system

Abstract

The system according to the embodiment aims to grasp the status of mobile phone users after a disaster occurs and provide appropriate countermeasures. [Solution] The system according to the embodiment comprises a detection unit, an identification unit, an understanding unit, a determination unit, and a provision unit. The detection unit detects the type or scale of a disaster. The identification unit identifies the location of the mobile phone owner based on information detected by the detection unit. The determination unit understands the condition of the mobile phone owner based on the location identified by the identification unit. The determination unit determines an appropriate response method based on the condition understood by the determination unit. The provision unit provides the response method determined by the determination unit to the mobile phone owner.

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 do not adequately grasp the detailed situation after a disaster occurs or provide specific countermeasures, so there is room for improvement.

[0005] The system according to the embodiment aims to grasp the status of mobile phone users after a disaster occurs and provide appropriate countermeasures. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, an identification unit, a grasp unit, a determination unit, and a provision unit. The detection unit detects the type or scale of a disaster. The identification unit identifies the location of the mobile phone owner based on information detected by the detection unit. The determination unit determines the condition of the mobile phone owner based on the location identified by the identification unit. The determination unit determines an appropriate response method based on the condition grasped by the determination unit. The provision unit provides the response method determined by the determination unit to the mobile phone owner. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the status of mobile phone users after a disaster occurs and provide appropriate countermeasures. [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 next-generation emergency alert system according to an embodiment of the present invention is an evolved version of the emergency alert system sent to mobile phones during a disaster. This system uses AI to instantly analyze the type and scale of the disaster, as well as the mobile phone user's location and status, and provides specific countermeasures. While conventional emergency alerts simply notify users of a disaster, this system implements detailed countermeasures in the following steps. First, when a disaster occurs, the system detects the type (earthquake, tsunami, typhoon, etc.) and scale of the disaster in real time. Next, it acquires the mobile phone's location information to identify the mobile phone user's location. It also determines the mobile phone user's status (e.g., traveling, at home, or in a public facility). Based on this information, the AI ​​instantly determines the optimal countermeasure. For example, in the event of an earthquake, the AI ​​considers factors such as the distance from the epicenter and the earthquake resistance of the building to suggest evacuation sites and evacuation routes. Furthermore, if a tsunami is predicted, the AI ​​encourages evacuation to higher ground and suggests the optimal evacuation route. Furthermore, the system provides specific instructions to the mobile phone user. For example, it sends a specific message such as, "The nearest evacuation site is a 10-minute walk from your current location. Please begin evacuating immediately." It also notifies users of precautions to take during evacuation and what items they need to bring.In this way, the next-generation emergency alert system aims to support a quick and accurate response in the event of a disaster and ensure the safety of mobile phone users.In this way, the next-generation emergency alert system aims to support a quick and accurate response in the event of a disaster and ensure the safety of mobile phone users.

[0029] A next-generation emergency alert system according to an embodiment includes a detection unit, an identification unit, a grasp unit, a determination unit, and a provision unit. The detection unit detects the type or scale of a disaster. For example, the detection unit can detect types of disasters, such as earthquakes, tsunamis, and typhoons, in real time. The detection unit can also detect the scale of a disaster in real time. The identification unit identifies the location of a mobile phone user based on information detected by the detection unit. For example, the identification unit can acquire location information from a mobile phone and identify the location of the mobile phone user. The identification unit can acquire location information using GPS, Wi-Fi, base station information, etc. The grasp unit grasps the mobile phone user's status based on the location identified by the identification unit. For example, the grasp unit can grasp whether the mobile phone user is traveling, at home, or in a public facility. The grasp unit can grasp the mobile phone user's health condition, travel status, surrounding conditions, etc. The determination unit determines the optimal response method based on the status grasped by the grasp unit. For example, the determination unit can determine the optimal evacuation site and evacuation route, taking into account factors such as the distance from the epicenter and the earthquake resistance of the building. The determination unit can determine response methods such as evacuation instructions, first aid, and contact methods. The provision unit provides the response method determined by the determination unit to the mobile phone owner. For example, the provision unit can provide specific instructions to the mobile phone owner. The provision unit can provide specific instructions such as evacuation routes, evacuation locations, and first aid methods. As a result, the next-generation emergency alert system according to the embodiment can support a quick and accurate response in the event of a disaster and ensure the safety of the mobile phone owner.

[0030] The detection unit can detect the type and scale of a disaster in real time. The detection unit can detect the type of disaster, such as an earthquake, tsunami, or typhoon, in real time. The detection unit can use sensors such as a seismometer, tsunami sensor, or weather radar to detect the type of disaster. For example, a seismometer can detect the occurrence of an earthquake and measure its seismic intensity. A tsunami sensor can detect the occurrence of a tsunami and measure its height. A weather radar can detect the occurrence of a typhoon and measure its wind speed and rainfall. The detection unit can also detect the scale of a disaster in real time. For example, the detection unit can measure the seismic intensity of an earthquake, the height of a tsunami, the wind speed and rainfall of a typhoon, etc. in real time. This enables rapid response by detecting the type and scale of a disaster in real time. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input data from a sensor to a generation AI and have the generation AI detect the type and scale of a disaster.

[0031] The identification unit can acquire location information of a mobile phone and identify the location of the mobile phone owner. For example, the identification unit can acquire location information of a mobile phone and identify the location of the mobile phone owner. The identification unit can use GPS, Wi-Fi, base station information, etc. to acquire location information. For example, GPS can receive signals from satellites and identify the location of a mobile phone. Wi-Fi can identify the location of a mobile phone based on information about surrounding Wi-Fi access points. Base station information can identify the location of a mobile phone based on information about the base station to which the mobile phone is connected. The identification unit must clarify the method and accuracy of acquiring location information. For example, GPS can identify a location with an accuracy of several meters, but accuracy may decrease inside buildings or underground. Wi-Fi can identify a location with an accuracy of tens of meters, but accuracy may vary depending on the placement of access points. Base station information can identify a location with an accuracy of several hundred meters, but accuracy may vary depending on the density of base stations. This allows the mobile phone owner's location to be accurately identified, thereby providing an appropriate response method. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or without AI. For example, the identification unit can input location information into the generation AI and cause the generation AI to identify the location of the mobile phone owner.

[0032] The grasping unit can grasp the mobile phone user's status. For example, the grasping unit can grasp whether the mobile phone user is on the move, at home, or in a public facility. The grasping unit can grasp the mobile phone user's health condition, movement status, surrounding conditions, etc. For example, the grasping unit can determine whether the mobile phone user is on the move using the mobile phone's acceleration sensor or gyro sensor. The grasping unit can also determine whether the mobile phone user is at home based on the mobile phone's location information. Furthermore, the grasping unit can determine whether the mobile phone user is in a public facility based on information about Wi-Fi access points around the mobile phone. The grasping unit needs to clarify the specific content and grasping method of the mobile phone user's status. For example, biometric data such as heart rate and body temperature can be used to grasp the health condition. Data from the acceleration sensor and gyro sensor can be used to grasp the movement status. Information about Wi-Fi access points and surrounding audio data can be used to grasp the surrounding conditions. This allows the mobile phone user's status to be grasped, thereby providing a more appropriate response method. Some or all of the above-mentioned processing by the grasping unit may be performed, for example, using AI or without AI. For example, the grasping unit can input the mobile phone owner's status data into the generation AI and cause the generation AI to grasp the status.

[0033] The determination unit can determine an appropriate evacuation site or evacuation route by taking into account the distance from the epicenter or the earthquake resistance of the building. The determination unit can determine the optimal evacuation site or evacuation route by taking into account, for example, the distance from the epicenter and the earthquake resistance of the building. The determination unit needs to clarify a specific measurement method and standard for the distance from the epicenter. For example, the distance from the epicenter can be measured by straight-line distance or travel distance. The straight-line distance is a method of measuring the shortest distance between the epicenter and the mobile phone carrier's location, and the travel distance is a method of measuring the distance along the actual travel path. The earthquake resistance of a building can be evaluated by the building's structure, age, earthquake resistance standards, etc. For example, the building's structure can be evaluated by type, such as reinforced concrete, wood, or steel frame construction. The age can be evaluated by the number of years the building was constructed. The earthquake resistance standards can be evaluated by the earthquake resistance standards at the time the building was designed. This makes it possible to provide optimal evacuation sites and evacuation routes by taking into account the distance from the epicenter and the building's earthquake resistance. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input data such as the distance from the epicenter and the earthquake resistance of buildings into the generation AI, and have the generation AI determine the optimal evacuation site and evacuation route.

[0034] The providing unit can provide specific instructions to the mobile phone user. For example, the providing unit can provide specific instructions to the mobile phone user. The providing unit can provide specific instructions such as evacuation routes, evacuation locations, and first aid procedures. For example, the providing unit can send a specific message such as, "The nearest evacuation location is a 10-minute walk from your current location. Please begin evacuating immediately." The providing unit can also notify the user of precautions to take during evacuation and necessary items to bring with you. For example, the providing unit can provide instructions such as, "Remain calm and pay attention to your surroundings while evacuating" or "When evacuating, please bring drinking water, emergency food, a flashlight, etc." The providing unit must clearly define the specific content of the instructions and the method of providing them. For example, the content of the instructions can include evacuation routes, evacuation locations, and first aid procedures, and the method of providing the instructions can be a text message, an audio message, a visual message, or the like. By providing specific instructions, the mobile phone user can act quickly and appropriately. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input instruction content to the generation AI and cause the generation AI to generate specific instructions.

[0035] The detection unit can improve detection accuracy by referring to past disaster data when detecting the type and scale of a disaster. For example, the detection unit can improve detection accuracy by referring to past disaster data when detecting the type and scale of a disaster. The detection unit needs to clarify the specific type and reference method of past disaster data. For example, the detection unit can refer to past earthquake data, flood data, fire data, etc. Past earthquake data includes information such as the location of an earthquake, seismic intensity, and damage extent. Past flood data includes information such as the location of a flood, water level, and damage extent. Past fire data includes information such as the location of a fire, fire scale, and damage extent. The detection unit can improve disaster detection accuracy by referring to past disaster data. For example, the detection unit can improve the accuracy of identifying an epicenter by referring to past earthquake data. The detection unit can predict the arrival time of a tsunami by referring to past tsunami data. The detection unit can improve prediction accuracy of wind speed and rainfall by referring to past typhoon data. As a result, disaster detection accuracy is improved by referring to past disaster data. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input past disaster data into the generation AI and cause the generation AI to detect the type and scale of the disaster.

