System

Abstract

An object of a system according to an embodiment is to enhance safety and privacy protection of communication between a patient and a doctor in remote medical care.SOLUTION: A system includes a biometric authentication part, an encryption part, a document sharing part, a translation part, and an emergency notification part. The biometric authentication unit performs biometric authentication. The encryption unit encrypts communication data. The document sharing unit securely shares a document. The translation unit translates different languages in real time. The emergency reporting unit reports an emergency situation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not adequately ensuring the safety and privacy of communication between patients and doctors in remote medical care.

[0005] The system according to the embodiment aims to enhance the safety and privacy of communication between patients and doctors in remote medical care. [Means for solving the problem]

[0006] The system according to the embodiment includes a biometric authentication unit, an encryption unit, a document sharing unit, a translation unit, and an emergency notification unit. The biometric authentication unit performs biometric authentication. The encryption unit encrypts communication data. The document sharing unit securely shares documents. The translation unit translates between different languages ​​in real time. The emergency notification unit notifies users of emergencies. [Effects of the Invention]

[0007] The system according to the embodiment can enhance the safety and privacy of communication between patients and doctors in remote medical care. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The medical consultation security enhancement service according to an embodiment of the present invention is a system that uses audio glasses to provide secure communication between patients and doctors with an emphasis on privacy, and improves the safety of remote medical care with five main functions, including biometric authentication, encryption, secure document sharing, real-time translation, and emergency notification. As a result, the medical consultation security enhancement service can establish reliable communication between patients and medical professionals and improve the safety of remote medical care.

[0029] The medical consultation security enhancement service according to the embodiment includes a biometric authentication unit, an encryption unit, a document sharing unit, a translation unit, and an emergency call unit. The biometric authentication unit performs biometric authentication. For example, the biometric authentication unit verifies the identity of a patient using fingerprint authentication. The biometric authentication unit can also verify the identity of a doctor using facial authentication. The biometric authentication unit can also verify the identity of a patient using iris authentication. The encryption unit encrypts communication data. For example, the encryption unit encrypts communication data using AES. The encryption unit can also encrypt communication data using RSA. The encryption unit can also encrypt communication data using SHA. The document sharing unit securely shares documents. For example, the document sharing unit encrypts and shares a medical certificate. The document sharing unit can also encrypt and share a prescription. The document sharing unit can also encrypt and share a contract. The translation unit translates between different languages ​​in real time. For example, the translation unit translates between a Japanese-speaking patient and an English-speaking doctor in real time. The translation unit can also perform real-time translation between an English-speaking patient and a French-speaking doctor. The translation unit can also perform real-time translation between a Spanish-speaking patient and a German-speaking doctor. The emergency notification unit reports an emergency. For example, the emergency notification unit makes an emergency call when a patient suddenly becomes ill. The emergency notification unit can also make an emergency call when a doctor faces an emergency. The emergency notification unit can also make an emergency call when a patient loses consciousness. As a result, the medical consultation security enhancement service according to the embodiment can establish reliable communication between patients and medical professionals and improve the safety of remote medical care.

[0030] The biometric authentication unit can verify the identities of patients and doctors using fingerprint and facial recognition. For example, a patient wears audio glasses and introduces themselves verbally. The AI ​​analyzes the audio data and performs voiceprint authentication. This increases the accuracy of identity verification by combining voice authentication with fingerprint and facial recognition. The biometric authentication unit also instructs the patient to speak a specific phrase, and the AI ​​analyzes the audio data to perform voiceprint authentication. For example, if the patient says, "I am ____," the AI ​​acquires voiceprint data and verifies the patient's identity. The biometric authentication unit also registers the patient's voiceprint data in advance and compares it with the voiceprint data during medical consultations. The AI ​​analyzes the voice data in real time to confirm whether it matches the registered voiceprint data. This increases the accuracy of identity verification between patients and doctors.

[0031] The encryption unit can encrypt confidential information such as a patient's medical history and diagnosis results. For example, the encryption unit measures the patient's heart rate and body temperature using sensors built into the audio glasses, and the generation AI analyzes the data. If an abnormality is detected, it alerts the doctor. The encryption unit also uses additional sensors to monitor the patient's health during the biometric authentication process. For example, a heart rate monitor and thermometer are used in combination, and the generation AI analyzes the data to detect abnormalities. The encryption unit also creates a system that continuously monitors the patient's health while the audio glasses are worn, and immediately notifies the doctor if an abnormality occurs. The generation AI analyzes the data in real time and detects abnormalities. This prevents the leakage of confidential information.

[0032] The document sharing unit can encrypt and share important documents such as medical certificates and prescriptions. For example, during biometric authentication, the document sharing unit analyzes the patient's facial expressions and voice to estimate their emotional state. The generation AI provides relaxation guidance in cases of high stress or anxiety. The document sharing unit also uses the camera and microphone built into the audio glasses to monitor the patient's emotional state in real time. The generation AI analyzes the emotional data and provides music or messages to help patients relax as needed. The document sharing unit also uses its emotion estimation function to analyze the patient's emotional state and provides audio guidance for deep breathing and relaxation in cases of high stress or anxiety. The generation AI generates appropriate guidance based on the emotional data. This allows important medical documents to be shared safely.

[0033] The translation unit can perform real-time translation between Japanese-speaking patients and English-speaking doctors. For example, when the generation AI creates a summary, the translation unit automatically collects relevant background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The translation unit also uses a topic model to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The translation unit also builds a system to understand the context by referring to relevant background information and topic models when the generation AI creates a summary. For example, it automatically collects related information and reflects it in the summary. This enables smooth communication between patients and doctors who speak different languages.

