system

Abstract

The system according to the embodiment aims to provide information and training for prompt and appropriate evacuation in an emergency. [Solution] A system according to an embodiment includes an information generation unit, a navigation unit, an audio guidance unit, and a training unit. The information generation unit generates information corresponding to an emergency scenario. The navigation unit displays an evacuation route based on the information generated by the information generation unit. The audio guidance unit provides audio guidance based on the evacuation route displayed by the navigation unit. The training unit provides training in a virtual reality space.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not provide sufficient information or training for quick and appropriate evacuation in an emergency, and there is room for improvement.

[0005] The system according to the embodiment aims to provide information and training for prompt and appropriate evacuation in an emergency. [Means for solving the problem]

[0006] The system according to the embodiment includes an information generation unit, a navigation unit, an audio guidance unit, and a training unit. The information generation unit generates information corresponding to an emergency scenario. The navigation unit displays an evacuation route based on the information generated by the information generation unit. The audio guidance unit provides audio guidance based on the evacuation route displayed by the navigation unit. The training unit provides training in a virtual reality space. [Effects of the Invention]

[0007] The system according to the embodiment can provide information and training for prompt and appropriate evacuation in an emergency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An emergency support system according to an embodiment of the present invention uses a generative AI embedded in AR glasses to provide user support in emergency situations. This emergency support system generates information corresponding to various emergency scenarios and guides users to safe evacuation shelters by combining navigation via the AR glasses and audio guidance. Furthermore, training in a virtual reality space improves users' ability to respond to emergencies in advance. For example, information corresponding to scenarios such as fires, earthquakes, floods, and terrorist attacks is generated. When a user wears the AR glasses, evacuation routes are displayed in the user's field of vision and appropriate instructions are provided via audio guidance. Training in a virtual reality space allows users to prepare for real emergencies. This allows the emergency support system to provide users with prompt and appropriate support.

[0029] An emergency support system according to an embodiment includes an information generation unit, a navigation unit, an audio guidance unit, and a training unit. The information generation unit generates information corresponding to emergency scenarios. For example, the information generation unit generates information corresponding to emergency scenarios such as fires, earthquakes, floods, and terrorist attacks. The information generation unit uses a generation AI to collect and analyze data related to the emergency. For example, the generation AI can learn from past emergency data and generate new information in real time. The navigation unit displays an evacuation route on AR glasses based on the information generated by the information generation unit. For example, the navigation unit uses the generation AI to identify a user's current location and display an optimal evacuation route. The navigation unit can use AR technology to display an evacuation route superimposed on the user's field of view. The audio guidance unit provides audio guidance based on the evacuation route displayed by the navigation unit. For example, the audio guidance unit uses the generation AI to provide appropriate instructions to the user via audio. The audio guidance unit can adjust the content of the audio guidance depending on the user's situation. The training unit provides training in a virtual reality space to proactively improve a user's ability to deal with emergencies. For example, the training unit uses the generation AI to perform a simulation in a virtual reality space and train the user on how to respond to an emergency. The training unit can record the user's training history and provide an individual training plan. This allows the emergency support system according to the embodiment to provide the user with prompt and appropriate support.

[0030] The information generation unit can generate information corresponding to emergency scenarios such as fires, earthquakes, floods, and terrorist attacks. The information generation unit, for example, generates evacuation instructions in the event of a fire. For example, the information generation unit can analyze the location and progress of a fire in real time and generate an optimal evacuation route. The information generation unit can also generate safety assurance methods in the event of an earthquake. For example, the information generation unit can analyze the epicenter and seismic intensity of an earthquake and generate evacuation instructions for the user to a safe location. The information generation unit can also generate evacuation instructions in the event of a flood. For example, the information generation unit can analyze the progress and water level of a flood and generate evacuation instructions for the user to higher ground. The information generation unit can also generate evacuation instructions in the event of a terrorist attack. For example, the information generation unit can analyze the location and situation of a terrorist attack and generate instructions for the user to a safe evacuation location. In this way, the information generation unit can provide information corresponding to a variety of emergency situations.

[0031] The navigation unit can display an evacuation route on the AR glasses based on the information generated by the information generation unit. The navigation unit, for example, identifies the user's current location and displays an optimal evacuation route. For example, the navigation unit can identify the user's current location using GPS data and display the evacuation route. The navigation unit can also use AR technology to superimpose the evacuation route on the user's field of view. For example, the navigation unit displays the evacuation route on the user's field of view through the AR glasses, allowing the user to intuitively understand the evacuation route. The navigation unit can also update the evacuation route in real time. For example, the navigation unit updates the evacuation route in real time according to the user's movement and provides the optimal route. In this way, the navigation unit can provide the user with a visual evacuation route.

