System

Abstract

An object of a system according to an embodiment is to accurately determine whether a user should call an ambulance.SOLUTION: A system includes a voice input part, an analysis part, a diagnosis part, a determination part, and a notification part. The voice input unit acquires voice data of a user. The analysis unit analyzes the voice data acquired by the voice input unit. The diagnosis unit performs expert-level diagnosis based on the data analyzed by the analysis unit. The determination unit determines whether or not to call an ambulance based on the diagnosis result obtained by the diagnosis unit. The notification unit notifies the determination result obtained by the determination unit.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] With conventional technology, it is difficult to determine whether or not to call an ambulance, and there is a risk of inappropriate use due to incorrect judgment.

[0005] The system according to the embodiment aims to enable a user to accurately determine whether or not to call an ambulance. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice input unit, an analysis unit, a diagnosis unit, a determination unit, and a notification unit. The voice input unit acquires voice data of a user. The analysis unit analyzes the voice data acquired by the voice input unit. The diagnosis unit performs an expert-level diagnosis based on the data analyzed by the analysis unit. The determination unit determines whether an ambulance should be called based on the diagnosis result obtained by the diagnosis unit. The notification unit notifies the user of the determination result obtained by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately determine whether the user should call an ambulance. [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) A healthcare system according to an embodiment of the present invention is a system in which a user inputs symptoms by voice, and a generation AI performs an expert-level diagnosis and determines whether an ambulance should be called. This allows the healthcare system to allow a user to input symptoms by voice, receive an expert-level diagnosis and determine whether an ambulance should be called, and notify the user of the results.

[0029] The healthcare system according to the embodiment includes a voice input unit, an analysis unit, a diagnosis unit, a determination unit, and a notification unit. The voice input unit acquires voice data from a user. For example, the voice data may be collected using a smartphone microphone. Alternatively, the voice data may be acquired using a smartwatch microphone. The voice input unit may also use noise canceling technology to remove ambient sounds and acquire clear voice data. The analysis unit analyzes the voice data acquired by the voice input unit. For example, the analysis unit converts the voice data into text data using voice recognition technology. The analysis unit may also analyze the content of the voice data using natural language processing technology. The analysis unit may also analyze emotions in the voice data. The diagnosis unit performs an expert-level diagnosis based on the data analyzed by the analysis unit. For example, the generation AI may perform a diagnosis based on previously learned medical data and guidelines. The generation AI may also comprehensively consider the user's symptoms, medical history, and personal information to provide the most appropriate diagnosis. The generation AI may also refer to the latest medical research data and papers in real time to improve the accuracy of the diagnosis. The determination unit determines whether to call an ambulance based on the diagnosis result obtained by the diagnosis unit. For example, if the possibility of a heart attack is high, the generation AI instructs the user to call an ambulance immediately. Alternatively, if the symptoms are mild, the generation AI may determine that an ambulance is not necessary and guide the user to the appropriate contact information. The determination unit may also make optimal decisions by referring to the local ambulance operation status and hospital admission status in real time. The notification unit notifies the user of the determination result obtained by the determination unit. For example, the notification unit may notify the user of the result via SMS or email. The notification unit may also notify the user's family or emergency contacts of the result. The notification unit may also cooperate with local medical institutions and emergency services to ensure a prompt response. This allows the healthcare system according to the embodiment to allow the user to input symptoms via voice, receive an expert-level diagnosis and determine whether an ambulance should be called, and notify the user of the result. For example, if the user complains of chest pain, the generation AI may diagnose the possibility of a heart attack and instruct the user to immediately call an ambulance.If symptoms are mild, the app will provide contact information for nearby hospitals and clinics, and will notify the user's family and emergency contacts of the results to encourage prompt action.

[0030] The analysis unit can simultaneously collect biometric data such as facial expressions, body temperature, and heart rate in addition to voice data, and perform comprehensive analysis. For example, when a user describes their symptoms into a smartphone or smartwatch, the analysis unit simultaneously collects biometric data such as facial expressions, body temperature, and heart rate using a camera and sensors. This allows the generation AI to integrate the voice data and biometric data for analysis. The analysis unit also utilizes the smartwatch's sensors to monitor the user's heart rate and body temperature in real time. This data is input into the generation AI, which then combines it with the voice data for comprehensive analysis. Furthermore, when the user describes their symptoms, the analysis unit analyzes facial expressions using the smartphone's camera and simultaneously collects data obtained from the thermometer and heart rate monitor. The generation AI then performs more accurate analysis based on this data. This allows for more accurate diagnoses by integrating and analyzing voice data and biometric data.

[0031] The analysis unit can perform more accurate analysis by referring to the user's past health condition and lifestyle habit data. For example, when the user inputs symptoms, the analysis unit refers to past health checkup results and medical records, and the generation AI performs analysis based on that data. For example, past blood pressure and electrocardiogram data are taken into consideration. The analysis unit also collects the user's lifestyle habit data (e.g., amount of exercise and dietary content), and the generation AI refers to that data when symptoms are input and performs analysis. This takes into account the impact of lifestyle habits on symptoms. The analysis unit also uses the user's past medical history and medical history data to perform more accurate analysis when symptoms are input. For example, if the user has suffered from heart disease in the past, this information is taken into consideration when performing analysis. This enables more accurate analysis by referring to past health condition and lifestyle habit data.

[0032] The voice input unit allows users to input symptoms not only by voice, but also by text, images, and videos, which the generation AI can analyze. For example, when users input symptoms, the voice input unit allows them to input text as well as voice, and the generation AI analyzes the text data. For example, the user inputs symptoms in sentences. The voice input unit also allows users to upload images and videos when inputting symptoms, and the generation AI analyzes the data. For example, it analyzes photos of a skin rash or videos of a cough. The voice input unit also allows users to select voice, text, images, or videos when inputting symptoms, and the generation AI integrates and analyzes the data. For example, the user can attach an image while explaining the symptoms through voice. This allows users to input symptoms by selecting voice, text, images, or videos.

[0033] The analysis unit allows the generation AI to perform comparative analysis based on similar case data from other users. For example, when a user inputs symptoms, the analysis unit references similar case data from other users and the generation AI compares that data to perform analysis. For example, the analysis is based on the data of other users with the same symptoms. Furthermore, when symptoms are input, the analysis unit references data from users who have reported similar symptoms in the past and the generation AI compares that data to perform analysis. For example, the frequency of occurrence of the same symptoms and treatment results are taken into consideration. Furthermore, the analysis unit collects similar case data from other users and, when symptoms are input, the generation AI performs comparative analysis based on that data. For example, the age and gender of users with the same symptoms are taken into consideration. This allows for more reliable analysis by referencing similar case data from other users.

[0034] The diagnostic unit can improve the accuracy of diagnosis by referring to the latest medical research data or papers in real time. For example, when the generating AI makes a diagnosis, the diagnostic unit refers to the latest medical research databases and papers in real time and makes a diagnosis based on that information. For example, it incorporates new treatments and diagnostic criteria. Furthermore, during the diagnostic process, the diagnostic unit automatically collects the latest medical research data and improves the accuracy of diagnosis based on that data. For example, it refers to new case reports and clinical trial results. Furthermore, when the generating AI makes a diagnosis, the diagnostic unit refers to the latest medical papers and guidelines in real time and makes a diagnosis based on that information. For example, it incorporates new diagnostic criteria and treatments. In this way, by referring to the latest medical research data and papers, the accuracy of diagnosis can be improved.

