System

Abstract

An object of a system according to an embodiment is to provide an appropriate medical examination even to a patient who feels embarrassed with a face-to-face medical examination.SOLUTION: A system according to an embodiment includes a AI avatar, a machine learner, an online examiner, and a privacy protector. The AI avatar learns medical knowledges. The machine learner causes the AI avatar to learn medical knowledges. In the online consultation unit, a AI avatar interacts with a patient online to perform a consultation. The privacy protector allows the AI avatar to protect the privacy of the subject.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 technology has had the problem of making it difficult to provide appropriate medical examinations to patients who feel embarrassed about face-to-face examinations.

[0005] The system according to the embodiment aims to provide an appropriate medical examination even to patients who feel embarrassed about face-to-face medical examinations. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI avatar, a machine learning unit, an online consultation unit, and a privacy protection unit. The AI ​​avatar learns medical knowledge. The machine learning unit causes the AI ​​avatar to learn medical knowledge. The online consultation unit allows the AI ​​avatar to interact with and examine patients online. The privacy protection unit allows the AI ​​avatar to protect the privacy of patients. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate medical examinations even to patients who feel embarrassed about face-to-face examinations. [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 virtual doctor system according to an embodiment of the present invention is a system that uses machine learning to train an AI avatar and provides online consultations. This system can handle not only standard treatments but also cases where a face-to-face consultation is embarrassing, such as ED treatment and AGA treatment. This allows the virtual doctor system to provide appropriate medical information to patients while avoiding face-to-face consultations.

[0029] The virtual doctor system according to the embodiment includes an AI avatar, a machine learning unit, an online consultation unit, and a privacy protection unit. The AI ​​avatar provides appropriate answers to patients' questions and symptoms. For example, if a patient asks, "My hair has been thinning recently. What should I do?", the AI ​​avatar responds, based on its knowledge of AGA treatment, with, "There are several methods for treating AGA. For example, you can use medications such as finasteride and minoxidil." The machine learning unit teaches the AI ​​avatar medical knowledge. For example, the generation AI learns diagnostic protocols and treatment guidelines and generates appropriate answers to patients' questions. The online consultation unit allows the AI ​​avatar to interact with patients online and conduct consultations. For example, if a patient consults with the AI ​​avatar about "I've recently started experiencing symptoms of erectile dysfunction," the AI ​​avatar responds, based on its knowledge of ED treatment, with, "There are several methods for treating ED. For example, you can use medications such as sildenafil and tadalafil." The privacy protection unit ensures that the AI ​​avatar protects the patient's privacy. For example, when a patient consults about sensitive issues such as erectile dysfunction (ED) or androgenetic alopecia (AGA), the AI ​​avatar can respond online, allowing the patient to avoid a face-to-face consultation. This allows the virtual doctor system of the embodiment to provide appropriate medical information to patients while avoiding a face-to-face consultation.

[0030] The AI ​​avatar includes a personalized advice unit that provides personalized advice based on the patient's individual medical history and lifestyle. The personalized advice unit, for example, learns the patient's medical history and lifestyle and provides personalized advice based on that. For example, it provides dietary management advice to diabetic patients. The personalized advice unit also builds a system that suggests appropriate treatments and preventative measures based on the patient's individual medical history. For example, it suggests improving exercise habits to patients at high risk of heart disease. The personalized advice unit also collects and analyzes data on the patient's daily life to provide advice based on their lifestyle. For example, it provides health management advice based on sleep patterns and exercise volume. This makes it possible to provide advice based on the patient's individual medical history and lifestyle.

[0031] The AI ​​avatar includes a non-verbal communication analysis unit that analyzes the patient's non-verbal communication to realize a more natural dialogue. The non-verbal communication analysis unit, for example, allows the AI ​​avatar to analyze the patient's facial expressions and understand the non-verbal communication. For example, if the patient smiles, the AI ​​avatar will also respond with a smile. The non-verbal communication analysis unit also analyzes the patient's tone of voice to build a system that understands their emotions and intentions. For example, if the patient speaks in a tired voice, the AI ​​avatar will respond in a gentle tone. The non-verbal communication analysis unit also realizes a more natural dialogue by analyzing non-verbal communication. For example, if the patient nods, the AI ​​avatar will also make a gesture of agreement. This enables a natural dialogue with the patient.