[0032] The voice guidance unit can provide voice guidance based on the evacuation route displayed by the navigation unit. The voice guidance unit, for example, provides appropriate instructions to the user by voice. For example, the voice guidance unit can provide voice instructions on the evacuation route to the user using a generation AI. The voice guidance unit can also adjust the content of the voice guidance depending on the user's situation. For example, if the user is in a panic, the voice guidance unit can provide guidance in a calm tone. If the user is calm, the voice guidance unit can also provide detailed guidance. Furthermore, if the user is feeling anxious, the voice guidance unit can provide guidance in a reassuring tone. In this way, the voice guidance unit can provide voice instructions to the user.

[0033] The training unit provides training in a virtual reality space, thereby enabling users to proactively improve their ability to respond to emergencies. For example, the training unit performs a simulation in the virtual reality space to train users on how to respond to emergencies. For example, the training unit can use a generative AI to perform a simulation in the virtual reality space to train users on how to evacuate in the event of a fire. The training unit can also train users on how to ensure safety in the event of an earthquake. For example, the training unit can perform an earthquake simulation in the virtual reality space to train users on how to evacuate to a safe location. The training unit can also train users on how to evacuate in the event of a flood. For example, the training unit can perform a flood simulation in the virtual reality space to train users on how to evacuate to higher ground. The training unit can also train users on how to evacuate in the event of a terrorist attack. For example, the training unit can perform a terrorist attack simulation in the virtual reality space to train users on how to evacuate to a safe evacuation location. In this way, the training unit can proactively improve users' ability to respond to emergencies.

[0034] When generating information about an emergency scenario, the information generation unit can generate appropriate information by referring to the user's past emergency response history. The information generation unit, for example, generates information about similar scenarios based on the user's past emergency response history. For example, the information generation unit can use a generation AI to analyze the user's past emergency response history and generate information about similar scenarios. The information generation unit can also generate optimal evacuation route information by referring to routes the user has used in the past. For example, the information generation unit can use a generation AI to analyze the user's past evacuation routes and generate optimal evacuation route information. Furthermore, the information generation unit can generate information about appropriate evacuation means by taking into account evacuation means used by the user in the past. For example, the information generation unit can use a generation AI to analyze the user's past evacuation means and generate information about appropriate evacuation means. This allows the information generation unit to provide optimal information based on the user's past history.

[0035] When generating information for an emergency scenario, the information generation unit can customize the information based on the user's current location information. For example, if the user's current location is in a high-rise building, the information generation unit can generate evacuation route information within the building. For example, the information generation unit can use a generation AI to detect that the user's current location is in a high-rise building and generate evacuation route information within the building. Furthermore, if the user's current location is a subway station, the information generation unit can also generate evacuation route information from the subway station. For example, the information generation unit can use a generation AI to detect that the user's current location is a subway station and generate evacuation route information from the subway station. Furthermore, if the user's current location is a park, the information generation unit can also generate evacuation route information within the park. For example, the information generation unit can use a generation AI to detect that the user's current location is a park and generate evacuation route information within the park. This allows the information generation unit to provide appropriate information based on the user's current location.

[0036] When generating information for an emergency scenario, the information generation unit can generate information based on the user's health condition. For example, if the user has heart disease, the information generation unit generates evacuation route information that puts less strain on the heart. For example, the information generation unit can use a generation AI to detect the user's health condition and generate evacuation route information that puts less strain on the heart. Furthermore, if the user is pregnant, the information generation unit can generate evacuation route information suitable for pregnant women. For example, the information generation unit can use a generation AI to detect that the user is pregnant and generate evacuation route information suitable for pregnant women. Furthermore, if the user is elderly, the information generation unit can generate evacuation route information suitable for elderly people. For example, the information generation unit can use a generation AI to detect that the user is elderly and generate evacuation route information suitable for elderly people. This allows the information generation unit to provide appropriate information according to the user's health condition.