[0034] The emergency reporting unit can make an emergency call if a patient suddenly becomes ill. For example, when an emergency call is made, the emergency reporting unit uses a generation AI to automatically generate the report content, enabling fast and accurate information provision. For example, the report content is generated based on the patient's condition and location information. The emergency reporting unit also uses a generation AI to build a system that automatically generates the report content when an emergency call is made. For example, the report content is generated by analyzing the patient's biometric data and voice data. The emergency reporting unit also develops a system that automatically generates the report content when an emergency call is made, enabling fast and accurate information provision. For example, the report content is generated based on the patient's condition and location information. This allows for a rapid response to emergencies.

[0035] The biometric authentication unit can strengthen identity verification by authenticating the patient's voiceprint and analyzing the voice data with a generation AI. For example, the patient wears audio glasses and introduces themselves verbally. The generation AI analyzes the voice data and performs voiceprint authentication. This increases the accuracy of identity verification by combining voice authentication with fingerprint and facial authentication. The biometric authentication unit can also instruct the patient to speak a specific phrase, and the generation AI analyzes the voice data to perform voiceprint authentication. For example, if the patient says, "I am ____," voiceprint data is obtained and identity verification is performed. The biometric authentication unit can also register the patient's voiceprint data in advance and compare it with that voiceprint data during medical consultations. The generation AI analyzes the voice data in real time to confirm whether it matches the registered voiceprint data. This increases the accuracy of identity verification.

[0036] The biometric authentication unit can simultaneously monitor the patient's health condition and issue an alert if an abnormality is detected. For example, the biometric authentication unit measures the patient's heart rate and body temperature using sensors built into the audio glasses, and the generation AI analyzes the data. If an abnormality is detected, it alerts the doctor. The biometric authentication unit also uses additional sensors to monitor the patient's health condition during the biometric authentication process. For example, a heart rate monitor and thermometer are used in combination, and the generation AI analyzes the data to detect abnormalities. The biometric authentication unit also continuously monitors the patient's health condition while the audio glasses are worn, creating a system that immediately notifies the doctor if an abnormality occurs. The generation AI analyzes the data in real time and detects abnormalities. This allows the patient's health condition to be monitored in real time and any abnormalities to be addressed quickly.

[0037] The encryption unit can use a generation AI to analyze the transmission and reception history of communication data and detect signs of unauthorized access. In addition to encrypting communication data, the encryption unit can also use a generation AI to analyze the transmission and reception history and detect signs of unauthorized access. For example, it can detect abnormal access patterns and suspicious IP addresses. The encryption unit can also use a generation AI to analyze the transmission and reception history of communication data in real time and detect signs of unauthorized access. For example, it can detect abnormal data transfer volumes and suspicious access times. In addition to encrypting communication data, the encryption unit can also use a generation AI to analyze the transmission and reception history and build a system to detect signs of unauthorized access. For example, it can detect abnormal access frequencies and suspicious devices. This allows for early detection of signs of unauthorized access and strengthens security.

[0038] The encryption unit can apply different encryption algorithms depending on the content of the communication data. For example, the encryption unit builds a system that applies different encryption algorithms depending on the content of the communication data. The generation AI selects the optimal encryption algorithm depending on the confidentiality and importance of the data. The encryption unit also uses the generation AI to analyze the content of the communication data and apply an appropriate encryption algorithm. For example, it uses an advanced encryption algorithm for confidential information and a lightweight encryption algorithm for general information. The encryption unit also develops a system that applies different encryption algorithms depending on the content of the communication data. The generation AI selects and applies the optimal encryption algorithm based on the type and importance of the data. This enables optimal encryption depending on the content of the communication data, strengthening security.

[0039] The document sharing unit can analyze the contents of a document using a generation AI and apply different encryption levels depending on the level of confidentiality. For example, when sharing a document, the document sharing unit uses a generation AI to analyze the contents of the document and apply different encryption levels depending on the level of confidentiality. For example, it may apply high-level encryption to confidential information and light encryption to general information. The document sharing unit also uses a generation AI to analyze the contents of a document and build a system that automatically applies an encryption level depending on the level of confidentiality. For example, it may apply high-level encryption to medical certificates and prescriptions. The document sharing unit also develops a system when sharing a document using a generation AI to analyze the contents of the document and apply different encryption levels depending on the level of confidentiality. For example, it may apply high-level encryption to confidential information and light encryption to general information. This enables optimal encryption depending on the level of confidentiality of the document, thereby strengthening security.

[0040] The document sharing unit uses generation AI to analyze document sharing history and detect signs of unauthorized access or tampering. For example, the document sharing unit uses generation AI to analyze document sharing history and detect signs of unauthorized access or tampering. For example, it detects abnormal access patterns and suspicious IP addresses. The document sharing unit also uses generation AI to analyze document sharing history in real time and detect signs of unauthorized access or tampering. For example, it detects abnormal data transfer volumes and suspicious access times. The document sharing unit also builds a system where generation AI analyzes document sharing history and detects signs of unauthorized access or tampering. For example, it detects abnormal access frequencies and suspicious devices. This enables early detection of signs of unauthorized access or tampering and strengthens security.

