System

Abstract

An object of a system according to an embodiment is to quickly and accurately determine a symptom or a situation of a user in an emergency and perform an appropriate rescue.SOLUTION: A system includes a symptom determination unit, a rescue execution unit, an information providing unit, and a route determination unit. The symptom determination unit determines a symptom or a situation of the user. The rescue execution unit promptly performs a necessary rescue based on the symptom and situation determined by the symptom determination unit. The information providing unit provides the emergency call receiving organization or the medical institution with information on the rescue performed by the rescue performing unit. The route determination unit determines deployment of the emergency vehicle and an optimal transport route based on the information provided by the information providing 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 technologies have had the problem that it is difficult to quickly and accurately assess a user's symptoms and situation in an emergency and provide appropriate rescue.

[0005] The system according to the embodiment aims to quickly and accurately assess the symptoms and situation of a user in an emergency and provide appropriate rescue. [Means for solving the problem]

[0006] The system according to the embodiment includes a symptom determination unit, a rescue execution unit, an information provision unit, and a route determination unit. The symptom determination unit determines the user's symptoms and condition. The rescue execution unit promptly performs necessary rescue based on the symptoms and condition determined by the symptom determination unit. The information provision unit provides information about the rescue performed by the rescue execution unit to emergency call receiving agencies and medical institutions. The route determination unit determines the deployment of emergency vehicles and the optimal transport route based on the information provided by the information provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately assess the symptoms and situation of a user in an emergency and provide appropriate rescue. [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 emergency call system according to the embodiment of the present invention is a system that accurately assesses the symptoms and situation of a user, promptly provides necessary rescue, and promptly provides advanced information to emergency call receiving agencies and medical institutions, thereby enabling the system to determine the deployment of emergency vehicles and the optimal transport route.

[0029] An emergency call system according to an embodiment includes a symptom determination unit, a rescue execution unit, an information provision unit, and a route determination unit. The symptom determination unit determines the user's symptoms and situation. For example, the generation AI analyzes information and voice data input by the user and accurately determines the symptoms and situation. For example, if the user inputs "I have chest pain," the generation AI analyzes the information and determines that there is a possibility of a heart attack. The rescue execution unit promptly performs necessary rescue based on the symptoms and situation determined by the symptom determination unit. For example, if the generation AI determines there is a possibility of a heart attack, it immediately contacts an emergency call receiving agency and arranges for an ambulance. The generation AI also obtains the user's location information and notifies the nearest medical institution. The information provision unit provides information about the rescue performed by the rescue execution unit to the emergency call receiving agency or medical institution. For example, the generation AI compiles the user's symptoms, situation, location information, etc., and transmits them to the emergency call receiving agency or medical institution. The route determination unit determines the deployment of emergency vehicles and the optimal transport route based on the information provided by the information provision unit. For example, the generation AI calculates the quickest route by taking into account traffic conditions and distance. As a result, the emergency call system according to the embodiment can accurately assess the user's symptoms and situation, quickly provide necessary rescue, and quickly provide advanced information to emergency call receiving agencies and medical institutions, thereby determining the deployment of emergency vehicles and the optimal transport route.

[0030] The symptom determination unit can determine the level of urgency by analyzing the voice data and analyzing the tone and rhythm. For example, the generation AI analyzes the user's voice data and determines the level of urgency by analyzing the tone and rhythm of the voice. For example, if the user is nervous, the tone of the voice often becomes higher, and the system determines that the level of urgency is high. In analyzing the voice data, the generation AI also analyzes the rhythm of the voice to detect irregularities or agitation in the user's breathing. For example, if the user's breathing becomes faster, the system determines that the level of urgency is high. The generation AI also analyzes the voice data and monitors changes in the tone and rhythm of the voice in real time. For example, if the user's speaking rate suddenly increases, the system determines that the level of urgency is high. In this way, by analyzing the user's voice data and determining the level of urgency, faster and more accurate rescue can be performed.

[0031] The symptom determination unit can make more accurate judgments by referring to health data and medical history and comparing them with symptoms. For example, the symptom determination unit uses the generation AI to refer to the user's past health data and medical history and compare them with current symptoms. For example, if a user with a history of heart attacks complains of chest pain, the generation AI determines that there is a high possibility of a heart attack. The generation AI also analyzes the frequency and patterns of specific symptoms based on the user's medical history. For example, if a user with a history of frequent asthma attacks complains of shortness of breath, the generation AI determines that the symptom is an asthma attack. The generation AI also updates the user's health data in real time and determines symptoms based on the latest information. For example, it references the results of a recent health check and sets a high level of urgency if any abnormal values ​​are found. This enables more accurate symptom determination by referring to past health data and medical history.

[0032] The symptom determination unit analyzes environmental sounds and can use the results to help determine the situation. In this case, for example, the generation AI analyzes the environmental sounds around the user and determines whether the situation is noisy or quiet. For example, if the surroundings are noisy, it determines that there is a possibility of an accident or incident. In analyzing the environmental sounds, the generation AI detects specific sounds (such as sirens or screams) and determines the level of urgency. For example, if a siren is heard, it determines that an emergency has occurred. The generation AI also monitors changes in the environmental sounds in real time to help determine the situation. For example, if there is a sudden silence, it determines that the user may have lost consciousness. In this way, analyzing the environmental sounds around the user can be useful for determining the situation.

