system

Abstract

The system according to this embodiment aims to securely manage health information and provide appropriate support. [Solution] The system according to the embodiment comprises a recording unit, an analysis unit, a support unit, a protection unit, and an access control unit. The recording unit records health information. The analysis unit analyzes the health information recorded by the recording unit. The support unit provides support based on the results of the analysis performed by the analysis unit. The protection unit protects the data using blockchain technology. The access control unit performs access control based on the concept of Zero Trust.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, the management and protection of health information are not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to securely manage health information and provide appropriate support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recording unit, an analysis unit, a support unit, a protection unit, and an access control unit. The recording unit records health information. The analysis unit analyzes the health information recorded by the recording unit. The support unit provides support based on the results of the analysis performed by the analysis unit. The protection unit protects the data using blockchain technology. The access control unit performs access control based on the concept of Zero Trust. [Effects of the Invention]

[0007] The system according to this embodiment can securely manage health information and provide appropriate support. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 secure health information management system according to an embodiment of the present invention is designed based on the concept of Zero Trust, utilizing generative AI and blockchain technology. This system enables individuals to record health information data in real time via smartphones or smartwatches and receive support from generative AI, thereby enabling health monitoring, appropriate guidance in case of illness, and early detection of diseases. Healthcare institutions can upload blockchain-protected electronic medical records to the cloud and access patient data as needed. When individuals feel unwell, they can access this service and receive real-time diagnostic support from generative AI and make appointments at partner hospitals on the spot. In emergencies such as sudden changes in heart rate or a sudden increase in blood pressure, they can be notified by an alert and connected to an emergency call. This record can also be shared with health insurance companies. For example, an individual records health information data in real time via a smartphone or smartwatch. At this time, data such as heart rate, blood pressure, and body temperature are recorded. For example, a smartwatch measures heart rate and sends that data to a smartphone. This allows for real-time recording of the individual's health information. Next, the generative AI analyzes the recorded data. The generating AI analyzes data such as heart rate, blood pressure, and body temperature to monitor health status. For example, it can detect sudden changes in heart rate or a sudden rise in blood pressure, enabling early detection of signs of illness. This allows individuals to take appropriate action. Furthermore, medical institutions can upload blockchain-protected electronic medical records to the cloud and access patient data as needed. For example, a doctor can upload a patient's electronic medical record to the cloud and share it with other medical institutions. This facilitates smooth data sharing between medical institutions. When individuals feel unwell, they can access this service and receive real-time diagnostic support from the generating AI and make appointments at partner hospitals on the spot. For example, when an individual feels unwell, they can access the service from their smartphone and receive a diagnosis from the generating AI. Based on the diagnosis, they can make an appointment at a partner hospital on the spot.Furthermore, in emergencies such as a sudden change in heart rate or a sudden rise in blood pressure, the system can notify users via alert and connect them to an emergency call. For example, if the heart rate suddenly changes, an alert will appear on the smartphone, and the user can connect to an emergency call. This allows for a quick response. This record can also be shared with health insurance companies. For example, personal health information can be provided to health insurance companies and used for insurance application and premium calculations. This ensures that personal health information is properly managed and that health insurance can be used smoothly. As a result, the secure health information management system can record personal health information in real time and provide analysis and support using generated AI. In addition, security can be enhanced by strengthening data protection using blockchain technology and implementing access control based on the concept of Zero Trust.

[0029] The secure health information management system according to this embodiment comprises a recording unit, an analysis unit, a support unit, a protection unit, and an access control unit. The recording unit allows individuals to record health information via a smartphone or smartwatch. The recording unit records data such as heart rate, blood pressure, and body temperature. For example, the smartwatch measures heart rate and transmits the data to the smartphone. The recording unit can also manually input health information using a smartphone application. Furthermore, the recording unit can record exercise data and sleep data using sensors in the smartwatch or smartphone. The analysis unit analyzes the health information recorded by the recording unit using generative AI. For example, the analysis unit analyzes data such as heart rate, blood pressure, and body temperature to monitor the health status. For example, the analysis unit can detect sudden fluctuations in heart rate or sudden increases in blood pressure to detect early signs of poor health. The analysis unit can also analyze trends in health information using generative AI to evaluate long-term health status. The support unit provides support to the individual based on the results analyzed by the analysis unit. The support unit provides appropriate guidance in case of illness and early detection of diseases. For example, it uses generative AI to provide advice tailored to an individual's health condition. It can also provide real-time diagnostic support in case of illness and make appointments at partner hospitals. Furthermore, it can notify alerts in emergencies such as sudden changes in heart rate or blood pressure and connect to emergency calls. The protection unit uses blockchain technology to protect data. For example, it protects electronic medical records with blockchain to prevent data tampering. It also protects electronic medical records uploaded by healthcare institutions to the cloud and allows access to patient data as needed. Additionally, it can protect data shared with health insurance companies. The access control unit performs access control based on the concept of Zero Trust. For example, it sets authentication methods and access permissions to enhance data security. It also manages logs and can detect unauthorized access.As a result, the secure health information management system according to this embodiment can record personal health information in real time and provide analysis and support using generated AI. Furthermore, security can be enhanced by strengthening data protection using blockchain technology and implementing access control based on the concept of Zero Trust.

[0030] The recording unit can record data such as heart rate, blood pressure, and body temperature via a smartphone or smartwatch. For example, the recording unit can use a smartwatch to measure heart rate and transmit that data to a smartphone. The recording unit can also manually input health information using a smartphone application. Furthermore, the recording unit can record exercise data and sleep data using sensors in the smartwatch or smartphone. This enables the collection of health information in real time by recording health data via a smartphone or smartwatch. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input heart rate data measured by a smartwatch into a generating AI, which can then analyze and record the data.

[0031] The analysis unit can analyze recorded data and monitor health status. For example, the analysis unit can analyze data such as heart rate, blood pressure, and body temperature to monitor health status. For example, the analysis unit can detect sudden fluctuations in heart rate or sudden increases in blood pressure to detect early signs of poor health. The analysis unit can also use generating AI to analyze trends in health information and evaluate long-term health status. This makes it possible to monitor health status by analyzing recorded data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input recorded data into generating AI, which can then analyze the data to monitor health status.

[0032] The support unit can provide appropriate guidance or early detection of diseases based on the analysis results. For example, the support unit can provide appropriate guidance or early detection of diseases when a person is unwell. The support unit can provide advice tailored to an individual's health condition using, for example, generative AI. The support unit can also provide real-time diagnostic support when a person is unwell and can make appointments at affiliated hospitals. Furthermore, the support unit can notify alerts in emergencies such as sudden changes in heart rate or a sudden increase in blood pressure and connect to emergency calls. This enables appropriate guidance and early detection of diseases based on the analysis results. Some or all of the above processes in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can use generative AI to provide appropriate guidance and advice to individuals based on the analysis results.

[0033] The protection unit can protect electronic medical records using blockchain technology. For example, the protection unit can protect electronic medical records with blockchain to prevent data tampering. The protection unit can also protect electronic medical records uploaded by medical institutions to the cloud and allow access to patient data as needed. Furthermore, the protection unit can protect data shared with health insurance companies. In this way, the protection of electronic medical records is enhanced by using blockchain technology. Some or all of the above processes in the protection unit may be performed using AI, for example, or not using AI. For example, the protection unit can protect electronic medical record data using blockchain technology, and a generating AI can detect data tampering.