[0036] The detection unit can predict the extent of a disaster's impact by taking into account the time of day when a disaster occurs and weather conditions. The detection unit can predict the extent of a disaster's impact by taking into account, for example, the time of day when a disaster occurs and weather conditions. The detection unit needs to clarify specific definitions and criteria for the time of day when a disaster occurs. For example, the time of day when a disaster occurs can be defined as daytime, nighttime, commuting hours, etc. Daytime refers to the daytime, and nighttime refers to the nighttime. Commuting hours refer to the morning commuting hours and the evening commuting hours. Weather conditions can be defined as sunny, rainy, snowy, wind speed, etc. For example, sunny means good weather, rainy means rainfall, snowy means snowfall, and wind speed indicates wind strength. The detection unit can predict the extent of a disaster's impact by taking into account the time of day when a disaster occurs and weather conditions. For example, in the case of an earthquake that occurs at night, the impact extent can be predicted by taking into account the difficulty of evacuation. In the case of a typhoon that occurs during rainy weather, the impact extent can be predicted by taking into account the risk of flooding. In the case of an earthquake that occurs in winter, the impact extent can be predicted by taking into account the difficulty of evacuation in cold regions. This allows for accurate prediction of the extent of a disaster's impact by taking into account the time of day the disaster will occur and weather conditions. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input data on the time of day the disaster will occur and weather conditions into the generation AI, and have the generation AI predict the extent of the disaster's impact.

[0037] The detection unit can improve detection accuracy by taking geographical characteristics into account when detecting the type and scale of a disaster. For example, the detection unit can improve detection accuracy by taking geographical characteristics into account when detecting the type and scale of a disaster. The detection unit needs to clarify the specific types of geographical characteristics and how they are taken into account. For example, the detection unit can take into account topography, geology, population density, etc. Topography can take into account topographical characteristics such as mountainous areas, plains, and coastal areas. Geology can take into account ground hardness, geological structure, etc. Population density can take into account population densities such as urban areas, suburban areas, and rural areas. The detection unit can improve disaster detection accuracy by taking geographical characteristics into account. For example, in the case of an earthquake in a mountainous area, the detection unit can identify the epicenter by taking into account the influence of topography. In the case of a tsunami in a coastal area, the detection unit can predict the reach of the tsunami by taking into account the shape of the coastline. In the case of a typhoon in an urban area, the detection unit can predict wind speed by taking into account the influence of high-rise buildings. As a result, disaster detection accuracy is improved by taking geographical characteristics into account. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input data on geographical characteristics to the generation AI and cause the generation AI to detect the type and scale of a disaster.

[0038] The detection unit can improve detection accuracy by referring to information from social media when detecting the type and scale of a disaster. For example, the detection unit can improve detection accuracy by referring to information from social media when detecting the type and scale of a disaster. The detection unit needs to clarify the specific method of acquiring information from social media and its reliability. For example, the detection unit can refer to information from social media such as X (formerly Twitter (registered trademark)), Facebook (registered trademark), and Instagram (registered trademark). Information from social media can be acquired in the form of posts, images, videos, comments, etc. The detection unit can analyze information from social media to detect the type and scale of a disaster. For example, the detection unit can analyze posts on social media to identify the location of an earthquake. The detection unit can analyze images and videos on social media to predict the reach of a tsunami. The detection unit can analyze comments on social media to grasp the damage caused by a typhoon. By referring to information from social media, the accuracy of disaster detection is improved. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input information from social media into the generation AI and have the generation AI detect the type and scale of disasters.

[0039] The identification unit can improve the identification accuracy by referring to past location history when acquiring location information of the mobile phone. For example, when acquiring location information of the mobile phone, the identification unit can improve the identification accuracy by referring to past location history. The identification unit needs to clarify the specific method for acquiring and referencing past location history. For example, the identification unit can refer to GPS logs, movement history data, etc. The GPS log is a history of location information previously acquired by the mobile phone, and movement history data is data on routes traveled by the mobile phone in the past. The identification unit can improve the identification accuracy of current location information by referring to past location history. For example, the identification unit can identify the current location based on places frequently visited by the user in the past. The identification unit can predict the current location by analyzing the user's past movement patterns. The identification unit can identify the current location by taking into account the time periods in which the user was previously present. As a result, the identification accuracy of location information is improved by referring to past location history. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input past location history data to a generation AI and cause the generation AI to identify the current location.

[0040] The identification unit can improve the identification accuracy by taking into account surrounding environmental information when acquiring location information of the mobile phone. For example, when acquiring location information of the mobile phone, the identification unit can improve the identification accuracy by taking into account surrounding environmental information. The identification unit needs to clarify the specific type of surrounding environmental information and the acquisition method. For example, the identification unit can consider the structure of the building, surrounding obstacles, traffic conditions, etc. The structure of the building can be evaluated by types such as reinforced concrete, wood, and steel frame. The presence of surrounding obstacles can be considered, such as the presence of buildings, trees, and vehicles. The traffic conditions can be considered, such as road congestion and the status of traffic signals. The identification unit can improve the identification accuracy of the location information by taking into account the surrounding environmental information. For example, the identification unit can identify the current location by taking into account the arrangement of surrounding buildings. The identification unit can identify the current location by taking into account surrounding traffic conditions. The identification unit can identify the current location by taking into account the surrounding topography. As a result, the identification accuracy of the location information is improved by taking into account the surrounding environmental information. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or without using AI. For example, the identification unit can input data on surrounding environmental information to the generation AI and cause the generation AI to identify location information.

[0041] The identification unit can improve the identification accuracy by taking geographical characteristics into account when acquiring location information of a mobile phone. For example, when acquiring location information of a mobile phone, the identification unit can improve the identification accuracy by taking geographical characteristics into account. The identification unit needs to clarify the specific types of geographical characteristics and the method of consideration. For example, the identification unit can consider topography, geology, population density, etc. Topography can consider topographical characteristics such as mountainous areas, plains, and coastal areas. Geology can consider ground hardness, geological structure, etc. Population density can consider population densities such as urban areas, suburban areas, and rural areas. The identification unit can improve the identification accuracy of the location information by taking geographical characteristics into account. For example, when acquiring location information in a mountainous area, the identification unit can improve the accuracy by taking into account the influence of topography. When acquiring location information in an urban area, the identification unit can improve the accuracy by taking into account the influence of high-rise buildings. When acquiring location information in a coastal area, the identification unit can improve the accuracy by taking into account the shape of the coastline. In this way, the identification accuracy of the location information is improved by taking geographical characteristics into account. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit may input data of geographical characteristics to the generation AI and cause the generation AI to identify the location information.

[0042] The identification unit can improve the accuracy of identification by referring to information from social media when acquiring location information of a mobile phone. For example, when acquiring location information of a mobile phone, the identification unit can improve the accuracy of identification by referring to information from social media. The identification unit needs to clarify the specific method of acquiring information from social media and its reliability. For example, the identification unit can refer to information from social media such as X (formerly Twitter), Facebook, and Instagram. Information from social media can be acquired in the form of posts, images, videos, comments, etc. The identification unit can analyze information from social media to identify the location information of a mobile phone. For example, the identification unit can analyze posts on social media to identify the current location. The identification unit can analyze images and videos on social media to identify the current location. The identification unit can analyze comments on social media to identify the current location. By referring to information from social media, the accuracy of identifying the location information can be improved. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input information from social media to a generation AI and cause the generation AI to identify the location information.

[0043] The grasping unit can improve the accuracy of grasping the mobile phone user's state by referring to past behavioral history. For example, when grasping the mobile phone user's state, the grasping unit can improve the accuracy of grasping by referring to past behavioral history. The grasping unit needs to clarify a specific method for acquiring and referencing the past behavioral history. For example, the grasping unit can refer to a movement history, an activity log, etc. The movement history is data on routes traveled by the mobile phone user in the past, and the activity log is data on activities performed by the mobile phone user in the past. The grasping unit can improve the accuracy of grasping the current state by referring to the past behavioral history. For example, the grasping unit can analyze the user's past behavioral patterns to grasp the current state. The grasping unit can grasp the current state based on places the user has visited in the past. The grasping unit can grasp the current state by referring to the user's past movement history. As a result, the accuracy of grasping the state is improved by referring to the past behavioral history. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input past behavioral history data to a generation AI and cause the generation AI to grasp the current state.

[0044] The grasping unit can improve the accuracy of grasping the state of the mobile phone user by taking into account surrounding environmental information. For example, when grasping the state of the mobile phone user, the grasping unit can improve the accuracy of grasping by taking into account surrounding environmental information. The grasping unit needs to clarify the specific type of surrounding environmental information and the method of acquiring it. For example, the grasping unit can consider the structure of the building, surrounding obstacles, traffic conditions, etc. The structure of the building can be evaluated by types such as reinforced concrete, wood, steel frame, etc. The presence of surrounding obstacles can be considered. The presence of road congestion, traffic signal status, etc. can be considered. The grasping unit can improve the accuracy of state grasping by taking into account surrounding environmental information. For example, the grasping unit can grasp the current state by taking into account the layout of surrounding buildings. The grasping unit can grasp the current state by taking into account surrounding traffic conditions. The grasping unit can grasp the current state by taking into account surrounding terrain. As a result, the accuracy of state grasping is improved by taking into account surrounding environmental information. Some or all of the above-mentioned processing in the grasping unit may be performed using AI, for example, or without AI. For example, the grasping unit can input data on surrounding environmental information to the generating AI and cause the generating AI to grasp the state.

[0045] The grasping unit can improve the grasping accuracy by taking geographical characteristics into consideration when grasping the status of a mobile phone user. For example, when grasping the status of a mobile phone user, the grasping unit can improve the grasping accuracy by taking geographical characteristics into consideration. The grasping unit needs to clarify the specific type of geographical characteristics and the method of consideration. For example, the grasping unit can consider topography, geology, population density, etc. Topography can consider topographical characteristics such as mountainous areas, plains, and coastal areas. Geology can consider ground hardness, geological structure, etc. Population density can consider population density such as urban areas, suburban areas, and rural areas. The grasping unit can improve the accuracy of status grasping by taking geographical characteristics into consideration. For example, when grasping the status in mountainous areas, the grasping unit can improve the accuracy by taking into consideration the influence of topography. When grasping the status in urban areas, the grasping unit can improve the accuracy by taking into consideration the influence of high-rise buildings. When grasping the status in coastal areas, the grasping unit can improve the accuracy by taking into consideration the shape of the coastline. In this way, the accuracy of status grasping is improved by taking geographical characteristics into consideration. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit may input data of geographical characteristics to the generating AI and have the generating AI grasp the state.

