System

Abstract

An object of a system according to an embodiment is to enable citizens to quickly find an optimal hospital in an emergency.SOLUTION: A system includes an input unit, an analysis unit, a selection unit, and a guide unit. The input unit inputs a medical history or a symptom of a user. The analysis unit analyzes the information input by the input unit. The selection unit selects an optimal hospital based on the information analyzed by the analysis unit. The guidance unit guides the shortest route to the hospital selected by the selection 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] Conventional technology has the problem that it is difficult for citizens to quickly find the most suitable hospital in an emergency.

[0005] The system according to the embodiment aims to enable citizens to quickly find the most suitable hospital in the event of an emergency. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a selection unit, and a guidance unit. The input unit inputs the user's medical history or symptoms. The analysis unit analyzes the information input by the input unit. The selection unit selects the most suitable hospital based on the information analyzed by the analysis unit. The guidance unit provides guidance on the shortest route to the hospital selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment allows citizens to quickly find the most suitable hospital in an emergency. [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 SOS app according to the embodiment of the present invention is an app that can be used by citizens in emergencies, and is a system that guides citizens to the shortest route to the most suitable hospital based on their medical history and current symptoms. This allows citizens to quickly reach the appropriate medical institution in an emergency.

[0029] An SOS app according to an embodiment includes an input unit, an analysis unit, a selection unit, and a guidance unit. The input unit inputs a user's medical history and symptoms. For example, a user launches the app and inputs a history of heart disease and current chest pain. The input unit can also input the user's past medical history and allergy information. The analysis unit analyzes the information input by the input unit. For example, the generation AI analyzes the input medical history and symptoms and generates basic data for selecting the most appropriate medical institution. The analysis unit can also analyze the information using data mining or machine learning algorithms. The selection unit selects the most appropriate hospital based on the information analyzed by the analysis unit. For example, if a user has a history of heart disease and is currently experiencing chest pain, the generation AI selects a medical institution specializing in cardiac care. The selection unit can also select a hospital taking into account factors such as the hospital's specialty, current congestion status, and distance. The guidance unit provides guidance on the shortest route to the hospital selected by the selection unit. For example, the generation AI displays transportation options and travel time from the user's current location to the hospital. The guidance unit can also analyze map data and traffic information to suggest the optimal route, allowing the SOS app according to the embodiment to select the most suitable hospital based on the user's medical history and symptoms and provide guidance on the shortest route.

[0030] The input unit analyzes the user's voice input in real time and can automatically convert medical history and symptoms into text. For example, when a user enters their medical history and symptoms into the app via voice, the generation AI analyzes the voice in real time and converts it into text data. For example, if a user says, "I have a history of heart disease and am currently experiencing chest pain," the content is automatically converted into text. In addition, when the input unit receives voice input, the generation AI analyzes the user's pronunciation and accent to generate accurate text data. For example, even if the user speaks in a dialect or accent, the generation AI recognizes this and converts it into accurate text. In addition, the input unit analyzes the information entered by the user via voice, understanding the context, and converts it into appropriate medical terminology. For example, if the user enters "my chest hurts," it converts it into text as "chest pain." This allows the user's voice input to be analyzed in real time and medical history and symptoms to be automatically converted into text.

[0031] The input unit can refer to the user's past medical data and confirm the accuracy of the information entered. For example, the input unit has the generation AI compare the medical history and symptoms entered by the user with past medical data to confirm accuracy. For example, if there is a record of a past diagnosis of heart disease, it checks whether the current symptoms match. The input unit also has the generation AI refer to the user's electronic medical record and medical records to check whether the entered information is consistent with past data. For example, if there is a record of past allergies, it checks against the newly entered allergy information. The input unit also has the generation AI compare the information entered by the user with past medical history and prescription history to confirm accuracy. For example, it checks whether the medications prescribed in the past match the current symptoms. This allows the user's past medical data to be referenced and the accuracy of the entered information to be confirmed.

[0032] The input unit collects biometric data from the user's wearable device and can complement the input of medical history and symptoms. The input unit collects biometric data such as heart rate and blood pressure from the wearable device worn by the user, and the generation AI complements the input of medical history and symptoms based on that data. For example, an abnormally high heart rate is entered as a heart-related symptom. The input unit also analyzes the data collected from the wearable device in real time, and the generation AI grasps the user's current health condition. For example, it evaluates fatigue and stress levels based on sleep data and exercise data. The input unit also automatically complements the input of medical history and symptoms based on the data from the user's wearable device. For example, it can confirm a history of diabetes based on blood glucose level data. This allows the input of biometric data from the user's wearable device to complement the input of medical history and symptoms.

[0033] The input unit collects information from the user's family or friends to understand more detailed medical history or symptoms. In the input unit, for example, with the user's permission, the generation AI collects information from family and friends to complement the input of the medical history and symptoms. For example, past medical history and allergy information known to family members are added. In addition, the input unit analyzes information provided by family and friends through the app to understand the user's medical history and symptoms in more detail. For example, past surgical history and treatment history known to family members are input. In addition, the input unit complements the input of the medical history and symptoms based on information from the user's family and friends. For example, recent health conditions and symptoms known to friends are added. In this way, information from the user's family and friends can be collected to understand more detailed medical history and symptoms.

[0034] The selection unit can analyze hospitals' past treatment performance data and select the hospital with the highest success rate. For example, the selection unit uses the generating AI to analyze hospitals' past treatment performance data and select hospitals with a high success rate for specific symptoms or illnesses. For example, it selects hospitals with a strong track record of treating heart disease. The selection unit also uses the generating AI to identify hospitals with high success rates based on the hospitals' treatment performance data and introduce them to the user. For example, it takes into account past surgical success rates and post-treatment recovery rates. The selection unit also uses the generating AI to analyze hospitals' past treatment performance data and select hospitals with a high success rate for specific symptoms. For example, it selects hospitals with a high success rate for cancer treatment. This allows the selection of the hospital with the highest success rate by analyzing hospitals' past treatment performance data.