[0035] The diagnosis unit can provide the most reliable diagnosis by simulating the opinions of multiple experts. For example, when the generation AI issues a diagnosis result, the diagnosis unit simulates the opinions of multiple experts and provides the most reliable diagnosis from among them. For example, it integrates the diagnosis results of different experts. Furthermore, during the diagnosis process, the diagnosis unit simulates the opinions of multiple experts and provides the most appropriate diagnosis based on the results. For example, it weights and integrates the expert opinions. Furthermore, when the generation AI makes a diagnosis, the diagnosis unit simulates the opinions of multiple experts and provides the most reliable diagnosis from among them. For example, it compares the diagnosis results of different experts and selects the optimal diagnosis. In this way, by simulating the opinions of multiple experts, a highly reliable diagnosis can be provided.

[0036] The diagnosis unit can automatically share the diagnosis results with different medical institutions or experts to obtain second opinions. For example, the diagnosis unit builds a system that automatically shares the diagnosis results with different medical institutions or experts to obtain second opinions. For example, the diagnosis results are shared by email or on a dedicated platform. Furthermore, after the generative AI issues a diagnosis result, the diagnosis unit automatically sends the result to different medical institutions or experts to obtain second opinions. For example, the diagnosis unit collects expert opinions and provides them to the user. Furthermore, the diagnosis unit develops a system that automatically shares the diagnosis results with different medical institutions or experts to obtain second opinions. For example, the diagnosis results are shared on the cloud and expert opinions are collected. In this way, by sharing the diagnosis results with different medical institutions or experts, second opinions can be obtained and the reliability of the diagnosis can be improved.

[0037] The diagnostic unit can automatically notify the user's family or emergency contacts of the diagnostic results, encouraging a prompt response. The diagnostic unit, for example, builds a system that automatically notifies the user's family or emergency contacts of the diagnostic results, encouraging a prompt response. For example, it sends the diagnostic results via SMS or email. In addition, after the generation AI issues a diagnostic result, the diagnostic unit automatically notifies the user's family or emergency contacts of the result, encouraging a prompt response. For example, it notifies the emergency contacts by phone. In addition, the diagnostic unit develops a system that automatically notifies the user's family or emergency contacts of the diagnostic results, encouraging a prompt response. For example, it notifies the family members of the diagnostic results via a dedicated app and sends an alert to the family members. This makes it possible to automatically notify the family members and emergency contacts of the diagnostic results, encouraging a prompt response.

[0038] The judgment unit can make optimal decisions by referring to the operating status of local ambulances or the hospital acceptance status in real time. For example, when the generating AI determines whether to call an ambulance, the judgment unit refers to the operating status of local ambulances in real time and makes optimal decisions. For example, it takes into account ambulance waiting times and operation rates. The judgment unit also refers to the acceptance status of local hospitals in real time and makes optimal decisions by referring to the accepting status of local hospitals in real time. For example, it takes into account the availability of hospital beds and emergency response capabilities. The judgment unit also refers to the operating status of local ambulances and the acceptance status of hospitals in an integrated manner when the generating AI determines whether to call an ambulance and makes optimal decisions by referring to the arrival time of the ambulance and the acceptance capacity of the hospital. This allows optimal decisions to be made by referring to the operating status of local ambulances and the acceptance status of hospitals in real time.

[0039] The judgment unit can improve the reliability of its judgment based on data from similar past cases. For example, when the generation AI determines whether to call an ambulance, the judgment unit refers to data from similar past cases and improves the reliability of its judgment based on that data. For example, it considers the results of cases where an ambulance was called for similar symptoms. The judgment unit also collects data from similar past cases and uses that data to determine whether an ambulance should be called. For example, it refers to the treatment results when an ambulance was called for the same symptoms. The judgment unit also refers to data from similar past cases when the generation AI determines whether an ambulance should be called and improves the reliability of its judgment based on that data. For example, it considers the survival rate when an ambulance was called for similar symptoms. In this way, the reliability of the judgment can be improved by referring to data from similar past cases.

[0040] The judgment unit can coordinate the judgment result with local medical institutions or emergency services to achieve a rapid response. For example, the judgment unit coordinates the judgment result, for example, when the generation AI determines whether an ambulance should be called, with local medical institutions or emergency services to achieve a rapid response. For example, it automatically sends the diagnosis result to a medical institution. The judgment unit also coordinates the judgment result with local emergency services to build a system to achieve a rapid response. For example, it automatically arranges for an ambulance. The judgment unit also coordinates the judgment result, for example, when the generation AI determines whether an ambulance should be called, with local medical institutions or emergency services to achieve a rapid response. For example, it shares the diagnosis result in real time. This allows the judgment result to be coordinated with local medical institutions and emergency services to achieve a rapid response.

[0041] The judgment unit can provide appropriate medical expense support based on the judgment result and the user's health insurance information. For example, the judgment unit links the result of the generation AI's judgment on whether to call an ambulance with the user's health insurance information and provides appropriate medical expense support. For example, it automatically checks the insurance coverage. The judgment unit also links the judgment result with the user's health insurance information and builds a system that provides appropriate medical expense support. For example, it automatically contacts an insurance company. The judgment unit also links the result of the generation AI's judgment on whether to call an ambulance with the user's health insurance information and provides appropriate medical expense support. For example, it checks the insurance coverage in real time. In this way, by linking the judgment result with the health insurance information, appropriate medical expense support can be provided.

[0042] The notification unit can learn from past data on improper use of ambulances and propose preventive measures to prevent improper use. For example, the generation AI learns from past data on improper use of ambulances and proposes preventive measures to prevent improper use based on that data. For example, it proposes appropriate responses based on past cases. Furthermore, if the risk of improper use is high, the notification unit has the generation AI propose preventive measures based on past data. For example, it analyzes past patterns of improper use and proposes appropriate responses. Furthermore, the notification unit has the generation AI learn from past data on improper use and proposes preventive measures to prevent improper use based on that data. For example, it proposes appropriate responses based on past cases. In this way, by learning from past data on improper use and proposing preventive measures, it is possible to prevent improper use of ambulances.

[0043] When there is a high risk of improper use, the notification unit can provide the user with educational content to encourage appropriate use. For example, when there is a high risk of improper use, the notification unit has the generation AI provide educational content to the user to encourage appropriate use. For example, the generation AI can provide a video explaining how to properly use an ambulance. Furthermore, when there is a high risk of improper use, the notification unit has the generation AI provide educational content to the user to encourage appropriate use. For example, the generation AI can provide guidelines explaining the standards for using an ambulance. Furthermore, when there is a high risk of improper use, the notification unit has the generation AI provide educational content to the user to encourage appropriate use. For example, the generation AI can provide an infographic explaining how to properly use an ambulance. In this way, by providing educational content when there is a high risk of improper use, appropriate use can be encouraged.

