitors the user's health data and automatically implements countermeasures when an abnormality is detected. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively analyze an individual's health data and suggest appropriate countermeasures and medical institutions to visit in emergency situations. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable 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 health management system according to an embodiment of the present invention is a system that analyzes an individual's "symptoms of discomfort" in real time based on their biometric data, health checkup results, and medication record information. This health management system collects the individual's biometric data, health checkup results, and medication record information, and uses a multimodal large-scale language model (LLM) to comprehensively diagnose the patient's symptoms in emergencies and propose appropriate countermeasures and medical institutions to visit in real time. In addition, an autonomous AI agent continuously monitors the user's health data and automatically takes countermeasures when an abnormality is detected. For example, it may contact family members or share information with medical institutions. Furthermore, it autonomously provides health management and lifestyle improvement suggestions on a daily basis. For example, the health management system collects the individual's biometric data, health checkup results, and medication record information. In this case, the user only needs to input their own health data. For example, they input biometric data such as blood pressure, heart rate, and body temperature, as well as past health checkup results and information on medications listed in their medication record. This information is input into the multimodal LLM. Next, the multimodal LLM analyzes the input information and comprehensively diagnoses the patient's symptoms in emergencies. For example, if a user experiences chest pain, LLM diagnoses whether there is a heart problem based on biometric data, health checkup results, and medication record information. Based on the diagnosis, it suggests appropriate treatment methods and medical institutions to visit in real time. For example, if a heart problem is suspected, it suggests a nearby cardiologist. Furthermore, the autonomous AI agent continuously monitors the user's health data. For example, it monitors the user's biometric data in real time and automatically takes countermeasures when an abnormality is detected. For example, if the user's heart rate suddenly increases, it will contact family members and share information with medical institutions. In addition, it autonomously provides health management and lifestyle improvement suggestions on a daily basis. For example, it suggests healthy lifestyle habits based on the user's diet and exercise records. This allows users to understand their health status in real time and receive appropriate medical services with peace of mind in emergencies. Moreover, the autonomous AI agent protects individual health and automatically takes the optimal action in emergencies, enabling everyone to live a safe and secure life.This allows the health management system to analyze individual health data in real time and suggest appropriate treatment methods and medical facilities.

[0029] The health management system according to this embodiment comprises a data collection unit, a data analysis unit, a response suggestion unit, and a monitoring unit. The data collection unit collects personal biometric data, health checkup results, and medication record information. For example, the data collection unit collects biometric data such as the user's blood pressure, heart rate, and body temperature. The data collection unit can also collect past health checkup results. Furthermore, the data collection unit can also collect information on medications listed in the medication record. For example, the data collection unit collects biometric data such as blood pressure, heart rate, and body temperature entered by the user. The data collection unit can also obtain past health checkup results from electronic medical records. The data collection unit can also scan medication information listed in the medication record and convert it into digital data. The data analysis unit analyzes the information collected by the data collection unit and makes a comprehensive diagnosis of the patient's symptoms in an emergency. For example, the data analysis unit makes a comprehensive diagnosis of the patient's symptoms in an emergency based on the collected biometric data, health checkup results, and medication record information. The data analysis unit can analyze the collected information using a multimodal LLM. For example, if a user experiences chest pain, the data analysis unit diagnoses whether there is a heart problem based on biometric data, health checkup results, and medication record information. Based on the collected information, the data analysis unit can suggest appropriate actions to take in an emergency and recommend medical institutions to visit. The action suggestion unit suggests appropriate actions to take and medical institutions to visit based on the diagnostic results obtained by the data analysis unit. For example, the action suggestion unit suggests appropriate actions to take and medical institutions to visit based on the diagnostic results. The action suggestion unit can suggest appropriate actions to take and medical institutions to visit based on the diagnostic results using multimodal LLM. For example, if a heart problem is suspected, the action suggestion unit will suggest a nearby cardiologist. Based on the diagnostic results, the action suggestion unit can suggest actions such as first aid and medication administration. The monitoring unit continuously monitors the user's health data and automatically takes countermeasures when it detects an abnormality. For example, the monitoring unit monitors the user's biometric data in real time and automatically takes countermeasures when it detects an abnormality.The monitoring unit can continuously monitor the user's health data using a multimodal LLM (Limited Life Management) system. For example, if the user's heart rate suddenly increases, the monitoring unit will contact family members or share information with medical institutions. The monitoring unit can also autonomously provide health management and lifestyle improvement suggestions on a daily basis. For example, based on the user's records of diet and exercise, the monitoring unit will suggest healthy lifestyle habits. As a result, the health management system according to this embodiment can analyze individual health data in real time and suggest appropriate countermeasures and medical institutions.

[0030] The data collection unit collects personal biometric data, health checkup results, and medication record information. Specifically, it uses wearable devices and smartphone applications to collect biometric data such as the user's blood pressure, heart rate, and body temperature. These devices are worn constantly by the user during their daily life, collecting data in real time and transmitting it to a cloud server. For example, a wearable device is worn on the user's wrist to periodically measure heart rate and blood pressure, and the data is collected through a smartphone application. The data collection unit can also obtain past health checkup results from electronic medical records. The electronic medical record system digitizes and stores medical records from healthcare institutions, and with the user's consent, the data collection unit can access this data. Furthermore, the data collection unit can scan medication information written in medication record books and convert it into digital data. Users take pictures of pages of their medication record books using their smartphone cameras and upload the data through a dedicated application. This allows the data collection unit to accurately understand the user's medication history and current medication status. This data is stored in a secure cloud environment and made accessible to the data analysis unit and the treatment suggestion unit as needed. The data acquisition unit performs regular calibration and device maintenance to ensure the quality of the collected data. Furthermore, it provides guidelines and checking functions to prevent input errors when users manually enter data. This allows the data acquisition unit to collect accurate and reliable data, improving the overall system performance.

[0031] The Data Analysis Department analyzes information collected by the Data Collection Department to comprehensively diagnose a patient's symptoms in emergencies. The Data Analysis Department can analyze collected information using a multimodal Large-Scale Language Model (LLM). Specifically, it integrates collected biometric data, health checkup results, and medication record information to comprehensively evaluate the user's health status. For example, if a user experiences chest pain, the Data Analysis Department diagnoses whether there is a heart problem based on heart rate and blood pressure fluctuations, past health checkup results, and medication record information. The multimodal LLM has the ability to comprehensively analyze data in different formats, such as text data, numerical data, and image data, enabling highly accurate assessment of the user's symptoms and health status. Based on the collected information, the Data Analysis Department can suggest appropriate actions and medical institutions to visit in emergencies. For example, if a heart problem is suspected, the Data Analysis Department can suggest a nearby cardiologist and, in an emergency, arrange for an ambulance. Furthermore, the Data Analysis Department can utilize historical data and statistical information to assess long-term health risks and perform trend analysis. For example, based on past health checkup results and changes in biometric data, the system can predict the risk of specific diseases and propose preventive measures. Furthermore, the data analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the data analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The Response Proposal Department proposes appropriate treatment methods and medical institutions to visit based on the diagnostic results obtained by the Data Analysis Department. Using multimodal LLM (Limited Licensing Management), the Response Proposal Department can propose appropriate treatment methods and medical institutions to visit based on the diagnostic results. Specifically, it proposes treatment methods such as first aid and medication administration based on the diagnostic results. For example, if a heart problem is suspected, the Response Proposal Department can suggest a nearby cardiologist and, in an emergency, can arrange for an ambulance. Furthermore, the Response Proposal Department can also suggest appropriate lifestyle improvements according to the user's symptoms and health condition. For example, it can suggest diet and exercise advice, stress management methods, and other support to help users lead healthy lives. The Response Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it collects user evaluations and opinions on the suggested treatment methods and medical institutions and incorporates them into future suggestions. The Response Proposal Department can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the response suggestion department to provide users with prompt and reliable action instructions, minimizing health risks. Furthermore, the response suggestion department can provide customized suggestions tailored to the individual needs and circumstances of each user. For example, for users with specific allergies or chronic illnesses, it can suggest appropriate treatment methods and medical facilities. In this way, the response suggestion department can comprehensively support users' health management and provide optimal suggestions that meet their individual needs.