[0035] The selection unit can select a hospital with the most suitable doctor based on the specialty or years of experience of the hospital's doctors. For example, the selection unit uses the generation AI to analyze the specialty and years of experience of the hospital's doctors and select a hospital with the most suitable doctor for a specific symptom or disease. For example, it selects a hospital with many cardiologists. The selection unit also identifies a hospital with the most suitable doctor based on the doctor's specialty and years of experience and guides the user to the hospital. For example, it selects a hospital with doctors with many years of experience. The selection unit also uses the generation AI to analyze the specialty and years of experience of the hospital's doctors and selects a hospital with the most suitable doctor for a specific symptom. For example, it selects a hospital with many cancer treatment specialists. This makes it possible to select a hospital with the most suitable doctor, taking into account the specialty and years of experience of the hospital's doctors.

[0036] The selection unit can analyze hospital facilities and the state of adoption of the latest technology to select a hospital where the most advanced treatment can be received. For example, the selection unit uses a generating AI to analyze a hospital's facilities and the state of adoption of the latest technology to select a hospital where the most advanced treatment can be received. For example, it selects a hospital that has the latest MRI equipment or robotic surgery system. Furthermore, based on the hospital's facilities and the state of adoption of technology, the selection unit uses the generating AI to identify a hospital where the most advanced treatment can be received and guides the user there. For example, it selects a hospital with the latest cancer treatment technology. Furthermore, the selection unit uses a generating AI to analyze a hospital's facilities and the state of adoption of the latest technology to select a hospital where the most advanced treatment can be received for a specific symptom. For example, it selects a hospital with the latest treatment technology for heart disease. This allows the selection of a hospital where the most advanced treatment can be received by analyzing a hospital's facilities and the state of adoption of the latest technology.

[0037] The selection unit can analyze hospital patient satisfaction data and select the hospital with the highest rating. In the selection unit, for example, the generation AI analyzes hospital patient satisfaction data and selects the hospital with the highest rating. For example, the selection unit evaluates based on feedback and reviews from patients. The selection unit also identifies the hospital with the highest rating based on the patient satisfaction data and guides the user to it. For example, the selection unit selects a hospital with high patient satisfaction. The selection unit also analyzes hospital patient satisfaction data and selects the hospital with the highest rating for a specific symptom. For example, the selection unit selects a hospital that has a high rating for treating heart disease. In this way, the selection unit can analyze hospital patient satisfaction data and select the hospital with the highest rating.

[0038] The guidance unit can analyze real-time traffic information and suggest the quickest route to reach the destination. For example, the generation AI in the guidance unit analyzes real-time traffic information and suggests the quickest route based on the current traffic conditions. For example, it selects the optimal route taking into account congestion and accident information. The guidance unit also obtains real-time data from a traffic information service, and the generation AI analyzes it to calculate the shortest route. For example, it suggests a route based on the operation status of public transportation and road congestion. The guidance unit also analyzes real-time traffic information using the generation AI to suggest the shortest route from the user's current location to the hospital. For example, it suggests an alternative route to avoid traffic congestion. This allows the system to analyze real-time traffic information and suggest the quickest route to reach the destination.

[0039] The guidance unit can customize the optimal route based on the user's means of transportation. In the guidance unit, for example, the generation AI takes into account the user's means of transportation and customizes the optimal route. For example, if the user is traveling on foot, the generation AI will suggest a pedestrian-only route. Furthermore, if the user is traveling by bicycle, the guidance unit will suggest the optimal route by taking into account bicycle-only roads and safe routes. For example, roads with bicycle lanes will be selected preferentially. Furthermore, if the user is traveling by car, the generation AI will suggest the optimal route by taking into account the location of parking lots and traffic regulations. For example, the guidance unit will provide information on parking lots near hospitals and provide guidance on the route from the parking lot to the hospital. This allows the optimal route to be customized by taking into account the user's means of transportation.

[0040] The guidance unit can analyze the operation status of public transportation and propose the most efficient transfer route. In the guidance unit, for example, the generation AI analyzes the operation status of public transportation in real time and proposes the most efficient transfer route. For example, it selects the optimal route taking into account train and bus schedules. In addition, the guidance unit uses the generation AI to propose a route that minimizes transfer and waiting times based on public transportation operation data. For example, it selects a route with minimal waiting time at transfer stations. In addition, the guidance unit uses the generation AI to analyze the operation status of public transportation and propose the most efficient transfer route from the user's current location to the hospital. For example, it selects a route with the fewest number of transfers. This makes it possible to analyze the operation status of public transportation and propose the most efficient transfer route.

[0041] The guidance unit can refer to the user's past travel history and suggest the most familiar route. For example, the generation AI in the guidance unit refers to the user's past travel history and suggests the most familiar route. For example, it prioritizes selecting routes that the user has used frequently in the past. Furthermore, the guidance unit identifies the most familiar route based on the user's travel history data and guides the user along it. For example, it takes into account roads and means of transportation that have been used in the past. Furthermore, the generation AI in the guidance unit analyzes the user's past travel history and suggests the most familiar route. For example, it selects public transportation routes that the user has used in the past. In this way, it is possible to refer to the user's past travel history and suggest the most familiar route.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The input unit analyzes the user's voice input in real time and can automatically convert their medical history and symptoms into text. For example, when a user enters their medical history and symptoms into the app via voice, the generation AI analyzes the voice in real time and converts it into text data. For example, if a user says, "I have a history of heart disease and am currently experiencing chest pain," the content is automatically converted into text. In addition, when the input unit receives voice input, the generation AI analyzes the user's pronunciation and accent to generate accurate text data. For example, even if the user speaks with a dialect or accent, the generation AI recognizes this and converts it into accurate text. In addition, the input unit analyzes the information entered by the user via voice, understanding the context, and converts it into appropriate medical terminology. For example, if the user enters "my chest hurts," it converts it into text as "chest pain." This allows the user's voice input to be analyzed in real time and medical history and symptoms to be automatically converted into text.