[0044] If there is a high risk of improper use, the notification unit can work with local medical institutions or emergency services to encourage appropriate action. For example, if the generation AI detects a high risk of improper use, the notification unit can work with local medical institutions or emergency services to encourage appropriate action. For example, it can automatically send diagnosis results to medical institutions. The notification unit can also build a system where, if there is a high risk of improper use, the generation AI can work with local emergency services to encourage appropriate action. For example, it can automatically arrange for an ambulance. The notification unit can also work with local medical institutions or emergency services to encourage appropriate action if there is a high risk of improper use. For example, it can share diagnosis results in real time. This makes it possible to work with local medical institutions or emergency services to encourage appropriate action if there is a high risk of improper use.

[0045] If the risk of improper use is high, the notification unit can notify the user's family or emergency contact and request support. For example, the notification unit will build a system in which, if the generation AI detects a high risk of improper use, it notifies the user's family or emergency contact and requests support. For example, it will send the diagnosis results via SMS or email. Furthermore, if the risk of improper use is high, the notification unit will notify the user's family or emergency contact and request support. For example, it will notify the emergency contact by phone. Furthermore, the notification unit will develop a system in which, if the generation AI detects a high risk of improper use, it notifies the user's family or emergency contact and requests support. For example, it will notify the family through a dedicated app and send an alert to the family. This makes it possible to request support and encourage appropriate action by notifying the family or emergency contact when there is a high risk of improper use.

[0046] The analysis unit can improve the accuracy of learning based on the latest medical database when learning the user's personal information and medical history. For example, when the generation AI learns the user's personal information and medical history, the analysis unit refers to the latest medical database and improves the accuracy of learning based on that information. For example, new treatments and diagnostic criteria are incorporated. In addition, when the generation AI learns the user's personal information and medical history, the analysis unit automatically collects the latest medical database and improves the accuracy of learning based on that data. For example, new case reports and clinical trial results are referenced. In addition, when the generation AI learns the user's personal information and medical history, the analysis unit refers to the latest medical database in real time and improves the accuracy of learning based on that information. For example, new diagnostic criteria and treatments are incorporated. In this way, by referring to the latest medical database, the learning accuracy of the user's personal information and medical history can be improved.

[0047] The analysis unit also learns the user's lifestyle and environmental data, enabling a more comprehensive understanding of the health condition. For example, the generation AI collects the user's lifestyle data (e.g., amount of exercise and dietary content) and performs learning based on that data. This takes into account the impact of lifestyle on health condition. The analysis unit also collects the user's environmental data (e.g., living environment and work environment) and the generation AI performs learning based on that data. This takes into account the impact of the environment on health condition. The analysis unit also learns the user's lifestyle and environmental data and enables a more comprehensive understanding of the health condition. For example, the health condition is evaluated taking into account the amount of exercise, dietary content, and living environment. This makes it possible to understand the health condition more comprehensively by learning the user's lifestyle and environmental data.

[0048] The analysis unit can share the user's personal information and medical history with different medical institutions or experts to provide comprehensive medical support. For example, the generation AI can share the user's personal information and medical history with different medical institutions to provide comprehensive medical support. For example, diagnostic results and treatment history can be shared in an electronic medical record. The analysis unit can also share the user's personal information and medical history with experts to build a system that provides comprehensive medical support. For example, it can collect expert opinions and provide them to the user. The analysis unit can also share the user's personal information and medical history with different medical institutions or experts to provide comprehensive medical support. For example, it can share diagnostic results on the cloud and collect expert opinions. This allows the generation AI to share the user's personal information and medical history with different medical institutions or experts to provide comprehensive medical support.

[0049] The analysis unit allows the generation AI to propose an individual health management plan based on the user's personal information and medical history. For example, the analysis unit allows the generation AI to propose an individual health management plan based on the user's personal information and medical history. For example, the analysis unit provides advice on diet and exercise. The analysis unit also builds a system in which the generation AI proposes an individual health management plan based on the user's personal information and medical history. For example, the analysis unit proposes a schedule for regular health checks. The analysis unit also allows the generation AI to propose an individual health management plan based on the user's personal information and medical history. For example, the analysis unit proposes preventive measures and treatments for specific diseases. This allows more appropriate health management to be achieved by proposing an individual health management plan based on the user's personal information and medical history.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The healthcare system may further include a location information acquisition unit. The location information acquisition unit acquires the user's current location and can use this information to provide a diagnosis or arrange for an ambulance. For example, when a user inputs symptoms, the location information acquisition unit acquires location information and takes into account the nearest medical institution and the arrival time of an ambulance. The location information acquisition unit can also optimally allocate local medical resources based on the user's location information. For example, it can refer to the ambulance operation status and hospital acceptance status in real time to select the most appropriate medical institution. The location information acquisition unit can also suggest the optimal route in an emergency based on the user's location information. For example, it can guide the user to the shortest route taking traffic conditions into consideration. In this way, utilizing location information enables faster and more appropriate medical treatment.

[0052] The healthcare system may further include a preventive measure suggestion unit. The preventive measure suggestion unit can suggest preventive measures against illness based on the user's health data. For example, it may analyze the user's lifestyle data and suggest improvements to lack of exercise and diet. The preventive measure suggestion unit can also predict the risk of a specific disease based on the user's past health checkup results and suggest preventive measures. For example, if the user has high blood pressure, it may suggest limiting salt intake. The preventive measure suggestion unit can also suggest preventive measures against allergies and infectious diseases based on the user's environmental data. For example, it may suggest wearing a mask when going out during pollen season. In this way, the preventive measures suggested can contribute to maintaining the user's health.

[0053] The healthcare system may further include a reminder unit. The reminder unit can remind the user to undergo regular health checkups and take medication. For example, the reminder unit may remind the user to undergo regular health checkups. The reminder unit can also manage the user's medication schedule and notify the user of medication times. For example, the reminder unit may remind the user to take medication in the morning and evening. The reminder unit can also remind the user of exercise and meal timings based on the user's health management plan. For example, the reminder unit may notify the user of daily walking and meal times. In this way, the reminder function can be utilized to support the user's health management.

[0054] The healthcare system may further include a community collaboration unit. The community collaboration unit may provide information to enable users to participate in local health events and support groups. For example, it may notify users of information about local health seminars and exercise events. The community collaboration unit may also support users in connecting with other users who have the same symptoms or health goals. For example, it may provide online forums and chat groups. The community collaboration unit may also strengthen collaboration with local medical institutions and specialists to enable users to receive the support they need. For example, it may provide a system for introducing local medical institutions and making reservations. This may strengthen collaboration with the community and support users' health management.