[0032] The AI ​​avatar is equipped with a specialty application unit that can be applied to different specialty fields and respond to a wide range of health consultations. The specialty application unit, for example, applies the AI ​​avatar to psychological counseling to respond to patients' mental health consultations. For example, it provides advice on stress management and anxiety relief. The specialty application unit also develops an AI avatar specialized in nutritional guidance to provide advice on patients' dietary management and nutritional balance. For example, it suggests diets and how to take in specific nutrients. The specialty application unit also develops AI avatars that have learned knowledge from different specialty fields to respond to a wide range of health consultations. For example, it provides exercise guidance and preventive measures for lifestyle-related diseases. This makes it possible to respond to a wide range of health consultations.

[0033] The AI ​​avatar is equipped with a multilingual support unit that supports multiple languages ​​and allows it to be used by international patients. The multilingual support unit, for example, allows the AI ​​avatar to support multiple languages ​​and allows it to be used by international patients. For example, it supports major languages ​​such as English, Spanish, and Chinese. The multilingual support unit also has a language translation function that allows patients to ask questions in their native language. For example, a question asked in Japanese can be translated into English, and the AI ​​avatar can respond in English. The multilingual support unit also develops multilingual AI avatars that can respond appropriately to patients with different cultural backgrounds. For example, it can provide advice that takes cultural differences into consideration. This makes it possible to accommodate international patients.

[0034] The AI ​​avatar includes a diagnostic accuracy improvement unit that integrates the patient's past medical history and health data to make a more accurate diagnosis. The diagnostic accuracy improvement unit, for example, integrates the patient's past medical history and health data to build a system that makes a more accurate diagnosis. For example, it makes a diagnosis based on past medical history and test results. The diagnostic accuracy improvement unit also integrates health data and makes a diagnosis based on the patient's symptoms and medical history. For example, it makes a diagnosis based on blood pressure and blood sugar data. The diagnostic accuracy improvement unit also analyzes the patient's symptoms and medical history based on the past medical history to make a more accurate diagnosis. For example, it makes a diagnosis by referring to past medical examination results. This enables a more accurate diagnosis.

[0035] The AI ​​avatar includes a living environment consideration unit that provides advice taking into account the patient's living environment. The living environment consideration unit, for example, allows the AI ​​avatar to consider the patient's living environment and provide appropriate advice. For example, if the air quality in the home is poor, it may suggest using an air purifier. The living environment consideration unit also analyzes the patient's eating habits and provides advice on healthy eating. For example, it may suggest a nutritionally balanced meal menu. The living environment consideration unit also collects and analyzes data on the patient's daily life in order to provide advice based on the patient's living environment. For example, it may provide health management advice based on the amount of exercise and sleep patterns. This makes it possible to provide advice that takes into account the patient's living environment.

[0036] The AI ​​avatar is equipped with an expert collaboration unit that collaborates with different medical experts to conduct examinations from multiple perspectives. The expert collaboration unit, for example, builds a system in which the AI ​​avatar collaborates with different medical experts to conduct examinations from multiple perspectives. For example, an internist and a dermatologist conduct an examination together. The expert collaboration unit also collaborates with doctors of different specialties to conduct examinations according to the patient's symptoms. For example, a patient at risk of heart disease is examined by a cardiologist. The expert collaboration unit also collaborates with multiple medical experts to conduct examinations based on the patient's symptoms and medical history. For example, an internist and a dermatologist conduct an examination together. This makes it possible to conduct examinations from multiple perspectives.

[0037] The AI ​​avatar includes a security protocol unit that implements advanced security protocols to encrypt patient data and prevent access by third parties. The security protocol unit, for example, encrypts patient data and implements advanced security protocols to prevent access by third parties. For example, AES-256 encryption technology is used. The security protocol unit also combines data encryption and security protocols to build a system that protects patient privacy. For example, the SSL / TLS protocol is used when sending and receiving data. The security protocol unit also encrypts patient data and strengthens access control to prevent unauthorized access by third parties. For example, two-factor authentication is implemented. This allows patient data to be safely protected.

[0038] The AI ​​avatar is equipped with an access management unit that provides an interface that allows patients to manage access rights to their own data. The access management unit provides, for example, an interface that allows patients to manage access rights to their own data. For example, it is equipped with functions that allow data to be viewed, modified, and deleted. The access management unit also provides a dashboard that allows patients to manage access rights to their own data. For example, it adds a function that allows data sharing settings and access history to be checked. The access management unit also develops an interface that allows patients to manage access rights to their own data, strengthening privacy protection. For example, it provides a function that allows data access rights to be set in detail. This allows patients to manage their own data.