[0033] The monitoring unit continuously monitors the user's health data and automatically takes countermeasures when an anomaly is detected. The monitoring unit can continuously monitor the user's health data using a multimodal LLM (Limited Lifecycle Management) architecture. Specifically, it monitors the user's biometric data in real time and automatically takes countermeasures when an anomaly is detected. For example, if the user's heart rate suddenly increases, the monitoring unit will contact family members and share information with medical institutions. The monitoring unit can also autonomously provide health management and lifestyle improvement suggestions on a daily basis. For example, it can suggest healthy lifestyle habits based on the user's diet and exercise records. The monitoring unit can also accumulate user health data over the long term and perform trend analysis and risk assessment. For example, it can predict the risk of specific diseases based on past data and suggest preventative measures. Furthermore, the monitoring unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the monitoring unit to not only grasp the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system. In addition, the monitoring unit can collect user feedback and continuously improve the accuracy and effectiveness of its monitoring. For example, the monitoring unit collects user evaluations and opinions on the monitoring results and incorporates them into the next monitoring session. Furthermore, the monitoring unit can reliably transmit information using multiple communication methods. For instance, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the monitoring unit to provide users with prompt and reliable action instructions, minimizing health risks.

[0034] The data collection unit can collect biometric data such as the user's blood pressure, heart rate, and body temperature. For example, the data collection unit can measure the user's blood pressure and collect the data. The data collection unit can also measure the heart rate and collect the data. The data collection unit can also measure the body temperature and collect the data. For example, the data collection unit can measure the user's blood pressure using a blood pressure monitor and collect the data. The data collection unit can also measure the user's heart rate using a heart rate monitor and collect the data. The data collection unit can also measure the user's body temperature using a thermometer and collect the data. By collecting the user's biometric data in this way, their health status can be understood. 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 collect biometric data such as the user's blood pressure, heart rate, and body temperature, input the collected data into a generating AI, and have the generating AI analyze the data.

[0035] The data collection unit can collect past health checkup results. For example, the data collection unit can obtain past health checkup results from electronic medical records. The data collection unit can also scan past health checkup results and convert them into digital data. The data collection unit can also manually input past health checkup results. For example, the data collection unit can automatically obtain past health checkup results from electronic medical records and collect the data. The data collection unit can also scan past health checkup results with a scanner, convert them into digital data, and collect them. The data collection unit can also manually input past health checkup results and collect the data. This allows the user's health history to be understood by collecting past health checkup results. 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 collect past health checkup results, input the collected data into a generating AI, and have the generating AI analyze the data.

[0036] The data collection unit can collect information about medications recorded in the medication record book. For example, the data collection unit can scan the medication information recorded in the medication record book and convert it into digital data. The data collection unit can also manually input medication information. The data collection unit can also obtain medication information from pharmacies. For example, the data collection unit can scan the medication information recorded in the medication record book with a scanner, convert it into digital data, and collect it. The data collection unit can also manually input medication information and collect the data. The data collection unit can also automatically obtain medication information from pharmacies and collect the data. This allows for an understanding of the user's medication adherence by collecting information from the medication record book. 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 collect medication information recorded in the medication record book, input the collected data into a generating AI, and have the generating AI analyze the data.

[0037] The data analysis unit can comprehensively diagnose a patient's symptoms in an emergency based on collected biometric data, health checkup results, and medication record information. For example, the data analysis unit can comprehensively diagnose a patient's symptoms in an emergency based on collected biometric data, health checkup results, and medication record information. The data analysis unit can analyze the collected information using a multimodal LLM (Limited Licensing Machine). For example, if a user experiences chest pain, the data analysis unit can diagnose whether there is a heart problem based on biometric data, health checkup results, and medication record information. Based on the collected information, the data analysis unit can suggest appropriate treatment methods and medical institutions to visit in an emergency. This allows for the suggestion of appropriate treatment methods by comprehensively diagnosing the patient's symptoms in an emergency. Some or all of the above-described processes in the data analysis unit may be performed using AI, or not. For example, the data analysis unit can input collected biometric data, health checkup results, and medication record information into a generating AI, which can then analyze the data.

[0038] The response suggestion unit can propose appropriate treatment methods and medical institutions to visit based on the diagnosis results. For example, the response suggestion unit can propose appropriate treatment methods and medical institutions to visit based on the diagnosis results. The response suggestion unit can propose appropriate treatment methods and medical institutions to visit based on the diagnosis results using multimodal LLM. For example, if a heart problem is suspected, the response suggestion unit will suggest a nearby cardiologist. The response suggestion unit can propose treatment methods such as emergency treatment and medication administration based on the diagnosis results. This enables a rapid response by proposing appropriate treatment methods and medical institutions to visit based on the diagnosis results. Some or all of the above processing in the response suggestion unit may be performed using AI, for example, or without AI. For example, the response suggestion unit can input the diagnosis results into a generating AI, and the generating AI can propose appropriate treatment methods and medical institutions to visit.

[0039] The monitoring unit can monitor the user's biometric data in real time and, if it detects an abnormality, can contact family members or share information with medical institutions. For example, the monitoring unit can continuously monitor the user's health data using a multimodal LLM. For example, if the user's heart rate suddenly increases, the monitoring unit will contact family members or share information with medical institutions. The monitoring unit can autonomously provide health management and lifestyle improvement suggestions on a daily basis. For example, the monitoring unit will suggest healthy lifestyle habits based on the user's diet and exercise records. This allows for real-time monitoring of the user's biometric data and rapid response when an abnormality is detected. Some or all of the above-described processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the user's biometric data into a generating AI, which can then detect abnormalities and implement countermeasures.

[0040] The monitoring unit can autonomously provide health management and lifestyle improvement suggestions on a daily basis. For example, the monitoring unit can suggest healthy lifestyle habits based on the user's records of meals and exercise. The monitoring unit can continuously monitor the user's health data using a multimodal LLM and make health management and lifestyle improvement suggestions. For example, the monitoring unit can analyze the user's meal records and suggest nutritionally balanced meals. The monitoring unit can analyze the user's exercise records and suggest an appropriate exercise plan. In this way, it can support the user's health maintenance by autonomously providing daily health management and lifestyle improvement suggestions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's meal and exercise records into a generating AI, and the generating AI can make health management and lifestyle improvement suggestions.