[0041] The translation department can automatically update the dictionary of medical terminology using generative AI and provide accurate translations based on the latest medical information. The translation department, for example, can automatically update the dictionary of medical terminology using generative AI and provide accurate translations based on the latest medical information. For example, the dictionary is updated when new medical terms or treatments are added. The translation department also uses generative AI to build a system that automatically updates the dictionary of medical terminology. For example, new terms are extracted from the latest medical research and papers and added to the dictionary. The translation department also develops a system that uses generative AI to automatically update the dictionary of medical terminology during translation and provide accurate translations based on the latest medical information. For example, new treatments and drug names are added to the dictionary. This allows the translation department to provide accurate translations based on the latest medical information.

[0042] The translation department can evaluate the accuracy of the translation results using a generation AI and continuously improve it based on user feedback. For example, the translation department builds a system that evaluates the accuracy of the translation results using a generation AI and continuously improves it based on user feedback. For example, it collects user ratings and comments and adjusts the translation algorithm. The translation department also uses a generation AI to evaluate the accuracy of the translation results and improves it based on user feedback. For example, it analyzes user feedback and improves the translation accuracy. The translation department also develops a system that evaluates the accuracy of the translation results using a generation AI and continuously improves it based on user feedback. For example, it collects user ratings and comments and adjusts the translation algorithm. This can improve the accuracy of the translation results and increase user satisfaction.

[0043] The emergency call unit can analyze the patient's location information using generation AI and quickly notify the nearest medical institution. For example, when an emergency call is made, the emergency call unit uses generation AI to analyze the patient's location information and quickly notify the nearest medical institution. For example, it identifies the nearest hospital based on GPS data and makes a call. The emergency call unit also uses generation AI to build a system that analyzes the patient's location information when an emergency call is made and notifies the nearest medical institution. For example, it selects the most suitable medical institution based on location data. The emergency call unit also develops a system that analyzes the patient's location information when an emergency call is made and quickly notifies the nearest medical institution. For example, it identifies the nearest hospital based on GPS data and makes a call. This allows the patient's location information to be quickly notified to medical institutions in the event of an emergency, enabling a rapid response.

[0044] The emergency call department can use generation AI to analyze emergency call history, understand frequency and patterns, and take preventive measures. For example, the emergency call department uses generation AI to analyze emergency call history, understand frequency and patterns, and take preventive measures. For example, if there are many calls during a particular time period or situation, the cause can be identified and measures can be taken. The emergency call department also uses generation AI to build a system that analyzes emergency call history and understands frequency and patterns. For example, it can identify areas and time periods with many calls and take preventive measures. The emergency call department also develops a system that uses generation AI to analyze emergency call history, understand frequency and patterns, and take preventive measures. For example, if there are many calls during a particular time period or situation, the cause can be identified and measures can be taken. In this way, by understanding the frequency and patterns of emergency calls and taking preventive measures, it is possible to reduce the occurrence of emergencies.

[0045] The emergency notification unit can also apply the emergency notification function to remote caregiving and remote education. For example, the emergency notification unit applies the emergency notification function to remote caregiving and supports emergency notifications between the caregiver and the care recipient. The generation AI monitors the care situation in real time and makes a notification in the event of an emergency. The emergency notification unit also applies the emergency notification function to remote education and supports emergency notifications between teachers and students. The generation AI monitors the education situation in real time and makes a notification in the event of an emergency. The emergency notification unit also applies the emergency notification function to other remote services and supports emergency notifications. The generation AI monitors the status of each service in real time and makes a notification in the event of an emergency. This makes it possible to apply the emergency notification function to other remote services, enabling emergency response in a wide range of fields.

[0046] The emergency call unit uses a generation AI to automatically generate the content of an emergency call, enabling rapid and accurate information provision. For example, the emergency call unit uses a generation AI to automatically generate the content of an emergency call, enabling rapid and accurate information provision. For example, the content of the call is generated based on the patient's condition and location information. The emergency call unit also uses a generation AI to build a system that automatically generates the content of an emergency call, enabling rapid and accurate information provision. For example, the content of the call is generated by analyzing the patient's biometric data and voice data. The emergency call unit also develops a system that uses a generation AI to automatically generate the content of an emergency call, enabling rapid and accurate information provision. For example, the content of the call is generated based on the patient's condition and location information. This enables rapid and accurate information provision when an emergency call is made, resulting in a faster response.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The medical consultation security enhancement service can also be equipped with a function to collect patient lifestyle data and support health management. For example, the acquisition unit records the patient's diet and exercise, and the generation AI analyzes the data to evaluate their health condition. The acquisition unit can also monitor the patient's sleep patterns, and the generation AI analyzes the data to evaluate the quality of sleep. Furthermore, the acquisition unit can measure the patient's stress level, and the generation AI analyzes the data to provide stress management advice. This can support the patient's comprehensive health management.

[0049] The medical consultation security enhancement service can further include a function to analyze a patient's health data and provide preventive medical advice. For example, the analysis unit analyzes the patient's past health data, and the generation AI predicts future health risks. The analysis unit can also analyze the patient's lifestyle data, and the generation AI can provide advice to reduce health risks. Furthermore, the analysis unit can analyze the patient's genetic information, and the generation AI can provide preventive medical advice based on the genetic risks. This enables early detection of patient health risks and supports preventive medical care.