[0033] The symptom judgment unit can incorporate real-time feedback from other users and utilize collective knowledge. In the symptom judgment unit, for example, the generation AI collects real-time feedback from other users and uses it to judge symptoms. For example, if many users in the same area complain of similar symptoms, it will determine that there is a possibility of an outbreak. Furthermore, based on the feedback from other users, the generation AI analyzes symptom patterns and makes more accurate judgments. For example, if a particular symptom occurs frequently, it will prioritize responding to that symptom. Furthermore, the generation AI incorporates feedback from other users in real time and utilizes collective knowledge. For example, if many users complain of the same symptom, it will set a high level of urgency for that symptom. In this way, by incorporating real-time feedback from other users, collective knowledge can be utilized to make more accurate symptom judgments.

[0034] The rescue execution unit can track the location of the nearest ambulance in real time based on the location information and make optimal rescue arrangements. In the rescue execution unit, for example, the generation AI acquires the user's location information and tracks the location of the nearest ambulance in real time. For example, the current location of the ambulance is identified based on GPS data and optimal rescue arrangements are made. Furthermore, based on the user's location information, the generation AI automatically selects the nearest ambulance and quickly dispatches it. For example, it calculates the ambulance's arrival time and selects the vehicle that can arrive the fastest. Furthermore, the generation AI tracks the ambulance's location in real time and calculates the optimal route by comparing it with the user's location information. For example, it selects the shortest route taking traffic conditions into consideration. This allows the nearest ambulance to be tracked in real time based on the user's location information and optimal rescue arrangements to be made, enabling quick rescue.

[0035] The rescue execution unit can automatically select and contact a specialist or specialized institution according to the symptoms. For example, the generation AI analyzes the user's symptoms and automatically selects a specialist or specialized institution according to those symptoms. For example, in the case of a heart attack, it will contact a cardiologist. Based on the user's symptoms, the generation AI also selects the most appropriate specialist or specialized institution and quickly contacts them. For example, in the case of a stroke, it will contact a neurosurgeon. Based on the user's symptom data, the generation AI also automatically selects the most appropriate option from a list of specialists and specialized institutions. For example, it will prioritize medical institutions that can handle specific symptoms. This allows the system to automatically select a specialist or specialized institution according to the user's symptoms and quickly contact them, enabling appropriate medical response.

[0036] The rescue execution unit can determine the level of urgency by utilizing data from sensors built into the device. For example, the generation AI acquires heart rate and body temperature data from sensors built into the user's device and determines the level of urgency. For example, if the heart rate is abnormally high, it determines that the level of urgency is high. The generation AI also determines the level of urgency in real time based on the sensor data acquired from the user's device. For example, if the body temperature rises rapidly, it determines that the level of urgency is high. The generation AI also analyzes the device's sensor data and monitors the user's health condition. For example, if it detects an abnormality in the heart rate or body temperature, it will immediately arrange for rescue. This allows the rescue execution unit to accurately determine the level of urgency by utilizing data from sensors built into the user's device.

[0037] The rescue execution unit can incorporate real-time traffic information provided by other users and select the optimal route. In the rescue execution unit, for example, the generation AI collects real-time traffic information provided by other users and selects the optimal route. For example, it calculates the shortest route based on congestion information. Furthermore, the generation AI updates the optimal route for the ambulance in real time based on traffic information from other users. For example, it selects a detour route taking into account accident information. Furthermore, the generation AI incorporates real-time traffic information and selects a route that will minimize the ambulance's arrival time. For example, it prioritizes routes with less traffic. In this way, by incorporating real-time traffic information provided by other users, the optimal route can be selected, enabling rapid rescue.

[0038] The information provision unit can visualize symptoms and conditions and provide them to emergency call receiving agencies and medical institutions in a format that is intuitively understandable. In the information provision unit, for example, the generation AI visualizes the user's symptoms and conditions and provides them to emergency call receiving agencies and medical institutions. For example, the progression of symptoms may be displayed in graphs or charts. The user's location information and symptoms may also be displayed on a map and provided to emergency call receiving agencies and medical institutions in a format that is intuitively understandable. For example, the user's current location and the nearest medical institution may be displayed on a map. The generation AI may also visualize the user's symptoms and conditions and provide them to emergency call receiving agencies and medical institutions in real time. For example, changes in the user's heart rate and body temperature may be displayed in graphs. In this way, visualizing the user's symptoms and conditions makes it easier for emergency call receiving agencies and medical institutions to intuitively understand, enabling them to respond more quickly.