[0046] The grasping unit can improve the accuracy of grasping the mobile phone owner's status by referring to information from social media. For example, when grasping the mobile phone owner's status, the grasping unit can improve the accuracy of grasping by referring to information from social media. The grasping unit needs to clarify the specific method of acquiring information from social media and its reliability. For example, the grasping unit can refer to information from social media such as X (formerly Twitter), Facebook, and Instagram. Information from social media can be acquired in the form of posts, images, videos, comments, etc. The grasping unit can analyze information from social media to grasp the mobile phone owner's status. For example, the grasping unit can analyze posts on social media to grasp the current status. The grasping unit can analyze images and videos on social media to grasp the current status. The grasping unit can analyze comments on social media to grasp the current status. By referring to information from social media, the accuracy of grasping the status can be improved. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input information from social media to a generation AI and cause the generation AI to grasp the status.

[0047] The determination unit can improve the accuracy of the determination when determining the optimal response method by referring to past response history. For example, when determining the optimal response method, the determination unit can improve the accuracy of the determination by referring to past response history. The determination unit needs to clarify a specific method for acquiring and referencing past response history. For example, the determination unit can refer to a response log, past disaster response data, etc. The response log is a record of response methods taken in the past, and past disaster response data is data on responses to disasters that have occurred in the past. By referring to the past response history, the determination unit can improve the accuracy of determining the current response method. For example, the determination unit can refer to past evacuation history to determine the optimal evacuation location. The determination unit can analyze past response methods and determine the most effective response method. The determination unit can refer to past disaster response history to determine the optimal evacuation route. As a result, the accuracy of determining the response method is improved by referring to the past response history. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input past response history data to a generation AI and have the generation AI determine a response method.

[0048] The determination unit can improve the accuracy of the determination when determining the optimal response method by taking into account surrounding environmental information. For example, when determining the optimal response method, the determination unit can improve the accuracy of the determination by taking into account surrounding environmental information. The determination unit needs to clarify the specific type of surrounding environmental information and how to acquire it. For example, the determination unit can consider the structure of the building, surrounding obstacles, traffic conditions, etc. The structure of the building can be evaluated by types such as reinforced concrete, wood, and steel frame. The presence of surrounding obstacles can be considered, such as the presence of buildings, trees, and vehicles. The traffic conditions can be considered, such as road congestion and the status of traffic signals. The determination unit can improve the accuracy of the determination when determining the response method by taking into account the surrounding environmental information. For example, the determination unit can determine the optimal evacuation site by taking into account the earthquake resistance of surrounding buildings. The determination unit can determine the optimal evacuation route by taking into account the surrounding traffic conditions. The determination unit can determine the optimal evacuation site by taking into account the surrounding topography. As a result, the accuracy of the determination when determining the response method is improved by taking into account the surrounding environmental information. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input data on surrounding environmental information into the generation AI and have the generation AI make a decision on how to deal with the situation.

[0049] The determination unit can improve the accuracy of the determination when determining the optimal response method by taking geographical characteristics into consideration. For example, the determination unit can improve the accuracy of the determination when determining the optimal response method by taking geographical characteristics into consideration. The determination unit needs to clarify the specific types of geographical characteristics and the method of consideration. For example, the determination unit can take into consideration topography, geology, population density, etc. The topography can take into consideration topographical characteristics such as mountainous areas, plains, and coastal areas. The geology can take into consideration ground hardness, geological structure, etc. The population density can take into consideration population densities such as urban areas, suburban areas, and rural areas. By taking geographical characteristics into consideration, the determination unit can improve the accuracy of the determination of the response method. For example, the determination unit can determine the optimal evacuation route by taking into consideration the influence of topography when evacuating in mountainous areas. The determination unit can determine the optimal evacuation route by taking into consideration the influence of high-rise buildings when evacuating in urban areas. The determination unit can determine the optimal evacuation route by taking into consideration the shape of the coastline when evacuating in coastal areas. In this way, the accuracy of the determination of the response method is improved by taking geographical characteristics into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input data on geographical characteristics to the generation AI and have the generation AI determine how to deal with the situation.

[0050] The determination unit can improve the accuracy of the determination when determining the optimal response method by referring to information from social media. For example, when determining the optimal response method, the determination unit can improve the accuracy of the determination by referring to information from social media. The determination unit needs to clarify the specific method of acquiring information from social media and its reliability. For example, the determination unit can refer to information from social media such as X (formerly Twitter), Facebook, and Instagram. The information from social media can be acquired in the form of posts, images, videos, comments, etc. The determination unit can analyze the information from social media to determine the optimal response method. For example, the determination unit can analyze posts on social media to determine the optimal evacuation location. The determination unit can analyze images and videos on social media to determine the optimal evacuation route. The determination unit can analyze comments on social media to determine the optimal response method. By referring to information from social media, the accuracy of determining the response method can be improved. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or without AI. For example, the determination unit can input information from social media into a generation AI and have the generation AI determine the response method.

[0051] The providing unit can improve the accuracy of providing specific instructions to a mobile phone user by referring to past instruction history. For example, when providing specific instructions to a mobile phone user, the providing unit can improve the accuracy of providing the instructions by referring to past instruction history. The providing unit needs to clarify a specific method for acquiring and referencing the past instruction history. For example, the providing unit can refer to an instruction log, past response data, etc. The instruction log is a record of instructions given in the past, and the past response data is data on responses to disasters that have occurred in the past. By referring to the past instruction history, the providing unit can improve the accuracy of providing current instructions. For example, the providing unit can refer to past evacuation instruction history to provide an optimal evacuation location. The providing unit can analyze the content of past instructions and provide the most effective instructions. The providing unit can refer to past disaster response history to provide an optimal evacuation route. By referring to the past instruction history, the accuracy of providing instructions can be improved. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input past instruction history data to a generation AI and cause the generation AI to provide instructions.

[0052] The providing unit can improve the accuracy of providing specific instructions to a mobile phone user by taking into account surrounding environmental information. For example, when providing specific instructions to a mobile phone user, the providing unit can improve the accuracy of providing the instructions by taking into account surrounding environmental information. The providing unit needs to clarify the specific type of surrounding environmental information and how to acquire it. For example, the providing unit can take into account the structure of the building, surrounding obstacles, traffic conditions, etc. The structure of the building can be evaluated by types such as reinforced concrete, wood, and steel frame construction. The presence of surrounding obstacles can be taken into account, such as the presence of buildings, trees, and vehicles. The traffic conditions can be taken into account, such as road congestion and the status of traffic signals. The providing unit can improve the accuracy of providing instructions by taking into account the surrounding environmental information. For example, the providing unit can provide an optimal evacuation site by taking into account the earthquake resistance of surrounding buildings. The providing unit can provide an optimal evacuation route by taking into account the surrounding traffic conditions. The providing unit can provide an optimal evacuation site by taking into account the surrounding topography. As a result, the accuracy of providing instructions is improved by taking into account the surrounding environmental information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input data on surrounding environmental information to the generating AI and cause the generating AI to provide instructions.

[0053] The providing unit can improve the accuracy of providing specific instructions to a mobile phone user by taking geographical characteristics into consideration. For example, when providing specific instructions to a mobile phone user, the providing unit can improve the accuracy of providing instructions by taking geographical characteristics into consideration. The providing unit needs to clarify the specific types of geographical characteristics and how they are considered. For example, the providing unit can take into consideration topography, geology, population density, etc. Topography can take into consideration topographical characteristics such as mountainous areas, plains, and coastal areas. Geology can take into consideration ground hardness, geological structure, etc. Population density can take into consideration population densities such as urban areas, suburban areas, and rural areas. The providing unit can improve the accuracy of providing instructions by taking geographical characteristics into consideration. For example, when evacuating in mountainous areas, the providing unit can provide an optimal evacuation route by taking into consideration the influence of topography. When evacuating in urban areas, the providing unit can provide an optimal evacuation route by taking into consideration the influence of high-rise buildings. When evacuating in coastal areas, the providing unit can provide an optimal evacuation route by taking into consideration the shape of the coastline. In this way, the accuracy of providing instructions is improved by taking into consideration geographical characteristics. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input data of geographical characteristics to the generating AI and cause the generating AI to provide instructions.

[0054] The providing unit can improve the accuracy of providing specific instructions to a mobile phone user by referring to information from social media. For example, when providing specific instructions to a mobile phone user, the providing unit can improve the accuracy of providing the instructions by referring to information from social media. The providing unit needs to clarify the specific acquisition method and reliability of the information from social media. For example, the providing unit can refer to information from social media such as X (formerly Twitter), Facebook, and Instagram. The information from social media can be acquired in the form of posts, images, videos, comments, etc. The providing unit can analyze the information from social media and provide optimal instructions. For example, the providing unit can analyze posts on social media and provide optimal evacuation locations. The providing unit can analyze images and videos on social media and provide optimal evacuation routes. The providing unit can analyze comments on social media and provide optimal ways to respond. By referring to information from social media, the accuracy of providing instructions can be improved. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input information from social media to a generation AI and cause the generation AI to provide instructions.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The next-generation emergency alert system can also include a battery management unit. The battery management unit can monitor the remaining battery level of a mobile phone during a disaster and switch to power-saving mode as necessary. For example, if the battery level is low, the battery management unit can automatically switch to power-saving mode and prioritize receiving emergency alerts and transmitting location information. The battery management unit can also notify mobile phone owners on how to conserve battery power. For example, it can send specific messages such as, "Your battery level is low. Please close unnecessary apps." Furthermore, the battery management unit can suggest optimal settings for efficient battery use during a disaster. Thus, the battery management unit can help mobile phone owners ensure they receive necessary information during a disaster.

[0057] The next-generation emergency alert system can further include a health monitoring unit. The health monitoring unit can monitor the health status of mobile phone users in real time when a disaster occurs and provide appropriate countermeasures as necessary. For example, the health monitoring unit can measure heart rate and body temperature, and if abnormalities are detected, the health monitoring unit can notify first aid instructions. The health monitoring unit can also adjust evacuation instructions based on the mobile phone user's health status. For example, if the user is feeling unwell, the health monitoring unit can suggest a reasonable evacuation route. Furthermore, the health monitoring unit can provide information on necessary medicines and medical institutions in the event of a disaster. Thus, by including a health monitoring unit, it is possible to support appropriate evacuation while protecting the health of the mobile phone user.

[0058] The next-generation emergency alert system can further include a communication support unit. The communication support unit can help mobile phone users contact family and friends in the event of a disaster. For example, when a disaster occurs, it can automatically send a message to emergency contacts to inform them of the mobile phone user's safety. In addition, even if communication is cut off during a disaster, the communication support unit can temporarily store messages and send them as soon as communication is restored. Furthermore, when a disaster occurs, the communication support unit can share the location information of family and friends to confirm each other's safety. In this way, the communication support unit can help mobile phone users act with peace of mind in the event of a disaster.