[0034] The access control unit can perform access control based on the concept of Zero Trust. For example, the access control unit can set authentication methods and access rights to enhance data security. The access control unit can also manage logs and detect unauthorized access. This enhances security through access control based on the concept of Zero Trust. Some or all of the above processes in the access control unit may be performed using AI, for example, or without AI. For example, the access control unit can use a generating AI to analyze access logs and detect unauthorized access.

[0035] The support department can provide real-time diagnostic support when a user is feeling unwell and make reservations at affiliated hospitals. For example, the support department can provide real-time diagnostic support when a user is feeling unwell. The support department uses generative AI to analyze an individual's health condition and make an appropriate diagnosis. For example, when an individual feels unwell, they can access the service from their smartphone and receive a diagnosis from the generative AI. Based on the diagnosis results, they can make a reservation at an affiliated hospital on the spot. This makes it possible to provide real-time diagnostic support and make reservations at affiliated hospitals when a user is feeling unwell. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can use generative AI to automatically make reservations at affiliated hospitals based on the diagnosis results.

[0036] The support unit can notify alerts and connect to emergency calls in emergencies such as a sudden change in heart rate or a sudden increase in blood pressure. For example, the support unit can detect a sudden change in heart rate or a sudden increase in blood pressure and notify an alert. The support unit uses generative AI to analyze heart rate and blood pressure data and detect abnormalities. For example, if the heart rate becomes suddenly irregular, an alert will be displayed on the smartphone and an emergency call can be connected. This enables alerts to be notified in emergencies and allows for a quick response. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use generative AI to analyze heart rate and blood pressure data, detect abnormalities, and notify alerts.

[0037] The protection unit can protect electronic medical records uploaded by medical institutions to the cloud. For example, the protection unit protects electronic medical records uploaded by medical institutions to the cloud using blockchain technology. The protection unit prevents data tampering and enhances security. For example, a doctor can upload a patient's electronic medical record to the cloud and share it with other medical institutions. This enhances the protection of electronic medical records uploaded to the cloud. Some or all of the above processes in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can use a generating AI to analyze the data in the electronic medical record and detect tampering.

[0038] The protection unit can protect data shared with health insurance companies. For example, the protection unit protects data shared with health insurance companies using blockchain technology. The protection unit prevents data tampering and enhances security. For example, personal health information is provided to health insurance companies and used for insurance application and premium calculation. This enhances the protection of data shared with health insurance companies. Some or all of the above processing in the protection unit may be performed using AI, for example, or not using AI. For example, the protection unit can use a generating AI to analyze data shared with health insurance companies and detect tampering.

[0039] The recording unit can automatically detect and record abnormal values ​​by referring to the user's past health data during recording. For example, the recording unit can refer to the user's past heart rate data to detect and record abnormal heart rates. The recording unit uses generation AI to analyze past health data and detect abnormal values. For example, it can refer to the user's past blood pressure data to detect and record abnormal blood pressure. The recording unit can also refer to the user's past body temperature data to detect and record abnormal body temperature. This improves the accuracy of abnormal value detection by referring to past health data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can use generation AI to analyze past health data and automatically detect and record abnormal values.

[0040] The recording unit can simultaneously record the user's lifestyle and dietary habits during recording and associate them with health information. For example, the recording unit can record the user's dietary habits and associate them with health information. The recording unit can use generative AI to analyze the user's lifestyle and dietary habits and associate them with health information. For example, it can record the user's exercise habits and associate them with health information. The recording unit can also record the user's sleep patterns and associate them with health information. This improves the relevance of health information by recording lifestyle habits and dietary habits. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can use generative AI to analyze the user's lifestyle and dietary habits and associate them with health information.

[0041] The recording unit can record environmental factors based on the user's geographical location information during recording. For example, if the user is at high altitude, the recording unit can record oxygen concentration. The recording unit uses a generation AI to analyze the user's geographical location information and record environmental factors. For example, if the user is in an urban area, it can record air pollution levels. The recording unit can also record indoor temperature if the user is indoors. In this way, environmental factors can be recorded by considering geographical location information. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can use a generation AI to analyze the user's geographical location information and automatically record environmental factors.

[0042] The recording unit can analyze the user's social media activity and record relevant health information at the time of recording. For example, if the user is experiencing stress on social media, the recording unit will record that information. The recording unit uses generative AI to analyze the user's social media activity and record relevant health information. For example, if the user posts about exercise on social media, that information can be recorded. The recording unit can also record information if the user posts about food on social media. In this way, relevant health information can be recorded by analyzing social media activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can use generative AI to analyze the user's social media activity and automatically record relevant health information.

[0043] The analysis unit can improve the accuracy of detecting anomalies by referring to past health data during analysis. For example, the analysis unit can refer to the user's past heart rate data to detect abnormal heart rates with high accuracy. The analysis unit uses a generation AI to analyze past health data and detect anomalies. For example, it can refer to the user's past blood pressure data to detect abnormal blood pressure with high accuracy. The analysis unit can also refer to the user's past body temperature data to detect abnormal body temperature with high accuracy. In this way, the accuracy of detecting anomalies is improved by referring to past health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use a generation AI to analyze past health data and detect anomalies with high accuracy.

[0044] The analysis unit can optimize the analysis algorithm based on the user's lifestyle or diet during analysis. For example, the analysis unit can optimize the analysis algorithm by considering the user's diet. The analysis unit can use generative AI to analyze the user's lifestyle and diet and optimize the analysis algorithm. For example, it can optimize the analysis algorithm by considering the user's exercise habits. The analysis unit can also optimize the analysis algorithm by considering the user's sleep patterns. This improves the accuracy of the analysis algorithm by considering lifestyle and diet. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to analyze the user's lifestyle and diet and automatically optimize the analysis algorithm.

[0045] The analysis unit can incorporate environmental factors into the analysis by considering the user's geographical location information during the analysis. For example, if the user is at high altitude, the analysis unit can incorporate oxygen concentration into the analysis. The analysis unit uses a generation AI to analyze the user's geographical location information and incorporate environmental factors into the analysis. For example, if the user is in an urban area, the air pollution level can be incorporated into the analysis. The analysis unit can also incorporate indoor temperature into the analysis if the user is indoors. In this way, environmental factors can be incorporated into the analysis by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generation AI analyze the user's geographical location information and automatically incorporate environmental factors into the analysis.

[0046] The analysis unit can analyze a user's social media activity during analysis and incorporate relevant data into the analysis. For example, if a user is experiencing stress on social media, the analysis unit can incorporate that information into the analysis. The analysis unit uses generative AI to analyze a user's social media activity and incorporate relevant data into the analysis. For example, if a user posts about exercise on social media, that information can be incorporated into the analysis. The analysis unit can also incorporate information about food if a user posts about food on social media. In this way, by analyzing social media activity, relevant data can be incorporated into the analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to analyze a user's social media activity and automatically incorporate relevant data into the analysis.

[0047] The support unit can select the optimal support method by referring to past health data during support. For example, the support unit can select the optimal support method by referring to the user's past heart rate data. The support unit can also select the optimal support method by analyzing past health data using a generation AI. For example, it can select the optimal support method by referring to the user's past blood pressure data. The support unit can also select the optimal support method by referring to the user's past body temperature data. In this way, the optimal support method can be selected by referring to past health data. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI. For example, the support unit can have a generation AI analyze past health data and automatically select the optimal support method.