[0039] The information provision unit can refer to data from similar cases and propose the optimal response method. In the information provision unit, for example, the generation AI refers to data from similar cases in the past and proposes the optimal response method for the current situation. For example, the response method is determined based on data from users who have previously complained of similar symptoms. Furthermore, based on data from similar cases, the generation AI proposes the optimal response method to emergency call receiving agencies and medical institutions. For example, it analyzes past data and proposes the most effective treatment method. Furthermore, the generation AI refers to data from similar cases in the past in real time and proposes the optimal response method according to the current situation. For example, it selects a response method based on past success stories. In this way, by referring to data from similar cases in the past, it is possible to propose the optimal response method for the current situation.

[0040] The information provision unit can automatically translate information into different languages ​​and provide information that can be handled internationally. For example, the information provision unit uses a generation AI to automatically translate a user's symptoms and situation into different languages ​​and provide the information to emergency call receiving agencies and medical institutions. For example, it supports multiple languages ​​such as English, Spanish, and Chinese. Furthermore, based on the information automatically translated into different languages, the generation AI provides information that can be handled internationally. For example, it responds appropriately to emergency calls from foreign users. Furthermore, the generation AI translates information into different languages ​​in real time and provides it to emergency call receiving agencies and medical institutions. For example, it automatically translates information entered by a user in a foreign language and provides it. This allows for international handling by automatically translating information into different languages.

[0041] The information provision unit can link with other emergency notification systems and medical systems to share and integrate information. For example, the generation AI in the information provision unit links with other emergency notification systems and medical systems to share and integrate information. For example, it shares a user's symptoms and situation between different systems. By linking with other emergency notification systems and medical systems, the generation AI also integrates information and provides it to emergency notification receiving agencies and medical institutions. For example, it centralizes data from multiple systems. The generation AI also links with other emergency notification systems and medical systems in real time to share and integrate information. For example, it integrates and provides information from different systems. This makes it possible to share and integrate information by linking with other emergency notification systems and medical systems, enabling more effective responses.

[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 symptom determination unit can make more accurate determinations by referring to the user's health data and medical history and comparing them with symptoms. For example, if a user with a history of heart attacks complains of chest pain, it can determine that the possibility of a heart attack is high. It can also analyze the frequency and pattern of specific symptoms based on the user's medical history. For example, if a user who has frequently suffered asthma attacks in the past complains of shortness of breath, it can determine that the symptom is an asthma attack. It can also update the user's health data in real time and determine symptoms based on the latest information. For example, it can refer to the results of a recent health check and set a high level of urgency if any abnormal values ​​are found. This allows for more accurate symptom determinations by referring to past health data and medical history.

[0044] The symptom determination unit can determine the level of urgency by analyzing the voice data and analyzing the tone and rhythm. For example, if the user is nervous, the voice tone will often become higher, and the level of urgency will be determined to be high. In analyzing the voice data, the voice rhythm is also analyzed to detect irregularities or agitation in the user's breathing. For example, if the user's breathing becomes faster, the level of urgency will be determined to be high. The voice data is also analyzed to monitor changes in the voice tone and rhythm in real time. For example, if the user's speaking rate suddenly increases, the level of urgency will be determined to be high. In this way, by analyzing the user's voice data and determining the level of urgency, faster and more accurate rescue can be performed.

[0045] The symptom determination unit can analyze environmental sounds and use the results to help determine the situation. For example, it can analyze the environmental sounds around the user and determine whether the situation is noisy or quiet. For example, if the surroundings are noisy, it can determine that there is a possibility of an accident or incident. In analyzing the environmental sounds, it can also detect specific sounds (such as sirens or screams) and determine the level of urgency. For example, if a siren is heard, it can determine that an emergency has occurred. It can also monitor changes in the environmental sounds in real time to help determine the situation. For example, if there is a sudden silence, it can determine that the user may have lost consciousness. In this way, analyzing the environmental sounds around the user can be useful for determining the situation.

[0046] The symptom determination unit can incorporate real-time feedback from other users and utilize collective knowledge. For example, real-time feedback from other users is collected and used to determine symptoms. For example, if many users in the same area complain of similar symptoms, it can determine that there is a possibility of an outbreak. Furthermore, based on the feedback from other users, symptom patterns can be analyzed to make more accurate determinations. For example, if a particular symptom occurs frequently, prioritizing responses to that symptom. Furthermore, feedback from other users can be incorporated in real time and collective knowledge can be utilized. For example, if many users complain of the same symptom, the urgency level for that symptom can be set high. In this way, by incorporating real-time feedback from other users, collective knowledge can be utilized to make more accurate symptom determinations.

[0047] The rescue execution unit can track the location of the nearest ambulance in real time based on the location information and make optimal rescue arrangements. For example, it acquires the user's location information and tracks the location of the nearest ambulance in real time. For example, it identifies the current location of the ambulance based on GPS data and makes optimal rescue arrangements. It also automatically selects the nearest ambulance based on the user's location information and quickly dispatches it. For example, it calculates the ambulance's arrival time and selects the vehicle that can arrive the fastest. It also tracks the ambulance's location in real time and calculates the optimal route by comparing it with the user's location information. For example, it selects the shortest route taking traffic conditions into consideration. This allows it to track the nearest ambulance in real time based on the user's location information and make optimal rescue arrangements, enabling quick rescue.