[0059] The next-generation emergency alert system can also be equipped with a translation unit. This translation unit can provide multilingual emergency alerts in the event of a disaster. For example, it can send emergency alerts in the native language of foreign tourists and foreign residents. The translation unit can also translate disaster information and evacuation instructions in real time and provide them to mobile phone users. For example, it can provide messages such as, "The nearest evacuation shelter is a 10-minute walk from your current location. Please begin evacuating immediately." in multiple languages. Furthermore, the translation unit can also respond to inquiries in foreign languages ​​in the event of a disaster. Thus, by incorporating a translation unit, it is possible to provide fast and accurate information to many people, regardless of language barriers.

[0060] The next-generation emergency alert system can further include an evacuation simulation unit. The evacuation simulation unit can provide simulations for mobile phone users to conduct evacuation drills before a disaster occurs. For example, it can conduct evacuation simulations assuming the occurrence of an earthquake or tsunami, and identify optimal evacuation routes and evacuation locations. The evacuation simulation unit can also provide evacuation scenarios customized based on the mobile phone user's location and status. For example, it can provide different evacuation scenarios for users at home and users in public facilities. Furthermore, the evacuation simulation unit can provide feedback to mobile phone users on areas for improvement and caution based on the simulation results. Thus, the evacuation simulation unit can help mobile phone users take prompt and appropriate action in the event of a disaster.

[0061] The next-generation emergency alert system can further include a disaster prediction unit. The disaster prediction unit can predict future disasters based on past disaster data and weather data. For example, it can predict the probability of an earthquake or the arrival time of a tsunami and notify mobile phone users in advance. The disaster prediction unit can also analyze weather data and predict the occurrence of typhoons and heavy rain. Furthermore, the disaster prediction unit can encourage mobile phone users to make advance preparations based on the prediction results. For example, it can send specific messages such as, "A typhoon is forecast to approach tomorrow. Please check your evacuation location." Thus, by including a disaster prediction unit, mobile phone users can make advance preparations and strengthen their disaster preparedness.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The detection unit detects the type or scale of the disaster. For example, the detection unit can detect the type of disaster (such as an earthquake, tsunami, or typhoon) in real time, and can also detect the scale of the disaster in real time. Step 2: The identification unit identifies the location of the mobile phone owner based on the information detected by the detection unit. For example, the location information of the mobile phone can be acquired and the location of the mobile phone owner can be identified using GPS, Wi-Fi, base station information, etc. Step 3: The grasping unit grasps the mobile phone carrier's status based on the location identified by the identifying unit. For example, it can grasp the mobile phone carrier's status, such as whether they are on the move, at home, or in a public facility, and can also grasp their health status, movement status, and surrounding conditions. Step 4: The decision unit determines the optimal response method based on the conditions grasped by the grasp unit. For example, it can determine the optimal evacuation site and evacuation route, taking into account factors such as the distance from the epicenter and the earthquake resistance of the building, and determine response methods such as evacuation instructions, first aid, and communication methods. Step 5: The providing unit provides the mobile phone owner with the countermeasure determined by the determining unit. For example, specific instructions such as evacuation routes, evacuation locations, and first aid methods can be provided.

[0064] (Example 2) The next-generation emergency alert system according to an embodiment of the present invention is an evolved version of the emergency alert system sent to mobile phones during a disaster. This system uses AI to instantly analyze the type and scale of the disaster, as well as the mobile phone user's location and status, and provides specific countermeasures. While conventional emergency alerts simply notify users of a disaster, this system implements detailed countermeasures in the following steps. First, when a disaster occurs, the system detects the type (earthquake, tsunami, typhoon, etc.) and scale of the disaster in real time. Next, it acquires the mobile phone's location information to identify the mobile phone user's location. It also determines the mobile phone user's status (e.g., traveling, at home, or in a public facility). Based on this information, the AI ​​instantly determines the optimal countermeasure. For example, in the event of an earthquake, the AI ​​considers factors such as the distance from the epicenter and the earthquake resistance of the building to suggest evacuation sites and evacuation routes. Furthermore, if a tsunami is predicted, the AI ​​encourages evacuation to higher ground and suggests the optimal evacuation route. Furthermore, the system provides specific instructions to the mobile phone user. For example, it sends a specific message such as, "The nearest evacuation site is a 10-minute walk from your current location. Please begin evacuating immediately." It also notifies users of precautions to take during evacuation and what items they need to bring.In this way, the next-generation emergency alert system aims to support a quick and accurate response in the event of a disaster and ensure the safety of mobile phone users.In this way, the next-generation emergency alert system aims to support a quick and accurate response in the event of a disaster and ensure the safety of mobile phone users.

[0065] A next-generation emergency alert system according to an embodiment includes a detection unit, an identification unit, a grasp unit, a determination unit, and a provision unit. The detection unit detects the type or scale of a disaster. For example, the detection unit can detect types of disasters, such as earthquakes, tsunamis, and typhoons, in real time. The detection unit can also detect the scale of a disaster in real time. The identification unit identifies the location of a mobile phone user based on information detected by the detection unit. For example, the identification unit can acquire location information from a mobile phone and identify the location of the mobile phone user. The identification unit can acquire location information using GPS, Wi-Fi, base station information, etc. The grasp unit grasps the mobile phone user's status based on the location identified by the identification unit. For example, the grasp unit can grasp whether the mobile phone user is traveling, at home, or in a public facility. The grasp unit can grasp the mobile phone user's health condition, travel status, surrounding conditions, etc. The determination unit determines the optimal response method based on the status grasped by the grasp unit. For example, the determination unit can determine the optimal evacuation site and evacuation route, taking into account factors such as the distance from the epicenter and the earthquake resistance of the building. The determination unit can determine response methods such as evacuation instructions, first aid, and contact methods. The provision unit provides the response method determined by the determination unit to the mobile phone owner. For example, the provision unit can provide specific instructions to the mobile phone owner. The provision unit can provide specific instructions such as evacuation routes, evacuation locations, and first aid methods. As a result, the next-generation emergency alert system according to the embodiment can support a quick and accurate response in the event of a disaster and ensure the safety of the mobile phone owner.

[0066] The detection unit can detect the type and scale of a disaster in real time. The detection unit can detect the type of disaster, such as an earthquake, tsunami, or typhoon, in real time. The detection unit can use sensors such as a seismometer, tsunami sensor, or weather radar to detect the type of disaster. For example, a seismometer can detect the occurrence of an earthquake and measure its seismic intensity. A tsunami sensor can detect the occurrence of a tsunami and measure its height. A weather radar can detect the occurrence of a typhoon and measure its wind speed and rainfall. The detection unit can also detect the scale of a disaster in real time. For example, the detection unit can measure the seismic intensity of an earthquake, the height of a tsunami, the wind speed and rainfall of a typhoon, etc. in real time. This enables rapid response by detecting the type and scale of a disaster in real time. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without AI. For example, the detection unit can input data from a sensor to a generation AI and have the generation AI detect the type and scale of a disaster.

[0067] The identification unit can acquire location information of a mobile phone and identify the location of the mobile phone owner. For example, the identification unit can acquire location information of a mobile phone and identify the location of the mobile phone owner. The identification unit can use GPS, Wi-Fi, base station information, etc. to acquire location information. For example, GPS can receive signals from satellites and identify the location of a mobile phone. Wi-Fi can identify the location of a mobile phone based on information about surrounding Wi-Fi access points. Base station information can identify the location of a mobile phone based on information about the base station to which the mobile phone is connected. The identification unit must clarify the method and accuracy of acquiring location information. For example, GPS can identify a location with an accuracy of several meters, but accuracy may decrease inside buildings or underground. Wi-Fi can identify a location with an accuracy of tens of meters, but accuracy may vary depending on the placement of access points. Base station information can identify a location with an accuracy of several hundred meters, but accuracy may vary depending on the density of base stations. This allows the mobile phone owner's location to be accurately identified, thereby providing an appropriate response method. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or without AI. For example, the identification unit can input location information into the generation AI and cause the generation AI to identify the location of the mobile phone owner.

[0068] The grasping unit can grasp the mobile phone user's status. For example, the grasping unit can grasp whether the mobile phone user is on the move, at home, or in a public facility. The grasping unit can grasp the mobile phone user's health condition, movement status, surrounding conditions, etc. For example, the grasping unit can determine whether the mobile phone user is on the move using the mobile phone's acceleration sensor or gyro sensor. The grasping unit can also determine whether the mobile phone user is at home based on the mobile phone's location information. Furthermore, the grasping unit can determine whether the mobile phone user is in a public facility based on information about Wi-Fi access points around the mobile phone. The grasping unit needs to clarify the specific content and grasping method of the mobile phone user's status. For example, biometric data such as heart rate and body temperature can be used to grasp the health condition. Data from the acceleration sensor and gyro sensor can be used to grasp the movement status. Information about Wi-Fi access points and surrounding audio data can be used to grasp the surrounding conditions. This allows the mobile phone user's status to be grasped, thereby providing a more appropriate response method. Some or all of the above-mentioned processing by the grasping unit may be performed, for example, using AI or without AI. For example, the grasping unit can input the mobile phone owner's status data into the generation AI and cause the generation AI to grasp the status.

[0069] The determination unit can determine an appropriate evacuation site or evacuation route by taking into account the distance from the epicenter or the earthquake resistance of the building. The determination unit can determine the optimal evacuation site or evacuation route by taking into account, for example, the distance from the epicenter and the earthquake resistance of the building. The determination unit needs to clarify a specific measurement method and standard for the distance from the epicenter. For example, the distance from the epicenter can be measured by straight-line distance or travel distance. The straight-line distance is a method of measuring the shortest distance between the epicenter and the mobile phone carrier's location, and the travel distance is a method of measuring the distance along the actual travel path. The earthquake resistance of a building can be evaluated by the building's structure, age, earthquake resistance standards, etc. For example, the building's structure can be evaluated by type, such as reinforced concrete, wood, or steel frame construction. The age can be evaluated by the number of years the building was constructed. The earthquake resistance standards can be evaluated by the earthquake resistance standards at the time the building was designed. This makes it possible to provide optimal evacuation sites and evacuation routes by taking into account the distance from the epicenter and the building's earthquake resistance. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input data such as the distance from the epicenter and the earthquake resistance of buildings into the generation AI, and have the generation AI determine the optimal evacuation site and evacuation route.