[0050] The medical consultation security enhancement service can further be equipped with a function to analyze a patient's health data and provide personalized nutritional advice. For example, the analysis unit analyzes the patient's dietary data, and the generation AI evaluates nutritional balance. The analysis unit can also analyze the patient's health condition and lifestyle data, and the generation AI can provide personalized nutritional advice. Furthermore, the analysis unit can analyze the patient's genetic information, and the generation AI can provide nutritional advice based on genetic factors. This makes it possible to provide appropriate nutritional advice according to the patient's health condition.

[0051] The medical consultation security enhancement service can further include a function to analyze a patient's health data and provide an exercise program. For example, the analysis unit analyzes the patient's exercise data, and the generation AI provides an appropriate exercise program. The analysis unit can also analyze the patient's health condition and lifestyle data, and the generation AI can provide personalized exercise advice. Furthermore, the analysis unit can analyze the patient's genetic information, and the generation AI can provide an exercise program based on genetic factors. This makes it possible to provide an appropriate exercise program according to the patient's health condition.

[0052] The medical consultation security enhancement service can further be equipped with a function to analyze a patient's health data and conduct regular health checks. For example, the analysis unit periodically collects the patient's health data, and the generation AI analyzes the data to evaluate the health condition. The analysis unit can also periodically monitor the patient's lifestyle data, allowing the generation AI to detect health risks early. Furthermore, the analysis unit can periodically analyze the patient's genetic information, allowing the generation AI to conduct health checks based on genetic risks. This allows the patient's health condition to be continuously monitored and health risks to be detected early.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The biometric authentication unit performs biometric authentication. For example, fingerprint authentication is used to confirm the patient's identity. It can also use facial or iris authentication to confirm the identity of the doctor or patient. Step 2: The encryption unit encrypts the communication data using an encryption method such as AES, RSA, or SHA. Step 3: The document sharing unit securely shares documents, such as medical certificates, prescriptions, and contracts, by encrypting them. Step 4: The translation unit translates between different languages ​​in real time, for example, between a Japanese-speaking patient and an English-speaking doctor, between an English-speaking patient and a French-speaking doctor, or between a Spanish-speaking patient and a German-speaking doctor. Step 5: The emergency notification department reports an emergency. For example, if a patient suddenly becomes ill or loses consciousness, or if a doctor faces an emergency, an emergency notification is made.

[0055] (Example 2) The medical consultation security enhancement service according to an embodiment of the present invention is a system that uses audio glasses to provide secure communication between patients and doctors with an emphasis on privacy, and improves the safety of remote medical care with five main functions, including biometric authentication, encryption, secure document sharing, real-time translation, and emergency notification. As a result, the medical consultation security enhancement service can establish reliable communication between patients and medical professionals and improve the safety of remote medical care.

[0056] The medical consultation security enhancement service according to the embodiment includes a biometric authentication unit, an encryption unit, a document sharing unit, a translation unit, and an emergency call unit. The biometric authentication unit performs biometric authentication. For example, the biometric authentication unit verifies the identity of a patient using fingerprint authentication. The biometric authentication unit can also verify the identity of a doctor using facial authentication. The biometric authentication unit can also verify the identity of a patient using iris authentication. The encryption unit encrypts communication data. For example, the encryption unit encrypts communication data using AES. The encryption unit can also encrypt communication data using RSA. The encryption unit can also encrypt communication data using SHA. The document sharing unit securely shares documents. For example, the document sharing unit encrypts and shares a medical certificate. The document sharing unit can also encrypt and share a prescription. The document sharing unit can also encrypt and share a contract. The translation unit translates between different languages ​​in real time. For example, the translation unit translates between a Japanese-speaking patient and an English-speaking doctor in real time. The translation unit can also perform real-time translation between an English-speaking patient and a French-speaking doctor. The translation unit can also perform real-time translation between a Spanish-speaking patient and a German-speaking doctor. The emergency notification unit reports an emergency. For example, the emergency notification unit makes an emergency call when a patient suddenly becomes ill. The emergency notification unit can also make an emergency call when a doctor faces an emergency. The emergency notification unit can also make an emergency call when a patient loses consciousness. As a result, the medical consultation security enhancement service according to the embodiment can establish reliable communication between patients and medical professionals and improve the safety of remote medical care.

[0057] The biometric authentication unit can verify the identities of patients and doctors using fingerprint and facial recognition. For example, a patient wears audio glasses and introduces themselves verbally. The AI ​​analyzes the audio data and performs voiceprint authentication. This increases the accuracy of identity verification by combining voice authentication with fingerprint and facial recognition. The biometric authentication unit also instructs the patient to speak a specific phrase, and the AI ​​analyzes the audio data to perform voiceprint authentication. For example, if the patient says, "I am ____," the AI ​​acquires voiceprint data and verifies the patient's identity. The biometric authentication unit also registers the patient's voiceprint data in advance and compares it with the voiceprint data during medical consultations. The AI ​​analyzes the voice data in real time to confirm whether it matches the registered voiceprint data. This increases the accuracy of identity verification between patients and doctors.

[0058] The encryption unit can encrypt confidential information such as a patient's medical history and diagnosis results. For example, the encryption unit measures the patient's heart rate and body temperature using sensors built into the audio glasses, and the generation AI analyzes the data. If an abnormality is detected, it alerts the doctor. The encryption unit also uses additional sensors to monitor the patient's health during the biometric authentication process. For example, a heart rate monitor and thermometer are used in combination, and the generation AI analyzes the data to detect abnormalities. The encryption unit also creates a system that continuously monitors the patient's health while the audio glasses are worn, and immediately notifies the doctor if an abnormality occurs. The generation AI analyzes the data in real time and detects abnormalities. This prevents the leakage of confidential information.