[0037] When generating information for an emergency scenario, the information generation unit can generate information based on the user's family composition. For example, if the user is with a child, the information generation unit generates evacuation route information suitable for the child. For example, the information generation unit can use a generation AI to detect the user's family composition and generate evacuation route information suitable for the child. Furthermore, if the user is accompanied by a pet, the information generation unit can generate route information that allows the user to evacuate with the pet. For example, the information generation unit can use a generation AI to detect that the user is accompanied by a pet and generate route information that allows the user to evacuate with the pet. Furthermore, if the user is accompanied by an elderly family member, the information generation unit can generate evacuation route information suitable for the elderly. For example, the information generation unit can use a generation AI to detect that the user is accompanied by an elderly family member and generate evacuation route information suitable for the elderly. This allows the information generation unit to provide appropriate information according to the user's family composition.

[0038] When displaying an evacuation route, the navigation unit can display an appropriate route by referring to the user's past evacuation history. The navigation unit, for example, displays an optimal route based on evacuation routes used by the user in the past. For example, the navigation unit can use a generation AI to analyze the user's past evacuation history and display an optimal route. The navigation unit can also display a route that avoids congestion based on the user's past evacuation history. For example, the navigation unit can use a generation AI to analyze the user's past evacuation history and display a route that avoids congestion. The navigation unit can also analyze the user's past evacuation history and display the most efficient route. For example, the navigation unit can use a generation AI to analyze the user's past evacuation history and display the most efficient route. This allows the navigation unit to provide an optimal route based on the user's past history.

[0039] When displaying an evacuation route, the navigation unit can update the route in real time based on the user's current location information. For example, the navigation unit can update the user's current location in real time while the user is moving and display the optimal route. For example, the navigation unit can use a generation AI to detect the user's current location in real time and display the optimal route. The navigation unit can also update the current location in real time and display the optimal route as the user approaches the destination. For example, the navigation unit can use a generation AI to detect the user's current location in real time and display the optimal route as the user approaches the destination. Furthermore, the navigation unit can update the current location in real time and perform navigation again if the user gets lost. For example, the navigation unit can use a generation AI to detect the user's current location in real time and perform navigation again if the user gets lost. This allows the navigation unit to provide the optimal route based on the user's current location.

[0040] When displaying an evacuation route, the navigation unit can adjust the route based on the user's moving speed. For example, if the user is walking fast, the navigation unit can display the shortest route. For example, the navigation unit can use a generation AI to detect the user's moving speed and display the shortest route. Furthermore, if the user is walking slowly, the navigation unit can display a detailed route. For example, the navigation unit can use a generation AI to detect the user's moving speed and display the detailed route. Furthermore, if the user is running, the navigation unit can display the safest route. For example, the navigation unit can use a generation AI to detect the user's moving speed and display the safest route. This allows the navigation unit to provide an appropriate route according to the user's moving speed.

[0041] When displaying an evacuation route, the navigation unit can optimize the route based on environmental information about the user's surroundings. For example, if there is an obstacle around the user, the navigation unit can display a detour route. For example, the navigation unit can use a generation AI to detect environmental information about the user's surroundings and display a detour route if there is an obstacle. The navigation unit can also display a route that avoids the congestion when the user's surroundings are congested. For example, the navigation unit can use a generation AI to detect environmental information about the user's surroundings and display a route that avoids the congestion. Furthermore, the navigation unit can display a well-lit route when the user's surroundings are dark. For example, the navigation unit can use a generation AI to detect environmental information about the user's surroundings and display a well-lit route when it is dark. This allows the navigation unit to provide an optimal route according to the environment around the user.

[0042] When providing an audio guide, the audio guide unit can provide the optimal audio guide by referring to the user's past audio guide usage history. The audio guide unit can provide the optimal audio guide based on, for example, the tones of audio guides used by the user in the past. For example, the audio guide unit can use a generation AI to analyze the user's past audio guide usage history and provide the optimal audio guide. The audio guide unit can also provide preferred audio guide content based on the user's past audio guide usage history. For example, the audio guide unit can use a generation AI to analyze the user's past audio guide usage history and provide the preferred audio guide content. The audio guide unit can also analyze the user's past audio guide usage history and provide the most effective audio guide. For example, the audio guide unit can use a generation AI to analyze the user's past audio guide usage history and provide the most effective audio guide. This allows the audio guide unit to provide the optimal audio guide based on the user's past history.

