System

Abstract

An object of a system according to an embodiment is to quickly and accurately determine whether a user should visit a hospital.SOLUTION: A system includes a basic information input unit, a medical condition input unit, a media upload unit, a determination unit, and a notification unit. The basic information input unit allows the user to input basic information such as age, sex, and presence or absence of a chronic disease. The medical condition input unit describes a medical condition in detail in text based on the basic information input by the basic information input unit. The media upload unit uploads a photograph or a moving image to supplement the disease condition input by the disease condition input unit. The determination unit comprehensively analyzes the basic information, the medical condition, the photograph, and the moving image, and determines the necessity of a hospital visit. The notification unit notifies the user of the possibility of the hospital visit determined 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 go to the hospital, and there is a risk that a person will not be able to visit a medical institution at the appropriate time.

[0005] The system according to the embodiment aims to quickly and accurately determine whether a user should go to a hospital. [Means for solving the problem]

[0006] The system according to the embodiment includes a basic information input unit, a medical condition input unit, a media upload unit, a determination unit, and a notification unit. The basic information input unit allows the user to input basic information such as age, gender, and whether or not the user has a chronic illness. The medical condition input unit provides a detailed text description of the medical condition based on the basic information input by the basic information input unit. The media upload unit uploads photos or videos to complement the medical condition input by the medical condition input unit. The determination unit comprehensively analyzes the basic information, medical condition, photos, and videos to determine whether or not a hospital visit is necessary. The notification unit notifies the user of the determination by the determination unit as to whether or not a hospital visit is necessary. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately determine whether a user should go to the hospital. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The hospital visit eligibility determination system according to the embodiment of the present invention is a system that determines whether or not a user should visit a hospital by inputting basic information and symptoms of a medical condition using a smartphone app and uploading photos and videos. This allows the hospital visit eligibility determination system to quickly and accurately determine whether or not a user should visit a hospital.

[0029] A hospital visit advising system according to an embodiment includes a basic information input unit, a medical condition input unit, a media upload unit, a determination unit, and a notification unit. The basic information input unit allows a user to input basic information such as age, gender, and whether or not they have a chronic illness. For example, a user may input information such as "45 years old, female, no chronic illnesses." The medical condition input unit provides a detailed text description of the patient's condition based on the basic information input by the basic information input unit. For example, the user may input a medical condition such as "I've had a bad headache and fever since yesterday." The media upload unit uploads photos or videos to supplement the medical condition input by the medical condition input unit. For example, if a patient has a rash, the user can take and upload a photo of the rash. It is also possible to upload a video of the patient's cough. The determination unit comprehensively analyzes the basic information, medical condition, photos, and videos to determine whether or not a hospital visit is necessary. For example, the generation AI may make a determination such as "This symptom is highly urgent, so you should go to the hospital immediately." The notification unit notifies the user of the determination by the determination unit regarding whether or not a hospital visit is necessary. For example, the notification may be in the form of, "There is no need to visit a hospital, but if your symptoms worsen, please consult a doctor." This allows the hospital visit advisability determination system according to the embodiment to quickly and accurately determine whether or not the user should visit a hospital.

[0030] The basic information input unit can input the user's past medical history or family medical history. For example, when the user inputs basic information, the basic information input unit also inputs past medical history and family medical history. For example, it inputs illnesses that the user has had in the past and whether there are any hereditary illnesses in the family. In addition, a function to automatically display the user's past medical history is added to the basic information input screen. For example, it displays past medical records and information on prescription medications. In addition, when inputting family medical history, questions are set that take into account family composition and genetic risks. For example, it inputs whether parents or siblings have a specific illness. This allows the generation AI to more accurately determine whether or not to visit the hospital.

[0031] The basic information input unit can input the user's lifestyle habits (diet, exercise, sleep patterns). The basic information input unit, for example, adds questions about the user's lifestyle habits to the basic information input screen. For example, meal frequency and content, exercise frequency and type, and sleep time and quality can be input. In addition, a function is added that allows the user to refer to past data when inputting lifestyle habits. For example, past meal records and exercise logs can be displayed. In addition, options are provided to make input easier for the user when inputting data about lifestyle habits. For example, the type of meal and exercise intensity can be selected from options. This allows the generation AI to evaluate the user's overall health condition and make a more appropriate decision on whether or not to visit the hospital.

[0032] The basic information input unit uses voice recognition technology to allow the user to input basic information simply by speaking. For example, the basic information input unit adds a voice recognition function to the basic information input screen, allowing the user to input information simply by speaking. For example, age, gender, and whether or not the user has a chronic illness are input by voice. Furthermore, using voice recognition technology, what the user speaks is converted into text in real time and displayed on the input screen. For example, if the user speaks, "45 years old, female, no chronic illnesses," it is automatically converted into text. Furthermore, using the voice recognition function, what the user speaks is analyzed and necessary information is automatically extracted. For example, age, gender, and whether or not the user has a chronic illness are automatically extracted from the speech and input. This allows the user to easily input basic information.

[0033] The basic information input unit can acquire the user's location information and display the congestion status or waiting time of local medical institutions. The basic information input unit, for example, adds a location information acquisition function to the basic information input screen and displays the congestion status and waiting time of local medical institutions based on the user's current location. For example, the waiting time of nearby hospitals is displayed in real time. In addition, a function is added to display the congestion status and waiting time of local medical institutions based on the user's location information. For example, the congestion status of the hospital closest to the current location is displayed. In addition, the location information acquisition function is used to display information about local medical institutions based on the basic information entered by the user. For example, the congestion status of medical institutions according to age and gender is displayed. This allows the user to understand the congestion status and waiting time of local medical institutions.

[0034] The medical condition input unit can add a function to the medical condition input screen, for example, where the generation AI analyzes the input content in real time and automatically generates additional questions to present to the user. For example, if you input "I have a bad headache," an additional question such as "When did your headache start?" will be displayed. The generation AI also analyzes the input text of the medical condition and automatically generates additional questions to complement the necessary information. For example, if you input "I have a fever," a question such as "What is your body temperature?" will be displayed. In addition, when inputting the medical condition text, the generation AI analyzes the input content in real time and automatically generates additional questions to present to the user. For example, if you input "I have a cough," a question such as "How often do you cough?" will be displayed. This allows the user to input more detailed medical conditions.

[0035] The medical condition input unit can refer to medical condition data previously entered by the user and automatically complete the information if there are similar symptoms. For example, the medical condition input unit adds a function to the medical condition text input screen that allows the user to refer to medical condition data previously entered by the user. For example, if there are symptoms similar to medical conditions previously entered, the information is automatically completed. In addition, a function is added in which the generation AI analyzes the user's past medical condition data and automatically completes the information if there are similar symptoms. For example, a question such as "Have you had similar symptoms in the past?" is displayed. In addition, when entering medical condition text, a function is added in which the user refers to medical condition data previously entered by the user and automatically completes the information if there are similar symptoms. For example, a question such as "If you have had similar symptoms in the past, please enter their details" is displayed. This allows the user to refer to past medical condition data and automatically complete similar symptoms.