[0059] The document sharing unit can encrypt and share important documents such as medical certificates and prescriptions. For example, during biometric authentication, the document sharing unit analyzes the patient's facial expressions and voice to estimate their emotional state. The generation AI provides relaxation guidance in cases of high stress or anxiety. The document sharing unit also uses the camera and microphone built into the audio glasses to monitor the patient's emotional state in real time. The generation AI analyzes the emotional data and provides music or messages to help patients relax as needed. The document sharing unit also uses its emotion estimation function to analyze the patient's emotional state and provides audio guidance for deep breathing and relaxation in cases of high stress or anxiety. The generation AI generates appropriate guidance based on the emotional data. This allows important medical documents to be shared safely.

[0060] The translation unit can perform real-time translation between Japanese-speaking patients and English-speaking doctors. For example, when the generation AI creates a summary, the translation unit automatically collects relevant background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The translation unit also uses a topic model to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The translation unit also builds a system to understand the context by referring to relevant background information and topic models when the generation AI creates a summary. For example, it automatically collects related information and reflects it in the summary. This enables smooth communication between patients and doctors who speak different languages.

[0061] The emergency reporting unit can make an emergency call if a patient suddenly becomes ill. For example, when an emergency call is made, the emergency reporting unit uses a generation AI to automatically generate the report content, enabling fast and accurate information provision. For example, the report content is generated based on the patient's condition and location information. The emergency reporting unit also uses a generation AI to build a system that automatically generates the report content when an emergency call is made. For example, the report content is generated by analyzing the patient's biometric data and voice data. The emergency reporting unit also develops a system that automatically generates the report content when an emergency call is made, enabling fast and accurate information provision. For example, the report content is generated based on the patient's condition and location information. This allows for a rapid response to emergencies.

[0062] The biometric authentication unit can strengthen identity verification by authenticating the patient's voiceprint and analyzing the voice data with a generation AI. For example, the patient wears audio glasses and introduces themselves verbally. The generation AI analyzes the voice data and performs voiceprint authentication. This increases the accuracy of identity verification by combining voice authentication with fingerprint and facial authentication. The biometric authentication unit can also instruct the patient to speak a specific phrase, and the generation AI analyzes the voice data to perform voiceprint authentication. For example, if the patient says, "I am ____," voiceprint data is obtained and identity verification is performed. The biometric authentication unit can also register the patient's voiceprint data in advance and compare it with that voiceprint data during medical consultations. The generation AI analyzes the voice data in real time to confirm whether it matches the registered voiceprint data. This increases the accuracy of identity verification.

[0063] The biometric authentication unit can simultaneously monitor the patient's health condition and issue an alert if an abnormality is detected. For example, the biometric authentication unit measures the patient's heart rate and body temperature using sensors built into the audio glasses, and the generation AI analyzes the data. If an abnormality is detected, it alerts the doctor. The biometric authentication unit also uses additional sensors to monitor the patient's health condition during the biometric authentication process. For example, a heart rate monitor and thermometer are used in combination, and the generation AI analyzes the data to detect abnormalities. The biometric authentication unit also continuously monitors the patient's health condition while the audio glasses are worn, creating a system that immediately notifies the doctor if an abnormality occurs. The generation AI analyzes the data in real time and detects abnormalities. This allows the patient's health condition to be monitored in real time and any abnormalities to be addressed quickly.

[0064] The biometric authentication unit uses the emotion estimation function to analyze the patient's emotional state at the time of authentication and can provide relaxation guidance if stress or anxiety is high. For example, the biometric authentication unit analyzes the patient's facial expressions and voice during biometric authentication to estimate their emotional state. The generation AI provides relaxation guidance if stress or anxiety is high. The biometric authentication unit also uses the camera and microphone installed in the audio glasses to monitor the patient's emotional state in real time. The generation AI analyzes the emotional data and provides music or messages to help them relax as needed. The biometric authentication unit also uses the emotion estimation function to analyze the patient's emotional state and provides audio guidance to take deep breaths or relax if stress or anxiety is high. The generation AI generates appropriate guidance based on the emotional data. This reduces the patient's stress and anxiety and allows authentication to be performed in a relaxed state.

[0065] The encryption unit can use a generation AI to analyze the transmission and reception history of communication data and detect signs of unauthorized access. In addition to encrypting communication data, the encryption unit can also use a generation AI to analyze the transmission and reception history and detect signs of unauthorized access. For example, it can detect abnormal access patterns and suspicious IP addresses. The encryption unit can also use a generation AI to analyze the transmission and reception history of communication data in real time and detect signs of unauthorized access. For example, it can detect abnormal data transfer volumes and suspicious access times. In addition to encrypting communication data, the encryption unit can also use a generation AI to analyze the transmission and reception history and build a system to detect signs of unauthorized access. For example, it can detect abnormal access frequencies and suspicious devices. This allows for early detection of signs of unauthorized access and strengthens security.

[0066] The encryption unit can apply different encryption algorithms depending on the content of the communication data. For example, the encryption unit builds a system that applies different encryption algorithms depending on the content of the communication data. The generation AI selects the optimal encryption algorithm depending on the confidentiality and importance of the data. The encryption unit also uses the generation AI to analyze the content of the communication data and apply an appropriate encryption algorithm. For example, it uses an advanced encryption algorithm for confidential information and a lightweight encryption algorithm for general information. The encryption unit also develops a system that applies different encryption algorithms depending on the content of the communication data. The generation AI selects and applies the optimal encryption algorithm based on the type and importance of the data. This enables optimal encryption depending on the content of the communication data, strengthening security.