[0070] The providing unit can provide specific instructions to the mobile phone user. For example, the providing unit can provide specific instructions to the mobile phone user. The providing unit can provide specific instructions such as evacuation routes, evacuation locations, and first aid procedures. For example, the providing unit can send a specific message such as, "The nearest evacuation location is a 10-minute walk from your current location. Please begin evacuating immediately." The providing unit can also notify the user of precautions to take during evacuation and necessary items to bring with you. For example, the providing unit can provide instructions such as, "Remain calm and pay attention to your surroundings while evacuating" or "When evacuating, please bring drinking water, emergency food, a flashlight, etc." The providing unit must clearly define the specific content of the instructions and the method of providing them. For example, the content of the instructions can include evacuation routes, evacuation locations, and first aid procedures, and the method of providing the instructions can be a text message, an audio message, a visual message, or the like. By providing specific instructions, the mobile phone user can act quickly and appropriately. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input instruction content to the generation AI and cause the generation AI to generate specific instructions.

[0071] The detection unit can estimate the user's emotions and adjust the notification method of disaster information based on the estimated user emotions. For example, the detection unit can estimate the user's emotions and adjust the notification method of disaster information based on the estimated user emotions. The detection unit needs to clarify the specific estimation method and criteria for emotions. For example, the detection unit can estimate the user's emotions using facial expression recognition, voice analysis, behavioral patterns, etc. Facial expression recognition is a method of estimating emotions by analyzing the user's facial expressions captured by a camera. Voice analysis is a method of estimating emotions by analyzing the tone and speed of the user's voice. Behavioral patterns are a method of estimating emotions by analyzing the user's movements and actions. The detection unit can adjust the notification method of disaster information based on the estimated user emotions. For example, if the user is in a panic, the disaster information can be notified in a calm tone to provide a sense of security. If the user is relaxed, detailed disaster information can be provided to deepen understanding. If the user is in a hurry, concise disaster information can be notified to the point. This enables more effective information provision by adjusting the notification method of disaster information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, or without, an AI. For example, the detection unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0072] The detection unit can improve detection accuracy by referring to past disaster data when detecting the type and scale of a disaster. For example, the detection unit can improve detection accuracy by referring to past disaster data when detecting the type and scale of a disaster. The detection unit needs to clarify the specific type and reference method of past disaster data. For example, the detection unit can refer to past earthquake data, flood data, fire data, etc. Past earthquake data includes information such as the location of an earthquake, seismic intensity, and damage extent. Past flood data includes information such as the location of a flood, water level, and damage extent. Past fire data includes information such as the location of a fire, fire scale, and damage extent. The detection unit can improve disaster detection accuracy by referring to past disaster data. For example, the detection unit can improve the accuracy of identifying an epicenter by referring to past earthquake data. The detection unit can predict the arrival time of a tsunami by referring to past tsunami data. The detection unit can improve prediction accuracy of wind speed and rainfall by referring to past typhoon data. As a result, disaster detection accuracy is improved by referring to past disaster data. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input past disaster data into the generation AI and cause the generation AI to detect the type and scale of the disaster.

[0073] The detection unit can predict the extent of a disaster's impact by taking into account the time of day when a disaster occurs and weather conditions. The detection unit can predict the extent of a disaster's impact by taking into account, for example, the time of day when a disaster occurs and weather conditions. The detection unit needs to clarify specific definitions and criteria for the time of day when a disaster occurs. For example, the time of day when a disaster occurs can be defined as daytime, nighttime, commuting hours, etc. Daytime refers to the daytime, and nighttime refers to the nighttime. Commuting hours refer to the morning commuting hours and the evening commuting hours. Weather conditions can be defined as sunny, rainy, snowy, wind speed, etc. For example, sunny means good weather, rainy means rainfall, snowy means snowfall, and wind speed indicates wind strength. The detection unit can predict the extent of a disaster's impact by taking into account the time of day when a disaster occurs and weather conditions. For example, in the case of an earthquake that occurs at night, the impact extent can be predicted by taking into account the difficulty of evacuation. In the case of a typhoon that occurs during rainy weather, the impact extent can be predicted by taking into account the risk of flooding. In the case of an earthquake that occurs in winter, the impact extent can be predicted by taking into account the difficulty of evacuation in cold regions. This allows for accurate prediction of the extent of a disaster's impact by taking into account the time of day the disaster will occur and weather conditions. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input data on the time of day the disaster will occur and weather conditions into the generation AI, and have the generation AI predict the extent of the disaster's impact.

[0074] The detection unit can estimate the user's emotions and prioritize disaster information based on the estimated user emotions. The detection unit can, for example, estimate the user's emotions and prioritize disaster information based on the estimated user emotions. The detection unit needs to clarify the specific emotion estimation method and criteria. For example, the detection unit can estimate the user's emotions using facial expression recognition, voice analysis, behavioral patterns, etc. Facial expression recognition is a method of estimating emotions by analyzing the user's facial expressions captured by a camera. Voice analysis is a method of estimating emotions by analyzing the tone and speed of the user's voice. Behavioral patterns are a method of estimating emotions by analyzing the user's movements and actions. The detection unit can prioritize disaster information based on the estimated user emotions. For example, if the user is in a panic, the most important information can be prioritized. If the user is relaxed, detailed information can be sequentially notified. If the user is in a hurry, concise and to-the-point information can be prioritized. This allows important information to be provided preferentially by prioritizing disaster information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, or without, an AI. For example, the detection unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0075] The detection unit can improve detection accuracy by taking geographical characteristics into account when detecting the type and scale of a disaster. For example, the detection unit can improve detection accuracy by taking geographical characteristics into account when detecting the type and scale of a disaster. The detection unit needs to clarify the specific types of geographical characteristics and how they are taken into account. For example, the detection unit can take into account topography, geology, population density, etc. Topography can take into account topographical characteristics such as mountainous areas, plains, and coastal areas. Geology can take into account ground hardness, geological structure, etc. Population density can take into account population densities such as urban areas, suburban areas, and rural areas. The detection unit can improve disaster detection accuracy by taking geographical characteristics into account. For example, in the case of an earthquake in a mountainous area, the detection unit can identify the epicenter by taking into account the influence of topography. In the case of a tsunami in a coastal area, the detection unit can predict the reach of the tsunami by taking into account the shape of the coastline. In the case of a typhoon in an urban area, the detection unit can predict wind speed by taking into account the influence of high-rise buildings. As a result, disaster detection accuracy is improved by taking geographical characteristics into account. Some or all of the above-described processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit may input data on geographical characteristics to the generation AI and cause the generation AI to detect the type and scale of a disaster.

[0076] The detection unit can improve detection accuracy by referring to information from social media when detecting the type and scale of a disaster. For example, the detection unit can improve detection accuracy by referring to information from social media when detecting the type and scale of a disaster. The detection unit needs to clarify the specific method of acquiring information from social media and its reliability. For example, the detection unit can refer to information from social media such as X (formerly Twitter), Facebook, and Instagram. Information from social media can be acquired in the form of posts, images, videos, comments, etc. The detection unit can analyze information from social media to detect the type and scale of a disaster. For example, the detection unit can analyze posts on social media to identify the location of an earthquake. The detection unit can analyze images and videos on social media to predict the reach of a tsunami. The detection unit can analyze comments on social media to grasp the damage caused by a typhoon. By referring to information from social media, the accuracy of disaster detection is improved. Some or all of the above-described processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input information from social media into the generation AI and have the generation AI detect the type and scale of disasters.

[0077] The identification unit can estimate the user's emotions and adjust the frequency of acquiring location information based on the estimated user's emotions. For example, the identification unit can estimate the user's emotions and adjust the frequency of acquiring location information based on the estimated user's emotions. The identification unit needs to clarify a specific emotion estimation method and criteria. For example, the identification unit can estimate the user's emotions using facial expression recognition, voice analysis, behavioral patterns, etc. Facial expression recognition is a method of estimating emotions by analyzing the user's facial expressions captured by a camera. Voice analysis is a method of estimating emotions by analyzing the tone and speed of the user's voice. Behavioral patterns are a method of estimating emotions by analyzing the user's movements and actions. The identification unit can adjust the frequency of acquiring location information based on the estimated user's emotions. For example, if the user is in a panic, location information can be acquired frequently to confirm safety. If the user is relaxed, location information can be acquired at a normal frequency. If the user is in a hurry, location information can be acquired quickly to suggest an optimal evacuation route. By adjusting the frequency of acquiring location information according to the user's emotions, more appropriate location information can be acquired. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0078] The identification unit can improve the identification accuracy by referring to past location history when acquiring location information of the mobile phone. For example, when acquiring location information of the mobile phone, the identification unit can improve the identification accuracy by referring to past location history. The identification unit needs to clarify the specific method for acquiring and referencing past location history. For example, the identification unit can refer to GPS logs, movement history data, etc. The GPS log is a history of location information previously acquired by the mobile phone, and movement history data is data on routes traveled by the mobile phone in the past. The identification unit can improve the identification accuracy of current location information by referring to past location history. For example, the identification unit can identify the current location based on places frequently visited by the user in the past. The identification unit can predict the current location by analyzing the user's past movement patterns. The identification unit can identify the current location by taking into account the time periods in which the user was previously present. As a result, the identification accuracy of location information is improved by referring to past location history. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input past location history data to a generation AI and cause the generation AI to identify the current location.

[0079] The identification unit can improve the identification accuracy by taking into account surrounding environmental information when acquiring location information of the mobile phone. For example, when acquiring location information of the mobile phone, the identification unit can improve the identification accuracy by taking into account surrounding environmental information. The identification unit needs to clarify the specific type of surrounding environmental information and the acquisition method. For example, the identification unit can consider the structure of the building, surrounding obstacles, traffic conditions, etc. The structure of the building can be evaluated by types such as reinforced concrete, wood, and steel frame. The presence of surrounding obstacles can be considered, such as the presence of buildings, trees, and vehicles. The traffic conditions can be considered, such as road congestion and the status of traffic signals. The identification unit can improve the identification accuracy of the location information by taking into account the surrounding environmental information. For example, the identification unit can identify the current location by taking into account the arrangement of surrounding buildings. The identification unit can identify the current location by taking into account surrounding traffic conditions. The identification unit can identify the current location by taking into account the surrounding topography. As a result, the identification accuracy of the location information is improved by taking into account the surrounding environmental information. Some or all of the above-mentioned processing in the identification unit may be performed, for example, using AI or without using AI. For example, the identification unit can input data on surrounding environmental information to the generation AI and cause the generation AI to identify location information.