[0055] The healthcare system may further include a feedback collection unit. The feedback collection unit may collect feedback from users and use it to improve the system. For example, users may provide their opinions on the usability of the system or on diagnostic results. The feedback collection unit may also improve the functions and interface of the system based on the user feedback. For example, new functions may be added in response to user requests. The feedback collection unit may also analyze the user feedback and provide data to improve the accuracy and reliability of the system. For example, the feedback collection unit may improve an algorithm to improve the accuracy of diagnostic results. In this way, the quality of the system may be improved by utilizing user feedback.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The voice input unit acquires the user's voice data. For example, voice data can be collected using a microphone on a smartphone or a smartwatch. It is also possible to use noise canceling technology to remove ambient noise and acquire clear voice data. Step 2: The analysis unit analyzes the voice data acquired by the voice input unit. For example, the analysis unit converts the voice data into text data using voice recognition technology and analyzes the content of the voice data using natural language processing technology. It can also analyze emotions in the voice data. Step 3: The diagnostic unit performs an expert-level diagnosis based on the data analyzed by the analytical unit. For example, the generative AI performs a diagnosis based on pre-trained medical data and guidelines, comprehensively considering the user's symptoms, medical history, and personal information to provide the most appropriate diagnosis. It can also refer to the latest medical research data and papers in real time to improve the accuracy of the diagnosis. Step 4: The judgment unit determines whether to call an ambulance based on the diagnosis results obtained by the diagnosis unit. For example, the generation AI will instruct the user to call an ambulance immediately if there is a high possibility of a heart attack, or direct the user to the appropriate contact information if the symptoms are mild. It can also make optimal decisions by referring to the operating status of local ambulances and hospital admissions in real time. Step 5: The notification unit notifies the user of the judgment result obtained by the judgment unit. For example, the result can be notified to the user via SMS or email, and the result can also be notified to the user's family or emergency contacts. It can also cooperate with local medical institutions and emergency services to ensure a prompt response.

[0058] (Example 2) A healthcare system according to an embodiment of the present invention is a system in which a user inputs symptoms by voice, and a generation AI performs an expert-level diagnosis and determines whether an ambulance should be called. This allows the healthcare system to allow a user to input symptoms by voice, receive an expert-level diagnosis and determine whether an ambulance should be called, and notify the user of the results.

[0059] The healthcare system according to the embodiment includes a voice input unit, an analysis unit, a diagnosis unit, a determination unit, and a notification unit. The voice input unit acquires voice data from a user. For example, the voice data may be collected using a smartphone microphone. Alternatively, the voice data may be acquired using a smartwatch microphone. The voice input unit may also use noise canceling technology to remove ambient sounds and acquire clear voice data. The analysis unit analyzes the voice data acquired by the voice input unit. For example, the analysis unit converts the voice data into text data using voice recognition technology. The analysis unit may also analyze the content of the voice data using natural language processing technology. The analysis unit may also analyze emotions in the voice data. The diagnosis unit performs an expert-level diagnosis based on the data analyzed by the analysis unit. For example, the generation AI may perform a diagnosis based on previously learned medical data and guidelines. The generation AI may also comprehensively consider the user's symptoms, medical history, and personal information to provide the most appropriate diagnosis. The generation AI may also refer to the latest medical research data and papers in real time to improve the accuracy of the diagnosis. The determination unit determines whether to call an ambulance based on the diagnosis result obtained by the diagnosis unit. For example, if the possibility of a heart attack is high, the generation AI instructs the user to call an ambulance immediately. Alternatively, if the symptoms are mild, the generation AI may determine that an ambulance is not necessary and guide the user to the appropriate contact information. The determination unit may also make optimal decisions by referring to the local ambulance operation status and hospital admission status in real time. The notification unit notifies the user of the determination result obtained by the determination unit. For example, the notification unit may notify the user of the result via SMS or email. The notification unit may also notify the user's family or emergency contacts of the result. The notification unit may also cooperate with local medical institutions and emergency services to ensure a prompt response. This allows the healthcare system according to the embodiment to allow the user to input symptoms via voice, receive an expert-level diagnosis and determine whether an ambulance should be called, and notify the user of the result. For example, if the user complains of chest pain, the generation AI may diagnose the possibility of a heart attack and instruct the user to immediately call an ambulance.If symptoms are mild, the app will provide contact information for nearby hospitals and clinics, and will notify the user's family and emergency contacts of the results to encourage prompt action.

[0060] The analysis unit can simultaneously collect biometric data such as facial expressions, body temperature, and heart rate in addition to voice data, and perform comprehensive analysis. For example, when a user describes their symptoms into a smartphone or smartwatch, the analysis unit simultaneously collects biometric data such as facial expressions, body temperature, and heart rate using a camera and sensors. This allows the generation AI to integrate the voice data and biometric data for analysis. The analysis unit also utilizes the smartwatch's sensors to monitor the user's heart rate and body temperature in real time. This data is input into the generation AI, which then combines it with the voice data for comprehensive analysis. Furthermore, when the user describes their symptoms, the analysis unit analyzes facial expressions using the smartphone's camera and simultaneously collects data obtained from the thermometer and heart rate monitor. The generation AI then performs more accurate analysis based on this data. This allows for more accurate diagnoses by integrating and analyzing voice data and biometric data.

[0061] The analysis unit can perform more accurate analysis by referring to the user's past health condition and lifestyle habit data. For example, when the user inputs symptoms, the analysis unit refers to past health checkup results and medical records, and the generation AI performs analysis based on that data. For example, past blood pressure and electrocardiogram data are taken into consideration. The analysis unit also collects the user's lifestyle habit data (e.g., amount of exercise and dietary content), and the generation AI refers to that data when symptoms are input and performs analysis. This takes into account the impact of lifestyle habits on symptoms. The analysis unit also uses the user's past medical history and medical history data to perform more accurate analysis when symptoms are input. For example, if the user has suffered from heart disease in the past, this information is taken into consideration when performing analysis. This enables more accurate analysis by referring to past health condition and lifestyle habit data.

[0062] The analysis unit uses the emotion estimation function to analyze the user's emotional state and consider the impact of stress or anxiety on symptoms. For example, when the user describes their symptoms, the analysis unit analyzes their emotional state from their voice and facial expressions, and the generation AI considers the impact of stress or anxiety on their symptoms. For example, it analyzes changes in voice tone and facial expressions. The analysis unit also uses the emotion estimation function to monitor the user's emotional state in real time, and if stress or anxiety is high, it analyzes the symptoms taking that impact into account. For example, it reflects heart rate fluctuations in the analysis. The analysis unit also collects facial expressions and voice using the smartphone's camera and microphone to analyze the user's emotional state, and the generation AI considers the impact of stress and anxiety on symptoms. For example, it analyzes facial tension and voice tremors. This makes it possible to analyze the emotional state and consider the impact of stress and anxiety on symptoms.

[0063] The voice input unit allows users to input symptoms not only by voice, but also by text, images, and videos, which the generation AI can analyze. For example, when users input symptoms, the voice input unit allows them to input text as well as voice, and the generation AI analyzes the text data. For example, the user inputs symptoms in sentences. The voice input unit also allows users to upload images and videos when inputting symptoms, and the generation AI analyzes the data. For example, it analyzes photos of a skin rash or videos of a cough. The voice input unit also allows users to select voice, text, images, or videos when inputting symptoms, and the generation AI integrates and analyzes the data. For example, the user can attach an image while explaining the symptoms through voice. This allows users to input symptoms by selecting voice, text, images, or videos.

[0064] The analysis unit allows the generation AI to perform comparative analysis based on similar case data from other users. For example, when a user inputs symptoms, the analysis unit references similar case data from other users and the generation AI compares that data to perform analysis. For example, the analysis is based on the data of other users with the same symptoms. Furthermore, when symptoms are input, the analysis unit references data from users who have reported similar symptoms in the past and the generation AI compares that data to perform analysis. For example, the frequency of occurrence of the same symptoms and treatment results are taken into consideration. Furthermore, the analysis unit collects similar case data from other users and, when symptoms are input, the generation AI performs comparative analysis based on that data. For example, the age and gender of users with the same symptoms are taken into consideration. This allows for more reliable analysis by referencing similar case data from other users.