[0036] The medical condition input unit uses natural language processing technology to allow the user to input medical conditions simply by speaking. The medical condition input unit, for example, adds a voice input function to the medical condition text input screen, allowing the user to input medical conditions simply by speaking. For example, if the user speaks, "I've had a bad headache and a fever since yesterday," it is automatically converted into text. In addition, using natural language processing technology, what the user speaks is converted into text in real time and displayed on the medical condition input screen. For example, if the user speaks, "I have a cough," it is automatically converted into text. In addition, using the voice input function, what the user speaks is analyzed and necessary information is automatically extracted. For example, if the user speaks, "I have a rash," it is automatically converted into text and displayed on the medical condition input screen. This allows the user to easily input medical conditions.

[0037] The media upload unit allows the generation AI to perform image analysis in real time and automatically highlight abnormal areas. For example, the media upload unit adds a function to the photo and video upload screen where the generation AI performs image analysis in real time and automatically highlights abnormal areas. For example, when a photo of a rash is uploaded, abnormal areas are highlighted in red. The generation AI also adds a function to analyze uploaded photos and videos and automatically highlight abnormal areas. For example, when a video of a cough is uploaded, abnormal movements are detected and highlighted. The generation AI also adds a function to analyze images in real time when photos and videos are uploaded and automatically highlight abnormal areas. For example, when a photo of skin is uploaded, abnormal areas are automatically highlighted. This allows abnormal areas to be automatically highlighted in images and videos uploaded by users.

[0038] The media upload unit enables the generation AI to compare with past image data and evaluate the progression of symptoms. The media upload unit adds a function to the photo or video upload screen, for example, that enables the generation AI to compare with past image data and evaluate the progression of symptoms. For example, it compares past photos of a rash with current photos to evaluate the progression. In addition, it adds a function that enables the generation AI to compare uploaded photos or videos with past data and evaluate the progression of symptoms. For example, it compares past videos of a cough with current videos to evaluate the progression. In addition, it adds a function that enables the generation AI to compare with past image data when uploading photos or videos and evaluate the progression of symptoms. For example, it compares past photos of skin with current photos to evaluate the progression. This allows the user to evaluate the progression of symptoms.

[0039] The media upload unit works in conjunction with cloud storage, allowing users to easily refer to past medical data. The media upload unit adds a cloud storage linkage function to the photo and video upload screen, for example, allowing users to easily refer to past medical data. For example, past medical records and image data are retrieved from cloud storage and displayed. The unit also works in conjunction with cloud storage to add a function that allows users to easily refer to past medical data. For example, past photos of rashes and medical records are retrieved from cloud storage and displayed. The unit also works in conjunction with cloud storage when uploading photos and videos, adding a function that allows users to easily refer to past medical data. For example, past videos of coughs and medical records are retrieved from cloud storage and displayed. This allows users to easily refer to past medical data.

[0040] The media upload unit enables the generation AI to automatically classify image data, allowing medical professionals to make efficient diagnoses. The media upload unit adds, for example, a function to the photo and video upload screen that enables the generation AI to automatically classify image data, allowing medical professionals to make efficient diagnoses. For example, it automatically classifies photos of a rash into dermatology and videos of coughing into respiratory medicine. The unit also adds a function to enable the generation AI to automatically classify uploaded photos and videos, allowing medical professionals to make efficient diagnoses. For example, it automatically classifies photos of skin into dermatology and videos of coughing into respiratory medicine. The unit also adds a function to enable the generation AI to automatically classify image data when photos and videos are uploaded, allowing medical professionals to make efficient diagnoses. For example, it automatically classifies photos of a rash into dermatology and videos of coughing into respiratory medicine. This allows medical professionals to make efficient diagnoses.

[0041] The judgment unit can enable the generation AI to compare with past judgment results and evaluate the consistency of the judgment. For example, the judgment unit adds a function to the judgment screen for whether or not to visit the hospital, where the generation AI compares with past judgment results and evaluates the consistency of the judgment. For example, it compares past judgment results with current judgment results to evaluate consistency. In addition, a function is added where the generation AI compares the judgment result for whether or not to visit the hospital with past data and evaluates consistency. For example, it compares past judgment results with current judgment results to evaluate consistency. In addition, when judging whether or not to visit the hospital, a function is added where the generation AI compares with past judgment results and evaluates consistency. For example, it compares past judgment results with current judgment results to evaluate consistency. In this way, the consistency of the judgment can be evaluated.

[0042] The judgment unit enables the generating AI to take the user's living environment into consideration and make a more appropriate judgment. For example, the judgment unit adds a function to the judgment screen for whether or not to visit a hospital that enables the generating AI to take the user's living environment into consideration and make a more appropriate judgment. For example, a judgment is made taking into consideration the risk of infection within the household. In addition, a function is added that enables the generating AI to take the user's living environment into consideration and make a more appropriate judgment when determining whether or not to visit a hospital ... This enables an appropriate judgment to be made taking into consideration the user's living environment.

[0043] The determination unit can link the result of the determination as to whether or not a visit is possible with the user's electronic medical record, allowing the medical institution to respond quickly. The determination unit, for example, adds a function that links the result of the determination as to whether or not a visit is possible with the user's electronic medical record, allowing the medical institution to respond quickly. For example, the determination result is automatically recorded in the electronic medical record. In addition, a function is added in which the generation AI links the result of the determination as to whether or not a visit is possible with the user's electronic medical record, allowing the medical institution to respond quickly. For example, the determination result is automatically recorded in the electronic medical record. In addition, a function is added in which the result of the determination as to whether or not a visit is possible is linked with the user's electronic medical record, allowing the medical institution to respond quickly. For example, the determination result is automatically recorded in the electronic medical record. This allows the medical institution to respond quickly.

[0044] The judgment unit enables the generating AI to take into account local medical resources and suggest the most appropriate medical institution. The judgment unit adds a function that, for example, when determining whether or not a hospital visit is possible, the generating AI takes into account local medical resources and suggests the most appropriate medical institution. For example, it suggests the most appropriate hospital based on the number of hospital beds and doctor availability. In addition, a function is added that, when determining whether or not a hospital visit is possible, the generating AI takes into account local medical resources and suggests the most appropriate medical institution. For example, it suggests the most appropriate hospital based on the number of hospital beds and doctor availability. In addition, a function is added that, when determining whether or not a hospital visit is possible, the generating AI takes into account local medical resources and suggests the most appropriate medical institution. For example, it suggests the most appropriate hospital based on the number of hospital beds and doctor availability. In this way, it is possible to suggest the most appropriate medical institution taking into account local medical resources.