[0039] The AI ​​avatar is equipped with a privacy education unit that educates patients about privacy protection, enabling them to safely manage their own data. The privacy education unit, for example, educates patients about privacy protection, enabling them to safely manage their own data. For example, it explains the importance of data encryption and password management. The privacy education unit also provides educational content on privacy protection, enabling patients to acquire the knowledge to safely manage their own data. For example, it provides online seminars and tutorials. The privacy education unit also explains the importance of privacy protection to patients and suggests specific measures. For example, it recommends how to set up two-factor authentication and install security software. This allows patients to safely manage their own data.

[0040] The AI ​​avatar includes an anonymous examination unit that performs examinations using anonymized data to protect privacy. The anonymous examination unit, for example, builds a system in which the AI ​​avatar performs examinations using anonymized data to protect privacy. For example, the anonymous examination unit anonymizes data so that individuals cannot be identified. The anonymous examination unit also anonymizes patient data and performs examinations while protecting privacy. For example, personal information such as name and address is deleted and anonymized data is used. The anonymous examination unit also protects patient privacy by performing examinations using anonymized data. For example, data related to the examination content and symptoms is anonymized. This makes it possible to perform examinations while protecting patient privacy.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The virtual teacher system can also be equipped with a lifestyle monitoring unit that monitors the patient's lifestyle and supports health management. For example, it can record the patient's exercise and dietary habits and evaluate their health condition. The lifestyle monitoring unit can also analyze the patient's sleep patterns and provide advice on improving sleep quality. Furthermore, the lifestyle monitoring unit can measure the patient's stress level and suggest specific measures for stress management. This makes it possible to manage health by taking into account the patient's overall lifestyle.

[0043] The virtual doctor system can also be equipped with a diagnostic accuracy improvement unit that integrates a patient's past medical history and health data to make a more accurate diagnosis. For example, a system can be constructed that integrates a patient's past medical history and health data to make a more accurate diagnosis. For example, a diagnosis can be made based on past medical history and test results. The diagnostic accuracy improvement unit can also integrate health data and make a diagnosis based on the patient's symptoms and medical history. For example, a diagnosis can be made based on blood pressure and blood sugar data. The diagnostic accuracy improvement unit can also analyze the patient's symptoms and medical history based on past medical history to make a more accurate diagnosis. For example, a diagnosis can be made by referring to past medical examination results. This enables more accurate diagnoses.

[0044] The virtual doctor system can also be equipped with a living environment consideration unit that provides advice taking into account the patient's living environment. For example, an AI avatar can consider the patient's living environment and provide appropriate advice. For example, if the air quality in the home is poor, it can suggest the use of an air purifier. The living environment consideration unit can also analyze the patient's eating habits and provide advice on healthy eating. For example, it can suggest nutritionally balanced meal menus. The living environment consideration unit can also collect and analyze data on the patient's daily life in order to provide advice based on the patient's living environment. For example, it can provide health management advice based on the amount of exercise and sleep patterns. This makes it possible to provide advice that takes into account the patient's living environment.

[0045] The virtual doctor system can also be equipped with an expert collaboration unit that collaborates with different medical professionals to conduct examinations from multiple perspectives. For example, a system can be built in which an AI avatar collaborates with different medical professionals to conduct examinations from multiple perspectives. For example, an internist and a dermatologist may conduct an examination jointly. The expert collaboration unit can also collaborate with doctors from different specialties to conduct examinations based on the patient's symptoms. For example, a patient at risk of heart disease would be examined by a cardiologist. The expert collaboration unit can also collaborate with multiple medical professionals to conduct examinations based on the patient's symptoms and medical history. For example, an internist and a dermatologist may conduct an examination jointly. This makes it possible to conduct examinations from multiple perspectives.

[0046] The virtual doctor system can also be equipped with a security protocol unit that implements advanced security protocols to encrypt patient data and prevent access by third parties. For example, AES-256 encryption technology is used. The security protocol unit also combines data encryption and security protocols to build a system that protects patient privacy. For example, the SSL / TLS protocol is used when sending and receiving data. The security protocol unit also encrypts patient data and strengthens access control to prevent unauthorized access by third parties. For example, two-factor authentication is implemented. This allows patient data to be safely protected.