[0048] The support unit can customize the support content based on the user's lifestyle or diet during support. For example, the support unit can customize the support content by considering the user's diet. The support unit can use generative AI to analyze the user's lifestyle and diet and customize the support content. For example, it can customize the support content by considering the user's exercise habits. The support unit can also customize the support content by considering the user's sleep patterns. In this way, the support content can be customized by considering lifestyle and diet. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use generative AI to analyze the user's lifestyle and diet and automatically customize the support content.

[0049] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, if the user is at high altitude, the support unit can provide support that takes oxygen concentration into account. The support unit uses generative AI to analyze the user's geographical location information and select the optimal support method. For example, if the user is in an urban area, it can provide support that takes air pollution levels into account. Also, if the user is indoors, the support unit can provide support that takes indoor temperature into account. In this way, the optimal support method can be selected by considering geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use generative AI to analyze the user's geographical location information and automatically select the optimal support method.

[0050] The support unit can analyze a user's social media activity and provide relevant support during support sessions. For example, if a user is experiencing stress on social media, the support unit can provide support based on that information. The support unit uses generative AI to analyze a user's social media activity and provide relevant support. For example, if a user posts about exercise on social media, the support unit can provide support based on that information. Similarly, if a user posts about food on social media, the support unit can provide support based on that information. In this way, relevant support can be provided by analyzing social media activity. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use generative AI to analyze a user's social media activity and automatically provide relevant support.

[0051] The protection unit can optimize its protection algorithm by referring to past data breach incidents during protection. For example, the protection unit can strengthen its protection algorithm by referring to past data breach incidents. The protection unit can analyze past data breach incidents using generative AI and optimize its protection algorithm. For example, it can analyze past data breach incidents, identify vulnerabilities, and optimize its protection algorithm. The protection unit can also optimize its protection algorithm by taking preventative measures based on past data breach incidents. This improves the accuracy of the protection algorithm by referring to past data breach incidents. Some or all of the above processes in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can use generative AI to analyze past data breach incidents and automatically optimize its protection algorithm.

[0052] The protection unit can customize the protection method based on the user's lifestyle or devices used during protection. For example, if the user is using a smartphone, the protection unit provides a protection method optimized for the smartphone. The protection unit uses generative AI to analyze the user's lifestyle and devices used and customize the protection method. For example, if the user is using a smartwatch, it can provide a protection method optimized for the smartwatch. The protection unit can also consider the user's lifestyle and provide the optimal protection method. This allows for the provision of the optimal protection method by considering lifestyle and devices used. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can use generative AI to analyze the user's lifestyle and devices used and automatically customize the protection method.

[0053] The protection unit can select the optimal protection method by considering the user's geographical location information during protection. For example, if the user is at high altitude, the protection unit can provide a protection method that takes oxygen concentration into account. The protection unit uses a generating AI to analyze the user's geographical location information and select the optimal protection method. For example, if the user is in an urban area, it can provide a protection method that takes air pollution levels into account. Furthermore, if the user is indoors, the protection unit can provide a protection method that takes indoor temperature into account. In this way, the protection unit can provide the optimal protection method by considering geographical location information. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can use a generating AI to analyze the user's geographical location information and automatically select the optimal protection method.

[0054] The protection unit can analyze the user's social media activity and provide relevant data protection methods during protection. For example, if the user is experiencing stress on social media, the protection unit can provide data protection methods based on that information. The protection unit uses generative AI to analyze the user's social media activity and provide relevant data protection methods. For example, if the user posts about exercise on social media, the protection unit can provide data protection methods based on that information. The protection unit can also provide data protection methods based on the user's posts about food on social media. In this way, relevant data protection methods can be provided by analyzing social media activity. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can use generative AI to analyze the user's social media activity and automatically provide relevant data protection methods.

[0055] The access control unit can select the optimal access control method by referring to past access history during access control. For example, the access control unit can refer to the user's past access history and select the optimal access control method. The access control unit can use a generation AI to analyze past access history and select the optimal access control method. For example, it can analyze the user's past access history, identify vulnerabilities, and optimize the access control method. The access control unit can also optimize the access control method by taking preventative measures based on the user's past access history. In this way, the optimal access control method can be selected by referring to past access history. Some or all of the above processing in the access control unit may be performed using AI, for example, or without using AI. For example, the access control unit can have a generation AI analyze past access history and automatically select the optimal access control method.

[0056] The access control unit can customize the access control method based on the user's lifestyle or devices used during access control. For example, if the user is using a smartphone, the access control unit can provide an access control method optimized for the smartphone. The access control unit uses generative AI to analyze the user's lifestyle and devices used and customize the access control method. For example, if the user is using a smartwatch, it can provide an access control method optimized for the smartwatch. The access control unit can also consider the user's lifestyle and provide the optimal access control method. This allows for the provision of the optimal access control method by considering lifestyle and devices used. Some or all of the above processing in the access control unit may be performed using AI, for example, or without AI. For example, the access control unit can use generative AI to analyze the user's lifestyle and devices used and automatically customize the access control method.

[0057] The access control unit can select the optimal access control method by considering the user's geographical location information during access control. For example, if the user is at high altitude, the access control unit can provide an access control method that takes oxygen concentration into account. The access control unit uses a generation AI to analyze the user's geographical location information and select the optimal access control method. For example, if the user is in an urban area, it can provide an access control method that takes air pollution levels into account. Furthermore, if the user is indoors, the access control unit can provide an access control method that takes indoor temperature into account. In this way, by considering geographical location information, the optimal access control method can be provided. Some or all of the above processing in the access control unit may be performed using AI, for example, or without AI. For example, the access control unit can have a generation AI analyze the user's geographical location information and automatically select the optimal access control method.

[0058] The access control unit can analyze a user's social media activity and provide relevant access control methods when controlling access. For example, if a user is experiencing stress on social media, the access control unit can provide access control methods based on that information. The access control unit can use generative AI to analyze a user's social media activity and provide relevant access control methods. For example, if a user posts about exercise on social media, the access control unit can provide access control methods based on that information. The access control unit can also provide access control methods based on information if a user posts about food on social media. In this way, relevant access control methods can be provided by analyzing social media activity. Some or all of the above processing in the access control unit may be performed using AI, for example, or without AI. For example, the access control unit can use generative AI to analyze a user's social media activity and automatically provide relevant access control methods.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The recording unit can record data that takes environmental factors into account, based on the user's geographical location. For example, if the user is at high altitude, it can record oxygen concentration and atmospheric pressure. If the user is in an urban area, it can record air pollution levels. Furthermore, if the user is indoors, it can record indoor temperature and humidity. This enables the collection of detailed health data that takes geographical location into account.

[0061] The analysis unit can optimize its analysis algorithm based on the user's lifestyle and diet. For example, it can perform analysis based on nutritional balance, taking into account the user's diet. It can also perform analysis based on exercise volume, taking into account the user's exercise habits. Furthermore, it can perform analysis based on sleep quality, taking into account the user's sleep patterns. This enables highly accurate health status analysis that takes lifestyle and diet into account.

[0062] The support department can select the optimal support method by referring to the user's past health data. For example, it can refer to the user's past heart rate data to provide optimal exercise advice. It can also refer to the user's past blood pressure data to provide optimal dietary advice. Furthermore, it can refer to the user's past body temperature data to provide optimal sleep advice. This enables personalized support that utilizes past health data.