[0041] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can identify the time of day when the most accurate data can be obtained from the user's past health data and collect data during that time. Based on the user's past health data, the data collection unit can collect data using specific devices or sensors. The data collection unit can analyze the user's past health data and optimize the data collection method under specific circumstances. This allows the optimal collection method to be selected by analyzing the user's past health 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 user's past health data into a generating AI, which can then select the optimal collection method.

[0042] The data collection unit can filter biometric data based on the user's current activity level and environment. For example, if the user is exercising, the data collection unit can filter the data to account for the effects of exercise. If the user is stationary, the data collection unit can prioritize collecting data from that stationary state. If the user is outdoors, the data collection unit can filter the data to eliminate the influence of the external environment. This allows for the collection of more accurate data by filtering the data based on the user's 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 user activity level and environmental data into a generating AI, which can then filter the data.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting biometric data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of biometric data specific to high altitude. If the user is in an urban area, the data collection unit can prioritize the collection of biometric data considering environmental data specific to urban areas. If the user is traveling, the data collection unit can prioritize the collection of data adapted to the environment of the travel destination. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. 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 user's geographical location information into a generating AI, and the generating AI can determine the priority of highly relevant data.

[0044] The data collection unit can analyze a user's social media activity and collect relevant data when collecting biometric data. For example, if a user is experiencing stress on social media, the data collection unit can collect stress-related biometric data. If a user is relaxed on social media, the data collection unit can collect biometric data indicating a relaxed state. If a user is active on social media, the data collection unit can collect biometric data indicating an active state. This allows for the collection of relevant data by analyzing the user's social media activity. 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 user's social media activity data into a generating AI, which can then collect relevant data.

[0045] The data analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the data analysis unit can perform a detailed analysis on highly important data. For less important data, it can perform a simplified analysis. For moderately important data, it can perform an analysis with an appropriate level of detail. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0046] The data analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the data analysis unit can apply a specific biometric data analysis algorithm to biometric data. For health checkup results, the data analysis unit can apply a specific diagnostic data analysis algorithm. For medication record information, the data analysis unit can apply a specific drug data analysis algorithm. By applying different analysis algorithms depending on the data category, more accurate analysis results can be provided. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the data category into a generating AI, and the generating AI can apply an appropriate analysis algorithm.

[0047] The data analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the data analysis unit can prioritize the analysis of the most recent data. The data analysis unit can analyze the most recent data while referring to past data. The data analysis unit can prioritize the analysis of data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the data collection period. Some or all of the above processes in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the data collection period into a generating AI, and the generating AI can determine the priority of analysis.

[0048] The data analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the data analysis unit can prioritize the analysis of highly relevant data. The data analysis unit can also postpone the analysis of less relevant data. The data analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for the provision of more accurate analysis results by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the relevance of the data into a generating AI, and the generating AI can adjust the order of analysis.

[0049] The response suggestion unit can adjust the level of detail of its suggestions based on the severity of the symptoms. For example, it can provide detailed suggestions for highly severe symptoms, simplified suggestions for less severe symptoms, and suggestions with an appropriate level of detail for moderately severe symptoms. By adjusting the level of detail of suggestions based on the severity of the symptoms, efficient suggestions become possible. Some or all of the above processing in the response suggestion unit may be performed using AI, for example, or without AI. For example, the response suggestion unit can input the severity of the symptoms into a generating AI, which can then adjust the level of detail of the suggestions.

[0050] The treatment suggestion unit can apply different suggestion algorithms depending on the symptom category when making a suggestion. For example, the treatment suggestion unit can apply a specific cardiac disease suggestion algorithm to cardiac-related symptoms. For respiratory-related symptoms, it can apply a specific respiratory disease suggestion algorithm. For digestive-related symptoms, it can apply a specific digestive disease suggestion algorithm. By applying different suggestion algorithms depending on the symptom category, it is possible to provide more accurate suggestions. Some or all of the above processing in the treatment suggestion unit may be performed using AI, for example, or without AI. For example, the treatment suggestion unit can input the symptom category into a generating AI, and the generating AI can apply an appropriate suggestion algorithm.

[0051] The suggested response unit can determine the priority of suggestions based on the timing of symptom onset. For example, the suggested response unit may prioritize suggesting the most recent symptoms. The suggested response unit can suggest the most recent symptoms while referring to past symptoms. The suggested response unit can prioritize suggesting symptoms that occurred within a specific period. This allows for efficient suggestions by determining the priority of suggestions based on the timing of symptom onset. Some or all of the above processing in the suggested response unit may be performed using AI, for example, or without AI. For example, the suggested response unit can input the timing of symptom onset into a generating AI, which can then determine the priority of suggestions.

[0052] The treatment suggestion unit can adjust the order of suggestions based on the relevance of symptoms when making suggestions. For example, the treatment suggestion unit may prioritize suggesting highly relevant symptoms. The treatment suggestion unit may postpone suggesting less relevant symptoms. The treatment suggestion unit can dynamically adjust the order of suggestions according to the relevance of symptoms. This allows for the provision of more accurate suggestions by adjusting the order of suggestions based on the relevance of symptoms. Some or all of the above processing in the treatment suggestion unit may be performed using AI, for example, or without AI. For example, the treatment suggestion unit can input the relevance of symptoms into a generating AI, and the generating AI can adjust the order of suggestions.

[0053] The monitoring unit can analyze the user's past health data during monitoring to select the optimal monitoring method. For example, the monitoring unit can identify the most effective monitoring method from the user's past health data and apply that method. The monitoring unit can perform monitoring using specific devices or sensors based on the user's past health data. The monitoring unit can analyze the user's past health data and optimize the monitoring method under specific circumstances. This allows the optimal monitoring method to be selected by analyzing the user's past health data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past health data into a generating AI, and the generating AI can select the optimal monitoring method.

[0054] The monitoring unit can customize the monitoring methods based on the user's current living situation during monitoring. For example, if the user is at work, the monitoring unit can adjust the monitoring frequency so as not to interfere with work. If the user is exercising, the monitoring unit can customize the monitoring methods to take the effects of exercise into consideration. If the user is resting, the monitoring unit can adjust the monitoring methods so as not to disturb rest. In this way, by customizing the monitoring methods based on the user's living situation, more appropriate monitoring can be provided. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's living situation into a generating AI, and the generating AI can customize the monitoring methods.

[0055] The monitoring unit can select the optimal monitoring method by considering the user's geographical location information during monitoring. For example, if the user is at high altitude, the monitoring unit will apply a monitoring method specific to high altitude. If the user is in an urban area, the monitoring unit can perform monitoring considering the environment specific to urban areas. If the user is traveling, the monitoring unit can select a monitoring method adapted to the environment of the travel destination. In this way, the optimal monitoring method can be selected by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the user's geographical location information into a generating AI, and the generating AI can select the optimal monitoring method.