[0067] The encryption unit uses the emotion estimation function to analyze the patient's emotions during communication, and can take measures to improve communication stability if stress is high. The encryption unit, for example, analyzes the patient's emotional state during communication, and can take measures to improve communication stability if stress is high. The generation AI adjusts communication priority based on the emotion data. The encryption unit also uses the emotion estimation function to monitor the patient's emotions during communication in real time. The generation AI takes measures to improve communication stability if stress is high. The encryption unit also builds a system that analyzes the patient's emotional state during communication, and can take measures to improve communication stability if stress is high. The generation AI adjusts communication priority based on the emotion data. This makes it possible to reduce patient stress and improve communication stability.

[0068] The document sharing unit can analyze the contents of a document using a generation AI and apply different encryption levels depending on the level of confidentiality. For example, when sharing a document, the document sharing unit uses a generation AI to analyze the contents of the document and apply different encryption levels depending on the level of confidentiality. For example, it may apply high-level encryption to confidential information and light encryption to general information. The document sharing unit also uses a generation AI to analyze the contents of a document and build a system that automatically applies an encryption level depending on the level of confidentiality. For example, it may apply high-level encryption to medical certificates and prescriptions. The document sharing unit also develops a system when sharing a document using a generation AI to analyze the contents of the document and apply different encryption levels depending on the level of confidentiality. For example, it may apply high-level encryption to confidential information and light encryption to general information. This enables optimal encryption depending on the level of confidentiality of the document, thereby strengthening security.

[0069] The document sharing unit uses generation AI to analyze document sharing history and detect signs of unauthorized access or tampering. For example, the document sharing unit uses generation AI to analyze document sharing history and detect signs of unauthorized access or tampering. For example, it detects abnormal access patterns and suspicious IP addresses. The document sharing unit also uses generation AI to analyze document sharing history in real time and detect signs of unauthorized access or tampering. For example, it detects abnormal data transfer volumes and suspicious access times. The document sharing unit also builds a system where generation AI analyzes document sharing history and detects signs of unauthorized access or tampering. For example, it detects abnormal access frequencies and suspicious devices. This enables early detection of signs of unauthorized access or tampering and strengthens security.

[0070] The document sharing unit can use the emotion estimation function to analyze the patient's emotions when sharing documents and provide guidance to give a sense of security. The document sharing unit, for example, analyzes the patient's emotional state when sharing documents and provides guidance to give a sense of security. The generation AI provides appropriate messages and advice based on the emotion data. The document sharing unit also uses the emotion estimation function to monitor the patient's emotions in real time when sharing documents. The generation AI provides guidance to give a sense of security. The document sharing unit also builds a system that analyzes the patient's emotional state when sharing documents and provides guidance to give a sense of security. The generation AI provides appropriate messages and advice based on the emotion data. This gives the patient a sense of security and reduces stress when sharing documents.

[0071] The translation department can automatically update the dictionary of medical terminology using generative AI and provide accurate translations based on the latest medical information. The translation department, for example, can automatically update the dictionary of medical terminology using generative AI and provide accurate translations based on the latest medical information. For example, the dictionary is updated when new medical terms or treatments are added. The translation department also uses generative AI to build a system that automatically updates the dictionary of medical terminology. For example, new terms are extracted from the latest medical research and papers and added to the dictionary. The translation department also develops a system that uses generative AI to automatically update the dictionary of medical terminology during translation and provide accurate translations based on the latest medical information. For example, new treatments and drug names are added to the dictionary. This allows the translation department to provide accurate translations based on the latest medical information.

[0072] The translation department can evaluate the accuracy of the translation results using a generation AI and continuously improve it based on user feedback. For example, the translation department builds a system that evaluates the accuracy of the translation results using a generation AI and continuously improves it based on user feedback. For example, it collects user ratings and comments and adjusts the translation algorithm. The translation department also uses a generation AI to evaluate the accuracy of the translation results and improves it based on user feedback. For example, it analyzes user feedback and improves the translation accuracy. The translation department also develops a system that evaluates the accuracy of the translation results using a generation AI and continuously improves it based on user feedback. For example, it collects user ratings and comments and adjusts the translation algorithm. This can improve the accuracy of the translation results and increase user satisfaction.

[0073] The translation unit uses the emotion estimation function to analyze the patient's emotions during translation, and can adjust the tone of the translation if the patient is under high stress. The translation unit, for example, analyzes the patient's emotional state during translation, and adjusts the tone of the translation if the patient is under high stress. The generation AI softens the tone of the translation based on the emotion data. The translation unit also uses the emotion estimation function to monitor the patient's emotions during translation in real time. The generation AI adjusts the tone of the translation if the patient is under high stress. The translation unit also builds a system that analyzes the patient's emotional state during translation, and adjusts the tone of the translation if the patient is under high stress. The generation AI softens the tone of the translation based on the emotion data. This reduces the patient's stress and adjusts the tone of the translation, making it possible to provide a translation that is easier to understand.