[0063] The protection unit can optimize its protection algorithms by referring to past data breach incidents. For example, it can analyze past data breach incidents to identify vulnerabilities and strengthen the protection algorithms. It can also optimize the protection algorithms by implementing preventative measures based on past data breach incidents. Furthermore, it can enhance real-time threat detection by referring to past data breach incidents. This enables highly accurate data protection by leveraging past data breach incidents.

[0064] The access control unit can analyze a user's social media activity and provide relevant access control methods. For example, if a user is experiencing stress on social media, it can provide access control methods based on that information. Similarly, if a user posts about exercise on social media, it can provide access control methods based on that information. Furthermore, if a user posts about food on social media, it can provide access control methods based on that information. This enables appropriate access control based on an analysis of social media activity.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The recording unit allows individuals to record health information via their smartphone or smartwatch. The unit records data such as heart rate, blood pressure, and body temperature, and the smartwatch measures heart rate and transmits that data to the smartphone. Users can also manually input health information using a smartphone application. Furthermore, exercise and sleep data can be recorded using sensors in the smartwatch or smartphone. Step 2: The analysis unit uses generation AI to analyze the health information recorded by the recording unit. The analysis unit analyzes data such as heart rate, blood pressure, and body temperature to monitor the health status. It can detect sudden fluctuations in heart rate and sudden increases in blood pressure, enabling early detection of signs of poor health. It can also analyze trends in health information to evaluate long-term health status. Step 3: The support department provides support to individuals based on the results analyzed by the analysis department. The support department provides appropriate guidance when an individual is feeling unwell, enables early detection of diseases, and uses generated AI to provide advice tailored to the individual's health condition. Furthermore, it can provide real-time diagnostic support when an individual is feeling unwell and can also make appointments at partner hospitals. In emergencies, it can notify alerts and connect to emergency calls. Step 4: The protection unit uses blockchain technology to protect the data. The protection unit protects electronic medical records with blockchain to prevent data tampering. It also protects electronic medical records uploaded by healthcare institutions to the cloud and allows access to patient data as needed. Furthermore, it can protect data shared with health insurance companies. Step 5: The access control unit performs access control based on the Zero Trust concept. The access control unit sets authentication methods and access permissions to enhance data security. It can also manage logs and detect unauthorized access.

[0067] (Example of form 2) The secure health information management system according to an embodiment of the present invention is designed based on the concept of Zero Trust, utilizing generative AI and blockchain technology. This system enables individuals to record health information data in real time via smartphones or smartwatches and receive support from generative AI, thereby enabling health monitoring, appropriate guidance in case of illness, and early detection of diseases. Healthcare institutions can upload blockchain-protected electronic medical records to the cloud and access patient data as needed. When individuals feel unwell, they can access this service and receive real-time diagnostic support from generative AI and make appointments at partner hospitals on the spot. In emergencies such as sudden changes in heart rate or a sudden increase in blood pressure, they can be notified by an alert and connected to an emergency call. This record can also be shared with health insurance companies. For example, an individual records health information data in real time via a smartphone or smartwatch. At this time, data such as heart rate, blood pressure, and body temperature are recorded. For example, a smartwatch measures heart rate and sends that data to a smartphone. This allows for real-time recording of the individual's health information. Next, the generative AI analyzes the recorded data. The generating AI analyzes data such as heart rate, blood pressure, and body temperature to monitor health status. For example, it can detect sudden changes in heart rate or a sudden rise in blood pressure, enabling early detection of signs of illness. This allows individuals to take appropriate action. Furthermore, medical institutions can upload blockchain-protected electronic medical records to the cloud and access patient data as needed. For example, a doctor can upload a patient's electronic medical record to the cloud and share it with other medical institutions. This facilitates smooth data sharing between medical institutions. When individuals feel unwell, they can access this service and receive real-time diagnostic support from the generating AI and make appointments at partner hospitals on the spot. For example, when an individual feels unwell, they can access the service from their smartphone and receive a diagnosis from the generating AI. Based on the diagnosis, they can make an appointment at a partner hospital on the spot.Furthermore, in emergencies such as a sudden change in heart rate or a sudden rise in blood pressure, the system can notify users via alert and connect them to an emergency call. For example, if the heart rate suddenly changes, an alert will appear on the smartphone, and the user can connect to an emergency call. This allows for a quick response. This record can also be shared with health insurance companies. For example, personal health information can be provided to health insurance companies and used for insurance application and premium calculations. This ensures that personal health information is properly managed and that health insurance can be used smoothly. As a result, the secure health information management system can record personal health information in real time and provide analysis and support using generated AI. In addition, security can be enhanced by strengthening data protection using blockchain technology and implementing access control based on the concept of Zero Trust.

[0068] The secure health information management system according to this embodiment comprises a recording unit, an analysis unit, a support unit, a protection unit, and an access control unit. The recording unit allows individuals to record health information via a smartphone or smartwatch. The recording unit records data such as heart rate, blood pressure, and body temperature. For example, the smartwatch measures heart rate and transmits the data to the smartphone. The recording unit can also manually input health information using a smartphone application. Furthermore, the recording unit can record exercise data and sleep data using sensors in the smartwatch or smartphone. The analysis unit analyzes the health information recorded by the recording unit using generative AI. For example, the analysis unit analyzes data such as heart rate, blood pressure, and body temperature to monitor the health status. For example, the analysis unit can detect sudden fluctuations in heart rate or sudden increases in blood pressure to detect early signs of poor health. The analysis unit can also analyze trends in health information using generative AI to evaluate long-term health status. The support unit provides support to the individual based on the results analyzed by the analysis unit. The support unit provides appropriate guidance in case of illness and early detection of diseases. For example, it uses generative AI to provide advice tailored to an individual's health condition. It can also provide real-time diagnostic support in case of illness and make appointments at partner hospitals. Furthermore, it can notify alerts in emergencies such as sudden changes in heart rate or blood pressure and connect to emergency calls. The protection unit uses blockchain technology to protect data. For example, it protects electronic medical records with blockchain to prevent data tampering. It also protects electronic medical records uploaded by healthcare institutions to the cloud and allows access to patient data as needed. Additionally, it can protect data shared with health insurance companies. The access control unit performs access control based on the concept of Zero Trust. For example, it sets authentication methods and access permissions to enhance data security. It also manages logs and can detect unauthorized access.As a result, the secure health information management system according to this embodiment can record personal health information in real time and provide analysis and support using generated AI. Furthermore, security can be enhanced by strengthening data protection using blockchain technology and implementing access control based on the concept of Zero Trust.

[0069] The recording unit can record data such as heart rate, blood pressure, and body temperature via a smartphone or smartwatch. For example, the recording unit can use a smartwatch to measure heart rate and transmit that data to a smartphone. The recording unit can also manually input health information using a smartphone application. Furthermore, the recording unit can record exercise data and sleep data using sensors in the smartwatch or smartphone. This enables the collection of health information in real time by recording health data via a smartphone or smartwatch. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input heart rate data measured by a smartwatch into a generating AI, which can then analyze and record the data.