[0056] The monitoring unit can analyze the user's social media activity during monitoring and propose monitoring methods. For example, if the user is experiencing stress on social media, the monitoring unit can propose stress-related monitoring. If the user is relaxed on social media, the monitoring unit can propose monitoring of their relaxed state. If the user is active on social media, the monitoring unit can propose monitoring of their activity state. In this way, by analyzing the user's social media activity, it is possible to propose relevant monitoring methods. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's social media activity data into a generating AI, and the generating AI can propose monitoring methods.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, it can identify the time of day when the most accurate data can be obtained from the user's past health data and collect data during that time. Based on the user's past health data, the data collection unit can collect data using specific devices or sensors. The data collection unit can analyze the user's past health data and optimize the data collection method under specific circumstances. This allows the optimal collection method to be selected by analyzing the user's past health data.

[0059] The monitoring unit can filter data based on the user's current activity level and environment. For example, if the user is exercising, the data can be filtered to account for the effects of exercise. If the user is stationary, data from that stationary state can be prioritized. If the user is outdoors, the data can be filtered to eliminate the influence of the external environment. By filtering data based on the user's activity level and environment, more accurate data can be collected.

[0060] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is at high altitude, it can prioritize the collection of biometric data specific to high altitude. If the user is in an urban area, it can prioritize the collection of biometric data considering environmental data specific to urban areas. If the user is traveling, it can prioritize the collection of data adapted to the environment of the travel destination. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant data.

[0061] The data analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with an appropriate level of detail on moderately important data. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible.

[0062] The response suggestion unit can adjust the level of detail in its suggestions based on the severity of the symptoms. For example, it can provide detailed suggestions for highly severe symptoms, simplified suggestions for less severe symptoms, and suggestions with an appropriate level of detail for moderately severe symptoms. By adjusting the level of detail in suggestions based on the severity of the symptoms, efficient suggestions become possible.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects personal biometric data, health checkup results, and medication record information. For example, it collects biometric data such as the user's blood pressure, heart rate, and body temperature, retrieves past health checkup results from electronic medical records, and scans medication information recorded in the medication record book and converts it into digital data. Step 2: The data analysis unit analyzes the information collected by the data collection unit to make a comprehensive diagnosis of the patient's symptoms in an emergency. For example, it diagnoses whether there is a heart problem based on the collected biometric data, health checkup results, and medication record information. Step 3: The treatment proposal department proposes appropriate treatment methods and medical institutions to visit based on the diagnostic results obtained by the data analysis department. For example, if a heart problem is suspected, it will suggest a nearby cardiologist and propose treatment methods such as emergency treatment and medication administration. Step 4: The monitoring unit continuously monitors the user's health data and automatically takes action when it detects an abnormality. For example, if the user's heart rate suddenly increases, it will contact family members and share information with medical institutions. It also autonomously provides suggestions for health management and lifestyle improvements on a daily basis.

[0065] (Example of form 2) The health management system according to an embodiment of the present invention is a system that analyzes an individual's "symptoms of discomfort" in real time based on their biometric data, health checkup results, and medication record information. This health management system collects the individual's biometric data, health checkup results, and medication record information, and uses a multimodal large-scale language model (LLM) to comprehensively diagnose the patient's symptoms in emergencies and propose appropriate countermeasures and medical institutions to visit in real time. In addition, an autonomous AI agent continuously monitors the user's health data and automatically takes countermeasures when an abnormality is detected. For example, it may contact family members or share information with medical institutions. Furthermore, it autonomously provides health management and lifestyle improvement suggestions on a daily basis. For example, the health management system collects the individual's biometric data, health checkup results, and medication record information. In this case, the user only needs to input their own health data. For example, they input biometric data such as blood pressure, heart rate, and body temperature, as well as past health checkup results and information on medications listed in their medication record. This information is input into the multimodal LLM. Next, the multimodal LLM analyzes the input information and comprehensively diagnoses the patient's symptoms in emergencies. For example, if a user experiences chest pain, LLM diagnoses whether there is a heart problem based on biometric data, health checkup results, and medication record information. Based on the diagnosis, it suggests appropriate treatment methods and medical institutions to visit in real time. For example, if a heart problem is suspected, it suggests a nearby cardiologist. Furthermore, the autonomous AI agent continuously monitors the user's health data. For example, it monitors the user's biometric data in real time and automatically takes countermeasures when an abnormality is detected. For example, if the user's heart rate suddenly increases, it will contact family members and share information with medical institutions. In addition, it autonomously provides health management and lifestyle improvement suggestions on a daily basis. For example, it suggests healthy lifestyle habits based on the user's diet and exercise records. This allows users to understand their health status in real time and receive appropriate medical services with peace of mind in emergencies. Moreover, the autonomous AI agent protects individual health and automatically takes the optimal action in emergencies, enabling everyone to live a safe and secure life.This allows the health management system to analyze individual health data in real time and suggest appropriate treatment methods and medical facilities.

[0066] The health management system according to this embodiment comprises a data collection unit, a data analysis unit, a response suggestion unit, and a monitoring unit. The data collection unit collects personal biometric data, health checkup results, and medication record information. For example, the data collection unit collects biometric data such as the user's blood pressure, heart rate, and body temperature. The data collection unit can also collect past health checkup results. Furthermore, the data collection unit can also collect information on medications listed in the medication record. For example, the data collection unit collects biometric data such as blood pressure, heart rate, and body temperature entered by the user. The data collection unit can also obtain past health checkup results from electronic medical records. The data collection unit can also scan medication information listed in the medication record and convert it into digital data. The data analysis unit analyzes the information collected by the data collection unit and makes a comprehensive diagnosis of the patient's symptoms in an emergency. For example, the data analysis unit makes a comprehensive diagnosis of the patient's symptoms in an emergency based on the collected biometric data, health checkup results, and medication record information. The data analysis unit can analyze the collected information using a multimodal LLM. For example, if a user experiences chest pain, the data analysis unit diagnoses whether there is a heart problem based on biometric data, health checkup results, and medication record information. Based on the collected information, the data analysis unit can suggest appropriate actions to take in an emergency and recommend medical institutions to visit. The action suggestion unit suggests appropriate actions to take and medical institutions to visit based on the diagnostic results obtained by the data analysis unit. For example, the action suggestion unit suggests appropriate actions to take and medical institutions to visit based on the diagnostic results. The action suggestion unit can suggest appropriate actions to take and medical institutions to visit based on the diagnostic results using multimodal LLM. For example, if a heart problem is suspected, the action suggestion unit will suggest a nearby cardiologist. Based on the diagnostic results, the action suggestion unit can suggest actions such as first aid and medication administration. The monitoring unit continuously monitors the user's health data and automatically takes countermeasures when it detects an abnormality. For example, the monitoring unit monitors the user's biometric data in real time and automatically takes countermeasures when it detects an abnormality.The monitoring unit can continuously monitor the user's health data using a multimodal LLM (Limited Life Management) system. For example, if the user's heart rate suddenly increases, the monitoring unit will contact family members or share information with medical institutions. The monitoring unit can also autonomously provide health management and lifestyle improvement suggestions on a daily basis. For example, based on the user's records of diet and exercise, the monitoring unit will suggest healthy lifestyle habits. As a result, the health management system according to this embodiment can analyze individual health data in real time and suggest appropriate countermeasures and medical institutions.