[0044] The input unit can refer to the user's past medical data and confirm the accuracy of the information entered. For example, the generation AI compares the medical history and symptoms entered by the user with past medical data to confirm accuracy. For example, if there is a record of a past diagnosis of heart disease, it checks whether the current symptoms match. The input unit also refers to the user's electronic medical record and medical records to check whether the entered information is consistent with past data. For example, if there is a record of past allergies, it checks against the newly entered allergy information. The input unit also compares the information entered by the user with past medical history and prescription history to confirm accuracy. For example, it checks whether the medications prescribed in the past match the current symptoms. This allows the generation AI to refer to the user's past medical data and confirm the accuracy of the entered information.

[0045] The input unit collects biometric data from the user's wearable device and can complement the input of medical history and symptoms. For example, biometric data such as heart rate and blood pressure can be collected from the wearable device worn by the user, and the generation AI can complement the input of medical history and symptoms based on that data. For example, an abnormally high heart rate can be entered as a heart-related symptom. The input unit also analyzes the data collected from the wearable device in real time, and the generation AI can grasp the user's current health condition. For example, it can evaluate fatigue and stress levels based on sleep data and exercise data. The input unit also automatically complements the input of medical history and symptoms based on the data from the user's wearable device. For example, it can confirm a history of diabetes based on blood glucose level data. This allows the input of biometric data from the user's wearable device to complement the input of medical history and symptoms.

[0046] The input unit can collect information from the user's family or friends to understand more detailed medical history or symptoms. For example, with the user's permission, the generation AI collects information from family and friends to complement the input of medical history and symptoms. For example, past medical history and allergy information known to family members are added. The input unit also analyzes information provided by family and friends through the app to understand the user's medical history and symptoms in more detail. For example, past surgical history and treatment history known to family members are input. The input unit also complements the input of medical history and symptoms based on information from the user's family and friends. For example, recent health conditions and symptoms known to friends are added. This allows information to be collected from the user's family and friends to understand more detailed medical history and symptoms.

[0047] The selection unit can analyze hospitals' past treatment performance data and select the hospital with the highest success rate. For example, the generation AI analyzes hospitals' past treatment performance data and selects hospitals with a high success rate for specific symptoms or illnesses. For example, it selects hospitals with a strong track record of treating heart disease. The selection unit also uses the generation AI to identify hospitals with high success rates based on the hospitals' treatment performance data and guide the user to these hospitals. For example, it takes into account past surgical success rates and post-treatment recovery rates. The selection unit also uses the generation AI to analyze hospitals' treatment performance data and select hospitals with a high success rate for specific symptoms. For example, it selects hospitals with a high success rate for cancer treatment. This allows the generation AI to analyze hospitals' past treatment performance data and select the hospital with the highest success rate.

[0048] The selection unit can select a hospital with the most suitable doctor based on the specialty or years of experience of the hospital's doctors. For example, the generation AI analyzes the specialty and years of experience of the hospital's doctors and selects a hospital with the most suitable doctor for a specific symptom or illness. For example, it selects a hospital with many cardiologists. The selection unit also identifies hospitals with the most suitable doctors based on the doctors' specialty and years of experience and guides the user to the hospital. For example, it selects a hospital with doctors with many years of experience. The selection unit also analyzes the specialty and years of experience of the hospital's doctors and selects a hospital with the most suitable doctor for a specific symptom. For example, it selects a hospital with many cancer treatment specialists. This makes it possible to select a hospital with the most suitable doctor, taking into account the specialty and years of experience of the hospital's doctors.

[0049] The selection unit can analyze hospital facilities and the latest technology implementation status to select a hospital that offers the most advanced treatment. For example, the generating AI can analyze a hospital's facilities and the latest technology implementation status to select a hospital that offers the most advanced treatment. For example, it can select a hospital that has the latest MRI equipment or robotic surgery system. Based on the hospital's facilities and technology implementation status, the generating AI can identify a hospital that offers the most advanced treatment and guide the user to that hospital. For example, it can select a hospital with the latest cancer treatment technology. The selection unit can also analyze a hospital's facilities and the latest technology implementation status to select a hospital that offers the most advanced treatment for a specific symptom. For example, it can select a hospital with the latest heart disease treatment technology. This allows the generating AI to analyze a hospital's facilities and the latest technology implementation status to select a hospital that offers the most advanced treatment.

[0050] The guidance unit can analyze real-time traffic information and suggest the quickest route to reach the destination. For example, the generation AI analyzes real-time traffic information and suggests the quickest route based on the current traffic conditions. For example, it selects the optimal route taking into account congestion and accident information. The guidance unit also obtains real-time data from traffic information services, and the generation AI analyzes it to calculate the shortest route. For example, it suggests a route based on the operation status of public transportation and road congestion. The guidance unit also analyzes real-time traffic information and suggests the shortest route from the user's current location to the hospital. For example, it suggests an alternative route to avoid traffic congestion. This allows the system to analyze real-time traffic information and suggest the quickest route to reach the destination.

[0051] The guidance unit can customize the optimal route based on the user's means of transportation. For example, the generation AI takes into account the user's means of transportation and customizes the optimal route. For example, if the user is traveling on foot, the generation AI will suggest a pedestrian-only route. Furthermore, if the user is traveling by bicycle, the generation AI will suggest the optimal route by taking into account bicycle-only roads and safe routes. For example, roads with bicycle lanes will be selected with priority. Furthermore, if the user is traveling by car, the generation AI will suggest the optimal route by taking into account the location of parking lots and traffic regulations. For example, the generation AI will provide information on parking lots near hospitals and guide the user on the route from the parking lot to the hospital. This allows the optimal route to be customized by taking into account the user's means of transportation.