[0070] The analysis unit can analyze recorded data and monitor health status. For example, the analysis unit can analyze data such as heart rate, blood pressure, and body temperature to monitor health status. For example, the analysis unit can detect sudden fluctuations in heart rate or sudden increases in blood pressure to detect early signs of poor health. The analysis unit can also use generating AI to analyze trends in health information and evaluate long-term health status. This makes it possible to monitor health status by analyzing recorded data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input recorded data into generating AI, which can then analyze the data to monitor health status.

[0071] The support unit can provide appropriate guidance or early detection of diseases based on the analysis results. For example, the support unit can provide appropriate guidance or early detection of diseases when a person is unwell. The support unit can provide advice tailored to an individual's health condition using, for example, generative AI. The support unit can also provide real-time diagnostic support when a person is unwell and can make appointments at affiliated hospitals. Furthermore, the support unit can notify alerts in emergencies such as sudden changes in heart rate or a sudden increase in blood pressure and connect to emergency calls. This enables appropriate guidance and early detection of diseases based on the analysis results. Some or all of the above processes in the support unit may be performed using, for example, AI, or not using AI. For example, the support unit can use generative AI to provide appropriate guidance and advice to individuals based on the analysis results.

[0072] The protection unit can protect electronic medical records using blockchain technology. For example, the protection unit can protect electronic medical records with blockchain to prevent data tampering. The protection unit can also protect electronic medical records uploaded by medical institutions to the cloud and allow access to patient data as needed. Furthermore, the protection unit can protect data shared with health insurance companies. In this way, the protection of electronic medical records is enhanced by using blockchain technology. Some or all of the above processes in the protection unit may be performed using AI, for example, or not using AI. For example, the protection unit can protect electronic medical record data using blockchain technology, and a generating AI can detect data tampering.

[0073] The access control unit can perform access control based on the concept of Zero Trust. For example, the access control unit can set authentication methods and access rights to enhance data security. The access control unit can also manage logs and detect unauthorized access. This enhances security through access control based on the concept of Zero Trust. Some or all of the above processes in the access control unit may be performed using AI, for example, or without AI. For example, the access control unit can use a generating AI to analyze access logs and detect unauthorized access.

[0074] The support department can provide real-time diagnostic support when a user is feeling unwell and make reservations at affiliated hospitals. For example, the support department can provide real-time diagnostic support when a user is feeling unwell. The support department uses generative AI to analyze an individual's health condition and make an appropriate diagnosis. For example, when an individual feels unwell, they can access the service from their smartphone and receive a diagnosis from the generative AI. Based on the diagnosis results, they can make a reservation at an affiliated hospital on the spot. This makes it possible to provide real-time diagnostic support and make reservations at affiliated hospitals when a user is feeling unwell. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can use generative AI to automatically make reservations at affiliated hospitals based on the diagnosis results.

[0075] The support unit can notify alerts and connect to emergency calls in emergencies such as a sudden change in heart rate or a sudden increase in blood pressure. For example, the support unit can detect a sudden change in heart rate or a sudden increase in blood pressure and notify an alert. The support unit uses generative AI to analyze heart rate and blood pressure data and detect abnormalities. For example, if the heart rate becomes suddenly irregular, an alert will be displayed on the smartphone and an emergency call can be connected. This enables alerts to be notified in emergencies and allows for a quick response. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use generative AI to analyze heart rate and blood pressure data, detect abnormalities, and notify alerts.

[0076] The protection unit can protect electronic medical records uploaded by medical institutions to the cloud. For example, the protection unit protects electronic medical records uploaded by medical institutions to the cloud using blockchain technology. The protection unit prevents data tampering and enhances security. For example, a doctor can upload a patient's electronic medical record to the cloud and share it with other medical institutions. This enhances the protection of electronic medical records uploaded to the cloud. Some or all of the above processes in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can use a generating AI to analyze the data in the electronic medical record and detect tampering.

[0077] The protection unit can protect data shared with health insurance companies. For example, the protection unit protects data shared with health insurance companies using blockchain technology. The protection unit prevents data tampering and enhances security. For example, personal health information is provided to health insurance companies and used for insurance application and premium calculation. This enhances the protection of data shared with health insurance companies. Some or all of the above processing in the protection unit may be performed using AI, for example, or not using AI. For example, the protection unit can use a generating AI to analyze data shared with health insurance companies and detect tampering.

[0078] The recording unit can estimate the user's emotions and adjust the frequency of recording health information based on the estimated emotions. For example, if the user is stressed, the recording unit can reduce the recording frequency to alleviate the user's burden. The recording unit uses generative AI to estimate the user's emotions and adjust the recording frequency. For example, if the user is relaxed, the recording frequency can be increased to collect more detailed data. The recording unit can also minimize the recording frequency and quickly record data if the user is in a hurry. In this way, the user's burden can be reduced by adjusting the recording frequency according to the user's emotions. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit may use generative AI to analyze the user's emotions and automatically adjust the recording frequency.

[0079] The recording unit can automatically detect and record abnormal values ​​by referring to the user's past health data during recording. For example, the recording unit can refer to the user's past heart rate data to detect and record abnormal heart rates. The recording unit uses generation AI to analyze past health data and detect abnormal values. For example, it can refer to the user's past blood pressure data to detect and record abnormal blood pressure. The recording unit can also refer to the user's past body temperature data to detect and record abnormal body temperature. This improves the accuracy of abnormal value detection by referring to past health data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can use generation AI to analyze past health data and automatically detect and record abnormal values.

[0080] The recording unit can simultaneously record the user's lifestyle and dietary habits during recording and associate them with health information. For example, the recording unit can record the user's dietary habits and associate them with health information. The recording unit can use generative AI to analyze the user's lifestyle and dietary habits and associate them with health information. For example, it can record the user's exercise habits and associate them with health information. The recording unit can also record the user's sleep patterns and associate them with health information. This improves the relevance of health information by recording lifestyle habits and dietary habits. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can use generative AI to analyze the user's lifestyle and dietary habits and associate them with health information.

[0081] The recording unit can estimate the user's emotions and determine the priority of data to record based on the estimated emotions. For example, if the user is stressed, the recording unit may prioritize recording heart rate data. The recording unit uses generative AI to estimate the user's emotions and determine the priority of data to record. For example, if the user is relaxed, it may prioritize recording blood pressure data. The recording unit may also prioritize recording body temperature data if the user is in a hurry. This allows important data to be recorded preferentially by determining the priority of data to record according to the user's emotions. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit may use generative AI to analyze the user's emotions and automatically determine the priority of data to record.

[0082] The recording unit can record environmental factors based on the user's geographical location information during recording. For example, if the user is at high altitude, the recording unit can record oxygen concentration. The recording unit uses a generation AI to analyze the user's geographical location information and record environmental factors. For example, if the user is in an urban area, it can record air pollution levels. The recording unit can also record indoor temperature if the user is indoors. In this way, environmental factors can be recorded by considering geographical location information. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can use a generation AI to analyze the user's geographical location information and automatically record environmental factors.

[0083] The recording unit can analyze the user's social media activity and record relevant health information at the time of recording. For example, if the user is experiencing stress on social media, the recording unit will record that information. The recording unit uses generative AI to analyze the user's social media activity and record relevant health information. For example, if the user posts about exercise on social media, that information can be recorded. The recording unit can also record information if the user posts about food on social media. In this way, relevant health information can be recorded by analyzing social media activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can use generative AI to analyze the user's social media activity and automatically record relevant health information.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. The analysis unit uses generative AI to estimate the user's emotions and adjust the display method of the analysis results. For example, if the user is relaxed, it can provide a display method that includes detailed information. The analysis unit can also provide a concise display method if the user is in a hurry. By adjusting the display method of the analysis results according to the user's emotions, visibility is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have generative AI analyze the user's emotions and automatically adjust the display method of the analysis results.