[0043] When providing the audio guidance, the audio guidance unit can update the content of the guidance in real time based on the user's current location information. For example, the audio guidance unit can update the user's current location in real time while the user is moving and provide optimal guidance. For example, the audio guidance unit can use a generation AI to detect the user's current location in real time and provide optimal guidance. The audio guidance unit can also update the current location in real time as the user approaches the destination and provide optimal guidance. For example, the audio guidance unit can use a generation AI to detect the user's current location in real time and provide optimal guidance as the user approaches the destination. Furthermore, if the user gets lost, the audio guidance unit can update the current location in real time and provide guidance again. For example, the audio guidance unit can use a generation AI to detect the user's current location in real time and provide guidance again if the user gets lost. This allows the audio guidance unit to provide optimal audio guidance based on the user's current location.

[0044] When providing audio guidance, the audio guide unit can customize the content of the guidance based on the user's hearing characteristics. For example, if the user has a hearing impairment, the audio guide unit can adjust the volume of the guidance when providing the guidance. For example, the audio guide unit can use a generation AI to detect the user's hearing characteristics and adjust the volume when providing the guidance. Furthermore, if the user is elderly, the audio guide unit can provide guidance in a tone that is easy to hear. For example, the audio guide unit can use a generation AI to detect that the user is elderly and provide guidance in a tone that is easy to hear. Furthermore, if the user is young, the audio guide unit can provide guidance in a brighter tone. For example, the audio guide unit can use a generation AI to detect that the user is young and provide guidance in a brighter tone. This allows the audio guide unit to provide optimal audio guidance according to the user's hearing characteristics.

[0045] When providing audio guidance, the audio guidance unit can adjust the volume of the guidance based on the noise level around the user. For example, if the user's surroundings are noisy, the audio guidance unit can increase the volume of the guidance. For example, the audio guidance unit can use a generation AI to detect the noise level around the user and increase the volume of the guidance. Furthermore, if the user's surroundings are quiet, the audio guidance unit can decrease the volume of the guidance. For example, the audio guidance unit can use a generation AI to detect the noise level around the user and decrease the volume of the guidance. Furthermore, the audio guidance unit can provide guidance at an appropriate volume depending on the noise level around the user. For example, the audio guidance unit can use a generation AI to detect the noise level around the user and provide guidance at an appropriate volume. This allows the audio guidance unit to provide audio guidance at an optimal volume depending on the noise level around the user.

[0046] When providing training, the training unit can provide optimal training by referring to the user's past training history. The training unit can, for example, provide similar training based on the content of training the user has done in the past. For example, the training unit can use a generation AI to analyze the user's past training history and provide similar training. The training unit can also prioritize providing effective training from the user's past training history. For example, the training unit can use a generation AI to analyze the user's past training history and prioritize providing effective training. Furthermore, the training unit can analyze the user's past training history and provide the most effective training. For example, the training unit can use a generation AI to analyze the user's past training history and provide the most effective training. This allows the training unit to provide optimal training based on the user's past history.

[0047] When providing training, the training unit can customize the training content based on the user's current physical condition and health state. For example, if the user is tired, the training unit can provide lighter training. For example, the training unit can use the generation AI to detect the user's physical condition and provide lighter training. Furthermore, the training unit can also provide practical training if the user is in good health. For example, the training unit can use the generation AI to detect the user's health condition and provide practical training. Furthermore, the training unit can also provide training to help the user relax if the user is not feeling well. For example, the training unit can use the generation AI to detect the user's poor physical condition and provide training to help the user relax. This allows the training unit to provide optimal training according to the user's physical condition and health state.

[0048] When providing training, the training unit can select training content based on the user's interests. For example, the training unit can provide training related to a field in which the user is interested. For example, the training unit can use a generation AI to detect the user's interests and provide training related to the field in which the user is interested. The training unit can also select training content based on a theme in which the user is interested. For example, the training unit can use a generation AI to detect the user's interests and select training content based on the theme in which the user is interested. Furthermore, the training unit can provide training that reflects the user's interests. For example, the training unit can use a generation AI to detect the user's interests and provide training that reflects them. This allows the training unit to provide optimal training according to the user's interests.

[0049] When providing training, the training unit can adjust the timing of the training based on the user's lifestyle rhythm. For example, if the user is a morning person, the training unit can provide training in the morning. For example, the training unit can use the generation AI to detect the user's lifestyle rhythm and provide training in the morning if the user is a morning person. Furthermore, the training unit can also provide training in the evening if the user is a night owl. For example, the training unit can use the generation AI to detect the user's lifestyle rhythm and provide training in the evening if the user is a night owl. Furthermore, the training unit can also provide training at the optimal timing in accordance with the user's lifestyle rhythm. For example, the training unit can use the generation AI to detect the user's lifestyle rhythm and provide training at the optimal timing in accordance with that. This allows the training unit to provide training at the optimal timing according to the user's lifestyle rhythm.