[0047] The virtual doctor system can also be equipped with an access management unit that provides an interface that allows patients to manage access rights to their own data. For example, it provides an interface that allows patients to manage access rights to their own data. For example, it provides functions that allow them to view, modify, and delete data. The access management unit also provides a dashboard that allows patients to manage access rights to their own data. For example, it adds functions that allow them to check data sharing settings and access history. The access management unit also develops an interface that allows patients to manage access rights to their own data, strengthening privacy protection. For example, it provides a function that allows them to set data access rights in detail. This allows patients to manage their own data.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The AI ​​avatar provides appropriate answers to the patient's questions and symptoms. For example, if a patient asks, "My hair has been thinning recently. What should I do?", the AI ​​avatar will use its knowledge of AGA treatment to respond, "There are several ways to treat AGA. For example, you can use medications such as finasteride and minoxidil." Step 2: The machine learning unit trains the AI ​​avatar with medical knowledge. For example, the generative AI learns diagnostic protocols and treatment guidelines and generates appropriate answers to patient questions. Step 3: In the online consultation department, an AI avatar will conduct an online consultation with the patient. For example, if a patient says, "I've recently started experiencing symptoms of erectile dysfunction," the AI ​​avatar will respond, based on its knowledge of ED treatment, by saying, "There are several ways to treat ED. For example, you can use medications such as sildenafil and tadalafil." Step 4: Privacy Protection: The AI ​​avatar protects patients' privacy. For example, when patients consult about sensitive issues such as erectile dysfunction (ED) or androgenetic alopecia (AGA), the AI ​​avatar can handle the consultation online, allowing patients to avoid face-to-face consultations.

[0050] (Example 2) The virtual doctor system according to an embodiment of the present invention is a system that uses machine learning to train an AI avatar and provides online consultations. This system can handle not only standard treatments but also cases where a face-to-face consultation is embarrassing, such as ED treatment and AGA treatment. This allows the virtual doctor system to provide appropriate medical information to patients while avoiding face-to-face consultations.

[0051] The virtual doctor system according to the embodiment includes an AI avatar, a machine learning unit, an online consultation unit, and a privacy protection unit. The AI ​​avatar provides appropriate answers to patients' questions and symptoms. For example, if a patient asks, "My hair has been thinning recently. What should I do?", the AI ​​avatar responds, based on its knowledge of AGA treatment, with, "There are several methods for treating AGA. For example, you can use medications such as finasteride and minoxidil." The machine learning unit teaches the AI ​​avatar medical knowledge. For example, the generation AI learns diagnostic protocols and treatment guidelines and generates appropriate answers to patients' questions. The online consultation unit allows the AI ​​avatar to interact with patients online and conduct consultations. For example, if a patient consults with the AI ​​avatar about "I've recently started experiencing symptoms of erectile dysfunction," the AI ​​avatar responds, based on its knowledge of ED treatment, with, "There are several methods for treating ED. For example, you can use medications such as sildenafil and tadalafil." The privacy protection unit ensures that the AI ​​avatar protects the patient's privacy. For example, when a patient consults about sensitive issues such as erectile dysfunction (ED) or androgenetic alopecia (AGA), the AI ​​avatar can respond online, allowing the patient to avoid a face-to-face consultation. This allows the virtual doctor system of the embodiment to provide appropriate medical information to patients while avoiding a face-to-face consultation.

[0052] The AI ​​avatar includes an emotion estimation unit that analyzes the patient's emotional state in real time and responds accordingly. The emotion estimation unit, for example, is equipped with an emotion estimation function in the AI ​​avatar and analyzes the patient's facial expressions and tone of voice to grasp the patient's emotional state in real time. For example, if the patient is feeling anxious, the AI ​​avatar will speak to them in a gentle tone. The emotion estimation unit also uses the emotion estimation function to respond according to the patient's emotional state. For example, if the patient is feeling stressed, it will provide advice to help them relax. The emotion estimation unit also builds a system that analyzes the patient's emotional state in real time and responds appropriately. For example, if the patient is feeling angry, the AI ​​avatar will be adjusted to respond calmly. This makes it possible to respond according to the patient's emotional state.

[0053] The AI ​​avatar includes a personalized advice unit that provides personalized advice based on the patient's individual medical history and lifestyle. The personalized advice unit, for example, learns the patient's medical history and lifestyle and provides personalized advice based on that. For example, it provides dietary management advice to diabetic patients. The personalized advice unit also builds a system that suggests appropriate treatments and preventative measures based on the patient's individual medical history. For example, it suggests improving exercise habits to patients at high risk of heart disease. The personalized advice unit also collects and analyzes data on the patient's daily life to provide advice based on their lifestyle. For example, it provides health management advice based on sleep patterns and exercise volume. This makes it possible to provide advice based on the patient's individual medical history and lifestyle.