[0048] The rescue execution unit can automatically select a specialist or specialized institution according to the symptoms and contact them. For example, it analyzes the user's symptoms and automatically selects a specialist or specialized institution according to those symptoms. For example, in the case of a heart attack, it contacts a cardiologist. It also selects the most appropriate specialist or specialized institution based on the user's symptoms and contacts them quickly. For example, in the case of a stroke, it contacts a neurosurgeon. It also automatically selects the most appropriate option from a list of specialists and specialized institutions based on the user's symptom data. For example, it prioritizes selecting medical institutions that can handle specific symptoms. This allows it to automatically select a specialist or specialized institution according to the user's symptoms and contact them quickly, enabling appropriate medical response.

[0049] The rescue execution unit can determine the level of urgency by utilizing data from sensors built into the device. For example, it obtains heart rate and body temperature data from sensors built into the user's device and determines the level of urgency. For example, if the heart rate is abnormally high, it determines that the level of urgency is high. It also determines the level of urgency in real time based on sensor data obtained from the user's device. For example, if the body temperature rises rapidly, it determines that the level of urgency is high. It also analyzes the device's sensor data to monitor the user's health condition. For example, if an abnormality in the heart rate or body temperature is detected, it will immediately arrange for rescue. This allows for accurate determination of the level of urgency by utilizing data from sensors built into the user's device.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The symptom determination unit determines the user's symptoms and situation. For example, the generation AI analyzes the information and voice data entered by the user and accurately determines the symptoms and situation. For example, if the user enters "I have chest pain," the generation AI analyzes that information and determines that there is a possibility of a heart attack. Step 2: The rescue execution unit promptly carries out the necessary rescue based on the symptoms and situation determined by the symptom determination unit. For example, if the generation AI determines that there is a possibility of a heart attack, it will immediately contact the emergency call receiving agency and arrange for an ambulance. The generation AI also obtains the user's location information and notifies the nearest medical institution. Step 3: The information provision unit provides information about the rescue performed by the rescue execution unit to the emergency call receiving agency or medical institution. For example, the generation AI compiles the user's symptoms, situation, location information, etc. and sends it to the emergency call receiving agency or medical institution. Step 4: The route determination unit determines the deployment of emergency vehicles and the optimal transport route based on the information provided by the information provision unit. For example, the generation AI calculates the quickest route, taking into account traffic conditions and distance.

[0052] (Example 2) The emergency call system according to the embodiment of the present invention is a system that accurately assesses the symptoms and situation of a user, promptly provides necessary rescue, and promptly provides advanced information to emergency call receiving agencies and medical institutions, thereby enabling the system to determine the deployment of emergency vehicles and the optimal transport route.

[0053] An emergency call system according to an embodiment includes a symptom determination unit, a rescue execution unit, an information provision unit, and a route determination unit. The symptom determination unit determines the user's symptoms and situation. For example, the generation AI analyzes information and voice data input by the user and accurately determines the symptoms and situation. For example, if the user inputs "I have chest pain," the generation AI analyzes the information and determines that there is a possibility of a heart attack. The rescue execution unit promptly performs necessary rescue based on the symptoms and situation determined by the symptom determination unit. For example, if the generation AI determines there is a possibility of a heart attack, it immediately contacts an emergency call receiving agency and arranges for an ambulance. The generation AI also obtains the user's location information and notifies the nearest medical institution. The information provision unit provides information about the rescue performed by the rescue execution unit to the emergency call receiving agency or medical institution. For example, the generation AI compiles the user's symptoms, situation, location information, etc., and transmits them to the emergency call receiving agency or medical institution. The route determination unit determines the deployment of emergency vehicles and the optimal transport route based on the information provided by the information provision unit. For example, the generation AI calculates the quickest route by taking into account traffic conditions and distance. As a result, the emergency call system according to the embodiment can accurately assess the user's symptoms and situation, quickly provide necessary rescue, and quickly provide advanced information to emergency call receiving agencies and medical institutions, thereby determining the deployment of emergency vehicles and the optimal transport route.

[0054] The symptom determination unit can determine the level of urgency by analyzing the voice data and analyzing the tone and rhythm. For example, the generation AI analyzes the user's voice data and determines the level of urgency by analyzing the tone and rhythm of the voice. For example, if the user is nervous, the tone of the voice often becomes higher, and the system determines that the level of urgency is high. In analyzing the voice data, the generation AI also analyzes the rhythm of the voice to detect irregularities or agitation in the user's breathing. For example, if the user's breathing becomes faster, the system determines that the level of urgency is high. The generation AI also analyzes the voice data and monitors changes in the tone and rhythm of the voice in real time. For example, if the user's speaking rate suddenly increases, the system determines that the level of urgency is high. In this way, by analyzing the user's voice data and determining the level of urgency, faster and more accurate rescue can be performed.