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

[0051] The emergency support system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit acquires biometric information, such as the user's heart rate, blood pressure, and oxygen saturation, in real time to monitor the user's health condition in an emergency. For example, if the user is feeling excessive stress, the health monitoring unit acquires that information and transmits it to the information generation unit, thereby generating evacuation instructions according to the user's health condition. Furthermore, if the user has a health problem, the health monitoring unit may notify the audio guidance unit and provide the user with appropriate health management advice. Furthermore, the health monitoring unit may adjust the training content of the training unit based on the user's health condition. This allows the emergency support system to provide optimal support according to the user's health condition.

[0052] The emergency support system may further include an environmental information acquisition unit that acquires surrounding environmental information based on the user's location information. The environmental information acquisition unit collects weather information, traffic information, disaster information, and the like around the user's current location in real time and provides the information to the information generation unit. For example, if the user is in an area at risk of flooding, the environmental information acquisition unit acquires the information and transmits it to the information generation unit to generate evacuation instructions for the flood. Also, if the user is stuck in traffic congestion, the environmental information acquisition unit acquires the information and transmits it to the navigation unit to display the optimal evacuation route to avoid the congestion. Furthermore, the environmental information acquisition unit may detect the noise level around the user and notify the audio guidance unit to provide audio guidance at an appropriate volume. This allows the emergency support system to provide optimal support according to the user's surrounding environment.

[0053] The emergency support system may further include a history reference unit that references the user's past emergency response history and generates appropriate information. The history reference unit analyzes the history of emergencies the user has experienced in the past and provides the information to the information generation unit. For example, if the user has experienced a fire in the past, the history reference unit can obtain that information and generate evacuation instructions for fires with priority. Also, if the user has experienced an earthquake in the past, the history reference unit can obtain that information and generate evacuation instructions for earthquakes with priority. Furthermore, if the user has experienced a flood in the past, the history reference unit can obtain that information and generate evacuation instructions for floods with priority. This allows the emergency support system to provide optimal information based on the user's past history.

[0054] The emergency support system may further include a family structure reference unit that generates information based on the user's family structure. The family structure reference unit acquires the user's family structure information and provides it to the information generation unit. For example, if the user is with children, the family structure reference unit can acquire the information and generate evacuation route information suitable for children. Also, if the user is accompanied by a pet, the family structure reference unit can acquire the information and generate route information that allows the user to evacuate with the pet. Furthermore, if the user is with elderly family members, the family structure reference unit can acquire the information and generate evacuation route information suitable for elderly people. This allows the emergency support system to provide optimal information according to the user's family structure.

[0055] The emergency support system may further include a location information customization unit that customizes information based on the user's current location information. The location information customization unit acquires the user's current location in real time and provides it to the information generation unit. For example, if the user's current location is in a high-rise building, the location information customization unit can generate evacuation route information within the building. Also, if the user's current location is a subway station, the location information customization unit can generate evacuation route information from the subway station. Furthermore, if the user's current location is a park, the location information customization unit can generate evacuation route information within the park. This allows the emergency support system to provide optimal information based on the user's current location.

[0056] The emergency support system can further include a historical voice guide unit that provides the optimal guide by referring to the user's past voice guide usage history. The historical voice guide unit analyzes the history of voice guides used by the user in the past and provides it to the voice guide unit. For example, the historical voice guide unit can provide the optimal guide based on the tone of the voice guide used by the user in the past. It can also provide preferred guide content based on the user's past voice guide usage history. Furthermore, it can analyze the user's past voice guide usage history and provide the most effective guide. This allows the emergency support system to provide the optimal voice guide based on the user's past history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The information generation unit generates information corresponding to emergency scenarios. For example, it generates information corresponding to emergency scenarios such as fires, earthquakes, floods, and terrorist attacks. The information generation unit uses generation AI to collect and analyze data related to emergencies. The generation AI can learn from past emergency data and generate new information in real time. Step 2: The navigation unit displays an evacuation route on the AR glasses based on the information generated by the information generation unit. The navigation unit uses generation AI to identify the user's current location and display the optimal evacuation route. AR technology can be used to superimpose the evacuation route on the user's field of vision. Step 3: The voice guidance unit provides voice guidance based on the evacuation route displayed by the navigation unit. The voice guidance unit uses generation AI to provide appropriate instructions to the user by voice. The content of the voice guidance can be adjusted depending on the user's situation. Step 4: The training department provides training in a virtual reality space to proactively improve the user's ability to respond to emergencies. The training department uses generative AI to conduct simulations in a virtual reality space and train the user on how to respond to emergencies. The training history of the user can be recorded and an individual training plan can be provided.