[0065] The analysis unit uses the emotion estimation function to analyze the emotions of the user when entering symptoms in real time and can provide appropriate advice. For example, when the user enters symptoms, the analysis unit uses the emotion estimation function to analyze the emotions in real time, and the generation AI provides advice according to those emotions. For example, if the user is feeling very anxious, a reassuring message is displayed. The analysis unit also uses the emotion estimation function to analyze the emotional state of the user when entering symptoms, and the generation AI provides appropriate advice based on those emotions. For example, if the user is feeling very stressed, the generation AI suggests ways to relax. The analysis unit also uses the emotion estimation function to analyze the emotions of the user when entering symptoms in real time, and the generation AI provides advice according to those emotions. For example, if the user is feeling very scared, the generation AI provides advice to stay calm. In this way, the user's emotions can be analyzed in real time and appropriate advice can be provided, thereby increasing the user's sense of security.

[0066] The diagnostic unit can improve the accuracy of diagnosis by referring to the latest medical research data or papers in real time. For example, when the generating AI makes a diagnosis, the diagnostic unit refers to the latest medical research databases and papers in real time and makes a diagnosis based on that information. For example, it incorporates new treatments and diagnostic criteria. Furthermore, during the diagnostic process, the diagnostic unit automatically collects the latest medical research data and improves the accuracy of diagnosis based on that data. For example, it refers to new case reports and clinical trial results. Furthermore, when the generating AI makes a diagnosis, the diagnostic unit refers to the latest medical papers and guidelines in real time and makes a diagnosis based on that information. For example, it incorporates new diagnostic criteria and treatments. In this way, by referring to the latest medical research data and papers, the accuracy of diagnosis can be improved.

[0067] The diagnosis unit can provide the most reliable diagnosis by simulating the opinions of multiple experts. For example, when the generation AI issues a diagnosis result, the diagnosis unit simulates the opinions of multiple experts and provides the most reliable diagnosis from among them. For example, it integrates the diagnosis results of different experts. Furthermore, during the diagnosis process, the diagnosis unit simulates the opinions of multiple experts and provides the most appropriate diagnosis based on the results. For example, it weights and integrates the expert opinions. Furthermore, when the generation AI makes a diagnosis, the diagnosis unit simulates the opinions of multiple experts and provides the most reliable diagnosis from among them. For example, it compares the diagnosis results of different experts and selects the optimal diagnosis. In this way, by simulating the opinions of multiple experts, a highly reliable diagnosis can be provided.

[0068] The diagnosis unit can use the emotion estimation function to analyze the user's emotional reaction to the diagnosis result and suggest appropriate follow-up. For example, when providing the diagnosis result, the diagnosis unit uses the emotion estimation function to analyze the user's emotional reaction and suggests appropriate follow-up based on the result. For example, if anxiety is strong, counseling is suggested. The diagnosis unit also analyzes the user's emotional reaction to the diagnosis result in real time and suggests appropriate follow-up based on the result. For example, if fear is strong, information to reassure the user is provided. The diagnosis unit also uses the emotion estimation function to analyze the user's emotional reaction to the diagnosis result and suggests appropriate follow-up based on the result. For example, if stress is high, methods to relax are suggested. In this way, the user's sense of security can be increased by analyzing the user's emotional reaction to the diagnosis result and suggesting appropriate follow-up.

[0069] The diagnosis unit can automatically share the diagnosis results with different medical institutions or experts to obtain second opinions. For example, the diagnosis unit builds a system that automatically shares the diagnosis results with different medical institutions or experts to obtain second opinions. For example, the diagnosis results are shared by email or on a dedicated platform. Furthermore, after the generative AI issues a diagnosis result, the diagnosis unit automatically sends the result to different medical institutions or experts to obtain second opinions. For example, the diagnosis unit collects expert opinions and provides them to the user. Furthermore, the diagnosis unit develops a system that automatically shares the diagnosis results with different medical institutions or experts to obtain second opinions. For example, the diagnosis results are shared on the cloud and expert opinions are collected. In this way, by sharing the diagnosis results with different medical institutions or experts, second opinions can be obtained and the reliability of the diagnosis can be improved.

[0070] The diagnostic unit can automatically notify the user's family or emergency contacts of the diagnostic results, encouraging a prompt response. The diagnostic unit, for example, builds a system that automatically notifies the user's family or emergency contacts of the diagnostic results, encouraging a prompt response. For example, it sends the diagnostic results via SMS or email. In addition, after the generation AI issues a diagnostic result, the diagnostic unit automatically notifies the user's family or emergency contacts of the result, encouraging a prompt response. For example, it notifies the emergency contacts by phone. In addition, the diagnostic unit develops a system that automatically notifies the user's family or emergency contacts of the diagnostic results, encouraging a prompt response. For example, it notifies the family members of the diagnostic results via a dedicated app and sends an alert to the family members. This makes it possible to automatically notify the family members and emergency contacts of the diagnostic results, encouraging a prompt response.

[0071] The diagnosis unit can use the emotion estimation function to analyze the user's emotions regarding the diagnosis result and provide counseling to give a sense of security. For example, when providing the diagnosis result, the diagnosis unit uses the emotion estimation function to analyze the user's emotions and provides counseling to give a sense of security based on the result. For example, if the user is highly anxious, the diagnosis unit suggests counseling by a specialist. The diagnosis unit also analyzes the user's emotions regarding the diagnosis result in real time and provides counseling to give a sense of security based on the result. For example, if the user is highly afraid, the diagnosis unit introduces the user to a psychological counselor. The diagnosis unit also uses the emotion estimation function to analyze the user's emotions regarding the diagnosis result and provides counseling to give a sense of security based on the result. For example, if the user is highly stressed, the diagnosis unit suggests a relaxation session. In this way, the user's sense of security can be increased by analyzing the user's emotions regarding the diagnosis result and providing counseling to give a sense of security.

[0072] The judgment unit can make optimal decisions by referring to the operating status of local ambulances or the hospital acceptance status in real time. For example, when the generating AI determines whether to call an ambulance, the judgment unit refers to the operating status of local ambulances in real time and makes optimal decisions. For example, it takes into account ambulance waiting times and operation rates. The judgment unit also refers to the acceptance status of local hospitals in real time and makes optimal decisions by referring to the accepting status of local hospitals in real time. For example, it takes into account the availability of hospital beds and emergency response capabilities. The judgment unit also refers to the operating status of local ambulances and the acceptance status of hospitals in an integrated manner when the generating AI determines whether to call an ambulance and makes optimal decisions by referring to the arrival time of the ambulance and the acceptance capacity of the hospital. This allows optimal decisions to be made by referring to the operating status of local ambulances and the acceptance status of hospitals in real time.

[0073] The judgment unit can improve the reliability of its judgment based on data from similar past cases. For example, when the generation AI determines whether to call an ambulance, the judgment unit refers to data from similar past cases and improves the reliability of its judgment based on that data. For example, it considers the results of cases where an ambulance was called for similar symptoms. The judgment unit also collects data from similar past cases and uses that data to determine whether an ambulance should be called. For example, it refers to the treatment results when an ambulance was called for the same symptoms. The judgment unit also refers to data from similar past cases when the generation AI determines whether an ambulance should be called and improves the reliability of its judgment based on that data. For example, it considers the survival rate when an ambulance was called for similar symptoms. In this way, the reliability of the judgment can be improved by referring to data from similar past cases.