[0055] The symptom determination unit can make more accurate judgments by referring to health data and medical history and comparing them with symptoms. For example, the symptom determination unit uses the generation AI to refer to the user's past health data and medical history and compare them with current symptoms. For example, if a user with a history of heart attacks complains of chest pain, the generation AI determines that there is a high possibility of a heart attack. The generation AI also analyzes the frequency and patterns of specific symptoms based on the user's medical history. For example, if a user with a history of frequent asthma attacks complains of shortness of breath, the generation AI determines that the symptom is an asthma attack. The generation AI also updates the user's health data in real time and determines symptoms based on the latest information. For example, it references the results of a recent health check and sets a high level of urgency if any abnormal values ​​are found. This enables more accurate symptom determination by referring to past health data and medical history.

[0056] The symptom determination unit uses the emotion estimation function to analyze the emotional state, detect emotions with high urgency, and prioritize responses. For example, the symptom determination unit uses the generation AI to analyze the user's voice and text data and detect emotions such as fear and panic using the emotion estimation function. For example, if the user is crying out for help, it determines that the level of urgency is high. The emotion estimation function is also used to analyze the user's facial expression and detect emotions with high urgency. For example, if the user is distorting their face, it determines that they are feeling pain or fear. The generation AI also analyzes the user's input data in real time and uses the emotion estimation function to prioritize responses to emotions with high urgency. For example, if the user is in a state of panic, it immediately makes an emergency call. In this way, the emotion estimation function allows priority responses to be given to users with emotions with high urgency.

[0057] The symptom determination unit analyzes environmental sounds and can use the results to help determine the situation. In this case, for example, the generation AI analyzes the environmental sounds around the user and determines whether the situation is noisy or quiet. For example, if the surroundings are noisy, it determines that there is a possibility of an accident or incident. In analyzing the environmental sounds, the generation AI detects specific sounds (such as sirens or screams) and determines the level of urgency. For example, if a siren is heard, it determines that an emergency has occurred. The generation AI also monitors changes in the environmental sounds in real time to help determine the situation. For example, if there is a sudden silence, it determines that the user may have lost consciousness. In this way, analyzing the environmental sounds around the user can be useful for determining the situation.

[0058] The symptom judgment unit can incorporate real-time feedback from other users and utilize collective knowledge. In the symptom judgment unit, for example, the generation AI collects real-time feedback from other users and uses it to judge symptoms. For example, if many users in the same area complain of similar symptoms, it will determine that there is a possibility of an outbreak. Furthermore, based on the feedback from other users, the generation AI analyzes symptom patterns and makes more accurate judgments. For example, if a particular symptom occurs frequently, it will prioritize responding to that symptom. Furthermore, the generation AI incorporates feedback from other users in real time and utilizes collective knowledge. For example, if many users complain of the same symptom, it will set a high level of urgency for that symptom. In this way, by incorporating real-time feedback from other users, collective knowledge can be utilized to make more accurate symptom judgments.

[0059] The symptom determination unit can use the emotion estimation function to estimate emotions in real time and provide advice to elicit positive emotions. For example, the symptom determination unit uses the emotion estimation function to estimate the emotions a user is feeling when entering data in real time and provide advice to elicit positive emotions. For example, if the user is feeling anxious, a reassuring message is displayed. The generation AI also analyzes the user's input data and uses the emotion estimation function to provide advice to elicit positive emotions. For example, if the user is nervous, it suggests ways to relax. The emotion estimation function also monitors the user's emotional state in real time and provides advice to elicit positive emotions. For example, if the user is depressed, an encouraging message is displayed. In this way, the user's emotions can be estimated in real time and advice to elicit positive emotions provided, thereby achieving psychological stability.

[0060] The rescue execution unit can track the location of the nearest ambulance in real time based on the location information and make optimal rescue arrangements. In the rescue execution unit, for example, the generation AI acquires the user's location information and tracks the location of the nearest ambulance in real time. For example, the current location of the ambulance is identified based on GPS data and optimal rescue arrangements are made. Furthermore, based on the user's location information, the generation AI automatically selects the nearest ambulance and quickly dispatches it. For example, it calculates the ambulance's arrival time and selects the vehicle that can arrive the fastest. Furthermore, the generation AI tracks the ambulance's location in real time and calculates the optimal route by comparing it with the user's location information. For example, it selects the shortest route taking traffic conditions into consideration. This allows the nearest ambulance to be tracked in real time based on the user's location information and optimal rescue arrangements to be made, enabling quick rescue.

[0061] The rescue execution unit can automatically select and contact a specialist or specialized institution according to the symptoms. For example, the generation AI analyzes the user's symptoms and automatically selects a specialist or specialized institution according to those symptoms. For example, in the case of a heart attack, it will contact a cardiologist. Based on the user's symptoms, the generation AI also selects the most appropriate specialist or specialized institution and quickly contacts them. For example, in the case of a stroke, it will contact a neurosurgeon. Based on the user's symptom data, the generation AI also automatically selects the most appropriate option from a list of specialists and specialized institutions. For example, it will prioritize medical institutions that can handle specific symptoms. This allows the system to automatically select a specialist or specialized institution according to the user's symptoms and quickly contact them, enabling appropriate medical response.