[0052] The guidance unit can analyze the operation status of public transportation and suggest the most efficient transfer route. For example, the generation AI analyzes the operation status of public transportation in real time and suggests the most efficient transfer route. For example, it selects the optimal route by taking into account train and bus schedules. The guidance unit also uses the generation AI to suggest routes that minimize transfer and waiting times based on public transportation operation data. For example, it selects routes with minimal waiting times at transfer stations. The guidance unit also uses the generation AI to analyze the operation status of public transportation and suggest the most efficient transfer route from the user's current location to the hospital. For example, it selects a route with the fewest transfers. This makes it possible to analyze the operation status of public transportation and suggest the most efficient transfer route.

[0053] The guidance unit can refer to the user's past travel history and suggest the most familiar route. For example, the generation AI can refer to the user's past travel history and suggest the most familiar route. For example, it can prioritize routes that the user has used frequently in the past. The guidance unit also identifies the most familiar route based on the user's travel history data and guides the user along it. For example, it can take into account roads and means of transportation that have been used in the past. The guidance unit also analyzes the user's past travel history and suggests the most familiar route. For example, it can select public transportation routes that the user has used in the past. This allows the generation AI to refer to the user's past travel history and suggest the most familiar route.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The input unit inputs the user's medical history and symptoms. For example, the user starts the app and inputs their medical history of heart disease and current chest pain. The input unit also allows the user to input their past medical history and allergy information. Step 2: The analysis unit analyzes the information entered by the input unit. For example, the generation AI analyzes the entered medical history and symptoms to generate basic data for selecting the most appropriate medical institution. The analysis unit can also analyze the information using data mining and machine learning algorithms. Step 3: The selection unit selects the most suitable hospital based on the information analyzed by the analysis unit. For example, if a patient has a history of heart disease and is currently experiencing chest pain, the generation AI will select a medical institution specializing in cardiac care. The selection unit can also select a hospital based on factors such as the hospital's specialty, current congestion status, and distance. Step 4: The guidance unit guides the user along the shortest route to the hospital selected by the selection unit. For example, the generation AI displays the transportation options and travel time from the user's current location to the hospital. The guidance unit can also analyze map data and traffic information to suggest the optimal route.

[0056] (Example 2) The SOS app according to the embodiment of the present invention is an app that can be used by citizens in emergencies, and is a system that guides citizens to the shortest route to the most suitable hospital based on their medical history and current symptoms. This allows citizens to quickly reach the appropriate medical institution in an emergency.

[0057] An SOS app according to an embodiment includes an input unit, an analysis unit, a selection unit, and a guidance unit. The input unit inputs a user's medical history and symptoms. For example, a user launches the app and inputs a history of heart disease and current chest pain. The input unit can also input the user's past medical history and allergy information. The analysis unit analyzes the information input by the input unit. For example, the generation AI analyzes the input medical history and symptoms and generates basic data for selecting the most appropriate medical institution. The analysis unit can also analyze the information using data mining or machine learning algorithms. The selection unit selects the most appropriate hospital based on the information analyzed by the analysis unit. For example, if a user has a history of heart disease and is currently experiencing chest pain, the generation AI selects a medical institution specializing in cardiac care. The selection unit can also select a hospital taking into account factors such as the hospital's specialty, current congestion status, and distance. The guidance unit provides guidance on the shortest route to the hospital selected by the selection unit. For example, the generation AI displays transportation options and travel time from the user's current location to the hospital. The guidance unit can also analyze map data and traffic information to suggest the optimal route, allowing the SOS app according to the embodiment to select the most suitable hospital based on the user's medical history and symptoms and provide guidance on the shortest route.

[0058] The input unit analyzes the user's voice input in real time and can automatically convert medical history and symptoms into text. For example, when a user enters their medical history and symptoms into the app via voice, the generation AI analyzes the voice in real time and converts it into text data. For example, if a user says, "I have a history of heart disease and am currently experiencing chest pain," the content is automatically converted into text. In addition, when the input unit receives voice input, the generation AI analyzes the user's pronunciation and accent to generate accurate text data. For example, even if the user speaks in a dialect or accent, the generation AI recognizes this and converts it into accurate text. In addition, the input unit analyzes the information entered by the user via voice, understanding the context, and converts it into appropriate medical terminology. For example, if the user enters "my chest hurts," it converts it into text as "chest pain." This allows the user's voice input to be analyzed in real time and medical history and symptoms to be automatically converted into text.

[0059] The input unit can refer to the user's past medical data and confirm the accuracy of the information entered. For example, the input unit has the generation AI compare the medical history and symptoms entered by the user with past medical data to confirm accuracy. For example, if there is a record of a past diagnosis of heart disease, it checks whether the current symptoms match. The input unit also has the generation AI refer to the user's electronic medical record and medical records to check whether the entered information is consistent with past data. For example, if there is a record of past allergies, it checks against the newly entered allergy information. The input unit also has the generation AI compare the information entered by the user with past medical history and prescription history to confirm accuracy. For example, it checks whether the medications prescribed in the past match the current symptoms. This allows the user's past medical data to be referenced and the accuracy of the entered information to be confirmed.

[0060] The input unit can analyze the user's emotional state and provide advice to relax if stress or anxiety is high. For example, when the user inputs their medical history or symptoms, the generation AI uses an emotion estimation function to analyze the user's emotional state. For example, it measures the level of stress or anxiety from the tone of voice and facial expression. The input unit also uses the emotion estimation function to provide relaxation advice if the user feels high stress or anxiety while inputting. For example, it displays a message such as "Take a deep breath and relax." The input unit also analyzes the user's emotional state in real time, and if stress or anxiety is high, the generation AI suggests specific ways to relax. For example, it provides advice such as "Take a short break to relax." This allows the system to analyze the user's emotional state and provide relaxation advice if stress or anxiety is high.