[0054] The AI ​​avatar includes a non-verbal communication analysis unit that analyzes the patient's non-verbal communication to realize a more natural dialogue. The non-verbal communication analysis unit, for example, allows the AI ​​avatar to analyze the patient's facial expressions and understand the non-verbal communication. For example, if the patient smiles, the AI ​​avatar will also respond with a smile. The non-verbal communication analysis unit also analyzes the patient's tone of voice to build a system that understands their emotions and intentions. For example, if the patient speaks in a tired voice, the AI ​​avatar will respond in a gentle tone. The non-verbal communication analysis unit also realizes a more natural dialogue by analyzing non-verbal communication. For example, if the patient nods, the AI ​​avatar will also make a gesture of agreement. This enables a natural dialogue with the patient.

[0055] The AI ​​avatar is equipped with a specialty application unit that can be applied to different specialty fields and respond to a wide range of health consultations. The specialty application unit, for example, applies the AI ​​avatar to psychological counseling to respond to patients' mental health consultations. For example, it provides advice on stress management and anxiety relief. The specialty application unit also develops an AI avatar specialized in nutritional guidance to provide advice on patients' dietary management and nutritional balance. For example, it suggests diets and how to take in specific nutrients. The specialty application unit also develops AI avatars that have learned knowledge from different specialty fields to respond to a wide range of health consultations. For example, it provides exercise guidance and preventive measures for lifestyle-related diseases. This makes it possible to respond to a wide range of health consultations.

[0056] The AI ​​avatar is equipped with a multilingual support unit that supports multiple languages ​​and allows it to be used by international patients. The multilingual support unit, for example, allows the AI ​​avatar to support multiple languages ​​and allows it to be used by international patients. For example, it supports major languages ​​such as English, Spanish, and Chinese. The multilingual support unit also has a language translation function that allows patients to ask questions in their native language. For example, a question asked in Japanese can be translated into English, and the AI ​​avatar can respond in English. The multilingual support unit also develops multilingual AI avatars that can respond appropriately to patients with different cultural backgrounds. For example, it can provide advice that takes cultural differences into consideration. This makes it possible to accommodate international patients.

[0057] The AI ​​avatar includes a relaxation unit that uses an emotion estimation function to automatically select a conversation style that will help the patient relax and reduce stress. The relaxation unit, for example, uses the emotion estimation function to automatically select a conversation style that will help the patient relax. For example, if the patient is nervous, it will speak to them in a calm tone. The relaxation unit also analyzes the patient's emotional state in real time and selects a conversation style that will reduce stress. For example, it plays relaxing music in the background. The relaxation unit also uses the emotion estimation function to provide an environment that will help the patient relax. For example, it adjusts lighting and sound effects to help the patient relax. This reduces the patient's stress.

[0058] The AI ​​avatar includes an emotion examination unit that analyzes the patient's emotional state in real time and selects an examination method according to the emotion. The emotion examination unit, for example, analyzes the patient's emotional state in real time and selects an examination method according to the emotion. For example, if the patient is feeling anxious, it will speak to the patient in a gentle tone. The emotion examination unit also builds a system that selects an examination method according to the patient's emotional state based on the emotion analysis. For example, if the patient is feeling stressed, it will provide advice to help the patient relax. The emotion examination unit also analyzes the patient's emotional state in real time and selects an appropriate examination method. For example, if the patient is feeling angry, the AI ​​avatar will adjust to respond calmly. This makes it possible to select an examination method according to the patient's emotions.

[0059] The AI ​​avatar includes a diagnostic accuracy improvement unit that integrates the patient's past medical history and health data to make a more accurate diagnosis. The diagnostic accuracy improvement unit, for example, integrates the patient's past medical history and health data to build a system that makes a more accurate diagnosis. For example, it makes a diagnosis based on past medical history and test results. The diagnostic accuracy improvement unit also integrates health data and makes a diagnosis based on the patient's symptoms and medical history. For example, it makes a diagnosis based on blood pressure and blood sugar data. The diagnostic accuracy improvement unit also analyzes the patient's symptoms and medical history based on the past medical history to make a more accurate diagnosis. For example, it makes a diagnosis by referring to past medical examination results. This enables a more accurate diagnosis.

[0060] The AI ​​avatar includes a living environment consideration unit that provides advice taking into account the patient's living environment. The living environment consideration unit, for example, allows the AI ​​avatar to consider the patient's living environment and provide appropriate advice. For example, if the air quality in the home is poor, it may suggest using an air purifier. The living environment consideration unit also analyzes the patient's eating habits and provides advice on healthy eating. For example, it may suggest a nutritionally balanced meal menu. The living environment consideration unit also collects and analyzes data on the patient's daily life in order to provide advice based on the patient's living environment. For example, it may provide health management advice based on the amount of exercise and sleep patterns. This makes it possible to provide advice that takes into account the patient's living environment.