[0074] The judgment unit can use the emotion estimation function to analyze the user's emotional state and provide advice to encourage calm judgment. For example, when the generation AI is determining whether to call an ambulance, the judgment unit uses the emotion estimation function to analyze the user's emotional state and provide advice to encourage calm judgment. For example, if the user is very anxious, the judgment unit encourages the user to take a deep breath. The judgment unit also analyzes the user's emotional state in real time and provides advice to encourage the generation AI to make calm judgment. For example, if the user is very scared, the judgment unit suggests ways to relax. The judgment unit also uses the emotion estimation function to analyze the user's emotional state and provides advice to encourage the generation AI to make calm judgment. For example, if the user is very stressed, the judgment unit provides advice to stay calm. In this way, the user's emotional state is analyzed and advice to encourage calm judgment is provided, allowing the user to make an appropriate decision.

[0075] The judgment unit can coordinate the judgment result with local medical institutions or emergency services to achieve a rapid response. For example, the judgment unit coordinates the judgment result, for example, when the generation AI determines whether an ambulance should be called, with local medical institutions or emergency services to achieve a rapid response. For example, it automatically sends the diagnosis result to a medical institution. The judgment unit also coordinates the judgment result with local emergency services to build a system to achieve a rapid response. For example, it automatically arranges for an ambulance. The judgment unit also coordinates the judgment result, for example, when the generation AI determines whether an ambulance should be called, with local medical institutions or emergency services to achieve a rapid response. For example, it shares the diagnosis result in real time. This allows the judgment result to be coordinated with local medical institutions and emergency services to achieve a rapid response.

[0076] The judgment unit can provide appropriate medical expense support based on the judgment result and the user's health insurance information. For example, the judgment unit links the result of the generation AI's judgment on whether to call an ambulance with the user's health insurance information and provides appropriate medical expense support. For example, it automatically checks the insurance coverage. The judgment unit also links the judgment result with the user's health insurance information and builds a system that provides appropriate medical expense support. For example, it automatically contacts an insurance company. The judgment unit also links the result of the generation AI's judgment on whether to call an ambulance with the user's health insurance information and provides appropriate medical expense support. For example, it checks the insurance coverage in real time. In this way, by linking the judgment result with the health insurance information, appropriate medical expense support can be provided.

[0077] The judgment unit can use the emotion estimation function to analyze the user's emotions regarding the judgment result and provide support to give a sense of security. For example, when the generation AI determines whether an ambulance should be called, the judgment unit uses the emotion estimation function to analyze the user's emotions and provide support to give a sense of security. For example, if anxiety is strong, the judgment unit can suggest counseling. The judgment unit can also analyze the user's emotions regarding the judgment result in real time and provide support to give a sense of security based on the results. For example, if fear is strong, the judgment unit can introduce the user to a psychological counselor. The judgment unit can also use the emotion estimation function to analyze the user's emotions regarding the judgment result and provide support to give a sense of security based on the results. For example, if stress is high, the judgment unit can suggest a relaxation session. In this way, the user's feelings regarding the judgment result can be analyzed and support to give a sense of security can be provided, thereby increasing the user's sense of security.

[0078] The notification unit can learn from past data on improper use of ambulances and propose preventive measures to prevent improper use. For example, the generation AI learns from past data on improper use of ambulances and proposes preventive measures to prevent improper use based on that data. For example, it proposes appropriate responses based on past cases. Furthermore, if the risk of improper use is high, the notification unit has the generation AI propose preventive measures based on past data. For example, it analyzes past patterns of improper use and proposes appropriate responses. Furthermore, the notification unit has the generation AI learn from past data on improper use and proposes preventive measures to prevent improper use based on that data. For example, it proposes appropriate responses based on past cases. In this way, by learning from past data on improper use and proposing preventive measures, it is possible to prevent improper use of ambulances.

[0079] When there is a high risk of improper use, the notification unit can provide the user with educational content to encourage appropriate use. For example, when there is a high risk of improper use, the notification unit has the generation AI provide educational content to the user to encourage appropriate use. For example, the generation AI can provide a video explaining how to properly use an ambulance. Furthermore, when there is a high risk of improper use, the notification unit has the generation AI provide educational content to the user to encourage appropriate use. For example, the generation AI can provide guidelines explaining the standards for using an ambulance. Furthermore, when there is a high risk of improper use, the notification unit has the generation AI provide educational content to the user to encourage appropriate use. For example, the generation AI can provide an infographic explaining how to properly use an ambulance. In this way, by providing educational content when there is a high risk of improper use, appropriate use can be encouraged.

[0080] The notification unit can use the emotion estimation function to analyze the user's emotional state and provide advice to relieve anxiety and tension. For example, the notification unit can use the emotion estimation function to analyze the emotional state of a user for whom the generation AI is at high risk of improper use and provide advice to relieve anxiety and tension. For example, the notification unit can suggest ways to relax. The notification unit can also use the emotion estimation function to analyze the user's emotional state in real time and provide advice to relieve anxiety and tension. For example, the notification unit can encourage deep breathing. The notification unit can also use the emotion estimation function to analyze the emotional state of a user for whom the generation AI is at high risk of improper use and provide advice to relieve anxiety and tension. For example, the notification unit can suggest relaxation techniques. In this way, the user's sense of security can be increased by using the emotion estimation function to analyze the user's emotional state and provide advice to relieve anxiety and tension.

[0081] If there is a high risk of improper use, the notification unit can work with local medical institutions or emergency services to encourage appropriate action. For example, if the generation AI detects a high risk of improper use, the notification unit can work with local medical institutions or emergency services to encourage appropriate action. For example, it can automatically send diagnosis results to medical institutions. The notification unit can also build a system where, if there is a high risk of improper use, the generation AI can work with local emergency services to encourage appropriate action. For example, it can automatically arrange for an ambulance. The notification unit can also work with local medical institutions or emergency services to encourage appropriate action if there is a high risk of improper use. For example, it can share diagnosis results in real time. This makes it possible to work with local medical institutions or emergency services to encourage appropriate action if there is a high risk of improper use.

[0082] If the risk of improper use is high, the notification unit can notify the user's family or emergency contact and request support. For example, the notification unit will build a system in which, if the generation AI detects a high risk of improper use, it notifies the user's family or emergency contact and requests support. For example, it will send the diagnosis results via SMS or email. Furthermore, if the risk of improper use is high, the notification unit will notify the user's family or emergency contact and request support. For example, it will notify the emergency contact by phone. Furthermore, the notification unit will develop a system in which, if the generation AI detects a high risk of improper use, it notifies the user's family or emergency contact and requests support. For example, it will notify the family through a dedicated app and send an alert to the family. This makes it possible to request support and encourage appropriate action by notifying the family or emergency contact when there is a high risk of improper use.