[0061] The input unit collects biometric data from the user's wearable device and can complement the input of medical history and symptoms. The input unit collects biometric data such as heart rate and blood pressure from the wearable device worn by the user, and the generation AI complements the input of medical history and symptoms based on that data. For example, an abnormally high heart rate is entered as a heart-related symptom. The input unit also analyzes the data collected from the wearable device in real time, and the generation AI grasps the user's current health condition. For example, it evaluates fatigue and stress levels based on sleep data and exercise data. The input unit also automatically complements the input of medical history and symptoms based on the data from the user's wearable device. For example, it can confirm a history of diabetes based on blood glucose level data. This allows the input of biometric data from the user's wearable device to complement the input of medical history and symptoms.

[0062] The input unit collects information from the user's family or friends to understand more detailed medical history or symptoms. In the input unit, for example, with the user's permission, the generation AI collects information from family and friends to complement the input of the medical history and symptoms. For example, past medical history and allergy information known to family members are added. In addition, the input unit analyzes information provided by family and friends through the app to understand the user's medical history and symptoms in more detail. For example, past surgical history and treatment history known to family members are input. In addition, the input unit complements the input of the medical history and symptoms based on information from the user's family and friends. For example, recent health conditions and symptoms known to friends are added. In this way, information from the user's family and friends can be collected to understand more detailed medical history and symptoms.

[0063] The input unit can analyze the user's emotions and provide positive feedback to encourage the input of medical history or symptoms. For example, when a user inputs their medical history or symptoms, the input unit uses the emotion estimation function to analyze the user's emotions and provide positive feedback. For example, it displays a message such as, "Input is going well. You're almost there!". The input unit also uses the emotion estimation function to encourage input by providing positive feedback if the user feels negative emotions while inputting. For example, it displays a message such as, "Great! Keep going!". The input unit also analyzes the user's emotional state in real time and provides positive feedback to encourage input. For example, it displays a message such as, "Your health information is very important. Thank you!". In this way, the input unit can analyze the user's emotions and provide positive feedback to encourage input.

[0064] The selection unit can analyze hospitals' past treatment performance data and select the hospital with the highest success rate. For example, the selection unit uses the generating AI to analyze hospitals' past treatment performance data and select hospitals with a high success rate for specific symptoms or illnesses. For example, it selects hospitals with a strong track record of treating heart disease. The selection unit also uses the generating AI to identify hospitals with high success rates based on the hospitals' treatment performance data and introduce them to the user. For example, it takes into account past surgical success rates and post-treatment recovery rates. The selection unit also uses the generating AI to analyze hospitals' past treatment performance data and select hospitals with a high success rate for specific symptoms. For example, it selects hospitals with a high success rate for cancer treatment. This allows the selection of the hospital with the highest success rate by analyzing hospitals' past treatment performance data.

[0065] The selection unit can select a hospital with the most suitable doctor based on the specialty or years of experience of the hospital's doctors. For example, the selection unit uses the generation AI to analyze the specialty and years of experience of the hospital's doctors and select a hospital with the most suitable doctor for a specific symptom or disease. For example, it selects a hospital with many cardiologists. The selection unit also identifies a hospital with the most suitable doctor based on the doctor's specialty and years of experience and guides the user to the hospital. For example, it selects a hospital with doctors with many years of experience. The selection unit also uses the generation AI to analyze the specialty and years of experience of the hospital's doctors and selects a hospital with the most suitable doctor for a specific symptom. For example, it selects a hospital with many cancer treatment specialists. This makes it possible to select a hospital with the most suitable doctor, taking into account the specialty and years of experience of the hospital's doctors.

[0066] The selection unit can analyze the user's emotional state and select a hospital that gives a sense of security. For example, the selection unit uses an emotion estimation function to analyze the user's emotional state and select a hospital that gives a sense of security. For example, it selects a hospital that provides a relaxing environment for the user. The selection unit also uses the emotion estimation function to identify a hospital where the user feels secure, and the generation AI guides the user to that hospital. For example, it selects a hospital with high patient satisfaction. The selection unit also analyzes the user's emotional state in real time and selects a hospital that gives a sense of security. For example, it selects a hospital where the user has had a good experience in the past. In this way, it is possible to analyze the user's emotional state and select a hospital that gives a sense of security.

[0067] The selection unit can analyze hospital facilities and the state of adoption of the latest technology to select a hospital where the most advanced treatment can be received. For example, the selection unit uses a generating AI to analyze a hospital's facilities and the state of adoption of the latest technology to select a hospital where the most advanced treatment can be received. For example, it selects a hospital that has the latest MRI equipment or robotic surgery system. Furthermore, based on the hospital's facilities and the state of adoption of technology, the selection unit uses the generating AI to identify a hospital where the most advanced treatment can be received and guides the user there. For example, it selects a hospital with the latest cancer treatment technology. Furthermore, the selection unit uses a generating AI to analyze a hospital's facilities and the state of adoption of the latest technology to select a hospital where the most advanced treatment can be received for a specific symptom. For example, it selects a hospital with the latest treatment technology for heart disease. This allows the selection of a hospital where the most advanced treatment can be received by analyzing a hospital's facilities and the state of adoption of the latest technology.

[0068] The selection unit can analyze hospital patient satisfaction data and select the hospital with the highest rating. In the selection unit, for example, the generation AI analyzes hospital patient satisfaction data and selects the hospital with the highest rating. For example, the selection unit evaluates based on feedback and reviews from patients. The selection unit also identifies the hospital with the highest rating based on the patient satisfaction data and guides the user to it. For example, the selection unit selects a hospital with high patient satisfaction. The selection unit also analyzes hospital patient satisfaction data and selects the hospital with the highest rating for a specific symptom. For example, the selection unit selects a hospital that has a high rating for treating heart disease. In this way, the selection unit can analyze hospital patient satisfaction data and select the hospital with the highest rating.