[0045] The notification unit allows the generation AI to refer to the user's past behavioral history and select the optimal notification timing. For example, when notifying the user of the judgment result, the notification unit adds a function whereby the generation AI refers to the user's past behavioral history and selects the optimal notification timing. For example, the notification is sent during the time period when the user is most relaxed. In addition, a function whereby the generation AI analyzes the user's past behavioral history and selects the optimal notification timing. For example, the notification is sent during the time period when the user is most concentrated. In addition, when notifying the user of the judgment result, a function whereby the generation AI refers to the user's past behavioral history and selects the optimal notification timing. For example, the notification is sent during the time period when the user is most relaxed. This allows the user to be notified at the optimal time.

[0046] The notification unit enables the generation AI to consider the user's current activity status and select an appropriate notification method. For example, when notifying the judgment result, the notification unit adds a function that the generation AI considers the user's current activity status and selects an appropriate notification method. For example, if the user is driving, a voice notification is provided. The generation AI also adds a function that analyzes the user's current activity status and selects an appropriate notification method. For example, if the user is in a meeting, a vibration notification is provided. The generation AI also adds a function that considers the user's current activity status and selects an appropriate notification method when notifying the judgment result. For example, if the user is driving, a voice notification is provided. This makes it possible to select an appropriate notification method according to the user's activity status.

[0047] The notification unit can link with other devices such as a smartwatch or smart speaker, allowing the user to receive notifications wherever they are. For example, the notification unit adds a function that links the notification of the judgment result with a smartwatch, allowing the user to receive notifications wherever they are. For example, a notification is displayed on the smartwatch. In addition, a function is added in which the generation AI links the notification of the judgment result with a smart speaker, allowing the user to receive notifications wherever they are. For example, a notification is given by voice from the smart speaker. In addition, a function is added in which the notification of the judgment result is linked with a smartwatch or smart speaker, allowing the user to receive notifications wherever they are. For example, a notification is shown on the smartwatch and a notification is given by voice from the smart speaker. In this way, the user can receive notifications wherever they are.

[0048] The notification unit allows the generation AI to refer to the user's schedule and suggest the optimal date and time for a hospital visit. For example, when notifying the judgment result, the notification unit adds a function whereby the generation AI refers to the user's schedule and suggests the optimal date and time for a hospital visit. For example, it refers to the user's calendar and suggests a date and time when the hospital is available. In addition, a function whereby the generation AI analyzes the user's schedule and suggests the optimal date and time for a hospital visit. For example, it suggests a date and time when the user is free. In addition, when notifying the judgment result, a function whereby the generation AI refers to the user's schedule and suggests the optimal date and time for a hospital visit. For example, it refers to the user's calendar and suggests a date and time when the user is available. This makes it possible to suggest the optimal date and time for a hospital visit to the user.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The basic information input section can input the user's past medical history or family medical history. For example, when the user inputs basic information, they can also input their past medical history and family medical history. For example, they can input any illnesses they have had in the past or whether their family has any hereditary illnesses. In addition, a function can be added to the basic information input screen to automatically display the user's past medical history. For example, it can display past medical records and information on prescription medications. In addition, when inputting family medical history, questions can be added that take into account family composition and genetic risks. For example, they can input whether their parents or siblings have a specific illness. This allows the generated AI to more accurately determine whether or not to visit the hospital.

[0051] The basic information input unit can input the user's lifestyle habits (diet, exercise, and sleep patterns). For example, questions about the user's lifestyle habits can be added to the basic information input screen. For example, meal frequency and content, exercise frequency and type, and sleep time and quality can be input. In addition, a function can be added that allows the user to refer to past data when inputting lifestyle habits. For example, past meal records and exercise logs can be displayed. In addition, options can be provided to make input easier for the user when inputting data about lifestyle habits. For example, the type of meal and exercise intensity can be selected from options. This allows the generation AI to evaluate the user's overall health condition and make a more appropriate decision on whether or not to visit the hospital.

[0052] The basic information input unit uses voice recognition technology to allow the user to input basic information simply by speaking. For example, a voice recognition function can be added to the basic information input screen, allowing the user to input information simply by speaking. For example, age, gender, and whether or not the user has a chronic illness can be input by voice. In addition, voice recognition technology can be used to convert what the user says into text in real time and display it on the input screen. For example, if the user says "45 years old, female, no chronic illnesses," it will be automatically converted into text. In addition, the voice recognition function can be used to analyze what the user says and automatically extract the necessary information. For example, age, gender, and whether or not the user has a chronic illness can be automatically extracted and input from the speech. This allows the user to easily input basic information.

[0053] The basic information input unit can acquire the user's location information and display the congestion status or waiting time of local medical institutions. For example, a location information acquisition function can be added to the basic information input screen to display the congestion status and waiting time of local medical institutions based on the user's current location. For example, the waiting time of nearby hospitals can be displayed in real time. In addition, a function can be added to display the congestion status and waiting time of local medical institutions based on the user's location information. For example, the congestion status of the hospital closest to the current location can be displayed. In addition, the location information acquisition function can be used to display information about local medical institutions based on the basic information entered by the user. For example, the congestion status of medical institutions according to age and gender can be displayed. This allows the user to understand the congestion status and waiting time of local medical institutions.

[0054] The medical condition input section can have the generation AI analyze the input content in real time and automatically generate follow-up questions to present to the user. For example, a function can be added to the medical condition text input screen, where the generation AI can analyze the input content in real time and automatically generate follow-up questions to present to the user. For example, if you enter "I have a bad headache," a follow-up question such as "When did your headache start?" will be displayed. The generation AI can also analyze the text input of the medical condition and automatically generate follow-up questions to complement the necessary information. For example, if you enter "I have a fever," a question such as "What is your body temperature?" will be displayed. In addition, a function can be added where the generation AI can analyze the input content in real time when entering the medical condition text and automatically generate follow-up questions to present to the user. For example, if you enter "I have a cough," a question such as "How often do you cough?" will be displayed. This allows the user to enter more detailed medical conditions.

[0055] The medical condition input section can refer to medical condition data previously entered by the user and automatically complete the information if there are similar symptoms. For example, a function is added to the medical condition text input screen that allows the user to refer to medical condition data previously entered. For example, if there are symptoms similar to medical conditions previously entered, the information is automatically completed. In addition, a function is added in which the generation AI analyzes the user's past medical condition data and automatically completes the information if there are similar symptoms. For example, a question such as "Have you had similar symptoms in the past?" is displayed. In addition, a function is added in which, when entering medical condition text, the user refers to medical condition data previously entered by the user and automatically completes the information if there are similar symptoms. For example, a question such as "If you have had similar symptoms in the past, please enter their details" is displayed. This allows the user to refer to past medical condition data and automatically complete similar symptoms.