[0061] The AI ​​avatar is equipped with an expert collaboration unit that collaborates with different medical experts to conduct examinations from multiple perspectives. The expert collaboration unit, for example, builds a system in which the AI ​​avatar collaborates with different medical experts to conduct examinations from multiple perspectives. For example, an internist and a dermatologist conduct an examination together. The expert collaboration unit also collaborates with doctors of different specialties to conduct examinations according to the patient's symptoms. For example, a patient at risk of heart disease is examined by a cardiologist. The expert collaboration unit also collaborates with multiple medical experts to conduct examinations based on the patient's symptoms and medical history. For example, an internist and a dermatologist conduct an examination together. This makes it possible to conduct examinations from multiple perspectives.

[0062] The AI ​​avatar includes an examination relaxation unit that uses an emotion estimation function to introduce relaxation techniques to reduce the anxiety and tension that patients feel during examinations. The examination relaxation unit, for example, uses the emotion estimation function to introduce relaxation techniques to reduce the anxiety and tension that patients feel during examinations. For example, relaxing music may be played in the background. The examination relaxation unit also analyzes the patient's emotional state in real time and builds a system that applies relaxation techniques. For example, it may provide deep breathing instructions or relaxing videos. The examination relaxation unit also uses the emotion estimation function to provide an environment where the patient can relax. For example, it may adjust lighting and sound effects to help the patient relax. This reduces the patient's anxiety and tension.

[0063] The AI ​​avatar includes a privacy consideration unit that analyzes the emotional state of the patient and provides special consideration to patients who have privacy concerns. For example, the privacy consideration unit analyzes the emotional state of the patient and provides special consideration to patients who have privacy concerns. For example, if the patient is feeling anxious, it responds in a way that gives them a sense of security. The privacy consideration unit also builds a system that provides special consideration to patients who have privacy concerns based on the emotion analysis. For example, if the patient values ​​privacy, it encrypts the content of the conversation. The privacy consideration unit also analyzes the emotional state of the patient in real time and provides special consideration to patients who have privacy concerns. For example, if the patient values ​​privacy, it keeps the content of the conversation private. This makes it possible to provide special consideration to patients who have privacy concerns.

[0064] The AI ​​avatar includes a security protocol unit that implements advanced security protocols to encrypt patient data and prevent access by third parties. The security protocol unit, for example, encrypts patient data and implements advanced security protocols to prevent access by third parties. For example, AES-256 encryption technology is used. The security protocol unit also combines data encryption and security protocols to build a system that protects patient privacy. For example, the SSL / TLS protocol is used when sending and receiving data. The security protocol unit also encrypts patient data and strengthens access control to prevent unauthorized access by third parties. For example, two-factor authentication is implemented. This allows patient data to be safely protected.

[0065] The AI ​​avatar is equipped with an access management unit that provides an interface that allows patients to manage access rights to their own data. The access management unit provides, for example, an interface that allows patients to manage access rights to their own data. For example, it is equipped with functions that allow data to be viewed, modified, and deleted. The access management unit also provides a dashboard that allows patients to manage access rights to their own data. For example, it adds a function that allows data sharing settings and access history to be checked. The access management unit also develops an interface that allows patients to manage access rights to their own data, strengthening privacy protection. For example, it provides a function that allows data access rights to be set in detail. This allows patients to manage their own data.

[0066] The AI ​​avatar is equipped with a privacy education unit that educates patients about privacy protection, enabling them to safely manage their own data. The privacy education unit, for example, educates patients about privacy protection, enabling them to safely manage their own data. For example, it explains the importance of data encryption and password management. The privacy education unit also provides educational content on privacy protection, enabling patients to acquire the knowledge to safely manage their own data. For example, it provides online seminars and tutorials. The privacy education unit also explains the importance of privacy protection to patients and suggests specific measures. For example, it recommends how to set up two-factor authentication and install security software. This allows patients to safely manage their own data.

[0067] The AI ​​avatar includes an anonymous examination unit that performs examinations using anonymized data to protect privacy. The anonymous examination unit, for example, builds a system in which the AI ​​avatar performs examinations using anonymized data to protect privacy. For example, the anonymous examination unit anonymizes data so that individuals cannot be identified. The anonymous examination unit also anonymizes patient data and performs examinations while protecting privacy. For example, personal information such as name and address is deleted and anonymized data is used. The anonymous examination unit also protects patient privacy by performing examinations using anonymized data. For example, data related to the examination content and symptoms is anonymized. This makes it possible to perform examinations while protecting patient privacy.