[0069] The selection unit can analyze the emotions the user has toward the selected hospital and select a hospital that elicits positive emotions. For example, the selection unit uses an emotion estimation function by the generation AI to analyze the emotions the user has toward the selected hospital and selects a hospital that elicits positive emotions. For example, it selects a hospital that makes the user feel safe. The selection unit also uses the emotion estimation function to analyze whether the user has positive emotions toward the selected hospital and the generation AI guides the user to that hospital. For example, it selects a hospital where the user has had a good experience in the past. The selection unit also analyzes the user's emotional state in real time and selects a hospital that elicits positive emotions. For example, it selects a hospital that provides a relaxing environment for the user. In this way, it is possible to analyze the emotions the user has toward the selected hospital and select a hospital that elicits positive emotions.

[0070] The guidance unit can analyze real-time traffic information and suggest the quickest route to reach the destination. For example, the generation AI in the guidance unit analyzes real-time traffic information and suggests the quickest route based on the current traffic conditions. For example, it selects the optimal route taking into account congestion and accident information. The guidance unit also obtains real-time data from a traffic information service, and the generation AI analyzes it to calculate the shortest route. For example, it suggests a route based on the operation status of public transportation and road congestion. The guidance unit also analyzes real-time traffic information using the generation AI to suggest the shortest route from the user's current location to the hospital. For example, it suggests an alternative route to avoid traffic congestion. This allows the system to analyze real-time traffic information and suggest the quickest route to reach the destination.

[0071] The guidance unit can customize the optimal route based on the user's means of transportation. In the guidance unit, for example, the generation AI takes into account the user's means of transportation and customizes the optimal route. For example, if the user is traveling on foot, the generation AI will suggest a pedestrian-only route. Furthermore, if the user is traveling by bicycle, the guidance unit will suggest the optimal route by taking into account bicycle-only roads and safe routes. For example, roads with bicycle lanes will be selected preferentially. Furthermore, if the user is traveling by car, the generation AI will suggest the optimal route by taking into account the location of parking lots and traffic regulations. For example, the guidance unit will provide information on parking lots near hospitals and provide guidance on the route from the parking lot to the hospital. This allows the optimal route to be customized by taking into account the user's means of transportation.

[0072] The guidance unit can analyze the user's emotional state and suggest a route to reduce stress. For example, the generation AI uses an emotion estimation function to analyze the user's emotional state and suggest a route to reduce stress. For example, it selects a quiet road or a scenic route. The guidance unit also uses the emotion estimation function to identify a route that is less likely to cause stress to the user, and the generation AI guides the user along that route. For example, it suggests a route that avoids crowded areas. The guidance unit also analyzes the user's emotional state in real time and suggests a specific route to reduce stress. For example, it selects a route that passes through a park with lots of greenery. This makes it possible to analyze the user's emotional state and suggest a route to reduce stress.

[0073] The guidance unit can analyze the operation status of public transportation and propose the most efficient transfer route. In the guidance unit, for example, the generation AI analyzes the operation status of public transportation in real time and proposes the most efficient transfer route. For example, it selects the optimal route taking into account train and bus schedules. In addition, the guidance unit uses the generation AI to propose a route that minimizes transfer and waiting times based on public transportation operation data. For example, it selects a route with minimal waiting time at transfer stations. In addition, the guidance unit uses the generation AI to analyze the operation status of public transportation and propose the most efficient transfer route from the user's current location to the hospital. For example, it selects a route with the fewest number of transfers. This makes it possible to analyze the operation status of public transportation and propose the most efficient transfer route.

[0074] The guidance unit can refer to the user's past travel history and suggest the most familiar route. For example, the generation AI in the guidance unit refers to the user's past travel history and suggests the most familiar route. For example, it prioritizes selecting routes that the user has used frequently in the past. Furthermore, the guidance unit identifies the most familiar route based on the user's travel history data and guides the user along it. For example, it takes into account roads and means of transportation that have been used in the past. Furthermore, the generation AI in the guidance unit analyzes the user's past travel history and suggests the most familiar route. For example, it selects public transportation routes that the user has used in the past. In this way, it is possible to refer to the user's past travel history and suggest the most familiar route.

[0075] The guidance unit can analyze the emotions of the user when receiving route guidance and provide a route guidance method that gives a sense of security. For example, the generation AI uses an emotion estimation function to analyze the emotions of the user when receiving route guidance and provide a guidance method that gives a sense of security. For example, it provides audio guidance that provides guidance in a gentle voice. The guidance unit also uses the emotion estimation function to identify a guidance method that gives the user a sense of security, and the generation AI provides that method. For example, it displays a visually easy-to-understand map. The guidance unit also analyzes the user's emotional state in real time and provides a specific guidance method that gives a sense of security. For example, it provides guidance while playing music that the user finds relaxing. In this way, it is possible to analyze the emotions of the user when receiving route guidance and provide a guidance method that gives a sense of security.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The input unit analyzes the user's voice input in real time and can automatically convert their medical history and symptoms into text. For example, when a user enters their medical history and symptoms into the app via voice, the generation AI analyzes the voice in real time and converts it into text data. For example, if a user says, "I have a history of heart disease and am currently experiencing chest pain," the content is automatically converted into text. In addition, when the input unit receives voice input, the generation AI analyzes the user's pronunciation and accent to generate accurate text data. For example, even if the user speaks with a dialect or accent, the generation AI recognizes this and converts it into accurate text. In addition, the input unit analyzes the information entered by the user via voice, understanding the context, and converts it into appropriate medical terminology. For example, if the user enters "my chest hurts," it converts it into text as "chest pain." This allows the user's voice input to be analyzed in real time and medical history and symptoms to be automatically converted into text.