[0080] The identification unit can estimate the user's emotion and adjust the method for acquiring location information based on the estimated user's emotion. For example, the identification unit can estimate the user's emotion and adjust the method for acquiring location information based on the estimated user's emotion. The identification unit needs to clarify the specific method and criteria for estimating emotion. For example, the identification unit can estimate the user's emotion using facial expression recognition, voice analysis, behavioral patterns, etc. Facial expression recognition is a method of estimating emotion by analyzing the user's facial expression captured by a camera. Voice analysis is a method of estimating emotion by analyzing the tone and speed of the user's voice. Behavioral patterns are a method of estimating emotion by analyzing the user's movements and actions. The identification unit can adjust the method for acquiring location information based on the estimated user's emotion. For example, if the user is in a panic, GPS can be prioritized to quickly acquire location information. If the user is relaxed, a normal location information acquisition method can be used. If the user is in a hurry, Wi-Fi and Bluetooth can be used in combination to quickly acquire location information. This allows faster and more accurate location information to be acquired by adjusting the method for acquiring location information according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the identification unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0081] The identification unit can improve the identification accuracy by taking geographical characteristics into account when acquiring location information of a mobile phone. For example, when acquiring location information of a mobile phone, the identification unit can improve the identification accuracy by taking geographical characteristics into account. The identification unit needs to clarify the specific types of geographical characteristics and the method of consideration. For example, the identification unit can consider topography, geology, population density, etc. Topography can consider topographical characteristics such as mountainous areas, plains, and coastal areas. Geology can consider ground hardness, geological structure, etc. Population density can consider population densities such as urban areas, suburban areas, and rural areas. The identification unit can improve the identification accuracy of the location information by taking geographical characteristics into account. For example, when acquiring location information in a mountainous area, the identification unit can improve the accuracy by taking into account the influence of topography. When acquiring location information in an urban area, the identification unit can improve the accuracy by taking into account the influence of high-rise buildings. When acquiring location information in a coastal area, the identification unit can improve the accuracy by taking into account the shape of the coastline. In this way, the identification accuracy of the location information is improved by taking geographical characteristics into account. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit may input data of geographical characteristics to the generation AI and cause the generation AI to identify the location information.

[0082] The identification unit can improve the accuracy of identification by referring to information from social media when acquiring location information of a mobile phone. For example, when acquiring location information of a mobile phone, the identification unit can improve the accuracy of identification by referring to information from social media. The identification unit needs to clarify the specific method of acquiring information from social media and its reliability. For example, the identification unit can refer to information from social media such as X (formerly Twitter), Facebook, and Instagram. Information from social media can be acquired in the form of posts, images, videos, comments, etc. The identification unit can analyze information from social media to identify the location information of a mobile phone. For example, the identification unit can analyze posts on social media to identify the current location. The identification unit can analyze images and videos on social media to identify the current location. The identification unit can analyze comments on social media to identify the current location. By referring to information from social media, the accuracy of identifying the location information can be improved. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input information from social media to a generation AI and cause the generation AI to identify the location information.

[0083] The grasping unit can estimate the user's emotion and adjust the state grasping method based on the estimated user's emotion. For example, the grasping unit can estimate the user's emotion and adjust the state grasping method based on the estimated user's emotion. The grasping unit needs to clarify a specific emotion estimation method and criteria. For example, the grasping unit can estimate the user's emotion using facial expression recognition, voice analysis, behavioral patterns, etc. Facial expression recognition is a method of estimating the emotion by analyzing the user's facial expression captured by a camera. Voice analysis is a method of estimating the emotion by analyzing the tone and speed of the user's voice. Behavioral patterns are a method of estimating the emotion by analyzing the user's movements and actions. The grasping unit can adjust the state grasping method based on the estimated user's emotion. For example, if the user is in a panic, the grasping unit can ask simple questions to quickly grasp the state. If the user is relaxed, the grasping unit can ask detailed questions to grasp the state. If the user is in a hurry, the grasping unit can present options to quickly grasp the state. This allows for more accurate state grasping by adjusting the state grasping method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the comprehension unit may be performed using, or without, an AI. For example, the comprehension unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0084] The grasping unit can improve the accuracy of grasping the mobile phone user's state by referring to past behavioral history. For example, when grasping the mobile phone user's state, the grasping unit can improve the accuracy of grasping by referring to past behavioral history. The grasping unit needs to clarify a specific method for acquiring and referencing the past behavioral history. For example, the grasping unit can refer to a movement history, an activity log, etc. The movement history is data on routes traveled by the mobile phone user in the past, and the activity log is data on activities performed by the mobile phone user in the past. The grasping unit can improve the accuracy of grasping the current state by referring to the past behavioral history. For example, the grasping unit can analyze the user's past behavioral patterns to grasp the current state. The grasping unit can grasp the current state based on places the user has visited in the past. The grasping unit can grasp the current state by referring to the user's past movement history. As a result, the accuracy of grasping the state is improved by referring to the past behavioral history. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input past behavioral history data to a generation AI and cause the generation AI to grasp the current state.

[0085] The grasping unit can improve the accuracy of grasping the state of the mobile phone user by taking into account surrounding environmental information. For example, when grasping the state of the mobile phone user, the grasping unit can improve the accuracy of grasping by taking into account surrounding environmental information. The grasping unit needs to clarify the specific type of surrounding environmental information and the method of acquiring it. For example, the grasping unit can consider the structure of the building, surrounding obstacles, traffic conditions, etc. The structure of the building can be evaluated by types such as reinforced concrete, wood, steel frame, etc. The presence of surrounding obstacles can be considered. The presence of road congestion, traffic signal status, etc. can be considered. The grasping unit can improve the accuracy of state grasping by taking into account surrounding environmental information. For example, the grasping unit can grasp the current state by taking into account the layout of surrounding buildings. The grasping unit can grasp the current state by taking into account surrounding traffic conditions. The grasping unit can grasp the current state by taking into account surrounding terrain. As a result, the accuracy of state grasping is improved by taking into account surrounding environmental information. Some or all of the above-mentioned processing in the grasping unit may be performed using AI, for example, or without AI. For example, the grasping unit can input data on surrounding environmental information to the generating AI and cause the generating AI to grasp the state.

[0086] The grasping unit can estimate the user's emotions and adjust the frequency of state grasping based on the estimated user's emotions. For example, the grasping unit can estimate the user's emotions and adjust the frequency of state grasping based on the estimated user's emotions. The grasping unit needs to clarify a specific emotion estimation method and criteria. For example, the grasping unit can estimate the user's emotions using facial expression recognition, voice analysis, behavioral patterns, etc. Facial expression recognition is a method of analyzing the user's facial expressions captured by a camera to estimate emotions. Voice analysis is a method of analyzing the tone and speed of the user's voice to estimate emotions. Behavioral patterns are a method of analyzing the user's movements and actions to estimate emotions. The grasping unit can adjust the frequency of state grasping based on the estimated user's emotions. For example, if the user is in a panic, the state can be grasped frequently to ensure safety. If the user is relaxed, the state can be grasped at a normal frequency. If the user is in a hurry, the frequency can be adjusted to grasp the state quickly. As a result, by adjusting the frequency of state grasping according to the user's emotions, more appropriate state grasping is possible. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the comprehension unit may be performed using, or without, an AI. For example, the comprehension unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0087] The grasping unit can improve the grasping accuracy by taking geographical characteristics into consideration when grasping the status of a mobile phone user. For example, when grasping the status of a mobile phone user, the grasping unit can improve the grasping accuracy by taking geographical characteristics into consideration. The grasping unit needs to clarify the specific type of geographical characteristics and the method of consideration. For example, the grasping unit can consider topography, geology, population density, etc. Topography can consider topographical characteristics such as mountainous areas, plains, and coastal areas. Geology can consider ground hardness, geological structure, etc. Population density can consider population density such as urban areas, suburban areas, and rural areas. The grasping unit can improve the accuracy of status grasping by taking geographical characteristics into consideration. For example, when grasping the status in mountainous areas, the grasping unit can improve the accuracy by taking into consideration the influence of topography. When grasping the status in urban areas, the grasping unit can improve the accuracy by taking into consideration the influence of high-rise buildings. When grasping the status in coastal areas, the grasping unit can improve the accuracy by taking into consideration the shape of the coastline. In this way, the accuracy of status grasping is improved by taking geographical characteristics into consideration. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit may input data of geographical characteristics to the generating AI and have the generating AI grasp the state.

[0088] The grasping unit can improve the accuracy of grasping the mobile phone owner's status by referring to information from social media. For example, when grasping the mobile phone owner's status, the grasping unit can improve the accuracy of grasping by referring to information from social media. The grasping unit needs to clarify the specific method of acquiring information from social media and its reliability. For example, the grasping unit can refer to information from social media such as X (formerly Twitter), Facebook, and Instagram. Information from social media can be acquired in the form of posts, images, videos, comments, etc. The grasping unit can analyze information from social media to grasp the mobile phone owner's status. For example, the grasping unit can analyze posts on social media to grasp the current status. The grasping unit can analyze images and videos on social media to grasp the current status. The grasping unit can analyze comments on social media to grasp the current status. By referring to information from social media, the accuracy of grasping the status can be improved. Some or all of the above-described processing in the grasping unit may be performed using, for example, AI, or may be performed without using AI. For example, the grasping unit can input information from social media to a generation AI and cause the generation AI to grasp the status.

[0089] The determination unit can estimate the user's emotions and adjust the criteria for determining a response method based on the estimated user's emotions. For example, the determination unit can estimate the user's emotions and adjust the criteria for determining a response method based on the estimated user's emotions. The determination unit needs to clarify a specific emotion estimation method and criteria. For example, the determination unit can estimate the user's emotions using facial expression recognition, voice analysis, behavioral patterns, etc. Facial expression recognition is a method of estimating emotions by analyzing the user's facial expressions captured by a camera. Voice analysis is a method of estimating emotions by analyzing the tone and speed of the user's voice. Behavioral patterns are a method of estimating emotions by analyzing the user's movements and actions. The determination unit can adjust the criteria for determining a response method based on the estimated user's emotions. For example, if the user is in a panic, the determination unit can set criteria that prompt the user to evacuate quickly. If the user is relaxed, the determination unit can set criteria that provide detailed response methods. If the user is in a hurry, the determination unit can set criteria that provide concise and quick response methods. This allows the determination unit to provide a more appropriate response method by adjusting the criteria for determining a response method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0090] The determination unit can improve the accuracy of the determination when determining the optimal response method by referring to past response history. For example, when determining the optimal response method, the determination unit can improve the accuracy of the determination by referring to past response history. The determination unit needs to clarify a specific method for acquiring and referencing past response history. For example, the determination unit can refer to a response log, past disaster response data, etc. The response log is a record of response methods taken in the past, and past disaster response data is data on responses to disasters that have occurred in the past. By referring to the past response history, the determination unit can improve the accuracy of determining the current response method. For example, the determination unit can refer to past evacuation history to determine the optimal evacuation location. The determination unit can analyze past response methods and determine the most effective response method. The determination unit can refer to past disaster response history to determine the optimal evacuation route. As a result, the accuracy of determining the response method is improved by referring to the past response history. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input past response history data to a generation AI and have the generation AI determine a response method.