[0067] The data collection unit collects personal biometric data, health checkup results, and medication record information. Specifically, it uses wearable devices and smartphone applications to collect biometric data such as the user's blood pressure, heart rate, and body temperature. These devices are worn constantly by the user during their daily life, collecting data in real time and transmitting it to a cloud server. For example, a wearable device is worn on the user's wrist to periodically measure heart rate and blood pressure, and the data is collected through a smartphone application. The data collection unit can also obtain past health checkup results from electronic medical records. The electronic medical record system digitizes and stores medical records from healthcare institutions, and with the user's consent, the data collection unit can access this data. Furthermore, the data collection unit can scan medication information written in medication record books and convert it into digital data. Users take pictures of pages of their medication record books using their smartphone cameras and upload the data through a dedicated application. This allows the data collection unit to accurately understand the user's medication history and current medication status. This data is stored in a secure cloud environment and made accessible to the data analysis unit and the treatment suggestion unit as needed. The data acquisition unit performs regular calibration and device maintenance to ensure the quality of the collected data. Furthermore, it provides guidelines and checking functions to prevent input errors when users manually enter data. This allows the data acquisition unit to collect accurate and reliable data, improving the overall system performance.

[0068] The Data Analysis Department analyzes information collected by the Data Collection Department to comprehensively diagnose a patient's symptoms in emergencies. The Data Analysis Department can analyze collected information using a multimodal Large-Scale Language Model (LLM). Specifically, it integrates collected biometric data, health checkup results, and medication record information to comprehensively evaluate the user's health status. For example, if a user experiences chest pain, the Data Analysis Department diagnoses whether there is a heart problem based on heart rate and blood pressure fluctuations, past health checkup results, and medication record information. The multimodal LLM has the ability to comprehensively analyze data in different formats, such as text data, numerical data, and image data, enabling highly accurate assessment of the user's symptoms and health status. Based on the collected information, the Data Analysis Department can suggest appropriate actions and medical institutions to visit in emergencies. For example, if a heart problem is suspected, the Data Analysis Department can suggest a nearby cardiologist and, in an emergency, arrange for an ambulance. Furthermore, the Data Analysis Department can utilize historical data and statistical information to assess long-term health risks and perform trend analysis. For example, based on past health checkup results and changes in biometric data, the system can predict the risk of specific diseases and propose preventive measures. Furthermore, the data analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the data analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0069] The Response Proposal Department proposes appropriate treatment methods and medical institutions to visit based on the diagnostic results obtained by the Data Analysis Department. Using multimodal LLM (Limited Licensing Management), the Response Proposal Department can propose appropriate treatment methods and medical institutions to visit based on the diagnostic results. Specifically, it proposes treatment methods such as first aid and medication administration based on the diagnostic results. For example, if a heart problem is suspected, the Response Proposal Department can suggest a nearby cardiologist and, in an emergency, can arrange for an ambulance. Furthermore, the Response Proposal Department can also suggest appropriate lifestyle improvements according to the user's symptoms and health condition. For example, it can suggest diet and exercise advice, stress management methods, and other support to help users lead healthy lives. The Response Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it collects user evaluations and opinions on the suggested treatment methods and medical institutions and incorporates them into future suggestions. The Response Proposal Department can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the response suggestion department to provide users with prompt and reliable action instructions, minimizing health risks. Furthermore, the response suggestion department can provide customized suggestions tailored to the individual needs and circumstances of each user. For example, for users with specific allergies or chronic illnesses, it can suggest appropriate treatment methods and medical facilities. In this way, the response suggestion department can comprehensively support users' health management and provide optimal suggestions that meet their individual needs.

[0070] The monitoring unit continuously monitors the user's health data and automatically takes countermeasures when an anomaly is detected. The monitoring unit can continuously monitor the user's health data using a multimodal LLM (Limited Lifecycle Management) architecture. Specifically, it monitors the user's biometric data in real time and automatically takes countermeasures when an anomaly is detected. For example, if the user's heart rate suddenly increases, the monitoring unit will contact family members and share information with medical institutions. The monitoring unit can also autonomously provide health management and lifestyle improvement suggestions on a daily basis. For example, it can suggest healthy lifestyle habits based on the user's diet and exercise records. The monitoring unit can also accumulate user health data over the long term and perform trend analysis and risk assessment. For example, it can predict the risk of specific diseases based on past data and suggest preventative measures. Furthermore, the monitoring unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the monitoring unit to not only grasp the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system. In addition, the monitoring unit can collect user feedback and continuously improve the accuracy and effectiveness of its monitoring. For example, the monitoring unit collects user evaluations and opinions on the monitoring results and incorporates them into the next monitoring session. Furthermore, the monitoring unit can reliably transmit information using multiple communication methods. For instance, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the monitoring unit to provide users with prompt and reliable action instructions, minimizing health risks.

[0071] The data collection unit can collect biometric data such as the user's blood pressure, heart rate, and body temperature. For example, the data collection unit can measure the user's blood pressure and collect the data. The data collection unit can also measure the heart rate and collect the data. The data collection unit can also measure the body temperature and collect the data. For example, the data collection unit can measure the user's blood pressure using a blood pressure monitor and collect the data. The data collection unit can also measure the user's heart rate using a heart rate monitor and collect the data. The data collection unit can also measure the user's body temperature using a thermometer and collect the data. By collecting the user's biometric data in this way, their health status can be understood. 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 collect biometric data such as the user's blood pressure, heart rate, and body temperature, input the collected data into a generating AI, and have the generating AI analyze the data.

[0072] The data collection unit can collect past health checkup results. For example, the data collection unit can obtain past health checkup results from electronic medical records. The data collection unit can also scan past health checkup results and convert them into digital data. The data collection unit can also manually input past health checkup results. For example, the data collection unit can automatically obtain past health checkup results from electronic medical records and collect the data. The data collection unit can also scan past health checkup results with a scanner, convert them into digital data, and collect them. The data collection unit can also manually input past health checkup results and collect the data. This allows the user's health history to be understood by collecting past health checkup results. 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 collect past health checkup results, input the collected data into a generating AI, and have the generating AI analyze the data.

[0073] The data collection unit can collect information about medications recorded in the medication record book. For example, the data collection unit can scan the medication information recorded in the medication record book and convert it into digital data. The data collection unit can also manually input medication information. The data collection unit can also obtain medication information from pharmacies. For example, the data collection unit can scan the medication information recorded in the medication record book with a scanner, convert it into digital data, and collect it. The data collection unit can also manually input medication information and collect the data. The data collection unit can also automatically obtain medication information from pharmacies and collect the data. This allows for an understanding of the user's medication adherence by collecting information from the medication record book. 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 collect medication information recorded in the medication record book, input the collected data into a generating AI, and have the generating AI analyze the data.

[0074] The data analysis unit can comprehensively diagnose a patient's symptoms in an emergency based on collected biometric data, health checkup results, and medication record information. For example, the data analysis unit can comprehensively diagnose a patient's symptoms in an emergency based on collected biometric data, health checkup results, and medication record information. The data analysis unit can analyze the collected information using a multimodal LLM (Limited Licensing Machine). For example, if a user experiences chest pain, the data analysis unit can diagnose whether there is a heart problem based on biometric data, health checkup results, and medication record information. Based on the collected information, the data analysis unit can suggest appropriate treatment methods and medical institutions to visit in an emergency. This allows for the suggestion of appropriate treatment methods by comprehensively diagnosing the patient's symptoms in an emergency. Some or all of the above-described processes in the data analysis unit may be performed using AI, or not. For example, the data analysis unit can input collected biometric data, health checkup results, and medication record information into a generating AI, which can then analyze the data.