[0068] The AI ​​avatar is equipped with a privacy concern response unit that uses an emotion estimation function to immediately suggest countermeasures when a patient feels anxiety about privacy. The privacy concern response unit, for example, uses the emotion estimation function to build a system that immediately suggests countermeasures when a patient feels anxiety about privacy. For example, it suggests setting data encryption or access restrictions. The privacy concern response unit also analyzes the patient's emotional state in real time and suggests appropriate countermeasures when a patient feels anxiety about privacy. For example, it suggests setting data to private or strengthening security. The privacy concern response unit also uses the emotion estimation function to immediately suggest countermeasures when a patient feels anxiety about privacy. For example, it provides information about privacy protection to give a sense of security. This makes it possible to immediately suggest countermeasures when a patient feels anxiety about privacy.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The virtual teacher system can also be equipped with a lifestyle monitoring unit that monitors the patient's lifestyle and supports health management. For example, it can record the patient's exercise and dietary habits and evaluate their health condition. The lifestyle monitoring unit can also analyze the patient's sleep patterns and provide advice on improving sleep quality. Furthermore, the lifestyle monitoring unit can measure the patient's stress level and suggest specific measures for stress management. This makes it possible to manage health by taking into account the patient's overall lifestyle.

[0071] The virtual doctor system can also be equipped with an emotional examination unit that analyzes the patient's emotional state in real time and selects an examination method according to the emotion. For example, if the patient is feeling anxious, it will speak to them in a gentle tone. The emotional examination unit will also build a system that selects an examination method according to the patient's emotional state based on the emotion analysis. For example, if the patient is feeling stressed, it will provide advice to help them relax. The emotional examination unit will also analyze the patient's emotional state in real time and select an appropriate examination method. For example, if the patient is feeling angry, the AI ​​avatar will be adjusted to respond calmly. This makes it possible to select an examination method according to the patient's emotions.

[0072] The virtual doctor system can also be equipped with a diagnostic accuracy improvement unit that integrates a patient's past medical history and health data to make a more accurate diagnosis. For example, a system can be constructed that integrates a patient's past medical history and health data to make a more accurate diagnosis. For example, a diagnosis can be made based on past medical history and test results. The diagnostic accuracy improvement unit can also integrate health data and make a diagnosis based on the patient's symptoms and medical history. For example, a diagnosis can be made based on blood pressure and blood sugar data. The diagnostic accuracy improvement unit can also analyze the patient's symptoms and medical history based on past medical history to make a more accurate diagnosis. For example, a diagnosis can be made by referring to past medical examination results. This enables more accurate diagnoses.

[0073] The virtual doctor system can also be equipped with a living environment consideration unit that provides advice taking into account the patient's living environment. For example, an AI avatar can consider the patient's living environment and provide appropriate advice. For example, if the air quality in the home is poor, it can suggest the use of an air purifier. The living environment consideration unit can also analyze the patient's eating habits and provide advice on healthy eating. For example, it can suggest nutritionally balanced meal menus. The living environment consideration unit can also collect and analyze data on the patient's daily life in order to provide advice based on the patient's living environment. For example, it can provide health management advice based on the amount of exercise and sleep patterns. This makes it possible to provide advice that takes into account the patient's living environment.

[0074] The virtual doctor system can also be equipped with an expert collaboration unit that collaborates with different medical professionals to conduct examinations from multiple perspectives. For example, a system can be built in which an AI avatar collaborates with different medical professionals to conduct examinations from multiple perspectives. For example, an internist and a dermatologist may conduct an examination jointly. The expert collaboration unit can also collaborate with doctors from different specialties to conduct examinations based on the patient's symptoms. For example, a patient at risk of heart disease would be examined by a cardiologist. The expert collaboration unit can also collaborate with multiple medical professionals to conduct examinations based on the patient's symptoms and medical history. For example, an internist and a dermatologist may conduct an examination jointly. This makes it possible to conduct examinations from multiple perspectives.

[0075] The virtual teacher system can also be equipped with a relaxation section that uses an emotion estimation function to automatically select a conversation style that will help the patient relax and reduce stress. For example, the emotion estimation function can be used to automatically select a conversation style that will help the patient relax. For example, if the patient is nervous, the relaxation section can speak to them in a calm tone. The relaxation section can also analyze the patient's emotional state in real time and select a conversation style that will reduce stress. For example, it can play relaxing music in the background. The relaxation section can also use the emotion estimation function to provide an environment that will help the patient relax. For example, it can adjust lighting and sound effects to help the patient relax. This can reduce the patient's stress.