[0062] The rescue execution unit can determine the level of urgency by utilizing data from sensors built into the device. For example, the generation AI acquires heart rate and body temperature data from sensors built into the user's device and determines the level of urgency. For example, if the heart rate is abnormally high, it determines that the level of urgency is high. The generation AI also determines the level of urgency in real time based on the sensor data acquired from the user's device. For example, if the body temperature rises rapidly, it determines that the level of urgency is high. The generation AI also analyzes the device's sensor data and monitors the user's health condition. For example, if it detects an abnormality in the heart rate or body temperature, it will immediately arrange for rescue. This allows the rescue execution unit to accurately determine the level of urgency by utilizing data from sensors built into the user's device.

[0063] The rescue execution unit can incorporate real-time traffic information provided by other users and select the optimal route. In the rescue execution unit, for example, the generation AI collects real-time traffic information provided by other users and selects the optimal route. For example, it calculates the shortest route based on congestion information. Furthermore, the generation AI updates the optimal route for the ambulance in real time based on traffic information from other users. For example, it selects a detour route taking into account accident information. Furthermore, the generation AI incorporates real-time traffic information and selects a route that will minimize the ambulance's arrival time. For example, it prioritizes routes with less traffic. In this way, by incorporating real-time traffic information provided by other users, the optimal route can be selected, enabling rapid rescue.

[0064] The rescue execution unit can use the emotion estimation function to monitor the user's emotions in real time when dispatching rescue and send messages to provide a sense of security. The rescue execution unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when dispatching rescue and send messages to provide a sense of security. For example, it displays a message such as "An ambulance will arrive shortly." In addition, the generation AI analyzes the user's emotional state and automatically generates messages to provide a sense of security. For example, it sends a message such as "Please stay calm, rescue is on the way." In addition, the emotion estimation function is used to monitor the user's emotional changes in real time and send messages to provide a sense of security at appropriate times. For example, if the user panics, a message such as "Please take a deep breath" is displayed. In this way, by using the emotion estimation function to monitor the user's emotions in real time and sending messages to provide a sense of security, the user's anxiety is reduced and prompt rescue is possible.

[0065] The information provision unit can visualize symptoms and conditions and provide them to emergency call receiving agencies and medical institutions in a format that is intuitively understandable. In the information provision unit, for example, the generation AI visualizes the user's symptoms and conditions and provides them to emergency call receiving agencies and medical institutions. For example, the progression of symptoms may be displayed in graphs or charts. The user's location information and symptoms may also be displayed on a map and provided to emergency call receiving agencies and medical institutions in a format that is intuitively understandable. For example, the user's current location and the nearest medical institution may be displayed on a map. The generation AI may also visualize the user's symptoms and conditions and provide them to emergency call receiving agencies and medical institutions in real time. For example, changes in the user's heart rate and body temperature may be displayed in graphs. In this way, visualizing the user's symptoms and conditions makes it easier for emergency call receiving agencies and medical institutions to intuitively understand, enabling them to respond more quickly.

[0066] The information provision unit can refer to data from similar cases and propose the optimal response method. In the information provision unit, for example, the generation AI refers to data from similar cases in the past and proposes the optimal response method for the current situation. For example, the response method is determined based on data from users who have previously complained of similar symptoms. Furthermore, based on data from similar cases, the generation AI proposes the optimal response method to emergency call receiving agencies and medical institutions. For example, it analyzes past data and proposes the most effective treatment method. Furthermore, the generation AI refers to data from similar cases in the past in real time and proposes the optimal response method according to the current situation. For example, it selects a response method based on past success stories. In this way, by referring to data from similar cases in the past, it is possible to propose the optimal response method for the current situation.

[0067] The information providing unit can use the emotion estimation function to report the user's emotional state and adjust the response priority. For example, the information providing unit uses the emotion estimation function to report the user's emotional state to emergency call receiving agencies and medical institutions and adjust the response priority. For example, if the user is in a state of panic, the priority is set high. The generation AI also analyzes the user's emotional data and reports the emotional state to emergency call receiving agencies and medical institutions. For example, if emotions of fear or anxiety are strong, a prompt response is encouraged. The emotion estimation function also monitors the user's emotional state in real time and reports it to emergency call receiving agencies and medical institutions. For example, if the emotion score is high, the response priority is increased. In this way, by using the emotion estimation function to report the user's emotional state and adjust the response priority, a prompt and appropriate response is possible.

[0068] The information provision unit can automatically translate information into different languages ​​and provide information that can be handled internationally. For example, the information provision unit uses a generation AI to automatically translate a user's symptoms and situation into different languages ​​and provide the information to emergency call receiving agencies and medical institutions. For example, it supports multiple languages ​​such as English, Spanish, and Chinese. Furthermore, based on the information automatically translated into different languages, the generation AI provides information that can be handled internationally. For example, it responds appropriately to emergency calls from foreign users. Furthermore, the generation AI translates information into different languages ​​in real time and provides it to emergency call receiving agencies and medical institutions. For example, it automatically translates information entered by a user in a foreign language and provides it. This allows for international handling by automatically translating information into different languages.