[0078] The input unit can refer to the user's past medical data and confirm the accuracy of the information entered. For example, the generation AI compares the medical history and symptoms entered by the user with past medical data to confirm accuracy. For example, if there is a record of a past diagnosis of heart disease, it checks whether the current symptoms match. The input unit also refers to the user's electronic medical record and medical records to check whether the entered information is consistent with past data. For example, if there is a record of past allergies, it checks against the newly entered allergy information. The input unit also compares the information entered by the user with past medical history and prescription history to confirm accuracy. For example, it checks whether the medications prescribed in the past match the current symptoms. This allows the generation AI to refer to the user's past medical data and confirm the accuracy of the entered information.

[0079] The input unit can analyze the user's emotional state and provide advice to relax if stress or anxiety is high. For example, when the user inputs their medical history or symptoms, the generation AI uses the emotion estimation function to analyze the user's emotional state. For example, it measures the stress or anxiety level from the tone of voice and facial expression. The input unit also uses the emotion estimation function to provide relaxation advice if the user feels high stress or anxiety while inputting. For example, it displays a message such as "Take a deep breath and relax." The input unit also analyzes the user's emotional state in real time, and if stress or anxiety is high, the generation AI suggests specific ways to relax. For example, it provides advice such as "Take a short break to relax." This allows the system to analyze the user's emotional state and provide relaxation advice if stress or anxiety is high.

[0080] The input unit collects biometric data from the user's wearable device and can complement the input of medical history and symptoms. For example, biometric data such as heart rate and blood pressure can be collected from the wearable device worn by the user, and the generation AI can complement the input of medical history and symptoms based on that data. For example, an abnormally high heart rate can be entered as a heart-related symptom. The input unit also analyzes the data collected from the wearable device in real time, and the generation AI can grasp the user's current health condition. For example, it can evaluate fatigue and stress levels based on sleep data and exercise data. The input unit also automatically complements the input of medical history and symptoms based on the data from the user's wearable device. For example, it can confirm a history of diabetes based on blood glucose level data. This allows the input of biometric data from the user's wearable device to complement the input of medical history and symptoms.

[0081] The input unit can collect information from the user's family or friends to understand more detailed medical history or symptoms. For example, with the user's permission, the generation AI collects information from family and friends to complement the input of medical history and symptoms. For example, past medical history and allergy information known to family members are added. The input unit also analyzes information provided by family and friends through the app to understand the user's medical history and symptoms in more detail. For example, past surgical history and treatment history known to family members are input. The input unit also complements the input of medical history and symptoms based on information from the user's family and friends. For example, recent health conditions and symptoms known to friends are added. This allows information to be collected from the user's family and friends to understand more detailed medical history and symptoms.

[0082] The input unit can analyze the user's emotions and provide positive feedback to encourage input of medical history or symptoms. For example, when a user inputs their medical history or symptoms, the generation AI uses the emotion estimation function to analyze the user's emotions and provide positive feedback. For example, it displays a message such as, "Input is going well. You're almost there!". The input unit also uses the emotion estimation function to encourage input by providing positive feedback if the user feels negative emotions while inputting. For example, it displays a message such as, "Great! Keep going!". The input unit also analyzes the user's emotional state in real time and provides positive feedback to encourage input. For example, it displays a message such as, "Your health information is very important. Thank you!". This allows the input unit to analyze the user's emotions and provide positive feedback to encourage input.

[0083] The selection unit can analyze hospitals' past treatment performance data and select the hospital with the highest success rate. For example, the generation AI analyzes hospitals' past treatment performance data and selects hospitals with a high success rate for specific symptoms or illnesses. For example, it selects hospitals with a strong track record of treating heart disease. The selection unit also uses the generation AI to identify hospitals with high success rates based on the hospitals' treatment performance data and guide the user to these hospitals. For example, it takes into account past surgical success rates and post-treatment recovery rates. The selection unit also uses the generation AI to analyze hospitals' treatment performance data and select hospitals with a high success rate for specific symptoms. For example, it selects hospitals with a high success rate for cancer treatment. This allows the generation AI to analyze hospitals' past treatment performance data and select the hospital with the highest success rate.

[0084] The selection unit can select a hospital with the most suitable doctor based on the specialty or years of experience of the hospital's doctors. For example, the generation AI analyzes the specialty and years of experience of the hospital's doctors and selects a hospital with the most suitable doctor for a specific symptom or illness. For example, it selects a hospital with many cardiologists. The selection unit also identifies hospitals with the most suitable doctors based on the doctors' specialty and years of experience and guides the user to the hospital. For example, it selects a hospital with doctors with many years of experience. The selection unit also analyzes the specialty and years of experience of the hospital's doctors and selects a hospital with the most suitable doctor for a specific symptom. For example, it selects a hospital with many cancer treatment specialists. This makes it possible to select a hospital with the most suitable doctor, taking into account the specialty and years of experience of the hospital's doctors.

[0085] The selection unit can analyze the user's emotional state and select a hospital that gives a sense of security. For example, the generation AI uses an emotion estimation function to analyze the user's emotional state and select a hospital that gives a sense of security. For example, it selects a hospital that provides a relaxing environment for the user. The selection unit also uses the emotion estimation function to identify a hospital that gives the user a sense of security, and the generation AI guides the user to that hospital. For example, it selects a hospital with high patient satisfaction. The selection unit also analyzes the user's emotional state in real time and selects a hospital that gives a sense of security. For example, it selects a hospital where the user has had a good experience in the past. In this way, it is possible to analyze the user's emotional state and select a hospital that gives a sense of security.

[0086] The selection unit can analyze hospital facilities and the latest technology implementation status to select a hospital that offers the most advanced treatment. For example, the generating AI can analyze a hospital's facilities and the latest technology implementation status to select a hospital that offers the most advanced treatment. For example, it can select a hospital that has the latest MRI equipment or robotic surgery system. Based on the hospital's facilities and technology implementation status, the generating AI can identify a hospital that offers the most advanced treatment and guide the user to that hospital. For example, it can select a hospital with the latest cancer treatment technology. The selection unit can also analyze a hospital's facilities and the latest technology implementation status to select a hospital that offers the most advanced treatment for a specific symptom. For example, it can select a hospital with the latest heart disease treatment technology. This allows the generating AI to analyze a hospital's facilities and the latest technology implementation status to select a hospital that offers the most advanced treatment.