[0074] The emergency call unit can analyze the patient's location information using generation AI and quickly notify the nearest medical institution. For example, when an emergency call is made, the emergency call unit uses generation AI to analyze the patient's location information and quickly notify the nearest medical institution. For example, it identifies the nearest hospital based on GPS data and makes a call. The emergency call unit also uses generation AI to build a system that analyzes the patient's location information when an emergency call is made and notifies the nearest medical institution. For example, it selects the most suitable medical institution based on location data. The emergency call unit also develops a system that analyzes the patient's location information when an emergency call is made and quickly notifies the nearest medical institution. For example, it identifies the nearest hospital based on GPS data and makes a call. This allows the patient's location information to be quickly notified to medical institutions in the event of an emergency, enabling a rapid response.

[0075] The emergency call department can use generation AI to analyze emergency call history, understand frequency and patterns, and take preventive measures. For example, the emergency call department uses generation AI to analyze emergency call history, understand frequency and patterns, and take preventive measures. For example, if there are many calls during a particular time period or situation, the cause can be identified and measures can be taken. The emergency call department also uses generation AI to build a system that analyzes emergency call history and understands frequency and patterns. For example, it can identify areas and time periods with many calls and take preventive measures. The emergency call department also develops a system that uses generation AI to analyze emergency call history, understand frequency and patterns, and take preventive measures. For example, if there are many calls during a particular time period or situation, the cause can be identified and measures can be taken. In this way, by understanding the frequency and patterns of emergency calls and taking preventive measures, it is possible to reduce the occurrence of emergencies.

[0076] The emergency call unit can use the emotion estimation function to analyze the patient's emotion when making an emergency call and provide guidance to give a sense of security. The emergency call unit, for example, analyzes the patient's emotional state when making an emergency call and provides guidance to give a sense of security. The generation AI provides appropriate messages and advice based on the emotion data. The emergency call unit also uses the emotion estimation function to monitor the patient's emotion when making an emergency call in real time. The generation AI provides guidance to give a sense of security. The emergency call unit also builds a system that analyzes the patient's emotional state when making an emergency call and provides guidance to give a sense of security. The generation AI provides appropriate messages and advice based on the emotion data. This makes it possible to give patients a sense of security and reduce stress in emergencies.

[0077] The emergency notification unit can also apply the emergency notification function to remote caregiving and remote education. For example, the emergency notification unit applies the emergency notification function to remote caregiving and supports emergency notifications between the caregiver and the care recipient. The generation AI monitors the care situation in real time and makes a notification in the event of an emergency. The emergency notification unit also applies the emergency notification function to remote education and supports emergency notifications between teachers and students. The generation AI monitors the education situation in real time and makes a notification in the event of an emergency. The emergency notification unit also applies the emergency notification function to other remote services and supports emergency notifications. The generation AI monitors the status of each service in real time and makes a notification in the event of an emergency. This makes it possible to apply the emergency notification function to other remote services, enabling emergency response in a wide range of fields.

[0078] The emergency call unit uses a generation AI to automatically generate the content of an emergency call, enabling rapid and accurate information provision. For example, the emergency call unit uses a generation AI to automatically generate the content of an emergency call, enabling rapid and accurate information provision. For example, the content of the call is generated based on the patient's condition and location information. The emergency call unit also uses a generation AI to build a system that automatically generates the content of an emergency call, enabling rapid and accurate information provision. For example, the content of the call is generated by analyzing the patient's biometric data and voice data. The emergency call unit also develops a system that uses a generation AI to automatically generate the content of an emergency call, enabling rapid and accurate information provision. For example, the content of the call is generated based on the patient's condition and location information. This enables rapid and accurate information provision when an emergency call is made, resulting in a faster response.

[0079] The emergency call unit can use the emotion estimation function to analyze the patient's emotion when making an emergency call and provide an interface design that elicits positive emotions. The emergency call unit, for example, analyzes the patient's emotional state when making an emergency call and provides an interface design that elicits positive emotions. The generation AI adjusts the color and layout of the interface based on the emotion data. The emergency call unit also uses the emotion estimation function to monitor the patient's emotion when making an emergency call in real time. The generation AI provides an interface design that elicits positive emotions. The emergency call unit also builds a system that analyzes the patient's emotional state when making an emergency call and provides an interface design that elicits positive emotions. The generation AI adjusts the color and layout of the interface based on the emotion data. This makes it possible to elicit positive emotions in patients during an emergency and reduce stress.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The medical consultation security enhancement service can also be equipped with a function to collect patient lifestyle data and support health management. For example, the acquisition unit records the patient's diet and exercise, and the generation AI analyzes the data to evaluate their health condition. The acquisition unit can also monitor the patient's sleep patterns, and the generation AI analyzes the data to evaluate the quality of sleep. Furthermore, the acquisition unit can measure the patient's stress level, and the generation AI analyzes the data to provide stress management advice. This can support the patient's comprehensive health management.

[0082] The medical consultation security enhancement service can also be equipped with a function to analyze the patient's emotional state and provide medical advice based on that emotion. For example, the judgment unit can analyze the patient's facial expressions and voice to estimate their emotions, and the generation AI can provide appropriate medical advice based on that data. The judgment unit can also monitor the patient's emotional state in real time and provide guidance on how to relax if stress or anxiety is high. Furthermore, the judgment unit can provide advice to provide psychological support to the patient based on the emotional data. This makes it possible to provide appropriate medical advice according to the patient's emotional state.

[0083] The medical consultation security enhancement service can further include a function to analyze a patient's health data and provide preventive medical advice. For example, the analysis unit analyzes the patient's past health data, and the generation AI predicts future health risks. The analysis unit can also analyze the patient's lifestyle data, and the generation AI can provide advice to reduce health risks. Furthermore, the analysis unit can analyze the patient's genetic information, and the generation AI can provide preventive medical advice based on the genetic risks. This enables early detection of patient health risks and supports preventive medical care.