[0059] (Example 2) An emergency support system according to an embodiment of the present invention uses a generative AI embedded in AR glasses to provide user support in emergency situations. This emergency support system generates information corresponding to various emergency scenarios and guides users to safe evacuation shelters by combining navigation via the AR glasses and audio guidance. Furthermore, training in a virtual reality space improves users' ability to respond to emergencies in advance. For example, information corresponding to scenarios such as fires, earthquakes, floods, and terrorist attacks is generated. When a user wears the AR glasses, evacuation routes are displayed in the user's field of vision and appropriate instructions are provided via audio guidance. Training in a virtual reality space allows users to prepare for real emergencies. This allows the emergency support system to provide users with prompt and appropriate support.

[0060] An emergency support system according to an embodiment includes an information generation unit, a navigation unit, an audio guidance unit, and a training unit. The information generation unit generates information corresponding to emergency scenarios. For example, the information generation unit generates information corresponding to emergency scenarios such as fires, earthquakes, floods, and terrorist attacks. The information generation unit uses a generation AI to collect and analyze data related to the emergency. For example, the generation AI can learn from past emergency data and generate new information in real time. The navigation unit displays an evacuation route on AR glasses based on the information generated by the information generation unit. For example, the navigation unit uses the generation AI to identify a user's current location and display an optimal evacuation route. The navigation unit can use AR technology to display an evacuation route superimposed on the user's field of view. The audio guidance unit provides audio guidance based on the evacuation route displayed by the navigation unit. For example, the audio guidance unit uses the generation AI to provide appropriate instructions to the user via audio. The audio guidance unit can adjust the content of the audio guidance depending on the user's situation. The training unit provides training in a virtual reality space to proactively improve a user's ability to deal with emergencies. For example, the training unit uses the generation AI to perform a simulation in a virtual reality space and train the user on how to respond to an emergency. The training unit can record the user's training history and provide an individual training plan. This allows the emergency support system according to the embodiment to provide the user with prompt and appropriate support.

[0061] The information generation unit can generate information corresponding to emergency scenarios such as fires, earthquakes, floods, and terrorist attacks. The information generation unit, for example, generates evacuation instructions in the event of a fire. For example, the information generation unit can analyze the location and progress of a fire in real time and generate an optimal evacuation route. The information generation unit can also generate safety assurance methods in the event of an earthquake. For example, the information generation unit can analyze the epicenter and seismic intensity of an earthquake and generate evacuation instructions for the user to a safe location. The information generation unit can also generate evacuation instructions in the event of a flood. For example, the information generation unit can analyze the progress and water level of a flood and generate evacuation instructions for the user to higher ground. The information generation unit can also generate evacuation instructions in the event of a terrorist attack. For example, the information generation unit can analyze the location and situation of a terrorist attack and generate instructions for the user to a safe evacuation location. In this way, the information generation unit can provide information corresponding to a variety of emergency situations.

[0062] The navigation unit can display an evacuation route on the AR glasses based on the information generated by the information generation unit. The navigation unit, for example, identifies the user's current location and displays an optimal evacuation route. For example, the navigation unit can identify the user's current location using GPS data and display the evacuation route. The navigation unit can also use AR technology to superimpose the evacuation route on the user's field of view. For example, the navigation unit displays the evacuation route on the user's field of view through the AR glasses, allowing the user to intuitively understand the evacuation route. The navigation unit can also update the evacuation route in real time. For example, the navigation unit updates the evacuation route in real time according to the user's movement and provides the optimal route. In this way, the navigation unit can provide the user with a visual evacuation route.

[0063] The voice guidance unit can provide voice guidance based on the evacuation route displayed by the navigation unit. The voice guidance unit, for example, provides appropriate instructions to the user by voice. For example, the voice guidance unit can provide voice instructions on the evacuation route to the user using a generation AI. The voice guidance unit can also adjust the content of the voice guidance depending on the user's situation. For example, if the user is in a panic, the voice guidance unit can provide guidance in a calm tone. If the user is calm, the voice guidance unit can also provide detailed guidance. Furthermore, if the user is feeling anxious, the voice guidance unit can provide guidance in a reassuring tone. In this way, the voice guidance unit can provide voice instructions to the user.