[0091] The determination unit can improve the accuracy of the determination when determining the optimal response method by taking into account surrounding environmental information. For example, when determining the optimal response method, the determination unit can improve the accuracy of the determination by taking into account surrounding environmental information. The determination unit needs to clarify the specific type of surrounding environmental information and how to acquire it. For example, the determination unit can consider the structure of the building, surrounding obstacles, traffic conditions, etc. The structure of the building can be evaluated by types such as reinforced concrete, wood, and steel frame. The presence of surrounding obstacles can be considered, such as the presence of buildings, trees, and vehicles. The traffic conditions can be considered, such as road congestion and the status of traffic signals. The determination unit can improve the accuracy of the determination when determining the response method by taking into account the surrounding environmental information. For example, the determination unit can determine the optimal evacuation site by taking into account the earthquake resistance of surrounding buildings. The determination unit can determine the optimal evacuation route by taking into account the surrounding traffic conditions. The determination unit can determine the optimal evacuation site by taking into account the surrounding topography. As a result, the accuracy of the determination when determining the response method is improved by taking into account the surrounding environmental information. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input data on surrounding environmental information into the generation AI and have the generation AI make a decision on how to deal with the situation.

[0092] The determination unit can estimate the user's emotions and prioritize the countermeasures based on the estimated user emotions. The determination unit can, for example, estimate the user's emotions and prioritize the countermeasures based on the estimated user emotions. The determination unit needs to clarify a specific method and criteria for estimating emotions. For example, the determination unit can estimate the user's emotions using facial expression recognition, voice analysis, behavioral patterns, etc. Facial expression recognition is a method of estimating emotions by analyzing the user's facial expressions captured by a camera. Voice analysis is a method of estimating emotions by analyzing the tone and speed of the user's voice. Behavioral patterns are a method of estimating emotions by analyzing the user's movements and actions. The determination unit can prioritize the countermeasures based on the estimated user emotions. For example, if the user is in a panic, the most important countermeasure can be provided first. If the user is relaxed, detailed countermeasures can be provided sequentially. If the user is in a hurry, concise and to-the-point countermeasures can be provided first. Thus, by prioritizing the countermeasures according to the user's emotions, important countermeasures can be provided first. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0093] The determination unit can improve the accuracy of the determination when determining the optimal response method by taking geographical characteristics into consideration. For example, the determination unit can improve the accuracy of the determination when determining the optimal response method by taking geographical characteristics into consideration. The determination unit needs to clarify the specific types of geographical characteristics and the method of consideration. For example, the determination unit can take into consideration topography, geology, population density, etc. The topography can take into consideration topographical characteristics such as mountainous areas, plains, and coastal areas. The geology can take into consideration ground hardness, geological structure, etc. The population density can take into consideration population densities such as urban areas, suburban areas, and rural areas. By taking geographical characteristics into consideration, the determination unit can improve the accuracy of the determination of the response method. For example, the determination unit can determine the optimal evacuation route by taking into consideration the influence of topography when evacuating in mountainous areas. The determination unit can determine the optimal evacuation route by taking into consideration the influence of high-rise buildings when evacuating in urban areas. The determination unit can determine the optimal evacuation route by taking into consideration the shape of the coastline when evacuating in coastal areas. In this way, the accuracy of the determination of the response method is improved by taking geographical characteristics into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input data on geographical characteristics to the generation AI and have the generation AI determine how to deal with the situation.

[0094] The determination unit can improve the accuracy of the determination when determining the optimal response method by referring to information from social media. For example, when determining the optimal response method, the determination unit can improve the accuracy of the determination by referring to information from social media. The determination unit needs to clarify the specific method of acquiring information from social media and its reliability. For example, the determination unit can refer to information from social media such as X (formerly Twitter), Facebook, and Instagram. The information from social media can be acquired in the form of posts, images, videos, comments, etc. The determination unit can analyze the information from social media to determine the optimal response method. For example, the determination unit can analyze posts on social media to determine the optimal evacuation location. The determination unit can analyze images and videos on social media to determine the optimal evacuation route. The determination unit can analyze comments on social media to determine the optimal response method. By referring to information from social media, the accuracy of determining the response method can be improved. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or without AI. For example, the determination unit can input information from social media into a generation AI and have the generation AI determine the response method.

[0095] The providing unit can estimate the user's emotion and adjust the way in which instructions are expressed based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the way in which instructions are expressed based on the estimated user's emotion. The providing unit needs to clarify a specific emotion estimation method and criteria. For example, the providing unit can estimate the user's emotion using facial expression recognition, voice analysis, behavioral patterns, etc. Facial expression recognition is a method of estimating emotions by analyzing the user's facial expressions captured by a camera. Voice analysis is a method of estimating emotions by analyzing the tone and speed of the user's voice. Behavioral patterns are a method of estimating emotions by analyzing the user's movements and actions. The providing unit can adjust the way in which instructions are expressed based on the estimated user's emotion. For example, if the user is in a panic, a calm and reassuring way of expression can be used. If the user is relaxed, a detailed and polite way of expression can be used. If the user is in a hurry, a concise and quick way of expression can be used. This allows more effective instructions to be provided by adjusting the way in which instructions are expressed based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0096] The providing unit can improve the accuracy of providing specific instructions to a mobile phone user by referring to past instruction history. For example, when providing specific instructions to a mobile phone user, the providing unit can improve the accuracy of providing the instructions by referring to past instruction history. The providing unit needs to clarify a specific method for acquiring and referencing the past instruction history. For example, the providing unit can refer to an instruction log, past response data, etc. The instruction log is a record of instructions given in the past, and the past response data is data on responses to disasters that have occurred in the past. By referring to the past instruction history, the providing unit can improve the accuracy of providing current instructions. For example, the providing unit can refer to past evacuation instruction history to provide an optimal evacuation location. The providing unit can analyze the content of past instructions and provide the most effective instructions. The providing unit can refer to past disaster response history to provide an optimal evacuation route. By referring to the past instruction history, the accuracy of providing instructions can be improved. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input past instruction history data to a generation AI and cause the generation AI to provide instructions.

[0097] The providing unit can improve the accuracy of providing specific instructions to a mobile phone user by taking into account surrounding environmental information. For example, when providing specific instructions to a mobile phone user, the providing unit can improve the accuracy of providing the instructions by taking into account surrounding environmental information. The providing unit needs to clarify the specific type of surrounding environmental information and how to acquire it. For example, the providing unit can take into account the structure of the building, surrounding obstacles, traffic conditions, etc. The structure of the building can be evaluated by types such as reinforced concrete, wood, and steel frame construction. The presence of surrounding obstacles can be taken into account, such as the presence of buildings, trees, and vehicles. The traffic conditions can be taken into account, such as road congestion and the status of traffic signals. The providing unit can improve the accuracy of providing instructions by taking into account the surrounding environmental information. For example, the providing unit can provide an optimal evacuation site by taking into account the earthquake resistance of surrounding buildings. The providing unit can provide an optimal evacuation route by taking into account the surrounding traffic conditions. The providing unit can provide an optimal evacuation site by taking into account the surrounding topography. As a result, the accuracy of providing instructions is improved by taking into account the surrounding environmental information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or without AI. For example, the providing unit can input data on surrounding environmental information to the generating AI and cause the generating AI to provide instructions.

[0098] The providing unit can estimate the user's emotions and determine the priority of instructions based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and determine the priority of instructions based on the estimated user's emotions. The providing unit needs to clarify a specific emotion estimation method and criteria. For example, the providing unit can estimate the user's emotions using facial expression recognition, voice analysis, behavioral patterns, etc. Facial expression recognition is a method of estimating emotions by analyzing the user's facial expressions captured by a camera. Voice analysis is a method of estimating emotions by analyzing the tone and speed of the user's voice. Behavioral patterns are a method of estimating emotions by analyzing the user's movements and actions. The providing unit can determine the priority of instructions based on the estimated user's emotions. For example, if the user is in a panic, the most important instructions can be provided first. If the user is relaxed, detailed instructions can be provided sequentially. If the user is in a hurry, concise and to-the-point instructions can be provided first. In this way, by determining the priority of instructions according to the user's emotions, important instructions can be provided first. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0099] The providing unit can improve the accuracy of providing specific instructions to a mobile phone user by taking geographical characteristics into consideration. For example, when providing specific instructions to a mobile phone user, the providing unit can improve the accuracy of providing instructions by taking geographical characteristics into consideration. The providing unit needs to clarify the specific types of geographical characteristics and how they are considered. For example, the providing unit can take into consideration topography, geology, population density, etc. Topography can take into consideration topographical characteristics such as mountainous areas, plains, and coastal areas. Geology can take into consideration ground hardness, geological structure, etc. Population density can take into consideration population densities such as urban areas, suburban areas, and rural areas. The providing unit can improve the accuracy of providing instructions by taking geographical characteristics into consideration. For example, when evacuating in mountainous areas, the providing unit can provide an optimal evacuation route by taking into consideration the influence of topography. When evacuating in urban areas, the providing unit can provide an optimal evacuation route by taking into consideration the influence of high-rise buildings. When evacuating in coastal areas, the providing unit can provide an optimal evacuation route by taking into consideration the shape of the coastline. In this way, the accuracy of providing instructions is improved by taking into consideration geographical characteristics. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input data of geographical characteristics to the generating AI and cause the generating AI to provide instructions.