[0085] The analysis unit can improve the accuracy of detecting anomalies by referring to past health data during analysis. For example, the analysis unit can refer to the user's past heart rate data to detect abnormal heart rates with high accuracy. The analysis unit uses a generation AI to analyze past health data and detect anomalies. For example, it can refer to the user's past blood pressure data to detect abnormal blood pressure with high accuracy. The analysis unit can also refer to the user's past body temperature data to detect abnormal body temperature with high accuracy. In this way, the accuracy of detecting anomalies is improved by referring to past health data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use a generation AI to analyze past health data and detect anomalies with high accuracy.

[0086] The analysis unit can optimize the analysis algorithm based on the user's lifestyle or diet during analysis. For example, the analysis unit can optimize the analysis algorithm by considering the user's diet. The analysis unit can use generative AI to analyze the user's lifestyle and diet and optimize the analysis algorithm. For example, it can optimize the analysis algorithm by considering the user's exercise habits. The analysis unit can also optimize the analysis algorithm by considering the user's sleep patterns. This improves the accuracy of the analysis algorithm by considering lifestyle and diet. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to analyze the user's lifestyle and diet and automatically optimize the analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize the analysis of heart rate data. The analysis unit uses generative AI to estimate the user's emotions and determine the priority of analysis results. For example, if the user is relaxed, the analysis of blood pressure data may be prioritized. The analysis unit may also prioritize the analysis of body temperature data if the user is in a hurry. This allows for the prioritization of important data by determining the priority of analysis results according to the user's emotions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use generative AI to analyze the user's emotions and automatically determine the priority of analysis results.

[0088] The analysis unit can incorporate environmental factors into the analysis by considering the user's geographical location information during the analysis. For example, if the user is at high altitude, the analysis unit can incorporate oxygen concentration into the analysis. The analysis unit uses a generation AI to analyze the user's geographical location information and incorporate environmental factors into the analysis. For example, if the user is in an urban area, the air pollution level can be incorporated into the analysis. The analysis unit can also incorporate indoor temperature into the analysis if the user is indoors. In this way, environmental factors can be incorporated into the analysis by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generation AI analyze the user's geographical location information and automatically incorporate environmental factors into the analysis.

[0089] The analysis unit can analyze a user's social media activity during analysis and incorporate relevant data into the analysis. For example, if a user is experiencing stress on social media, the analysis unit can incorporate that information into the analysis. The analysis unit uses generative AI to analyze a user's social media activity and incorporate relevant data into the analysis. For example, if a user posts about exercise on social media, that information can be incorporated into the analysis. The analysis unit can also incorporate information about food if a user posts about food on social media. In this way, by analyzing social media activity, relevant data can be incorporated into the analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to analyze a user's social media activity and automatically incorporate relevant data into the analysis.

[0090] The support unit can estimate the user's emotions and adjust the support content based on those emotions. For example, if the user is feeling stressed, the support unit can provide advice on how to relax. The support unit uses generative AI to estimate the user's emotions and adjust the support content. For example, if the user is relaxed, it can provide advice on maintaining good health. The support unit can also provide advice on how to respond quickly if the user is in a hurry. By adjusting the support content according to the user's emotions, more appropriate support can be provided. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use generative AI to analyze the user's emotions and automatically adjust the support content.

[0091] The support unit can select the optimal support method by referring to past health data during support. For example, the support unit can select the optimal support method by referring to the user's past heart rate data. The support unit can also select the optimal support method by analyzing past health data using a generation AI. For example, it can select the optimal support method by referring to the user's past blood pressure data. The support unit can also select the optimal support method by referring to the user's past body temperature data. In this way, the optimal support method can be selected by referring to past health data. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI. For example, the support unit can have a generation AI analyze past health data and automatically select the optimal support method.

[0092] The support unit can customize the support content based on the user's lifestyle or diet during support. For example, the support unit can customize the support content by considering the user's diet. The support unit can use generative AI to analyze the user's lifestyle and diet and customize the support content. For example, it can customize the support content by considering the user's exercise habits. The support unit can also customize the support content by considering the user's sleep patterns. In this way, the support content can be customized by considering lifestyle and diet. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use generative AI to analyze the user's lifestyle and diet and automatically customize the support content.

[0093] The support unit can estimate the user's emotions and determine the priority of support based on those emotions. For example, if the user is stressed, the support unit will prioritize support to help them relax. The support unit uses generative AI to estimate the user's emotions and determine the priority of support. For example, if the user is relaxed, it can prioritize support to help them maintain their health. The support unit can also prioritize support that can be provided quickly if the user is in a hurry. This allows important support to be provided preferentially by determining the priority of support according to the user's emotions. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit may use generative AI to analyze the user's emotions and automatically determine the priority of support.

[0094] The support unit can select the optimal support method by considering the user's geographical location information during support. For example, if the user is at high altitude, the support unit can provide support that takes oxygen concentration into account. The support unit uses generative AI to analyze the user's geographical location information and select the optimal support method. For example, if the user is in an urban area, it can provide support that takes air pollution levels into account. Also, if the user is indoors, the support unit can provide support that takes indoor temperature into account. In this way, the optimal support method can be selected by considering geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use generative AI to analyze the user's geographical location information and automatically select the optimal support method.

[0095] The support unit can analyze a user's social media activity and provide relevant support during support sessions. For example, if a user is experiencing stress on social media, the support unit can provide support based on that information. The support unit uses generative AI to analyze a user's social media activity and provide relevant support. For example, if a user posts about exercise on social media, the support unit can provide support based on that information. Similarly, if a user posts about food on social media, the support unit can provide support based on that information. In this way, relevant support can be provided by analyzing social media activity. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can use generative AI to analyze a user's social media activity and automatically provide relevant support.

[0096] The protection unit can estimate the user's emotions and adjust the strength of data protection based on those emotions. For example, if the user is stressed, the protection unit will increase the strength of data protection. The protection unit uses generative AI to estimate the user's emotions and adjust the strength of data protection. For example, if the user is relaxed, the strength of data protection can be kept at a normal level. The protection unit can also minimize the strength of data protection if the user is in a hurry. This allows for appropriate data protection by adjusting the strength of data protection according to the user's emotions. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit may use generative AI to analyze the user's emotions and automatically adjust the strength of data protection.

[0097] The protection unit can optimize its protection algorithm by referring to past data breach incidents during protection. For example, the protection unit can strengthen its protection algorithm by referring to past data breach incidents. The protection unit can analyze past data breach incidents using generative AI and optimize its protection algorithm. For example, it can analyze past data breach incidents, identify vulnerabilities, and optimize its protection algorithm. The protection unit can also optimize its protection algorithm by taking preventative measures based on past data breach incidents. This improves the accuracy of the protection algorithm by referring to past data breach incidents. Some or all of the above processes in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can use generative AI to analyze past data breach incidents and automatically optimize its protection algorithm.