[0083] The notification unit can use the emotion estimation function to provide counseling to give a sense of security to users at high risk of improper use. For example, the notification unit uses the emotion estimation function to analyze the emotional state of users at high risk of improper use and provide counseling to give a sense of security. For example, if anxiety is high, the notification unit suggests counseling by a professional. The notification unit also uses the emotion estimation function to analyze the emotional state of users at high risk of improper use in real time and provides counseling to give a sense of security based on the results. For example, if fear is high, the notification unit introduces them to a psychological counselor. The notification unit also uses the emotion estimation function to analyze the emotional state of users at high risk of improper use and provides counseling to give a sense of security to users at high risk of improper use. For example, if stress is high, the notification unit suggests a relaxation session. In this way, by using the emotion estimation function to provide counseling to users at high risk of improper use, it is possible to give a sense of security and encourage appropriate action.

[0084] The analysis unit can improve the accuracy of learning based on the latest medical database when learning the user's personal information and medical history. For example, when the generation AI learns the user's personal information and medical history, the analysis unit refers to the latest medical database and improves the accuracy of learning based on that information. For example, new treatments and diagnostic criteria are incorporated. In addition, when the generation AI learns the user's personal information and medical history, the analysis unit automatically collects the latest medical database and improves the accuracy of learning based on that data. For example, new case reports and clinical trial results are referenced. In addition, when the generation AI learns the user's personal information and medical history, the analysis unit refers to the latest medical database in real time and improves the accuracy of learning based on that information. For example, new diagnostic criteria and treatments are incorporated. In this way, by referring to the latest medical database, the learning accuracy of the user's personal information and medical history can be improved.

[0085] The analysis unit also learns the user's lifestyle and environmental data, enabling a more comprehensive understanding of the health condition. For example, the generation AI collects the user's lifestyle data (e.g., amount of exercise and dietary content) and performs learning based on that data. This takes into account the impact of lifestyle on health condition. The analysis unit also collects the user's environmental data (e.g., living environment and work environment) and the generation AI performs learning based on that data. This takes into account the impact of the environment on health condition. The analysis unit also learns the user's lifestyle and environmental data and enables a more comprehensive understanding of the health condition. For example, the health condition is evaluated taking into account the amount of exercise, dietary content, and living environment. This makes it possible to understand the health condition more comprehensively by learning the user's lifestyle and environmental data.

[0086] The analysis unit can use the emotion estimation function to analyze the user's emotional state and consider the impact of stress or anxiety on health. For example, the analysis unit uses the generation AI to analyze the user's emotional state and consider the impact of stress or anxiety on health. For example, the emotion estimation function is used to calculate the user's emotion score and evaluate the health state based on that data. The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and, if stress or anxiety is high, considers the impact of that to evaluate the health state. For example, heart rate fluctuations are reflected in the analysis. The analysis unit also collects facial expressions and voice using the smartphone's camera and microphone so that the generation AI can analyze the user's emotional state and considers the impact of stress and anxiety on health. For example, facial tension and voice tremors are analyzed. This allows for a more accurate understanding of the user's health state by analyzing the user's emotional state using the emotion estimation function and considering the impact of stress and anxiety on health.

[0087] The analysis unit can share the user's personal information and medical history with different medical institutions or experts to provide comprehensive medical support. For example, the generation AI can share the user's personal information and medical history with different medical institutions to provide comprehensive medical support. For example, diagnostic results and treatment history can be shared in an electronic medical record. The analysis unit can also share the user's personal information and medical history with experts to build a system that provides comprehensive medical support. For example, it can collect expert opinions and provide them to the user. The analysis unit can also share the user's personal information and medical history with different medical institutions or experts to provide comprehensive medical support. For example, it can share diagnostic results on the cloud and collect expert opinions. This allows the generation AI to share the user's personal information and medical history with different medical institutions or experts to provide comprehensive medical support.

[0088] The analysis unit allows the generation AI to propose an individual health management plan based on the user's personal information and medical history. For example, the analysis unit allows the generation AI to propose an individual health management plan based on the user's personal information and medical history. For example, the analysis unit provides advice on diet and exercise. The analysis unit also builds a system in which the generation AI proposes an individual health management plan based on the user's personal information and medical history. For example, the analysis unit proposes a schedule for regular health checks. The analysis unit also allows the generation AI to propose an individual health management plan based on the user's personal information and medical history. For example, the analysis unit proposes preventive measures and treatments for specific diseases. This allows more appropriate health management to be achieved by proposing an individual health management plan based on the user's personal information and medical history.

[0089] The analysis unit can use the emotion estimation function to analyze the user's emotional state and reflect it in the health management plan. For example, the generation AI in the analysis unit analyzes the user's emotional state and proposes a health management plan based on that data. For example, if stress is high, a plan incorporating relaxation is proposed. The analysis unit also uses the emotion estimation function to analyze the user's emotional state in real time and adjust the health management plan based on the results. For example, if anxiety is high, a plan incorporating counseling is proposed. The analysis unit also uses the generation AI to analyze the user's emotional state and proposes a health management plan based on that data. For example, exercise and diet advice is adjusted based on the emotion score. In this way, more appropriate health management is possible by analyzing the user's emotional state using the emotion estimation function and reflecting it in the health management plan.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The healthcare system may further include a location information acquisition unit. The location information acquisition unit acquires the user's current location and can use this information to provide a diagnosis or arrange for an ambulance. For example, when a user inputs symptoms, the location information acquisition unit acquires location information and takes into account the nearest medical institution and the arrival time of an ambulance. The location information acquisition unit can also optimally allocate local medical resources based on the user's location information. For example, it can refer to the ambulance operation status and hospital acceptance status in real time to select the most appropriate medical institution. The location information acquisition unit can also suggest the optimal route in an emergency based on the user's location information. For example, it can guide the user to the shortest route taking traffic conditions into consideration. In this way, utilizing location information enables faster and more appropriate medical treatment.

[0092] The healthcare system may further include a preventive measure suggestion unit. The preventive measure suggestion unit can suggest preventive measures against illness based on the user's health data. For example, it may analyze the user's lifestyle data and suggest improvements to lack of exercise and diet. The preventive measure suggestion unit can also predict the risk of a specific disease based on the user's past health checkup results and suggest preventive measures. For example, if the user has high blood pressure, it may suggest limiting salt intake. The preventive measure suggestion unit can also suggest preventive measures against allergies and infectious diseases based on the user's environmental data. For example, it may suggest wearing a mask when going out during pollen season. In this way, the preventive measures suggested can contribute to maintaining the user's health.

[0093] The healthcare system may further include a reminder unit. The reminder unit can remind the user to undergo regular health checkups and take medication. For example, the reminder unit may remind the user to undergo regular health checkups. The reminder unit can also manage the user's medication schedule and notify the user of medication times. For example, the reminder unit may remind the user to take medication in the morning and evening. The reminder unit can also remind the user of exercise and meal timings based on the user's health management plan. For example, the reminder unit may notify the user of daily walking and meal times. In this way, the reminder function can be utilized to support the user's health management.

[0094] The healthcare system may further include a community collaboration unit. The community collaboration unit may provide information to enable users to participate in local health events and support groups. For example, it may notify users of information about local health seminars and exercise events. The community collaboration unit may also support users in connecting with other users who have the same symptoms or health goals. For example, it may provide online forums and chat groups. The community collaboration unit may also strengthen collaboration with local medical institutions and specialists to enable users to receive the support they need. For example, it may provide a system for introducing local medical institutions and making reservations. This may strengthen collaboration with the community and support users' health management.