[0100] The providing unit can improve the accuracy of providing specific instructions to a mobile phone user by referring to information from social media. For example, when providing specific instructions to a mobile phone user, the providing unit can improve the accuracy of providing the instructions by referring to information from social media. The providing unit needs to clarify the specific acquisition method and reliability of the information from social media. For example, the providing unit can refer to information from social media such as X (formerly Twitter), Facebook, and Instagram. The information from social media can be acquired in the form of posts, images, videos, comments, etc. The providing unit can analyze the information from social media and provide optimal instructions. For example, the providing unit can analyze posts on social media and provide optimal evacuation locations. The providing unit can analyze images and videos on social media and provide optimal evacuation routes. The providing unit can analyze comments on social media and provide optimal ways to respond. By referring to information from social media, the accuracy of providing instructions can be improved. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input information from social media to a generation AI and cause the generation AI to provide instructions. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned detection unit, identification unit, grasp unit, determination unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the detection unit can detect the type and scale of a disaster using the camera 42 or microphone 38B of the smart device 14. The identification unit can identify the location of the mobile phone owner using the GPS or Wi-Fi of the smart device 14. The determination unit can grasp the condition of the mobile phone owner using the sensors of the smart device 14. The determination unit can determine the optimal response method using the identification processing unit 290 of the data processing device 12. The provision unit can provide specific instructions using the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned detection unit, identification unit, grasp unit, determination unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit can detect the type and scale of a disaster using the camera 42 and microphone 238 of the smart glasses 214. The identification unit can identify the location of the mobile phone owner using the GPS and Wi-Fi of the smart glasses 214. The grasp unit can grasp the condition of the mobile phone owner using the sensors of the smart glasses 214. The determination unit can determine the optimal response method using the identification processing unit 290 of the data processing device 12. The provision unit can provide specific instructions using the display and speaker of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned detection unit, identification unit, grasp unit, determination unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the detection unit can detect the type and scale of a disaster using the camera 42 or microphone 238 of the headset type terminal 314. The identification unit can identify the location of the mobile phone owner using the GPS or Wi-Fi of the headset type terminal 314. The determination unit can grasp the condition of the mobile phone owner using the sensors of the headset type terminal 314. The determination unit can determine the optimal method of response using the identification processing unit 290 of the data processing device 12. The provision unit can provide specific instructions using the display 343 or speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned detection unit, identification unit, grasp unit, determination unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit can detect the type and scale of a disaster using the camera 42 and microphone 238 of the robot 414. The identification unit can identify the location of the mobile phone owner using the GPS and Wi-Fi of the robot 414. The determination unit can grasp the condition of the mobile phone owner using the sensors of the robot 414. The determination unit can determine the optimal response method using the identification processing unit 290 of the data processing device 12. The provision unit can provide specific instructions using the display and speaker 240 of the robot 414.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The next-generation emergency alert system can also be equipped with a voice recognition unit. When a disaster occurs, the voice recognition unit can analyze the mobile phone user's voice in real time and respond to emergency instructions and questions via voice. For example, if the mobile phone user asks, "Where is the evacuation site?", the voice recognition unit can analyze the question and provide voice guidance to the nearest evacuation site. The voice recognition unit can also analyze the tone and speed of the mobile phone user's voice to determine the level of urgency. For example, if the voice is trembling, it can determine the level of urgency and encourage a prompt response. Furthermore, the voice recognition unit can provide voice-based disaster information. For example, it can convey specific messages such as, "The nearest evacuation site is a 10-minute walk from your current location. Please begin evacuating immediately." This allows the voice recognition unit to assist mobile phone users in taking prompt and appropriate action.

[0103] The next-generation emergency alert system can also include a battery management unit. The battery management unit can monitor the remaining battery level of a mobile phone during a disaster and switch to power-saving mode as necessary. For example, if the battery level is low, the battery management unit can automatically switch to power-saving mode and prioritize receiving emergency alerts and transmitting location information. The battery management unit can also notify mobile phone owners on how to conserve battery power. For example, it can send specific messages such as, "Your battery level is low. Please close unnecessary apps." Furthermore, the battery management unit can suggest optimal settings for efficient battery use during a disaster. Thus, the battery management unit can help mobile phone owners ensure they receive necessary information during a disaster.

[0104] The next-generation emergency alert system can further include a health monitoring unit. The health monitoring unit can monitor the health status of mobile phone users in real time when a disaster occurs and provide appropriate countermeasures as necessary. For example, the health monitoring unit can measure heart rate and body temperature, and if abnormalities are detected, the health monitoring unit can notify first aid instructions. The health monitoring unit can also adjust evacuation instructions based on the mobile phone user's health status. For example, if the user is feeling unwell, the health monitoring unit can suggest a reasonable evacuation route. Furthermore, the health monitoring unit can provide information on necessary medicines and medical institutions in the event of a disaster. Thus, by including a health monitoring unit, it is possible to support appropriate evacuation while protecting the health of the mobile phone user.

[0105] The next-generation emergency alert system can further include a communication support unit. The communication support unit can help mobile phone users contact family and friends in the event of a disaster. For example, when a disaster occurs, it can automatically send a message to emergency contacts to inform them of the mobile phone user's safety. In addition, even if communication is cut off during a disaster, the communication support unit can temporarily store messages and send them as soon as communication is restored. Furthermore, when a disaster occurs, the communication support unit can share the location information of family and friends to confirm each other's safety. In this way, the communication support unit can help mobile phone users act with peace of mind in the event of a disaster.

[0106] The next-generation emergency alert system can further include a psychological support unit. The psychological support unit can monitor the mobile phone user's psychological state in real time when a disaster occurs and provide psychological support as needed. For example, if the mobile phone user is in a state of panic, the psychological support unit can suggest breathing techniques or mental exercises to help them relax. The psychological support unit can also estimate the mobile phone user's emotions and send messages based on those emotions. For example, it can send a reassuring message such as, "Please stay calm. You are safe." Furthermore, the psychological support unit can provide support for post-disaster stress and trauma. Thus, the psychological support unit can help mobile phone users remain calm during a disaster.

[0107] The next-generation emergency alert system can also be equipped with a translation unit. This translation unit can provide multilingual emergency alerts in the event of a disaster. For example, it can send emergency alerts in the native language of foreign tourists and foreign residents. The translation unit can also translate disaster information and evacuation instructions in real time and provide them to mobile phone users. For example, it can provide messages such as, "The nearest evacuation shelter is a 10-minute walk from your current location. Please begin evacuating immediately." in multiple languages. Furthermore, the translation unit can also respond to inquiries in foreign languages ​​in the event of a disaster. Thus, by incorporating a translation unit, it is possible to provide fast and accurate information to many people, regardless of language barriers.

[0108] The next-generation emergency alert system can further include an evacuation simulation unit. The evacuation simulation unit can provide simulations for mobile phone users to conduct evacuation drills before a disaster occurs. For example, it can conduct evacuation simulations assuming the occurrence of an earthquake or tsunami, and identify optimal evacuation routes and evacuation locations. The evacuation simulation unit can also provide evacuation scenarios customized based on the mobile phone user's location and status. For example, it can provide different evacuation scenarios for users at home and users in public facilities. Furthermore, the evacuation simulation unit can provide feedback to mobile phone users on areas for improvement and caution based on the simulation results. Thus, the evacuation simulation unit can help mobile phone users take prompt and appropriate action in the event of a disaster.

[0109] The next-generation emergency alert system may further include an emotion sharing unit. The emotion sharing unit may provide a function that allows mobile phone users to share their emotions with other users when a disaster occurs. For example, if a mobile phone user feels anxious or scared, they can share their emotions with other users through the emotion sharing unit and encourage each other. The emotion sharing unit may also analyze the emotions of the mobile phone user in real time when a disaster occurs and provide support according to the emotions. For example, if the mobile phone user is in a state of panic, the emotion sharing unit may send a message encouraging the user to relax. Furthermore, the emotion sharing unit may provide support for post-disaster stress and trauma. Thus, the emotion sharing unit may help mobile phone users to act with peace of mind when a disaster occurs.

[0110] The next-generation emergency alert system can further include a disaster prediction unit. The disaster prediction unit can predict future disasters based on past disaster data and weather data. For example, it can predict the probability of an earthquake or the arrival time of a tsunami and notify mobile phone users in advance. The disaster prediction unit can also analyze weather data and predict the occurrence of typhoons and heavy rain. Furthermore, the disaster prediction unit can encourage mobile phone users to make advance preparations based on the prediction results. For example, it can send specific messages such as, "A typhoon is forecast to approach tomorrow. Please check your evacuation location." Thus, by including a disaster prediction unit, mobile phone users can make advance preparations and strengthen their disaster preparedness.

[0111] The next-generation emergency alert system can further include an emotion analysis unit. The emotion analysis unit can analyze the emotions of mobile phone users in real time when a disaster occurs and provide a response appropriate to their emotions. For example, if the mobile phone user feels anxious or scared, the emotion analysis unit can send a message to help them relax. The emotion analysis unit can also analyze the emotions of the mobile phone user and provide evacuation instructions appropriate to their emotions. For example, if the mobile phone user is in a state of panic, the emotion analysis unit can provide specific instructions to act calmly. Furthermore, the emotion analysis unit can provide support for post-disaster stress and trauma. Thus, the emotion analysis unit can help mobile phone users act with peace of mind during a disaster.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The detection unit detects the type or scale of the disaster. For example, the detection unit can detect the type of disaster (such as an earthquake, tsunami, or typhoon) in real time, and can also detect the scale of the disaster in real time. Step 2: The identification unit identifies the location of the mobile phone owner based on the information detected by the detection unit. For example, the location information of the mobile phone can be acquired and the location of the mobile phone owner can be identified using GPS, Wi-Fi, base station information, etc. Step 3: The grasping unit grasps the mobile phone carrier's status based on the location identified by the identifying unit. For example, it can grasp the mobile phone carrier's status, such as whether they are on the move, at home, or in a public facility, and can also grasp their health status, movement status, and surrounding conditions. Step 4: The decision unit determines the optimal response method based on the conditions grasped by the grasp unit. For example, it can determine the optimal evacuation site and evacuation route, taking into account factors such as the distance from the epicenter and the earthquake resistance of the building, and determine response methods such as evacuation instructions, first aid, and communication methods. Step 5: The providing unit provides the mobile phone owner with the countermeasure determined by the determining unit. For example, specific instructions such as evacuation routes, evacuation locations, and first aid methods can be provided.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[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 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.

[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 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.

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 system comprising a detection unit that detects the type or scale of a disaster, an identification unit that identifies the location of a mobile phone holder based on information detected by the detection unit, an understanding unit that grasps the condition of the mobile phone holder based on the location identified by the identification unit, a judgment unit that determines an appropriate response method based on the condition grasped by the understanding unit, and a provision unit that provides the response method determined by the judgment unit to the mobile phone holder.

2. The detection unit Detecting the type and scale of disasters in real time The system of claim 1 .

3. The identification unit Obtaining mobile phone location information and identifying the mobile phone owner's location The system of claim 1 .

4. The grasping unit is Understand the status of mobile phone owners The system of claim 1 .

5. The system according to claim 1 , wherein the determination unit determines an appropriate evacuation site or evacuation route by taking into consideration the distance from the epicenter or the earthquake resistance of the building.

6. The providing unit Provide specific instructions to mobile phone owners The system of claim 1 .

7. The detection unit Estimates user emotions and adjusts disaster information notification methods based on the estimated user emotions The system of claim 1 .

8. The detection unit When detecting the type and scale of disasters, improve detection accuracy by referencing past disaster data. The system of claim 1 .

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