[0056] The medical condition input unit uses natural language processing technology to allow the user to input medical conditions simply by speaking. For example, a voice input function can be added to the medical condition text input screen, allowing the user to input medical conditions simply by speaking. For example, if the user says, "I've had a bad headache and fever since yesterday," it will be automatically converted into text. In addition, natural language processing technology is used to convert what the user says into text in real time and display it on the medical condition input screen. For example, if the user says, "I have a cough," it will be automatically converted into text. In addition, the voice input function is used to analyze what the user says and automatically extract the necessary information. For example, if the user says, "I have a rash," it will be automatically converted into text and displayed on the medical condition input screen. This allows the user to easily input their medical condition.

[0057] The media upload section allows the generation AI to perform image analysis in real time and automatically highlight any abnormalities. For example, a function will be added to the photo and video upload screen where the generation AI will perform image analysis in real time and automatically highlight any abnormalities. For example, when uploading a photo of a rash, abnormal areas will be highlighted in red. In addition, a function will be added where the generation AI will analyze uploaded photos and videos and automatically highlight any abnormalities. For example, when uploading a video of a cough, abnormal movements will be detected and highlighted. In addition, a function will be added where the generation AI will perform image analysis in real time when uploading photos and videos and automatically highlight any abnormalities. For example, when uploading a photo of skin, abnormal areas will be automatically highlighted. This allows abnormalities to be automatically highlighted in images and videos uploaded by users.

[0058] The media upload unit allows the generation AI to compare with past image data and evaluate the progression of symptoms. For example, a function is added to the photo or video upload screen where the generation AI compares with past image data and evaluates the progression of symptoms. For example, a past photo of a rash is compared with a current photo to evaluate the progression. In addition, a function is added where the generation AI compares uploaded photos or videos with past data and evaluates the progression of symptoms. For example, a past video of a cough is compared with a current video to evaluate the progression. In addition, a function is added where, when uploading photos or videos, the generation AI compares with past image data and evaluates the progression of symptoms. For example, a past photo of skin is compared with a current photo to evaluate the progression. This allows the user to evaluate the progression of symptoms.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: In the basic information input section, the user inputs basic information such as age, gender, whether or not they have a chronic illness, etc. For example, the user inputs information such as "45 years old, female, no chronic illnesses." Step 2: The symptom input unit describes the symptom in detail in text based on the basic information input by the basic information input unit. For example, the symptom is input in the form of "I've had a bad headache and fever since yesterday." Step 3: The media upload unit uploads photos or videos to complement the medical condition input by the medical condition input unit. For example, if the patient has a rash, the patient can take a photo of it and upload it. It is also possible to take a video of the patient coughing and upload it. Step 4: The judgment unit comprehensively analyzes basic information, medical condition, photos, and videos to determine the need for hospital visits. For example, the generation AI may make a judgment such as, "This symptom is highly urgent, so you should go to the hospital immediately." Step 5: The notification unit notifies the user of the decision made by the decision unit as to whether or not to visit the hospital. For example, the notification may say, "There is no need to visit the hospital, but if your symptoms worsen, please consult a doctor."

[0061] (Example 2) The hospital visit eligibility determination system according to the embodiment of the present invention is a system that determines whether or not a user should visit a hospital by inputting basic information and symptoms of a medical condition using a smartphone app and uploading photos and videos. This allows the hospital visit eligibility determination system to quickly and accurately determine whether or not a user should visit a hospital.

[0062] A hospital visit advising system according to an embodiment includes a basic information input unit, a medical condition input unit, a media upload unit, a determination unit, and a notification unit. The basic information input unit allows a user to input basic information such as age, gender, and whether or not they have a chronic illness. For example, a user may input information such as "45 years old, female, no chronic illnesses." The medical condition input unit provides a detailed text description of the patient's condition based on the basic information input by the basic information input unit. For example, the user may input a medical condition such as "I've had a bad headache and fever since yesterday." The media upload unit uploads photos or videos to supplement the medical condition input by the medical condition input unit. For example, if a patient has a rash, the user can take and upload a photo of the rash. It is also possible to upload a video of the patient's cough. The determination unit comprehensively analyzes the basic information, medical condition, photos, and videos to determine whether or not a hospital visit is necessary. For example, the generation AI may make a determination such as "This symptom is highly urgent, so you should go to the hospital immediately." The notification unit notifies the user of the determination by the determination unit regarding whether or not a hospital visit is necessary. For example, the notification may be in the form of, "There is no need to visit a hospital, but if your symptoms worsen, please consult a doctor." This allows the hospital visit advisability determination system according to the embodiment to quickly and accurately determine whether or not the user should visit a hospital.

[0063] The basic information input unit can input the user's past medical history or family medical history. For example, when the user inputs basic information, the basic information input unit also inputs past medical history and family medical history. For example, it inputs illnesses that the user has had in the past and whether there are any hereditary illnesses in the family. In addition, a function to automatically display the user's past medical history is added to the basic information input screen. For example, it displays past medical records and information on prescription medications. In addition, when inputting family medical history, questions are set that take into account family composition and genetic risks. For example, it inputs whether parents or siblings have a specific illness. This allows the generation AI to more accurately determine whether or not to visit the hospital.

[0064] The basic information input unit can input the user's lifestyle habits (diet, exercise, sleep patterns). The basic information input unit, for example, adds questions about the user's lifestyle habits to the basic information input screen. For example, meal frequency and content, exercise frequency and type, and sleep time and quality can be input. In addition, a function is added that allows the user to refer to past data when inputting lifestyle habits. For example, past meal records and exercise logs can be displayed. In addition, options are provided to make input easier for the user when inputting data about lifestyle habits. For example, the type of meal and exercise intensity can be selected from options. This allows the generation AI to evaluate the user's overall health condition and make a more appropriate decision on whether or not to visit the hospital.

[0065] The basic information input unit uses the emotion estimation function to analyze the emotional state of the user when entering information, and can provide advice to relax if the user is experiencing high levels of stress or anxiety. The basic information input unit uses the emotion estimation function to analyze the emotional state when the user enters basic information, for example. For example, it analyzes facial expressions and voice tone while entering information to measure the level of stress or anxiety. It also uses the emotion estimation function to provide advice to relax if the user is feeling stressed or anxious. For example, it suggests taking deep breaths or playing relaxing music. It also analyzes the emotional state of the user when entering information in real time, and displays advice to relax as needed. For example, it displays positive messages or words of encouragement. This allows the user to enter basic information in a relaxed state.

[0066] The basic information input unit uses voice recognition technology to allow the user to input basic information simply by speaking. For example, the basic information input unit adds a voice recognition function to the basic information input screen, allowing the user to input information simply by speaking. For example, age, gender, and whether or not the user has a chronic illness are input by voice. Furthermore, using voice recognition technology, what the user speaks is converted into text in real time and displayed on the input screen. For example, if the user speaks, "45 years old, female, no chronic illnesses," it is automatically converted into text. Furthermore, using the voice recognition function, what the user speaks is analyzed and necessary information is automatically extracted. For example, age, gender, and whether or not the user has a chronic illness are automatically extracted from the speech and input. This allows the user to easily input basic information.