[0087] The guidance unit can analyze real-time traffic information and suggest the quickest route to reach the destination. For example, the generation AI analyzes real-time traffic information and suggests the quickest route based on the current traffic conditions. For example, it selects the optimal route taking into account congestion and accident information. The guidance unit also obtains real-time data from traffic information services, and the generation AI analyzes it to calculate the shortest route. For example, it suggests a route based on the operation status of public transportation and road congestion. The guidance unit also analyzes real-time traffic information and suggests the shortest route from the user's current location to the hospital. For example, it suggests an alternative route to avoid traffic congestion. This allows the system to analyze real-time traffic information and suggest the quickest route to reach the destination.

[0088] The guidance unit can customize the optimal route based on the user's means of transportation. For example, the generation AI takes into account the user's means of transportation and customizes the optimal route. For example, if the user is traveling on foot, the generation AI will suggest a pedestrian-only route. Furthermore, if the user is traveling by bicycle, the generation AI will suggest the optimal route by taking into account bicycle-only roads and safe routes. For example, roads with bicycle lanes will be selected with priority. Furthermore, if the user is traveling by car, the generation AI will suggest the optimal route by taking into account the location of parking lots and traffic regulations. For example, the generation AI will provide information on parking lots near hospitals and guide the user on the route from the parking lot to the hospital. This allows the optimal route to be customized by taking into account the user's means of transportation.

[0089] The guidance unit can analyze the user's emotional state and suggest routes to reduce stress. For example, the generation AI uses an emotion estimation function to analyze the user's emotional state and suggest routes to reduce stress. For example, it may select quiet roads or scenic routes. The guidance unit also uses the emotion estimation function to identify routes that are less likely to cause stress to the user, and the generation AI guides the user along those routes. For example, it may suggest routes that avoid crowded areas. The guidance unit also analyzes the user's emotional state in real time and suggest specific routes to reduce stress. For example, it may select a route that passes through a park with lots of greenery. This makes it possible to analyze the user's emotional state and suggest routes to reduce stress.

[0090] The guidance unit can analyze the operation status of public transportation and suggest the most efficient transfer route. For example, the generation AI analyzes the operation status of public transportation in real time and suggests the most efficient transfer route. For example, it selects the optimal route by taking into account train and bus schedules. The guidance unit also uses the generation AI to suggest routes that minimize transfer and waiting times based on public transportation operation data. For example, it selects routes with minimal waiting times at transfer stations. The guidance unit also uses the generation AI to analyze the operation status of public transportation and suggest the most efficient transfer route from the user's current location to the hospital. For example, it selects a route with the fewest transfers. This makes it possible to analyze the operation status of public transportation and suggest the most efficient transfer route.

[0091] The guidance unit can refer to the user's past travel history and suggest the most familiar route. For example, the generation AI can refer to the user's past travel history and suggest the most familiar route. For example, it can prioritize routes that the user has used frequently in the past. The guidance unit also identifies the most familiar route based on the user's travel history data and guides the user along it. For example, it can take into account roads and means of transportation that have been used in the past. The guidance unit also analyzes the user's past travel history and suggests the most familiar route. For example, it can select public transportation routes that the user has used in the past. This allows the generation AI to refer to the user's past travel history and suggest the most familiar route.

[0092] The guidance unit can analyze the emotions of the user when receiving route guidance and provide a route guidance method that gives a sense of security. For example, the generation AI can use the emotion estimation function to analyze the emotions of the user when receiving route guidance and provide a guidance method that gives a sense of security. For example, it can provide an audio guide that provides guidance in a gentle voice. The guidance unit can also use the emotion estimation function to identify a guidance method that gives the user a sense of security, and the generation AI can provide that method. For example, it can display a visually easy-to-understand map. The guidance unit can also analyze the user's emotional state in real time and provide a specific guidance method that gives a sense of security. For example, it can provide guidance while playing music that the user finds relaxing. In this way, it can analyze the emotions of the user when receiving route guidance and provide a guidance method that gives a sense of security.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The input unit inputs the user's medical history and symptoms. For example, the user starts the app and inputs their medical history of heart disease and current chest pain. The input unit also allows the user to input their past medical history and allergy information. Step 2: The analysis unit analyzes the information entered by the input unit. For example, the generation AI analyzes the entered medical history and symptoms to generate basic data for selecting the most appropriate medical institution. The analysis unit can also analyze the information using data mining and machine learning algorithms. Step 3: The selection unit selects the most suitable hospital based on the information analyzed by the analysis unit. For example, if a patient has a history of heart disease and is currently experiencing chest pain, the generation AI will select a medical institution specializing in cardiac care. The selection unit can also select a hospital based on factors such as the hospital's specialty, current congestion status, and distance. Step 4: The guidance unit guides the user along the shortest route to the hospital selected by the selection unit. For example, the generation AI displays the transportation options and travel time from the user's current location to the hospital. The guidance unit can also analyze map data and traffic information to suggest the optimal route.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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. an input unit for inputting the user's medical history or symptoms; an analysis unit that analyzes the information input by the input unit; a selection unit that selects an optimal hospital based on the information analyzed by the analysis unit; a guidance unit that provides guidance on the shortest route to the hospital selected by the selection unit. A system characterized by:

2. The input unit Analyzing the user's voice input in real time and automatically converting the medical history or symptoms into text 2. The system of claim 1.

3. The input unit Refer to the user's past medical data to verify the accuracy of the medical history or symptom input information.

2. The system of claim 1.

4. The input unit Analyze the user's emotional state and provide relaxation advice if stress or anxiety levels are high 2. The system of claim 1.

5. The input unit Collecting biometric data from the user's wearable device to supplement the input of the medical history or the symptoms.

2. The system of claim 1.

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