[0064] The training unit provides training in a virtual reality space, thereby enabling users to proactively improve their ability to respond to emergencies. For example, the training unit performs a simulation in the virtual reality space to train users on how to respond to emergencies. For example, the training unit can use a generative AI to perform a simulation in the virtual reality space to train users on how to evacuate in the event of a fire. The training unit can also train users on how to ensure safety in the event of an earthquake. For example, the training unit can perform an earthquake simulation in the virtual reality space to train users on how to evacuate to a safe location. The training unit can also train users on how to evacuate in the event of a flood. For example, the training unit can perform a flood simulation in the virtual reality space to train users on how to evacuate to higher ground. The training unit can also train users on how to evacuate in the event of a terrorist attack. For example, the training unit can perform a terrorist attack simulation in the virtual reality space to train users on how to evacuate to a safe evacuation location. In this way, the training unit can proactively improve users' ability to respond to emergencies.

[0065] The information generation unit can estimate the user's emotions and adjust the generation of information for emergency scenarios based on the estimated user emotions. For example, if the user is in a panicked state, the information generation unit causes the generation AI to prioritize generating information encouraging the user to remain calm. For example, the information generation unit can use the generation AI to detect that the user is in a panicked state and generate information encouraging the user to remain calm. Furthermore, if the user remains calm, the information generation unit can generate information including detailed evacuation procedures. For example, the information generation unit can use the generation AI to detect that the user is calm and generate information including detailed evacuation procedures. Furthermore, if the user is feeling anxious, the information generation unit can generate information that provides a sense of security. For example, the information generation unit can use the generation AI to detect that the user is feeling anxious and generate information that provides a sense of security. This allows the information generation unit to provide appropriate information according to the user's emotions.

[0066] When generating information about an emergency scenario, the information generation unit can generate appropriate information by referring to the user's past emergency response history. The information generation unit, for example, generates information about similar scenarios based on the user's past emergency response history. For example, the information generation unit can use a generation AI to analyze the user's past emergency response history and generate information about similar scenarios. The information generation unit can also generate optimal evacuation route information by referring to routes the user has used in the past. For example, the information generation unit can use a generation AI to analyze the user's past evacuation routes and generate optimal evacuation route information. Furthermore, the information generation unit can generate information about appropriate evacuation means by taking into account evacuation means used by the user in the past. For example, the information generation unit can use a generation AI to analyze the user's past evacuation means and generate information about appropriate evacuation means. This allows the information generation unit to provide optimal information based on the user's past history.

[0067] When generating information for an emergency scenario, the information generation unit can customize the information based on the user's current location information. For example, if the user's current location is in a high-rise building, the information generation unit can generate evacuation route information within the building. For example, the information generation unit can use a generation AI to detect that the user's current location is in a high-rise building and generate evacuation route information within the building. Furthermore, if the user's current location is a subway station, the information generation unit can also generate evacuation route information from the subway station. For example, the information generation unit can use a generation AI to detect that the user's current location is a subway station and generate evacuation route information from the subway station. Furthermore, if the user's current location is a park, the information generation unit can also generate evacuation route information within the park. For example, the information generation unit can use a generation AI to detect that the user's current location is a park and generate evacuation route information within the park. This allows the information generation unit to provide appropriate information based on the user's current location.

[0068] The information generation unit can estimate the user's emotions and determine the priority of information to be generated based on the estimated user's emotions. For example, if the user is in a panic state, the information generation unit prioritizes generating the most important evacuation information. For example, the information generation unit can use a generation AI to detect that the user is in a panic state and prioritize generating the most important evacuation information. Furthermore, if the user is calm, the information generation unit can prioritize generating information including detailed evacuation procedures. For example, the information generation unit can use a generation AI to detect that the user is calm and prioritize generating information including detailed evacuation procedures. Furthermore, if the user is feeling anxious, the information generation unit can prioritize generating information that provides a sense of security. For example, the information generation unit can use a generation AI to detect that the user is feeling anxious and prioritize generating information that provides a sense of security. This allows the information generation unit to prioritize information according to the user's emotions.