[0075] The response suggestion unit can propose appropriate treatment methods and medical institutions to visit based on the diagnosis results. For example, the response suggestion unit can propose appropriate treatment methods and medical institutions to visit based on the diagnosis results. The response suggestion unit can propose appropriate treatment methods and medical institutions to visit based on the diagnosis results using multimodal LLM. For example, if a heart problem is suspected, the response suggestion unit will suggest a nearby cardiologist. The response suggestion unit can propose treatment methods such as emergency treatment and medication administration based on the diagnosis results. This enables a rapid response by proposing appropriate treatment methods and medical institutions to visit based on the diagnosis results. Some or all of the above processing in the response suggestion unit may be performed using AI, for example, or without AI. For example, the response suggestion unit can input the diagnosis results into a generating AI, and the generating AI can propose appropriate treatment methods and medical institutions to visit.

[0076] The monitoring unit can monitor the user's biometric data in real time and, if it detects an abnormality, can contact family members or share information with medical institutions. For example, the monitoring unit can continuously monitor the user's health data using a multimodal LLM. For example, if the user's heart rate suddenly increases, the monitoring unit will contact family members or share information with medical institutions. The monitoring unit can autonomously provide health management and lifestyle improvement suggestions on a daily basis. For example, the monitoring unit will suggest healthy lifestyle habits based on the user's diet and exercise records. This allows for real-time monitoring of the user's biometric data and rapid response when an abnormality is detected. Some or all of the above-described processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the user's biometric data into a generating AI, which can then detect abnormalities and implement countermeasures.

[0077] The monitoring unit can autonomously provide health management and lifestyle improvement suggestions on a daily basis. For example, the monitoring unit can suggest healthy lifestyle habits based on the user's records of meals and exercise. The monitoring unit can continuously monitor the user's health data using a multimodal LLM and make health management and lifestyle improvement suggestions. For example, the monitoring unit can analyze the user's meal records and suggest nutritionally balanced meals. The monitoring unit can analyze the user's exercise records and suggest an appropriate exercise plan. In this way, it can support the user's health maintenance by autonomously providing daily health management and lifestyle improvement suggestions. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's meal and exercise records into a generating AI, and the generating AI can make health management and lifestyle improvement suggestions.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of biometric data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay biometric data collection until the user is relaxed. If the user is relaxed, the data collection unit can immediately collect biometric data to obtain accurate data. If the user is in a hurry, the data collection unit can increase the collection frequency to collect the necessary data in a short time. This allows for the collection of more accurate data by adjusting the timing of biometric data collection based on the user'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 the user's emotions into a generative AI, which can then estimate the emotions and adjust the collection timing.

[0079] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, the data collection unit can identify the time of day when the most accurate data can be obtained from the user's past health data and collect data during that time. Based on the user's past health data, the data collection unit can collect data using specific devices or sensors. The data collection unit can analyze the user's past health data and optimize the data collection method under specific circumstances. This allows the optimal collection method to be selected by analyzing the user's past health 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 user's past health data into a generating AI, which can then select the optimal collection method.

[0080] The data collection unit can filter biometric data based on the user's current activity level and environment. For example, if the user is exercising, the data collection unit can filter the data to account for the effects of exercise. If the user is stationary, the data collection unit can prioritize collecting data from that stationary state. If the user is outdoors, the data collection unit can filter the data to eliminate the influence of the external environment. This allows for the collection of more accurate data by filtering the data based on the user's 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 user activity level and environmental data into a generating AI, which can then filter the data.

[0081] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related biometric data. If the user is relaxed, the data collection unit can collect a wide range of data to understand their overall health. If the user is in a hurry, the data collection unit can prioritize collecting only the most important data. This ensures that important data is collected preferentially by prioritizing data based on the user'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 the user's emotions into a generative AI, which can then estimate the emotions and determine the data prioritization.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information when collecting biometric data. For example, if the user is at high altitude, the data collection unit can prioritize the collection of biometric data specific to high altitude. If the user is in an urban area, the data collection unit can prioritize the collection of biometric data considering environmental data specific to urban areas. If the user is traveling, the data collection unit can prioritize the collection of data adapted to the environment of the travel destination. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. 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 user's geographical location information into a generating AI, and the generating AI can determine the priority of highly relevant data.

[0083] The data collection unit can analyze a user's social media activity and collect relevant data when collecting biometric data. For example, if a user is experiencing stress on social media, the data collection unit can collect stress-related biometric data. If a user is relaxed on social media, the data collection unit can collect biometric data indicating a relaxed state. If a user is active on social media, the data collection unit can collect biometric data indicating an active state. This allows for the collection of relevant data by analyzing the user's social media activity. 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 user's social media activity data into a generating AI, which can then collect relevant data.

[0084] The data analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the data analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the data analysis unit can provide detailed analysis results. If the user is in a hurry, the data analysis unit can provide concise analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the data analysis unit can input the user's emotions into a generative AI, which can then estimate the emotions and adjust the presentation of the analysis.

[0085] The data analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the data analysis unit can perform a detailed analysis on highly important data. For less important data, it can perform a simplified analysis. For moderately important data, it can perform an analysis with an appropriate level of detail. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the importance of the data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0086] The data analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the data analysis unit can apply a specific biometric data analysis algorithm to biometric data. For health checkup results, the data analysis unit can apply a specific diagnostic data analysis algorithm. For medication record information, the data analysis unit can apply a specific drug data analysis algorithm. By applying different analysis algorithms depending on the data category, more accurate analysis results can be provided. Some or all of the above processing in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the data category into a generating AI, and the generating AI can apply an appropriate analysis algorithm.

[0087] The data analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the data analysis unit can provide a short, concise analysis. If the user is relaxed, the data analysis unit can provide a detailed analysis. If the user is excited, the data analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the data analysis unit can input the user's emotions into a generative AI, which can then estimate the emotions and adjust the length of the analysis.

[0088] The data analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the data analysis unit can prioritize the analysis of the most recent data. The data analysis unit can analyze the most recent data while referring to past data. The data analysis unit can prioritize the analysis of data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the data collection period. Some or all of the above processes in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the data collection period into a generating AI, and the generating AI can determine the priority of analysis.

[0089] The data analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the data analysis unit can prioritize the analysis of highly relevant data. The data analysis unit can also postpone the analysis of less relevant data. The data analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for the provision of more accurate analysis results by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the data analysis unit may be performed using AI, for example, or without AI. For example, the data analysis unit can input the relevance of the data into a generating AI, and the generating AI can adjust the order of analysis.

[0090] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's emotions into a generative AI, which can then estimate the emotions and adjust the way suggestions are presented.