[0076] The virtual doctor system can also be equipped with a privacy-conscious unit that analyzes the patient's emotional state and provides special consideration to patients with privacy concerns. For example, an AI avatar can analyze the patient's emotional state and provide special consideration to patients with privacy concerns. For example, if the patient is feeling anxious, it can respond in a way that makes them feel at ease. The privacy-conscious unit can also build a system based on emotion analysis that provides special consideration to patients with privacy concerns. For example, if the patient values ​​privacy, it can encrypt the content of the conversation. The privacy-conscious unit can also analyze the patient's emotional state in real time and provide special consideration to patients with privacy concerns. For example, if the patient values ​​privacy, it can keep the content of the conversation private. This makes it possible to provide special consideration to patients with privacy concerns.

[0077] The virtual doctor system can also be equipped with a security protocol unit that implements advanced security protocols to encrypt patient data and prevent access by third parties. For example, AES-256 encryption technology is used. The security protocol unit also combines data encryption and security protocols to build a system that protects patient privacy. For example, the SSL / TLS protocol is used when sending and receiving data. The security protocol unit also encrypts patient data and strengthens access control to prevent unauthorized access by third parties. For example, two-factor authentication is implemented. This allows patient data to be safely protected.

[0078] The virtual doctor system can also be equipped with an access management unit that provides an interface that allows patients to manage access rights to their own data. For example, it provides an interface that allows patients to manage access rights to their own data. For example, it provides functions that allow them to view, modify, and delete data. The access management unit also provides a dashboard that allows patients to manage access rights to their own data. For example, it adds functions that allow them to check data sharing settings and access history. The access management unit also develops an interface that allows patients to manage access rights to their own data, strengthening privacy protection. For example, it provides a function that allows them to set data access rights in detail. This allows patients to manage their own data.

[0079] The virtual teacher system can also be equipped with a privacy concern response unit that uses the emotion estimation function to immediately suggest countermeasures when a patient feels anxious about privacy. For example, a system can be built that uses the emotion estimation function to immediately suggest countermeasures when a patient feels anxious about privacy. For example, it can suggest data encryption or access restriction settings. The privacy concern response unit can also analyze the patient's emotional state in real time and suggest appropriate countermeasures when a patient feels anxious about privacy. For example, it can suggest making data private or strengthening security. The privacy concern response unit can also use the emotion estimation function to immediately suggest countermeasures when a patient feels anxious about privacy. For example, it can provide information about privacy protection to give a sense of security. This makes it possible to immediately suggest countermeasures when a patient feels anxious about privacy.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The AI ​​avatar provides appropriate answers to the patient's questions and symptoms. For example, if a patient asks, "My hair has been thinning recently. What should I do?", the AI ​​avatar will use its knowledge of AGA treatment to respond, "There are several ways to treat AGA. For example, you can use medications such as finasteride and minoxidil." Step 2: The machine learning unit trains the AI ​​avatar with medical knowledge. For example, the generative AI learns diagnostic protocols and treatment guidelines and generates appropriate answers to patient questions. Step 3: In the online consultation department, an AI avatar will conduct an online consultation with the patient. For example, if a patient says, "I've recently started experiencing symptoms of erectile dysfunction," the AI ​​avatar will respond, based on its knowledge of ED treatment, by saying, "There are several ways to treat ED. For example, you can use medications such as sildenafil and tadalafil." Step 4: Privacy Protection: The AI ​​avatar protects patients' privacy. For example, when patients consult about sensitive issues such as erectile dysfunction (ED) or androgenetic alopecia (AGA), the AI ​​avatar can handle the consultation online, allowing patients to avoid face-to-face consultations.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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. AI avatars and a machine learning unit that trains the AI ​​avatar to learn medical knowledge; an online consultation unit in which the AI ​​avatar interacts with patients online and conducts consultations; A privacy protection unit that protects the patient's privacy. A system characterized by:

2. The AI ​​avatar is Equipped with an emotion estimation unit that analyzes the patient's emotional state in real time and responds accordingly 2. The system of claim 1.

3. The AI ​​avatar is Equipped with a personalized advice department that provides personalized advice based on each patient's individual medical history and lifestyle.

2. The system of claim 1.

4. The AI ​​avatar is Equipped with a non-verbal communication analysis unit that analyzes the patient's non-verbal communication to enable more natural dialogue 2. The system of claim 1.

5. The AI ​​avatar is We have a specialized field application department that can handle a wide range of health consultations across different fields.

2. The system of claim 1.

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