[0084] The medical consultation security enhancement service can further be equipped with a function to analyze the patient's emotional state and provide a rehabilitation program based on the emotion. For example, the provision unit can analyze the patient's facial expressions and voice to estimate their emotion, and the generation AI can provide an appropriate rehabilitation program based on that data. The provision unit can also monitor the patient's emotional state in real time and provide relaxation exercises if stress or anxiety is high. Furthermore, the provision unit can provide a rehabilitation program to provide psychological support to the patient based on the emotion data. This makes it possible to provide appropriate rehabilitation according to the patient's emotional state.

[0085] The medical consultation security enhancement service can further be equipped with a function to analyze a patient's health data and provide personalized nutritional advice. For example, the analysis unit analyzes the patient's dietary data, and the generation AI evaluates nutritional balance. The analysis unit can also analyze the patient's health condition and lifestyle data, and the generation AI can provide personalized nutritional advice. Furthermore, the analysis unit can analyze the patient's genetic information, and the generation AI can provide nutritional advice based on genetic factors. This makes it possible to provide appropriate nutritional advice according to the patient's health condition.

[0086] The medical consultation security enhancement service can further include a function to analyze the patient's emotional state and provide sleep improvement advice based on the emotion. For example, the providing unit can analyze the patient's facial expressions and voice to estimate their emotion, and the generating AI can provide appropriate sleep improvement advice based on that data. The providing unit can also monitor the patient's emotional state in real time and provide sleep guidance to help them relax if they are experiencing high levels of stress or anxiety. Furthermore, the providing unit can provide sleep improvement advice to provide psychological support to the patient based on the emotion data. This makes it possible to provide appropriate sleep improvement advice according to the patient's emotional state.

[0087] The medical consultation security enhancement service can further include a function to analyze a patient's health data and provide an exercise program. For example, the analysis unit analyzes the patient's exercise data, and the generation AI provides an appropriate exercise program. The analysis unit can also analyze the patient's health condition and lifestyle data, and the generation AI can provide personalized exercise advice. Furthermore, the analysis unit can analyze the patient's genetic information, and the generation AI can provide an exercise program based on genetic factors. This makes it possible to provide an appropriate exercise program according to the patient's health condition.

[0088] The medical consultation security enhancement service can further include a function to analyze the patient's emotional state and provide a stress management program based on the emotion. For example, the provision unit can analyze the patient's facial expressions and voice to estimate their emotion, and the generation AI can provide an appropriate stress management program based on that data. The provision unit can also monitor the patient's emotional state in real time and provide relaxation exercises if stress or anxiety is high. Furthermore, the provision unit can provide a stress management program to provide psychological support to the patient based on the emotion data. This makes it possible to provide appropriate stress management according to the patient's emotional state.

[0089] The medical consultation security enhancement service can further be equipped with a function to analyze a patient's health data and conduct regular health checks. For example, the analysis unit periodically collects the patient's health data, and the generation AI analyzes the data to evaluate the health condition. The analysis unit can also periodically monitor the patient's lifestyle data, allowing the generation AI to detect health risks early. Furthermore, the analysis unit can periodically analyze the patient's genetic information, allowing the generation AI to conduct health checks based on genetic risks. This allows the patient's health condition to be continuously monitored and health risks to be detected early.

[0090] The medical consultation security enhancement service can further be equipped with a function to analyze the patient's emotional state and provide emotionally based mental health support. For example, the provision unit can analyze the patient's facial expressions and voice to estimate their emotions, and the generation AI can provide appropriate mental health support based on that data. The provision unit can also monitor the patient's emotional state in real time and provide relaxation counseling if stress or anxiety is high. Furthermore, the provision unit can provide a mental health program to provide psychological support to the patient based on the emotional data. This makes it possible to provide appropriate mental health support according to the patient's emotional state.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The biometric authentication unit performs biometric authentication. For example, fingerprint authentication is used to confirm the patient's identity. It can also use facial or iris authentication to confirm the identity of the doctor or patient. Step 2: The encryption unit encrypts the communication data using an encryption method such as AES, RSA, or SHA. Step 3: The document sharing unit securely shares documents, such as medical certificates, prescriptions, and contracts, by encrypting them. Step 4: The translation unit translates between different languages ​​in real time, for example, between a Japanese-speaking patient and an English-speaking doctor, between an English-speaking patient and a French-speaking doctor, or between a Spanish-speaking patient and a German-speaking doctor. Step 5: The emergency notification department reports an emergency. For example, if a patient suddenly becomes ill or loses consciousness, or if a doctor faces an emergency, an emergency notification is made.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a biometric authentication unit that performs biometric authentication; an encryption unit that encrypts communication data; A document sharing section for securely sharing documents, A translation department that translates different languages ​​in real time, an emergency reporting unit that reports an emergency; A system characterized by:

2. The biometric authentication unit Verify patient and doctor identities using fingerprint and facial recognition 2. The system of claim 1.

3. The encryption unit Encrypt sensitive information such as patient history and diagnoses 2. The system of claim 1.

4. The document sharing unit Encrypt and share important documents like medical certificates and prescriptions 2. The system of claim 1.

5. The translation unit Real-time translation between Japanese-speaking patients and English-speaking doctors 2. The system of claim 1.

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