[0069] The information provision unit can link with other emergency notification systems and medical systems to share and integrate information. For example, the generation AI in the information provision unit links with other emergency notification systems and medical systems to share and integrate information. For example, it shares a user's symptoms and situation between different systems. By linking with other emergency notification systems and medical systems, the generation AI also integrates information and provides it to emergency notification receiving agencies and medical institutions. For example, it centralizes data from multiple systems. The generation AI also links with other emergency notification systems and medical systems in real time to share and integrate information. For example, it integrates and provides information from different systems. This makes it possible to share and integrate information by linking with other emergency notification systems and medical systems, enabling more effective responses.

[0070] The information providing unit can use the emotion estimation function to report emotional responses in real time, thereby improving the quality of response. For example, the information providing unit can use the emotion estimation function to report the user's emotional responses in real time to emergency call receiving agencies and medical institutions, thereby improving the quality of response. For example, if the user is feeling anxious, the information providing unit can encourage an appropriate response. The generation AI can also analyze the user's emotional data and report the emotional responses to emergency call receiving agencies and medical institutions. For example, if the user is feeling fear, the information providing unit can encourage a prompt response. The emotion estimation function can also be used to monitor the user's emotional responses in real time, and report them to emergency call receiving agencies and medical institutions. For example, if the emotion score is high, the quality of response can be improved. In this way, by using the emotion estimation function to report the user's emotional responses in real time, the quality of response by emergency call receiving agencies and medical institutions can be improved.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The symptom determination unit can make more accurate determinations by referring to the user's health data and medical history and comparing them with symptoms. For example, if a user with a history of heart attacks complains of chest pain, it can determine that the possibility of a heart attack is high. It can also analyze the frequency and pattern of specific symptoms based on the user's medical history. For example, if a user who has frequently suffered asthma attacks in the past complains of shortness of breath, it can determine that the symptom is an asthma attack. It can also update the user's health data in real time and determine symptoms based on the latest information. For example, it can refer to the results of a recent health check and set a high level of urgency if any abnormal values ​​are found. This allows for more accurate symptom determinations by referring to past health data and medical history.

[0073] The symptom determination unit can determine the level of urgency by analyzing the voice data and analyzing the tone and rhythm. For example, if the user is nervous, the voice tone will often become higher, and the level of urgency will be determined to be high. In analyzing the voice data, the voice rhythm is also analyzed to detect irregularities or agitation in the user's breathing. For example, if the user's breathing becomes faster, the level of urgency will be determined to be high. The voice data is also analyzed to monitor changes in the voice tone and rhythm in real time. For example, if the user's speaking rate suddenly increases, the level of urgency will be determined to be high. In this way, by analyzing the user's voice data and determining the level of urgency, faster and more accurate rescue can be performed.

[0074] The symptom determination unit can use the emotion estimation function to analyze the emotional state, detect emotions with high urgency, and respond with priority. For example, the emotion estimation function can be used to analyze the user's voice or text data and detect emotions such as fear or panic. For example, if the user is crying out for help, it is determined that the level of urgency is high. The emotion estimation function can also be used to analyze the user's facial expression and detect emotions with high urgency. For example, if the user is distorting their face, it is determined that they are feeling pain or fear. The symptom determination unit can also analyze the user's input data in real time and use the emotion estimation function to respond with priority to emotions with high urgency. For example, if the user is in a state of panic, an emergency call can be made immediately. In this way, by using the emotion estimation function, it is possible to respond with priority to users with emotions with high urgency.

[0075] The symptom determination unit can analyze environmental sounds and use the results to help determine the situation. For example, it can analyze the environmental sounds around the user and determine whether the situation is noisy or quiet. For example, if the surroundings are noisy, it can determine that there is a possibility of an accident or incident. In analyzing the environmental sounds, it can also detect specific sounds (such as sirens or screams) and determine the level of urgency. For example, if a siren is heard, it can determine that an emergency has occurred. It can also monitor changes in the environmental sounds in real time to help determine the situation. For example, if there is a sudden silence, it can determine that the user may have lost consciousness. In this way, analyzing the environmental sounds around the user can be useful for determining the situation.

[0076] The symptom determination unit can incorporate real-time feedback from other users and utilize collective knowledge. For example, real-time feedback from other users is collected and used to determine symptoms. For example, if many users in the same area complain of similar symptoms, it can determine that there is a possibility of an outbreak. Furthermore, based on the feedback from other users, symptom patterns can be analyzed to make more accurate determinations. For example, if a particular symptom occurs frequently, prioritizing responses to that symptom. Furthermore, feedback from other users can be incorporated in real time and collective knowledge can be utilized. For example, if many users complain of the same symptom, the urgency level for that symptom can be set high. In this way, by incorporating real-time feedback from other users, collective knowledge can be utilized to make more accurate symptom determinations.

[0077] The symptom determination unit can use the emotion estimation function to estimate emotions in real time and provide advice to elicit positive emotions. For example, the emotion estimation function can be used to estimate the emotions of the user when they input data in real time and provide advice to elicit positive emotions. For example, if the user is feeling anxious, a reassuring message can be displayed. The symptom determination unit can also analyze the user's input data and use the emotion estimation function to provide advice to elicit positive emotions. For example, if the user is nervous, a method for relaxing can be suggested. The symptom determination unit can also use the emotion estimation function to monitor the user's emotional state in real time and provide advice to elicit positive emotions. For example, if the user is depressed, an encouraging message can be displayed. In this way, the user's emotions can be estimated in real time and advice to elicit positive emotions can be provided, thereby achieving psychological stability for the user.