[0067] The basic information input unit can acquire the user's location information and display the congestion status or waiting time of local medical institutions. The basic information input unit, for example, adds a location information acquisition function to the basic information input screen and displays the congestion status and waiting time of local medical institutions based on the user's current location. For example, the waiting time of nearby hospitals is displayed in real time. In addition, a function is added to display the congestion status and waiting time of local medical institutions based on the user's location information. For example, the congestion status of the hospital closest to the current location is displayed. In addition, the location information acquisition function is used to display information about local medical institutions based on the basic information entered by the user. For example, the congestion status of medical institutions according to age and gender is displayed. This allows the user to understand the congestion status and waiting time of local medical institutions.

[0068] The medical condition input unit can add a function to the medical condition input screen, for example, where the generation AI analyzes the input content in real time and automatically generates additional questions to present to the user. For example, if you input "I have a bad headache," an additional question such as "When did your headache start?" will be displayed. The generation AI also analyzes the input text of the medical condition and automatically generates additional questions to complement the necessary information. For example, if you input "I have a fever," a question such as "What is your body temperature?" will be displayed. In addition, when inputting the medical condition text, the generation AI analyzes the input content in real time and automatically generates additional questions to present to the user. For example, if you input "I have a cough," a question such as "How often do you cough?" will be displayed. This allows the user to input more detailed medical conditions.

[0069] The medical condition input unit can refer to medical condition data previously entered by the user and automatically complete the information if there are similar symptoms. For example, the medical condition input unit adds a function to the medical condition text input screen that allows the user to refer to medical condition data previously entered by the user. For example, if there are symptoms similar to medical conditions previously entered, the information is automatically completed. In addition, a function is added in which the generation AI analyzes the user's past medical condition data and automatically completes the information if there are similar symptoms. For example, a question such as "Have you had similar symptoms in the past?" is displayed. In addition, when entering medical condition text, a function is added in which the user refers to medical condition data previously entered by the user and automatically completes the information if there are similar symptoms. For example, a question such as "If you have had similar symptoms in the past, please enter their details" is displayed. This allows the user to refer to past medical condition data and automatically complete similar symptoms.

[0070] The medical condition input unit uses the emotion estimation function to analyze the emotional state of the user when entering the medical condition, and can suggest counseling services if the user has strong negative emotions. For example, the medical condition input unit uses the emotion estimation function to analyze the user's emotional state when entering the medical condition as text, and can suggest counseling services if the user has strong negative emotions. For example, a message such as "You seem to be under a lot of stress. Would you like to try counseling?" is displayed. The medical condition input unit also uses the emotion estimation function to analyze the user's emotional state when entering the medical condition as text, and can suggest counseling services if the user has strong negative emotions. For example, a message such as "You seem to be feeling very anxious. Would you like to try counseling?" is displayed. The medical condition input unit also uses the emotion estimation function to analyze the user's emotional state when entering the medical condition as text, and can suggest counseling services if the user has strong negative emotions. For example, a message such as "You seem to be feeling depressed. Would you like to try counseling?" is displayed. This makes it possible to suggest counseling services if the user has negative emotions.

[0071] The medical condition input unit uses natural language processing technology to allow the user to input medical conditions simply by speaking. The medical condition input unit, for example, adds a voice input function to the medical condition text input screen, allowing the user to input medical conditions simply by speaking. For example, if the user speaks, "I've had a bad headache and a fever since yesterday," it is automatically converted into text. In addition, using natural language processing technology, what the user speaks is converted into text in real time and displayed on the medical condition input screen. For example, if the user speaks, "I have a cough," it is automatically converted into text. In addition, using the voice input function, what the user speaks is analyzed and necessary information is automatically extracted. For example, if the user speaks, "I have a rash," it is automatically converted into text and displayed on the medical condition input screen. This allows the user to easily input medical conditions.

[0072] The media upload unit allows the generation AI to perform image analysis in real time and automatically highlight abnormal areas. For example, the media upload unit adds a function to the photo and video upload screen where the generation AI performs image analysis in real time and automatically highlights abnormal areas. For example, when a photo of a rash is uploaded, abnormal areas are highlighted in red. The generation AI also adds a function to analyze uploaded photos and videos and automatically highlight abnormal areas. For example, when a video of a cough is uploaded, abnormal movements are detected and highlighted. The generation AI also adds a function to analyze images in real time when photos and videos are uploaded and automatically highlight abnormal areas. For example, when a photo of skin is uploaded, abnormal areas are automatically highlighted. This allows abnormal areas to be automatically highlighted in images and videos uploaded by users.

[0073] The media upload unit enables the generation AI to compare with past image data and evaluate the progression of symptoms. The media upload unit adds a function to the photo or video upload screen, for example, that enables the generation AI to compare with past image data and evaluate the progression of symptoms. For example, it compares past photos of a rash with current photos to evaluate the progression. In addition, it adds a function that enables the generation AI to compare uploaded photos or videos with past data and evaluate the progression of symptoms. For example, it compares past videos of a cough with current videos to evaluate the progression. In addition, it adds a function that enables the generation AI to compare with past image data when uploading photos or videos and evaluate the progression of symptoms. For example, it compares past photos of skin with current photos to evaluate the progression. This allows the user to evaluate the progression of symptoms.

[0074] The media upload unit can use the emotion estimation function to analyze the emotional state of the user when uploading photos or videos and display a message to give a sense of security. For example, the media upload unit can use the emotion estimation function to analyze the emotional state of the user when uploading photos or videos and display a message to give a sense of security. For example, it can display a message such as "Don't worry, we'll analyze it soon." The media upload unit can also use the emotion estimation function to analyze the emotional state of the user when uploading photos or videos and display a message to give a sense of security. For example, it can display a message such as "Don't worry, we'll get the results soon." The media upload unit can also use the emotion estimation function to analyze the emotional state of the user when uploading photos or videos and display a message to give a sense of security. For example, it can display a message such as "Relax, the analysis will be completed soon." This allows the user to upload photos or videos with peace of mind.

[0075] The media upload unit works in conjunction with cloud storage, allowing users to easily refer to past medical data. The media upload unit adds a cloud storage linkage function to the photo and video upload screen, for example, allowing users to easily refer to past medical data. For example, past medical records and image data are retrieved from cloud storage and displayed. The unit also works in conjunction with cloud storage to add a function that allows users to easily refer to past medical data. For example, past photos of rashes and medical records are retrieved from cloud storage and displayed. The unit also works in conjunction with cloud storage when uploading photos and videos, adding a function that allows users to easily refer to past medical data. For example, past videos of coughs and medical records are retrieved from cloud storage and displayed. This allows users to easily refer to past medical data.