[0091] The response suggestion unit can adjust the level of detail of its suggestions based on the severity of the symptoms. For example, it can provide detailed suggestions for highly severe symptoms, simplified suggestions for less severe symptoms, and suggestions with an appropriate level of detail for moderately severe symptoms. By adjusting the level of detail of suggestions based on the severity of the symptoms, efficient suggestions become possible. Some or all of the above processing in the response suggestion unit may be performed using AI, for example, or without AI. For example, the response suggestion unit can input the severity of the symptoms into a generating AI, which can then adjust the level of detail of the suggestions.

[0092] The treatment suggestion unit can apply different suggestion algorithms depending on the symptom category when making a suggestion. For example, the treatment suggestion unit can apply a specific cardiac disease suggestion algorithm to cardiac-related symptoms. For respiratory-related symptoms, it can apply a specific respiratory disease suggestion algorithm. For digestive-related symptoms, it can apply a specific digestive disease suggestion algorithm. By applying different suggestion algorithms depending on the symptom category, it is possible to provide more accurate suggestions. Some or all of the above processing in the treatment suggestion unit may be performed using AI, for example, or without AI. For example, the treatment suggestion unit can input the symptom category into a generating AI, and the generating AI can apply an appropriate suggestion algorithm.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input the user's emotions into a generative AI, which can then estimate the emotions and adjust the length of suggestions.

[0094] The suggested response unit can determine the priority of suggestions based on the timing of symptom onset. For example, the suggested response unit may prioritize suggesting the most recent symptoms. The suggested response unit can suggest the most recent symptoms while referring to past symptoms. The suggested response unit can prioritize suggesting symptoms that occurred within a specific period. This allows for efficient suggestions by determining the priority of suggestions based on the timing of symptom onset. Some or all of the above processing in the suggested response unit may be performed using AI, for example, or without AI. For example, the suggested response unit can input the timing of symptom onset into a generating AI, which can then determine the priority of suggestions.

[0095] The treatment suggestion unit can adjust the order of suggestions based on the relevance of symptoms when making suggestions. For example, the treatment suggestion unit may prioritize suggesting highly relevant symptoms. The treatment suggestion unit may postpone suggesting less relevant symptoms. The treatment suggestion unit can dynamically adjust the order of suggestions according to the relevance of symptoms. This allows for the provision of more accurate suggestions by adjusting the order of suggestions based on the relevance of symptoms. Some or all of the above processing in the treatment suggestion unit may be performed using AI, for example, or without AI. For example, the treatment suggestion unit can input the relevance of symptoms into a generating AI, and the generating AI can adjust the order of suggestions.

[0096] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated emotions. For example, if the user is nervous, the monitoring unit can provide a simple and highly visible monitoring method. If the user is relaxed, the monitoring unit can provide a detailed monitoring method. If the user is in a hurry, the monitoring unit can provide a concise monitoring method. By adjusting the monitoring method based on the user's emotions, more appropriate monitoring can be provided. 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's emotions into a generative AI, which can then estimate the emotions and adjust the monitoring method.

[0097] The monitoring unit can analyze the user's past health data during monitoring to select the optimal monitoring method. For example, the monitoring unit can identify the most effective monitoring method from the user's past health data and apply that method. The monitoring unit can perform monitoring using specific devices or sensors based on the user's past health data. The monitoring unit can analyze the user's past health data and optimize the monitoring method under specific circumstances. This allows the optimal monitoring method to be selected by analyzing the user's past health data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past health data into a generating AI, and the generating AI can select the optimal monitoring method.

[0098] The monitoring unit can customize the monitoring methods based on the user's current living situation during monitoring. For example, if the user is at work, the monitoring unit can adjust the monitoring frequency so as not to interfere with work. If the user is exercising, the monitoring unit can customize the monitoring methods to take the effects of exercise into consideration. If the user is resting, the monitoring unit can adjust the monitoring methods so as not to disturb rest. In this way, by customizing the monitoring methods based on the user's living situation, more appropriate monitoring can be provided. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's living situation into a generating AI, and the generating AI can customize the monitoring methods.

[0099] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is stressed, the monitoring unit will prioritize stress-related monitoring. If the user is relaxed, the monitoring unit can perform broad monitoring to understand their overall health. If the user is in a hurry, the monitoring unit can prioritize only the most important monitoring. This allows for prioritizing important monitoring based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's emotions into a generative AI, which can then estimate the emotions and determine monitoring priorities.

[0100] The monitoring unit can select the optimal monitoring method by considering the user's geographical location information during monitoring. For example, if the user is at high altitude, the monitoring unit will apply a monitoring method specific to high altitude. If the user is in an urban area, the monitoring unit can perform monitoring considering the environment specific to urban areas. If the user is traveling, the monitoring unit can select a monitoring method adapted to the environment of the travel destination. In this way, the optimal monitoring method can be selected by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input the user's geographical location information into a generating AI, and the generating AI can select the optimal monitoring method.

[0101] The monitoring unit can analyze the user's social media activity during monitoring and propose monitoring methods. For example, if the user is experiencing stress on social media, the monitoring unit can propose stress-related monitoring. If the user is relaxed on social media, the monitoring unit can propose monitoring of their relaxed state. If the user is active on social media, the monitoring unit can propose monitoring of their activity state. In this way, by analyzing the user's social media activity, it is possible to propose relevant monitoring methods. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's social media activity data into a generating AI, and the generating AI can propose monitoring methods.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] A health management system can estimate a user's emotions and adjust its approach based on those emotions. For example, if a user is stressed, it can suggest breathing exercises or meditation to promote relaxation. If a user is relaxed, it can suggest exercise or an active lifestyle. If a user is anxious, it can provide information on counseling or support groups to alleviate their anxiety. By providing a health management approach tailored to the user's emotions, it becomes possible to maintain health more effectively.

[0104] The data collection unit can analyze the user's past health data and select the optimal collection method. For example, it can identify the time of day when the most accurate data can be obtained from the user's past health data and collect data during that time. Based on the user's past health data, the data collection unit can collect data using specific devices or sensors. The data collection unit can analyze the user's past health data and optimize the data collection method under specific circumstances. This allows the optimal collection method to be selected by analyzing the user's past health data.

[0105] The data analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. By adjusting the presentation of the analysis based on the user's emotions, it is possible to provide more appropriate analysis results.

[0106] The monitoring unit can filter data based on the user's current activity level and environment. For example, if the user is exercising, the data can be filtered to account for the effects of exercise. If the user is stationary, data from that stationary state can be prioritized. If the user is outdoors, the data can be filtered to eliminate the influence of the external environment. By filtering data based on the user's activity level and environment, more accurate data can be collected.

[0107] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it can provide simple and easily understandable suggestions. If the user is relaxed, it can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, it can provide more appropriate suggestions.

[0108] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is at high altitude, it can prioritize the collection of biometric data specific to high altitude. If the user is in an urban area, it can prioritize the collection of biometric data considering environmental data specific to urban areas. If the user is traveling, it can prioritize the collection of data adapted to the environment of the travel destination. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant data.

[0109] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible monitoring method. If the user is relaxed, it can provide a detailed monitoring method. If the user is in a hurry, it can provide a concise monitoring method. By adjusting the monitoring method based on the user's emotions, it is possible to provide more appropriate monitoring.

[0110] The data analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with an appropriate level of detail on moderately important data. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible.