[0095] The healthcare system may further include a feedback collection unit. The feedback collection unit may collect feedback from users and use it to improve the system. For example, users may provide their opinions on the usability of the system or on diagnostic results. The feedback collection unit may also improve the functions and interface of the system based on the user feedback. For example, new functions may be added in response to user requests. The feedback collection unit may also analyze the user feedback and provide data to improve the accuracy and reliability of the system. For example, the feedback collection unit may improve an algorithm to improve the accuracy of diagnostic results. In this way, the quality of the system may be improved by utilizing user feedback.

[0096] The analysis unit uses the emotion estimation function to analyze the user's emotional state and consider the impact of stress or anxiety on symptoms. For example, when the user describes their symptoms, the analysis unit analyzes their emotional state from their voice and facial expressions, and the generation AI considers the impact of stress or anxiety on their symptoms. For example, it analyzes changes in voice tone and facial expressions. The analysis unit also uses the emotion estimation function to monitor the user's emotional state in real time, and if stress or anxiety is high, it analyzes the symptoms taking that impact into account. For example, it reflects heart rate fluctuations in the analysis. The analysis unit also collects facial expressions and voice using the smartphone's camera and microphone to analyze the user's emotional state, and the generation AI considers the impact of stress and anxiety on symptoms. For example, it analyzes facial tension and voice tremors. This makes it possible to analyze the emotional state and consider the impact of stress and anxiety on symptoms.

[0097] The analysis unit uses the emotion estimation function to analyze the emotions of the user when entering symptoms in real time and can provide appropriate advice. For example, when the user enters symptoms, the analysis unit uses the emotion estimation function to analyze the emotions in real time, and the generation AI provides advice according to those emotions. For example, if the user is feeling very anxious, a reassuring message is displayed. The analysis unit also uses the emotion estimation function to analyze the emotional state of the user when entering symptoms, and the generation AI provides appropriate advice based on those emotions. For example, if the user is feeling very stressed, the generation AI suggests ways to relax. The analysis unit also uses the emotion estimation function to analyze the emotions of the user when entering symptoms in real time, and the generation AI provides advice according to those emotions. For example, if the user is feeling very scared, the generation AI provides advice to stay calm. In this way, the user's emotions can be analyzed in real time and appropriate advice can be provided, thereby increasing the user's sense of security.

[0098] The diagnosis unit can use the emotion estimation function to analyze the user's emotional reaction to the diagnosis result and suggest appropriate follow-up. For example, when providing the diagnosis result, the diagnosis unit uses the emotion estimation function to analyze the user's emotional reaction and suggests appropriate follow-up based on the result. For example, if anxiety is strong, counseling is suggested. The diagnosis unit also analyzes the user's emotional reaction to the diagnosis result in real time and suggests appropriate follow-up based on the result. For example, if fear is strong, information to reassure the user is provided. The diagnosis unit also uses the emotion estimation function to analyze the user's emotional reaction to the diagnosis result and suggests appropriate follow-up based on the result. For example, if stress is high, methods to relax are suggested. In this way, the user's sense of security can be increased by analyzing the user's emotional reaction to the diagnosis result and suggesting appropriate follow-up.

[0099] The judgment unit can use the emotion estimation function to analyze the user's emotional state and provide advice to encourage calm judgment. For example, when the generation AI is determining whether to call an ambulance, the judgment unit uses the emotion estimation function to analyze the user's emotional state and provide advice to encourage calm judgment. For example, if the user is very anxious, the judgment unit encourages the user to take a deep breath. The judgment unit also analyzes the user's emotional state in real time and provides advice to encourage the generation AI to make calm judgment. For example, if the user is very scared, the judgment unit suggests ways to relax. The judgment unit also uses the emotion estimation function to analyze the user's emotional state and provides advice to encourage the generation AI to make calm judgment. For example, if the user is very stressed, the judgment unit provides advice to stay calm. In this way, the user's emotional state is analyzed and advice to encourage calm judgment is provided, allowing the user to make an appropriate decision.

[0100] The notification unit can use the emotion estimation function to analyze the user's emotional state and provide advice to relieve anxiety and tension. For example, the notification unit can use the emotion estimation function to analyze the emotional state of a user for whom the generation AI is at high risk of improper use and provide advice to relieve anxiety and tension. For example, the notification unit can suggest ways to relax. The notification unit can also use the emotion estimation function to analyze the user's emotional state in real time and provide advice to relieve anxiety and tension. For example, the notification unit can encourage deep breathing. The notification unit can also use the emotion estimation function to analyze the emotional state of a user for whom the generation AI is at high risk of improper use and provide advice to relieve anxiety and tension. For example, the notification unit can suggest relaxation techniques. In this way, the user's sense of security can be increased by using the emotion estimation function to analyze the user's emotional state and provide advice to relieve anxiety and tension.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The voice input unit acquires the user's voice data. For example, voice data can be collected using a microphone on a smartphone or a smartwatch. It is also possible to use noise canceling technology to remove ambient noise and acquire clear voice data. Step 2: The analysis unit analyzes the voice data acquired by the voice input unit. For example, the analysis unit converts the voice data into text data using voice recognition technology and analyzes the content of the voice data using natural language processing technology. It can also analyze emotions in the voice data. Step 3: The diagnostic unit performs an expert-level diagnosis based on the data analyzed by the analytical unit. For example, the generative AI performs a diagnosis based on pre-trained medical data and guidelines, comprehensively considering the user's symptoms, medical history, and personal information to provide the most appropriate diagnosis. It can also refer to the latest medical research data and papers in real time to improve the accuracy of the diagnosis. Step 4: The judgment unit determines whether to call an ambulance based on the diagnosis results obtained by the diagnosis unit. For example, the generation AI will instruct the user to call an ambulance immediately if there is a high possibility of a heart attack, or direct the user to the appropriate contact information if the symptoms are mild. It can also make optimal decisions by referring to the operating status of local ambulances and hospital admissions in real time. Step 5: The notification unit notifies the user of the judgment result obtained by the judgment unit. For example, the result can be notified to the user via SMS or email, and the result can also be notified to the user's family or emergency contacts. It can also cooperate with local medical institutions and emergency services to ensure a prompt response.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice input unit for acquiring voice data of a user; an analysis unit that analyzes the voice data acquired by the voice input unit; a diagnosis unit that performs an expert-level diagnosis based on the data analyzed by the analysis unit; a determination unit that determines whether or not an ambulance should be called based on the diagnosis result obtained by the diagnosis unit; a notification unit that notifies the determination result obtained by the determination unit. A system characterized by:

2. The analysis unit In addition to the voice data, facial expression, body temperature, and heart rate biometric data are simultaneously collected and analyzed comprehensively.

2. The system of claim 1.

3. The analysis unit Referencing the user's past health and lifestyle data for more accurate analysis 2. The system of claim 1.

4. The analysis unit Analyzes the user's emotional state and considers the impact of stress or anxiety on symptoms 2. The system of claim 1.

5. The voice input unit Symptoms can be input not only through voice but also through text, images, and videos, which are then analyzed by generative AI.

2. The system of claim 1.

6. The analysis unit The generative AI performs comparative analysis based on similar case data from other users.

2. The system of claim 1.

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