[0098] The protection unit can customize the protection method based on the user's lifestyle or devices used during protection. For example, if the user is using a smartphone, the protection unit provides a protection method optimized for the smartphone. The protection unit uses generative AI to analyze the user's lifestyle and devices used and customize the protection method. For example, if the user is using a smartwatch, it can provide a protection method optimized for the smartwatch. The protection unit can also consider the user's lifestyle and provide the optimal protection method. This allows for the provision of the optimal protection method by considering lifestyle and devices used. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can use generative AI to analyze the user's lifestyle and devices used and automatically customize the protection method.

[0099] The protection unit can estimate the user's emotions and determine data protection priorities based on those emotions. For example, if the user is stressed, the protection unit will prioritize the protection of important data. The protection unit uses generative AI to estimate the user's emotions and determine data protection priorities. For example, if the user is relaxed, it can perform normal data protection. The protection unit can also prioritize data that needs to be protected quickly if the user is in a hurry. This allows for the priority protection of important data by determining data protection priorities according to the user's emotions. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit may use generative AI to analyze the user's emotions and automatically determine data protection priorities.

[0100] The protection unit can select the optimal protection method by considering the user's geographical location information during protection. For example, if the user is at high altitude, the protection unit can provide a protection method that takes oxygen concentration into account. The protection unit uses a generating AI to analyze the user's geographical location information and select the optimal protection method. For example, if the user is in an urban area, it can provide a protection method that takes air pollution levels into account. Furthermore, if the user is indoors, the protection unit can provide a protection method that takes indoor temperature into account. In this way, the protection unit can provide the optimal protection method by considering geographical location information. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can use a generating AI to analyze the user's geographical location information and automatically select the optimal protection method.

[0101] The protection unit can analyze the user's social media activity and provide relevant data protection methods during protection. For example, if the user is experiencing stress on social media, the protection unit can provide data protection methods based on that information. The protection unit uses generative AI to analyze the user's social media activity and provide relevant data protection methods. For example, if the user posts about exercise on social media, the protection unit can provide data protection methods based on that information. The protection unit can also provide data protection methods based on the user's posts about food on social media. In this way, relevant data protection methods can be provided by analyzing social media activity. Some or all of the above processing in the protection unit may be performed using AI, for example, or without AI. For example, the protection unit can use generative AI to analyze the user's social media activity and automatically provide relevant data protection methods.

[0102] The access control unit can estimate the user's emotions and adjust the intensity of access control based on those emotions. For example, if the user is stressed, the access control unit will increase the intensity of access control. The access control unit uses generative AI to estimate the user's emotions and adjust the intensity of access control. For example, if the user is relaxed, the intensity of access control can be kept at a normal level. The access control unit can also minimize the intensity of access control if the user is in a hurry. This allows for appropriate access control by adjusting the intensity of access control according to the user's emotions. Some or all of the above processing in the access control unit may be performed using AI, for example, or without AI. For example, the access control unit may use generative AI to analyze the user's emotions and automatically adjust the intensity of access control.

[0103] The access control unit can select the optimal access control method by referring to past access history during access control. For example, the access control unit can refer to the user's past access history and select the optimal access control method. The access control unit can use a generation AI to analyze past access history and select the optimal access control method. For example, it can analyze the user's past access history, identify vulnerabilities, and optimize the access control method. The access control unit can also optimize the access control method by taking preventative measures based on the user's past access history. In this way, the optimal access control method can be selected by referring to past access history. Some or all of the above processing in the access control unit may be performed using AI, for example, or without using AI. For example, the access control unit can have a generation AI analyze past access history and automatically select the optimal access control method.

[0104] The access control unit can customize the access control method based on the user's lifestyle or devices used during access control. For example, if the user is using a smartphone, the access control unit can provide an access control method optimized for the smartphone. The access control unit uses generative AI to analyze the user's lifestyle and devices used and customize the access control method. For example, if the user is using a smartwatch, it can provide an access control method optimized for the smartwatch. The access control unit can also consider the user's lifestyle and provide the optimal access control method. This allows for the provision of the optimal access control method by considering lifestyle and devices used. Some or all of the above processing in the access control unit may be performed using AI, for example, or without AI. For example, the access control unit can use generative AI to analyze the user's lifestyle and devices used and automatically customize the access control method.

[0105] The access control unit can estimate the user's emotions and determine the priority of access control based on those emotions. For example, if the user is stressed, the access control unit will prioritize access to important data. The access control unit uses generative AI to estimate the user's emotions and determine the priority of access control. For example, if the user is relaxed, it can perform normal access control. The access control unit can also prioritize data that needs to be accessed quickly if the user is in a hurry. This allows for priority access to important data by determining access control priorities according to the user's emotions. Some or all of the above processing in the access control unit may be performed using AI, for example, or without AI. For example, the access control unit can use generative AI to analyze the user's emotions and automatically determine the priority of access control.

[0106] The access control unit can select the optimal access control method by considering the user's geographical location information during access control. For example, if the user is at high altitude, the access control unit can provide an access control method that takes oxygen concentration into account. The access control unit uses a generation AI to analyze the user's geographical location information and select the optimal access control method. For example, if the user is in an urban area, it can provide an access control method that takes air pollution levels into account. Furthermore, if the user is indoors, the access control unit can provide an access control method that takes indoor temperature into account. In this way, by considering geographical location information, the optimal access control method can be provided. Some or all of the above processing in the access control unit may be performed using AI, for example, or without AI. For example, the access control unit can have a generation AI analyze the user's geographical location information and automatically select the optimal access control method.

[0107] The access control unit can analyze a user's social media activity and provide relevant access control methods when controlling access. For example, if a user is experiencing stress on social media, the access control unit can provide access control methods based on that information. The access control unit can use generative AI to analyze a user's social media activity and provide relevant access control methods. For example, if a user posts about exercise on social media, the access control unit can provide access control methods based on that information. The access control unit can also provide access control methods based on information if a user posts about food on social media. In this way, relevant access control methods can be provided by analyzing social media activity. Some or all of the above processing in the access control unit may be performed using AI, for example, or without AI. For example, the access control unit can use generative AI to analyze a user's social media activity and automatically provide relevant access control methods. === Hard Collateral 1-1 === Each of the multiple elements described above, including the recording unit, analysis unit, support unit, protection unit, and access control unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the recording unit records health information using the sensors of the smart device 14 and transmits it to the data processing unit 12. The analysis unit analyzes the recorded data using the specific processing unit 290 of the data processing unit 12. The support unit provides support based on the analysis results using the specific processing unit 290 of the data processing unit 12. The protection unit protects the data using blockchain technology using the specific processing unit 290 of the data processing unit 12. The access control unit performs access control based on the concept of Zero Trust using the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-2 === Each of the multiple elements described above, including the recording unit, analysis unit, support unit, protection unit, and access control unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit records health information using the sensors of the smart glasses 214 and transmits it to the data processing unit 12. The analysis unit analyzes the recorded data using the specific processing unit 290 of the data processing unit 12. The support unit provides support based on the analysis results using the specific processing unit 290 of the data processing unit 12. The protection unit protects the data using blockchain technology using the specific processing unit 290 of the data processing unit 12. The access control unit performs access control based on the concept of Zero Trust using the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-3 === Each of the multiple elements described above, including the recording unit, analysis unit, support unit, protection unit, and access control unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit records health information using the sensors of the headset terminal 314 and transmits it to the data processing unit 12. The analysis unit analyzes the recorded data using the specific processing unit 290 of the data processing unit 12. The support unit provides support based on the analysis results using the specific processing unit 290 of the data processing unit 12. The protection unit protects the data using blockchain technology using the specific processing unit 290 of the data processing unit 12. The access control unit performs access control based on the concept of Zero Trust using the specific processing unit 290 of the data processing unit 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the recording unit, analysis unit, support unit, protection unit, and access control unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the recording unit records health information using the sensors of the robot 414 and transmits it to the data processing unit 12. The analysis unit analyzes the recorded data using the specific processing unit 290 of the data processing unit 12. The support unit provides support based on the analysis results using the specific processing unit 290 of the data processing unit 12. The protection unit protects the data using blockchain technology using the specific processing unit 290 of the data processing unit 12. The access control unit performs access control based on the concept of Zero Trust using the specific processing unit 290 of the data processing unit 12.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The recording unit can estimate the user's emotions and dynamically change the types of data recorded based on the estimated emotions. For example, if the user is stressed, stress-related data (heart rate, blood pressure, etc.) can be prioritized for recording. If the user is relaxed, data related to relaxation (sleep patterns, respiratory rate, etc.) can be recorded. Furthermore, if the user is exercising, exercise data (steps, calories burned, etc.) can be prioritized for recording. This allows for the efficient collection of the most relevant data according to the user's emotional state.