[0076] The media upload unit enables the generation AI to automatically classify image data, allowing medical professionals to make efficient diagnoses. The media upload unit adds, for example, a function to the photo and video upload screen that enables the generation AI to automatically classify image data, allowing medical professionals to make efficient diagnoses. For example, it automatically classifies photos of a rash into dermatology and videos of coughing into respiratory medicine. The unit also adds a function to enable the generation AI to automatically classify uploaded photos and videos, allowing medical professionals to make efficient diagnoses. For example, it automatically classifies photos of skin into dermatology and videos of coughing into respiratory medicine. The unit also adds a function to enable the generation AI to automatically classify image data when photos and videos are uploaded, allowing medical professionals to make efficient diagnoses. For example, it automatically classifies photos of a rash into dermatology and videos of coughing into respiratory medicine. This allows medical professionals to make efficient diagnoses.

[0077] The media upload unit can use the emotion estimation function to analyze the emotional state of a user when uploading photos or videos, and provide an interface design that elicits positive emotions. The media upload unit, for example, uses the emotion estimation function to analyze the emotional state of a user when uploading photos or videos, and provides an interface design that elicits positive emotions. For example, it displays bright colors and positive messages. It also analyzes the emotional state of a user when uploading photos or videos in real time, and provides an interface design that elicits positive emotions. For example, it changes the interface design according to the user's emotions. It also uses the emotion estimation function to analyze the emotional state of a user when uploading photos or videos, and provides an interface design that elicits positive emotions. For example, it changes the interface color or layout according to the user's emotions. This allows the user to upload photos or videos with positive emotions.

[0078] The judgment unit can enable the generation AI to compare with past judgment results and evaluate the consistency of the judgment. For example, the judgment unit adds a function to the judgment screen for whether or not to visit the hospital, where the generation AI compares with past judgment results and evaluates the consistency of the judgment. For example, it compares past judgment results with current judgment results to evaluate consistency. In addition, a function is added where the generation AI compares the judgment result for whether or not to visit the hospital with past data and evaluates consistency. For example, it compares past judgment results with current judgment results to evaluate consistency. In addition, when judging whether or not to visit the hospital, a function is added where the generation AI compares with past judgment results and evaluates consistency. For example, it compares past judgment results with current judgment results to evaluate consistency. In this way, the consistency of the judgment can be evaluated.

[0079] The judgment unit enables the generating AI to take the user's living environment into consideration and make a more appropriate judgment. For example, the judgment unit adds a function to the judgment screen for whether or not to visit a hospital that enables the generating AI to take the user's living environment into consideration and make a more appropriate judgment. For example, a judgment is made taking into consideration the risk of infection within the household. In addition, a function is added that enables the generating AI to take the user's living environment into consideration and make a more appropriate judgment when determining whether or not to visit a hospital ... This enables an appropriate judgment to be made taking into consideration the user's living environment.

[0080] The determination unit uses the emotion estimation function to analyze the emotional state of the user when receiving the determination result of whether or not the user can visit the hospital, and can suggest counseling services if the user has strong negative emotions. For example, when notifying the determination result of whether or not the user can visit the hospital, the determination unit uses the emotion estimation function to analyze the user's emotional state, and can suggest counseling services if the user has strong negative emotions. For example, a message such as "You seem to be under a lot of stress. Would you like to try counseling?" is displayed. The determination unit also uses the emotion estimation function to analyze the user's emotional state when receiving the determination result of whether or not the user can visit the hospital, and can suggest counseling services if the user has strong negative emotions. For example, a message such as "You seem to be feeling very anxious. Would you like to try counseling?" is displayed. The determination unit also uses the emotion estimation function to analyze the user's emotional state when notifying the determination result of whether or not the user can visit the hospital, and can suggest counseling services if the user has strong negative emotions. For example, a message such as "You seem to be feeling depressed. Would you like to try counseling?" is displayed. This makes it possible to suggest counseling services if the user has negative emotions.

[0081] The determination unit can link the result of the determination as to whether or not a visit is possible with the user's electronic medical record, allowing the medical institution to respond quickly. The determination unit, for example, adds a function that links the result of the determination as to whether or not a visit is possible with the user's electronic medical record, allowing the medical institution to respond quickly. For example, the determination result is automatically recorded in the electronic medical record. In addition, a function is added in which the generation AI links the result of the determination as to whether or not a visit is possible with the user's electronic medical record, allowing the medical institution to respond quickly. For example, the determination result is automatically recorded in the electronic medical record. In addition, a function is added in which the result of the determination as to whether or not a visit is possible is linked with the user's electronic medical record, allowing the medical institution to respond quickly. For example, the determination result is automatically recorded in the electronic medical record. This allows the medical institution to respond quickly.

[0082] The judgment unit enables the generating AI to take into account local medical resources and suggest the most appropriate medical institution. The judgment unit adds a function that, for example, when determining whether or not a hospital visit is possible, the generating AI takes into account local medical resources and suggests the most appropriate medical institution. For example, it suggests the most appropriate hospital based on the number of hospital beds and doctor availability. In addition, a function is added that, when determining whether or not a hospital visit is possible, the generating AI takes into account local medical resources and suggests the most appropriate medical institution. For example, it suggests the most appropriate hospital based on the number of hospital beds and doctor availability. In addition, a function is added that, when determining whether or not a hospital visit is possible, the generating AI takes into account local medical resources and suggests the most appropriate medical institution. For example, it suggests the most appropriate hospital based on the number of hospital beds and doctor availability. In this way, it is possible to suggest the most appropriate medical institution taking into account local medical resources.

[0083] The determination unit can use the emotion estimation function to analyze the emotional state of the user when receiving the determination result on whether or not they can visit the hospital, and provide an interface design that elicits positive emotions. For example, when notifying the determination result on whether or not they can visit the hospital, the determination unit can use the emotion estimation function to analyze the user's emotional state and provide an interface design that elicits positive emotions. For example, it can display bright colors or positive messages. The emotion estimation function can also be used to analyze the user's emotional state when receiving the determination result on whether or not they can visit the hospital, and provide an interface design that elicits positive emotions. For example, it can change the interface design depending on the user's emotions. The emotion estimation function can also be used to analyze the user's emotional state when notifying the determination result on whether or not they can visit the hospital, and provide an interface design that elicits positive emotions. For example, it can change the color or layout of the interface depending on the user's emotions. This allows the user to receive the determination result on whether or not they can visit the hospital with positive emotions.

[0084] The notification unit allows the generation AI to refer to the user's past behavioral history and select the optimal notification timing. For example, when notifying the user of the judgment result, the notification unit adds a function whereby the generation AI refers to the user's past behavioral history and selects the optimal notification timing. For example, the notification is sent during the time period when the user is most relaxed. In addition, a function whereby the generation AI analyzes the user's past behavioral history and selects the optimal notification timing. For example, the notification is sent during the time period when the user is most concentrated. In addition, when notifying the user of the judgment result, a function whereby the generation AI refers to the user's past behavioral history and selects the optimal notification timing. For example, the notification is sent during the time period when the user is most relaxed. This allows the user to be notified at the optimal time.