[0078] The rescue execution unit can track the location of the nearest ambulance in real time based on the location information and make optimal rescue arrangements. For example, it acquires the user's location information and tracks the location of the nearest ambulance in real time. For example, it identifies the current location of the ambulance based on GPS data and makes optimal rescue arrangements. It also automatically selects the nearest ambulance based on the user's location information and quickly dispatches it. For example, it calculates the ambulance's arrival time and selects the vehicle that can arrive the fastest. It also tracks the ambulance's location in real time and calculates the optimal route by comparing it with the user's location information. For example, it selects the shortest route taking traffic conditions into consideration. This allows it to track the nearest ambulance in real time based on the user's location information and make optimal rescue arrangements, enabling quick rescue.

[0079] The rescue execution unit can automatically select a specialist or specialized institution according to the symptoms and contact them. For example, it analyzes the user's symptoms and automatically selects a specialist or specialized institution according to those symptoms. For example, in the case of a heart attack, it contacts a cardiologist. It also selects the most appropriate specialist or specialized institution based on the user's symptoms and contacts them quickly. For example, in the case of a stroke, it contacts a neurosurgeon. It also automatically selects the most appropriate option from a list of specialists and specialized institutions based on the user's symptom data. For example, it prioritizes selecting medical institutions that can handle specific symptoms. This allows it to automatically select a specialist or specialized institution according to the user's symptoms and contact them quickly, enabling appropriate medical response.

[0080] The rescue execution unit can determine the level of urgency by utilizing data from sensors built into the device. For example, it obtains heart rate and body temperature data from sensors built into the user's device and determines the level of urgency. For example, if the heart rate is abnormally high, it determines that the level of urgency is high. It also determines the level of urgency in real time based on sensor data obtained from the user's device. For example, if the body temperature rises rapidly, it determines that the level of urgency is high. It also analyzes the device's sensor data to monitor the user's health condition. For example, if an abnormality in the heart rate or body temperature is detected, it will immediately arrange for rescue. This allows for accurate determination of the level of urgency by utilizing data from sensors built into the user's device.

[0081] The rescue execution unit can use the emotion estimation function to monitor emotions in real time when dispatching rescue and send messages to provide a sense of security. For example, the emotion estimation function can be used to monitor the user's emotions in real time when dispatching rescue and send messages to provide a sense of security. For example, a message such as "An ambulance will arrive shortly" can be displayed. The rescue execution unit can also analyze the user's emotional state and automatically generate messages to provide a sense of security. For example, a message such as "Please stay calm, rescue is on the way" can be sent. The emotion estimation function can also be used to monitor the user's emotional changes in real time and send messages to provide a sense of security at appropriate times. For example, if the user panics, a message such as "Please take a deep breath" can be displayed. In this way, by using the emotion estimation function to monitor the user's emotions in real time and sending messages to provide a sense of security, the user's anxiety can be reduced and rescue can be performed quickly.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The symptom determination unit determines the user's symptoms and situation. For example, the generation AI analyzes the information and voice data entered by the user and accurately determines the symptoms and situation. For example, if the user enters "I have chest pain," the generation AI analyzes that information and determines that there is a possibility of a heart attack. Step 2: The rescue execution unit promptly carries out the necessary rescue based on the symptoms and situation determined by the symptom determination unit. For example, if the generation AI determines that there is a possibility of a heart attack, it will immediately contact the emergency call receiving agency and arrange for an ambulance. The generation AI also obtains the user's location information and notifies the nearest medical institution. Step 3: The information provision unit provides information about the rescue performed by the rescue execution unit to the emergency call receiving agency or medical institution. For example, the generation AI compiles the user's symptoms, situation, location information, etc. and sends it to the emergency call receiving agency or medical institution. Step 4: The route determination unit determines the deployment of emergency vehicles and the optimal transport route based on the information provided by the information provision unit. For example, the generation AI calculates the quickest route, taking into account traffic conditions and distance.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a symptom determination unit that determines a symptom or situation of a user; a rescue execution unit that promptly carries out necessary rescue based on the symptoms and situations determined by the symptom determination unit; an information providing unit that provides information about the rescue performed by the rescue executing unit to an emergency call receiving organization or a medical institution; and a route determination unit that determines the deployment of emergency vehicles and the optimal transport route based on the information provided by the information providing unit. A system characterized by:

2. The symptom determination unit Analyzes voice data and determines urgency by analyzing tone and rhythm 2. The system of claim 1.

3. The symptom determination unit By referencing health data and medical history and comparing it with the symptoms, more accurate judgments can be made.

2. The system of claim 1.

4. The symptom determination unit Analyze emotional states, detect emotions with high urgency, and respond to them with priority 2. The system of claim 1.

5. The symptom determination unit Analyzing environmental sounds to help judge the situation 2. The system of claim 1.

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