[0110] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, it can send a notification that concisely summarizes the analysis results. If the user is relaxed, it can send a notification that includes detailed analysis results. Furthermore, if the user is in a hurry, it can send a notification that highlights only the essential points. This allows the system to provide analysis results in the most optimal way according to the user's emotional state.

[0111] The support system can estimate the user's emotions and dynamically change the priority of support based on those estimates. For example, if a user is stressed, it can prioritize providing advice on relaxation. If the user is relaxed, it can prioritize providing advice on maintaining good health. Furthermore, if the user is in a hurry, it can prioritize providing advice that can be implemented quickly. This allows the system to provide the most appropriate support according to the user's emotional state.

[0112] The protection unit can estimate the user's emotions and dynamically adjust the strength of data protection based on those emotions. For example, if the user is stressed, the strength of data protection can be increased. If the user is relaxed, the strength of data protection can be kept at a normal level. Furthermore, if the user is in a hurry, the strength of data protection can be minimized. This allows for optimal data protection according to the user's emotional state.

[0113] The access control unit can estimate the user's emotions and dynamically change the priority of access control based on those emotions. For example, if the user is stressed, access to important data can be prioritized. If the user is relaxed, normal access control can be applied. Furthermore, if the user is in a hurry, data that needs to be accessed quickly can be prioritized. This allows for the most appropriate access control depending on the user's emotional state.

[0114] The recording unit can record data that takes environmental factors into account, based on the user's geographical location. For example, if the user is at high altitude, it can record oxygen concentration and atmospheric pressure. If the user is in an urban area, it can record air pollution levels. Furthermore, if the user is indoors, it can record indoor temperature and humidity. This enables the collection of detailed health data that takes geographical location into account.

[0115] The analysis unit can optimize its analysis algorithm based on the user's lifestyle and diet. For example, it can perform analysis based on nutritional balance, taking into account the user's diet. It can also perform analysis based on exercise volume, taking into account the user's exercise habits. Furthermore, it can perform analysis based on sleep quality, taking into account the user's sleep patterns. This enables highly accurate health status analysis that takes lifestyle and diet into account.

[0116] The support department can select the optimal support method by referring to the user's past health data. For example, it can refer to the user's past heart rate data to provide optimal exercise advice. It can also refer to the user's past blood pressure data to provide optimal dietary advice. Furthermore, it can refer to the user's past body temperature data to provide optimal sleep advice. This enables personalized support that utilizes past health data.

[0117] The protection unit can optimize its protection algorithms by referring to past data breach incidents. For example, it can analyze past data breach incidents to identify vulnerabilities and strengthen the protection algorithms. It can also optimize the protection algorithms by implementing preventative measures based on past data breach incidents. Furthermore, it can enhance real-time threat detection by referring to past data breach incidents. This enables highly accurate data protection by leveraging past data breach incidents.

[0118] The access control unit can analyze a user's social media activity and provide relevant access control methods. For example, if a user is experiencing stress on social media, it can provide access control methods based on that information. Similarly, if a user posts about exercise on social media, it can provide access control methods based on that information. Furthermore, if a user posts about food on social media, it can provide access control methods based on that information. This enables appropriate access control based on an analysis of social media activity.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The recording unit allows individuals to record health information via their smartphone or smartwatch. The unit records data such as heart rate, blood pressure, and body temperature, and the smartwatch measures heart rate and transmits that data to the smartphone. Users can also manually input health information using a smartphone application. Furthermore, exercise and sleep data can be recorded using sensors in the smartwatch or smartphone. Step 2: The analysis unit uses generation AI to analyze the health information recorded by the recording unit. The analysis unit analyzes data such as heart rate, blood pressure, and body temperature to monitor the health status. It can detect sudden fluctuations in heart rate and sudden increases in blood pressure, enabling early detection of signs of poor health. It can also analyze trends in health information to evaluate long-term health status. Step 3: The support department provides support to individuals based on the results analyzed by the analysis department. The support department provides appropriate guidance when an individual is feeling unwell, enables early detection of diseases, and uses generated AI to provide advice tailored to the individual's health condition. Furthermore, it can provide real-time diagnostic support when an individual is feeling unwell and can also make appointments at partner hospitals. In emergencies, it can notify alerts and connect to emergency calls. Step 4: The protection unit uses blockchain technology to protect the data. The protection unit protects electronic medical records with blockchain to prevent data tampering. It also protects electronic medical records uploaded by healthcare institutions to the cloud and allows access to patient data as needed. Furthermore, it can protect data shared with health insurance companies. Step 5: The access control unit performs access control based on the Zero Trust concept. The access control unit sets authentication methods and access permissions to enhance data security. It can also manage logs and detect unauthorized access.

[0121] 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.

[0122] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0123] 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.

[0124] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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. 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.

[0139] 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.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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. 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.

[0155] 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.

[0156] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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. 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.

[0172] 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.

[0173] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of Symbols]

[0193] 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. A recording unit for recording health information, An analysis unit analyzes the health information recorded by the recording unit, A support unit provides support based on the results of the analysis performed by the aforementioned analysis unit, A protection unit that uses blockchain technology to protect data, It includes an access control unit that performs access control based on the concept of Zero Trust. A system characterized by the following features.

2. The aforementioned recording unit is Records data such as heart rate, blood pressure, and body temperature via a smartphone or smartwatch. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the recorded data and monitor your health status. The system according to feature 1.

4. The aforementioned support unit is Based on the analysis results, appropriate guidance will be provided when the patient is unwell, or diseases will be detected early. The system according to feature 1.

5. The aforementioned protective part is Using blockchain technology to protect electronic medical records The system according to feature 1.

6. The access control unit, Access control is implemented based on the concept of Zero Trust. The system according to feature 1.

7. The aforementioned support unit is We provide real-time diagnostic support and make appointments at affiliated hospitals when you feel unwell. The system according to feature 1.

8. The aforementioned support unit is In emergencies such as a sudden change in heart rate or a sudden increase in blood pressure, an alert will be sent and an emergency call will be connected. The system according to feature 1.

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