[0085] The notification unit enables the generation AI to consider the user's current activity status and select an appropriate notification method. For example, when notifying the judgment result, the notification unit adds a function that the generation AI considers the user's current activity status and selects an appropriate notification method. For example, if the user is driving, a voice notification is provided. The generation AI also adds a function that analyzes the user's current activity status and selects an appropriate notification method. For example, if the user is in a meeting, a vibration notification is provided. The generation AI also adds a function that considers the user's current activity status and selects an appropriate notification method when notifying the judgment result. For example, if the user is driving, a voice notification is provided. This makes it possible to select an appropriate notification method according to the user's activity status.

[0086] The notification unit can use the emotion estimation function to analyze the emotional state of the user when receiving the determination result, and can provide advice to relax if the user feels a strong negative emotion. For example, the notification unit can use the emotion estimation function to analyze the user's emotional state when notifying the user of the determination result, and can provide advice to relax if the user feels a strong negative emotion. For example, the notification unit can display a message such as "Take a deep breath and relax." The notification unit can also use the emotion estimation function to analyze the user's emotional state when receiving the determination result, and can provide advice to relax if the user feels a strong negative emotion. For example, the notification unit can display a message such as "Why not try listening to some music to relax?" The notification unit can also use the emotion estimation function to analyze the user's emotional state when notifying the user of the determination result, and can provide advice to relax if the user feels a strong negative emotion. For example, the notification unit can display a message such as "Why not try some stretching to relax?" This can provide advice to relax if the user feels a negative emotion.

[0087] The notification unit can link with other devices such as a smartwatch or smart speaker, allowing the user to receive notifications wherever they are. For example, the notification unit adds a function that links the notification of the judgment result with a smartwatch, allowing the user to receive notifications wherever they are. For example, a notification is displayed on the smartwatch. In addition, a function is added in which the generation AI links the notification of the judgment result with a smart speaker, allowing the user to receive notifications wherever they are. For example, a notification is given by voice from the smart speaker. In addition, a function is added in which the notification of the judgment result is linked with a smartwatch or smart speaker, allowing the user to receive notifications wherever they are. For example, a notification is shown on the smartwatch and a notification is given by voice from the smart speaker. In this way, the user can receive notifications wherever they are.

[0088] The notification unit allows the generation AI to refer to the user's schedule and suggest the optimal date and time for a hospital visit. For example, when notifying the judgment result, the notification unit adds a function whereby the generation AI refers to the user's schedule and suggests the optimal date and time for a hospital visit. For example, it refers to the user's calendar and suggests a date and time when the hospital is available. In addition, a function whereby the generation AI analyzes the user's schedule and suggests the optimal date and time for a hospital visit. For example, it suggests a date and time when the user is free. In addition, when notifying the judgment result, a function whereby the generation AI refers to the user's schedule and suggests the optimal date and time for a hospital visit. For example, it refers to the user's calendar and suggests a date and time when the user is available. This makes it possible to suggest the optimal date and time for a hospital visit to the user.

[0089] The notification unit can use the emotion estimation function to analyze the emotional state of the user when receiving the determination result and provide an interface design that elicits positive emotions. For example, when notifying the determination result, the notification unit can use the emotion estimation function to analyze the emotional state of the user and provide an interface design that elicits positive emotions. For example, it can display bright colors or positive messages. The emotion estimation function can also be used to analyze the emotional state of the user when receiving the determination result and provide an interface design that elicits positive emotions. For example, it can change the interface design depending on the user's emotions. The emotion estimation function can also be used to analyze the emotional state of the user when notifying the determination result and provide an interface design that elicits positive emotions. For example, it can change the color or layout of the interface depending on the user's emotions. This allows the user to receive the determination result with positive emotions.

[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 basic information input section can input the user's past medical history or family medical history. For example, when the user inputs basic information, they can also input their past medical history and family medical history. For example, they can input any illnesses they have had in the past or whether their family has any hereditary illnesses. In addition, a function can be added to the basic information input screen to automatically display the user's past medical history. For example, it can display past medical records and information on prescription medications. In addition, when inputting family medical history, questions can be added that take into account family composition and genetic risks. For example, they can input whether their parents or siblings have a specific illness. This allows the generated AI to more accurately determine whether or not to visit the hospital.

[0092] The basic information input unit can input the user's lifestyle habits (diet, exercise, and sleep patterns). For example, questions about the user's lifestyle habits can be added to the basic information input screen. For example, meal frequency and content, exercise frequency and type, and sleep time and quality can be input. In addition, a function can be added that allows the user to refer to past data when inputting lifestyle habits. For example, past meal records and exercise logs can be displayed. In addition, options can be provided to make input easier for the user when inputting data about lifestyle habits. For example, the type of meal and exercise intensity can be selected from options. This allows the generation AI to evaluate the user's overall health condition and make a more appropriate decision on whether or not to visit the hospital.

[0093] The basic information input unit can use the emotion estimation function to analyze the emotional state of the user when entering basic information, and provide advice to relax if the user is experiencing high levels of stress or anxiety. For example, the emotion estimation function is used to analyze the emotional state when the user enters basic information. For example, facial expressions and voice tone during entry are analyzed to measure the level of stress or anxiety. The emotion estimation function can also be used to provide advice to relax if the user is feeling stressed or anxious. For example, the emotion estimation function can suggest deep breathing or playing relaxing music. The emotional state of the user when entering basic information can also be analyzed in real time, and advice to relax can be displayed as needed. For example, positive messages or words of encouragement can be displayed. This allows the user to enter basic information in a relaxed state.

[0094] The basic information input unit uses voice recognition technology to allow the user to input basic information simply by speaking. For example, a voice recognition function can be added to the basic information input screen, allowing the user to input information simply by speaking. For example, age, gender, and whether or not the user has a chronic illness can be input by voice. In addition, voice recognition technology can be used to convert what the user says into text in real time and display it on the input screen. For example, if the user says "45 years old, female, no chronic illnesses," it will be automatically converted into text. In addition, the voice recognition function can be used to analyze what the user says and automatically extract the necessary information. For example, age, gender, and whether or not the user has a chronic illness can be automatically extracted and input from the speech. This allows the user to easily input basic information.

[0095] The basic information input unit can acquire the user's location information and display the congestion status or waiting time of local medical institutions. For example, a location information acquisition function can be added to the basic information input screen to display the congestion status and waiting time of local medical institutions based on the user's current location. For example, the waiting time of nearby hospitals can be displayed in real time. In addition, a function can be added to display the congestion status and waiting time of local medical institutions based on the user's location information. For example, the congestion status of the hospital closest to the current location can be displayed. In addition, the location information acquisition function can be used to display information about local medical institutions based on the basic information entered by the user. For example, the congestion status of medical institutions according to age and gender can be displayed. This allows the user to understand the congestion status and waiting time of local medical institutions.