[0111] The response suggestion unit can adjust the level of detail in its suggestions based on the severity of the symptoms. For example, it can provide detailed suggestions for highly severe symptoms, simplified suggestions for less severe symptoms, and suggestions with an appropriate level of detail for moderately severe symptoms. By adjusting the level of detail in suggestions based on the severity of the symptoms, efficient suggestions become possible.

[0112] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on those estimates. For example, if the user is stressed, stress-related monitoring will be prioritized. If the user is relaxed, broader monitoring can be performed to understand their overall health. If the user is in a hurry, only the most important monitoring can be prioritized. In this way, important monitoring can be prioritized by determining monitoring priorities based on the user's emotions.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects personal biometric data, health checkup results, and medication record information. For example, it collects biometric data such as the user's blood pressure, heart rate, and body temperature, retrieves past health checkup results from electronic medical records, and scans medication information recorded in the medication record book and converts it into digital data. Step 2: The data analysis unit analyzes the information collected by the data collection unit to make a comprehensive diagnosis of the patient's symptoms in an emergency. For example, it diagnoses whether there is a heart problem based on the collected biometric data, health checkup results, and medication record information. Step 3: The treatment proposal department proposes appropriate treatment methods and medical institutions to visit based on the diagnostic results obtained by the data analysis department. For example, if a heart problem is suspected, it will suggest a nearby cardiologist and propose treatment methods such as emergency treatment and medication administration. Step 4: The monitoring unit continuously monitors the user's health data and automatically takes action when it detects an abnormality. For example, if the user's heart rate suddenly increases, it will contact family members and share information with medical institutions. It also autonomously provides suggestions for health management and lifestyle improvements on a daily basis.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, data analysis unit, response suggestion unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart device 14 and collects the user's biometric data, health checkup results, and medication record information. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to comprehensively diagnose the patient's symptoms in an emergency. The response suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes appropriate response methods and medical institutions to visit based on the diagnosis results. The monitoring unit is implemented by the control unit 46A of the smart device 14 and continuously monitors the user's health data, automatically executing countermeasures when an abnormality is detected. 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.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, data analysis unit, response suggestion unit, and monitoring unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and collects the user's biometric data, health checkup results, and medication record information. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to comprehensively diagnose the patient's symptoms in an emergency. The response suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes appropriate response methods and medical institutions to visit based on the diagnosis results. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and continuously monitors the user's health data and automatically executes countermeasures when an abnormality is detected. 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.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the data collection unit, data analysis unit, response suggestion unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the headset terminal 314 and collects the user's biometric data, health checkup results, and medication record information. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to comprehensively diagnose the patient's symptoms in emergencies. The response suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes appropriate response methods and medical institutions to visit based on the diagnosis results. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and continuously monitors the user's health data, automatically executing countermeasures when an abnormality is detected. 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.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the data collection unit, data analysis unit, response suggestion unit, and monitoring unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the robot 414 and collects the user's biometric data, health checkup results, and medication record information. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to comprehensively diagnose the patient's symptoms in emergencies. The response suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes appropriate response methods and medical institutions to visit based on the diagnosis results. The monitoring unit is implemented by the control unit 46A of the robot 414 and continuously monitors the user's health data, automatically executing countermeasures when an abnormality is detected. 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) The data collection unit collects personal biometric data, health checkup results, and medication record information. A data analysis unit analyzes the information collected by the aforementioned data collection unit and makes a comprehensive diagnosis of the patient's symptoms in an emergency. Based on the diagnostic results obtained by the aforementioned data analysis unit, the response proposal unit proposes appropriate countermeasures and medical institutions to which patients should be consulted. It includes a monitoring unit that continuously monitors the user's health data and automatically takes countermeasures when an abnormality is detected. A system characterized by the following features. (Note 2) The aforementioned data acquisition unit is It collects biometric data from users, such as blood pressure, heart rate, and body temperature. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned data acquisition unit is Collect past health checkup results. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned data acquisition unit is Collect information about the medications listed in your medication record book. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned data analysis unit, Based on collected biometric data, health checkup results, and medication record information, a comprehensive diagnosis of the patient's symptoms is made in emergency situations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned countermeasure proposal unit, Based on the diagnosis, we will suggest appropriate treatment methods and medical institutions you should consult. The system described in Appendix 1, characterized by the features described herein. (Note 7) The monitoring unit, The system monitors the user's biometric data in real time and, if an anomaly is detected, contacts family members and shares information with medical institutions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, On a daily basis, they autonomously provide suggestions for health management and lifestyle improvements. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data acquisition unit is The system estimates the user's emotions and adjusts the timing of biometric data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned data acquisition unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data acquisition unit is When collecting biometric data, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned data acquisition unit is It estimates the user'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 13) The aforementioned data acquisition unit is When collecting biometric data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned data acquisition unit is When collecting biometric data, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned data analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned data 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 17) The aforementioned data 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 18) The aforementioned data analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned data 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 20) The aforementioned data 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 21) The aforementioned countermeasure proposal unit, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned countermeasure proposal unit, When making a suggestion, adjust the level of detail based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned countermeasure proposal unit, When making a proposal, different proposal algorithms are applied depending on the symptom category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned countermeasure proposal unit, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned countermeasure proposal unit, When making a proposal, prioritize the proposal based on when the symptoms started. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned countermeasure proposal unit, When making suggestions, adjust the order of suggestions based on the relevance of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 27) The monitoring unit, We estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The monitoring unit, During monitoring, the system analyzes the user's past health data to select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The monitoring unit, During monitoring, the monitoring methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The monitoring unit, During monitoring, the optimal monitoring method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The monitoring unit, During monitoring, we analyze users' social media activity and propose monitoring methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 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 data collection unit collects personal biometric data, health checkup results, and medication record information. A data analysis unit analyzes the information collected by the aforementioned data collection unit and makes a comprehensive diagnosis of the patient's symptoms in an emergency. Based on the diagnostic results obtained by the aforementioned data analysis unit, the response proposal unit proposes appropriate countermeasures and medical institutions to which patients should be consulted. It includes a monitoring unit that continuously monitors the user's health data and automatically takes countermeasures when an abnormality is detected. A system characterized by the following features.

2. The aforementioned data acquisition unit is It collects biometric data from users, such as blood pressure, heart rate, and body temperature. The system according to feature 1.

3. The aforementioned data acquisition unit is Collect past health checkup results. The system according to feature 1.

4. The aforementioned data acquisition unit is Collect information about the medications listed in your medication record book. The system according to feature 1.

5. The aforementioned data analysis unit, Based on collected biometric data, health checkup results, and medication record information, a comprehensive diagnosis of the patient's symptoms is made in emergency situations. The system according to feature 1.

6. The aforementioned countermeasure proposal unit, Based on the diagnosis, we will suggest appropriate treatment methods and medical institutions you should consult. The system according to feature 1.

7. The monitoring unit, The system monitors the user's biometric data in real time and, if an anomaly is detected, contacts family members and shares information with medical institutions. The system according to feature 1.

8. The monitoring unit, On a daily basis, they autonomously provide suggestions for health management and lifestyle improvements. The system according to feature 1.

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