[0096] The medical condition input section can have the generation AI analyze the input content in real time and automatically generate follow-up questions to present to the user. For example, a function can be added to the medical condition text input screen, where the generation AI can analyze the input content in real time and automatically generate follow-up questions to present to the user. For example, if you enter "I have a bad headache," a follow-up question such as "When did your headache start?" will be displayed. The generation AI can also analyze the text input of the medical condition and automatically generate follow-up questions to complement the necessary information. For example, if you enter "I have a fever," a question such as "What is your body temperature?" will be displayed. In addition, a function can be added where the generation AI can analyze the input content in real time when entering the medical condition text and automatically generate follow-up questions to present to the user. For example, if you enter "I have a cough," a question such as "How often do you cough?" will be displayed. This allows the user to enter more detailed medical conditions.

[0097] The medical condition input section can refer to medical condition data previously entered by the user and automatically complete the information if there are similar symptoms. For example, a function is added to the medical condition text input screen that allows the user to refer to medical condition data previously entered. For example, if there are symptoms similar to medical conditions previously entered, the information is automatically completed. In addition, a function is added in which the generation AI analyzes the user's past medical condition data and automatically completes the information if there are similar symptoms. For example, a question such as "Have you had similar symptoms in the past?" is displayed. In addition, a function is added in which, when entering medical condition text, the user refers to medical condition data previously entered by the user and automatically completes the information if there are similar symptoms. For example, a question such as "If you have had similar symptoms in the past, please enter their details" is displayed. This allows the user to refer to past medical condition data and automatically complete similar symptoms.

[0098] The medical condition input unit uses the emotion estimation function to analyze the emotional state of the user when entering the medical condition, and can suggest counseling services if the user has strong negative emotions. For example, when the user enters the medical condition as text, the emotion estimation function is used to analyze the user's emotional state, and if the user has strong negative emotions, counseling services are suggested. For example, a message such as "You seem to be under a lot of stress. Would you like to try counseling?" is displayed. The medical condition input unit also uses the emotion estimation function to analyze the user's emotional state when entering the medical condition, and if the user has strong negative emotions, counseling services are suggested. For example, a message such as "You seem to be feeling very anxious. Would you like to try counseling?" is displayed. The medical condition input unit also uses the emotion estimation function to analyze the user's emotional state when entering the medical condition, and if the user has strong negative emotions, counseling services are suggested. For example, a message such as "You seem to be feeling depressed. Would you like to try counseling?" is displayed. This makes it possible to suggest counseling services if the user has negative emotions.

[0099] The medical condition input unit uses natural language processing technology to allow the user to input medical conditions simply by speaking. For example, a voice input function can be added to the medical condition text input screen, allowing the user to input medical conditions simply by speaking. For example, if the user says, "I've had a bad headache and fever since yesterday," it will be automatically converted into text. In addition, natural language processing technology is used to convert what the user says into text in real time and display it on the medical condition input screen. For example, if the user says, "I have a cough," it will be automatically converted into text. In addition, the voice input function is used to analyze what the user says and automatically extract the necessary information. For example, if the user says, "I have a rash," it will be automatically converted into text and displayed on the medical condition input screen. This allows the user to easily input their medical condition.

[0100] The media upload section allows the generation AI to perform image analysis in real time and automatically highlight any abnormalities. For example, a function will be added to the photo and video upload screen where the generation AI will perform image analysis in real time and automatically highlight any abnormalities. For example, when uploading a photo of a rash, abnormal areas will be highlighted in red. In addition, a function will be added where the generation AI will analyze uploaded photos and videos and automatically highlight any abnormalities. For example, when uploading a video of a cough, abnormal movements will be detected and highlighted. In addition, a function will be added where the generation AI will perform image analysis in real time when uploading photos and videos and automatically highlight any abnormalities. For example, when uploading a photo of skin, abnormal areas will be automatically highlighted. This allows abnormalities to be automatically highlighted in images and videos uploaded by users.

[0101] The media upload unit allows the generation AI to compare with past image data and evaluate the progression of symptoms. For example, a function is added to the photo or video upload screen where the generation AI compares with past image data and evaluates the progression of symptoms. For example, a past photo of a rash is compared with a current photo to evaluate the progression. In addition, a function is added where the generation AI compares uploaded photos or videos with past data and evaluates the progression of symptoms. For example, a past video of a cough is compared with a current video to evaluate the progression. In addition, a function is added where, when uploading photos or videos, the generation AI compares with past image data and evaluates the progression of symptoms. For example, a past photo of skin is compared with a current photo to evaluate the progression. This allows the user to evaluate the progression of symptoms.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: In the basic information input section, the user inputs basic information such as age, gender, whether or not they have a chronic illness, etc. For example, the user inputs information such as "45 years old, female, no chronic illnesses." Step 2: The symptom input unit describes the symptom in detail in text based on the basic information input by the basic information input unit. For example, the symptom is input in the form of "I've had a bad headache and fever since yesterday." Step 3: The media upload unit uploads photos or videos to complement the medical condition input by the medical condition input unit. For example, if the patient has a rash, the patient can take a photo of it and upload it. It is also possible to take a video of the patient coughing and upload it. Step 4: The judgment unit comprehensively analyzes basic information, medical condition, photos, and videos to determine the need for hospital visits. For example, the generation AI may make a judgment such as, "This symptom is highly urgent, so you should go to the hospital immediately." Step 5: The notification unit notifies the user of the decision made by the decision unit as to whether or not to visit the hospital. For example, the notification may say, "There is no need to visit the hospital, but if your symptoms worsen, please consult a doctor."

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 basic information input section for inputting basic information such as age, gender, and whether or not a user has a chronic illness; a disease condition input unit that describes the disease condition in detail in text based on the basic information input by the basic information input unit; a media upload unit that uploads photos or videos to supplement the medical condition input by the medical condition input unit; a determination unit that comprehensively analyzes the basic information, the medical condition, the photograph, and the video and determines the need for hospital visits; a notification unit that notifies the user of the possibility of visiting the hospital determined by the determination unit. A system characterized by:

2. The basic information input unit Enter the user's past medical history or family medical history 2. The system of claim 1.

3. The basic information input unit Input the user's lifestyle habits (diet, exercise, sleep patterns) 2. The system of claim 1.

4. The basic information input unit Analyzing the user's emotional state as they type and providing advice to relax if stress or anxiety is high 2. The system of claim 1.

5. The basic information input unit Voice recognition technology allows the user to input basic information simply by speaking 2. The system of claim 1.

6. The basic information input unit Acquires the user's location information and displays the congestion status or waiting time of local medical institutions 2. The system of claim 1.

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