[0069] When generating information for an emergency scenario, the information generation unit can generate information based on the user's health condition. For example, if the user has heart disease, the information generation unit generates evacuation route information that puts less strain on the heart. For example, the information generation unit can use a generation AI to detect the user's health condition and generate evacuation route information that puts less strain on the heart. Furthermore, if the user is pregnant, the information generation unit can generate evacuation route information suitable for pregnant women. For example, the information generation unit can use a generation AI to detect that the user is pregnant and generate evacuation route information suitable for pregnant women. Furthermore, if the user is elderly, the information generation unit can generate evacuation route information suitable for elderly people. For example, the information generation unit can use a generation AI to detect that the user is elderly and generate evacuation route information suitable for elderly people. This allows the information generation unit to provide appropriate information according to the user's health condition.

[0070] When generating information for an emergency scenario, the information generation unit can generate information based on the user's family composition. For example, if the user is with a child, the information generation unit generates evacuation route information suitable for the child. For example, the information generation unit can use a generation AI to detect the user's family composition and generate evacuation route information suitable for the child. Furthermore, if the user is accompanied by a pet, the information generation unit can generate route information that allows the user to evacuate with the pet. For example, the information generation unit can use a generation AI to detect that the user is accompanied by a pet and generate route information that allows the user to evacuate with the pet. Furthermore, if the user is accompanied by an elderly family member, the information generation unit can generate evacuation route information suitable for the elderly. For example, the information generation unit can use a generation AI to detect that the user is accompanied by an elderly family member and generate evacuation route information suitable for the elderly. This allows the information generation unit to provide appropriate information according to the user's family composition.

[0071] The navigation unit can estimate the user's emotions and adjust the display method of evacuation routes based on the estimated user emotions. For example, if the user is in a panicked state, the navigation unit provides a simple, highly visible display method. For example, the navigation unit can use a generation AI to detect that the user is in a panicked state and provide a simple, highly visible display method. Furthermore, if the user is calm, the navigation unit can also display a detailed evacuation route. For example, the navigation unit can use a generation AI to detect that the user is calm and display a detailed evacuation route. Furthermore, if the user is feeling anxious, the navigation unit can also provide a display method that gives a sense of security. For example, the navigation unit can use a generation AI to detect that the user is feeling anxious and provide a display method that gives a sense of security. This allows the navigation unit to provide an appropriate display method according to the user's emotions.

[0072] When displaying an evacuation route, the navigation unit can display an appropriate route by referring to the user's past evacuation history. The navigation unit, for example, displays an optimal route based on evacuation routes used by the user in the past. For example, the navigation unit can use a generation AI to analyze the user's past evacuation history and display an optimal route. The navigation unit can also display a route that avoids congestion based on the user's past evacuation history. For example, the navigation unit can use a generation AI to analyze the user's past evacuation history and display a route that avoids congestion. The navigation unit can also analyze the user's past evacuation history and display the most efficient route. For example, the navigation unit can use a generation AI to analyze the user's past evacuation history and display the most efficient route. This allows the navigation unit to provide an optimal route based on the user's past history.

[0073] When displaying an evacuation route, the navigation unit can update the route in real time based on the user's current location information. For example, the navigation unit can update the user's current location in real time while the user is moving and display the optimal route. For example, the navigation unit can use a generation AI to detect the user's current location in real time and display the optimal route. The navigation unit can also update the current location in real time and display the optimal route as the user approaches the destination. For example, the navigation unit can use a generation AI to detect the user's current location in real time and display the optimal route as the user approaches the destination. Furthermore, the navigation unit can update the current location in real time and perform navigation again if the user gets lost. For example, the navigation unit can use a generation AI to detect the user's current location in real time and perform navigation again if the user gets lost. This allows the navigation unit to provide the optimal route based on the user's current location.

[0074] The navigation unit can estimate the user's emotions and adjust the display order of evacuation routes based on the estimated user's emotions. For example, if the user is in a panic, the navigation unit can prioritize displaying the most important evacuation route. For example, the navigation unit can use a generation AI to detect that the user is in a panic and prioritize displaying the most important evacuation route. Furthermore, if the user is calm, the navigation unit can display detailed evacuation routes in a sequential order. For example, the navigation unit can use a generation AI to detect that the user is calm and display detailed evacuation routes in a sequential order. Furthermore, if the user is feeling anxious, the navigation unit can prioritize displaying evacuation routes that give a sense of security. For example, the navigation unit can use a generation AI to detect that the user is feeling anxious and prioritize displaying evacuation routes that give a sense of security. This allows the navigation unit to provide an appropriate display order according to the user's emotions.

[0075] When displaying an evacuation route, the navigation unit can adjust the route based on the user's moving speed. For example, if the user is walking fast, the navigation unit can display the shortest route. For example, the navigation unit can use a generation AI to detect the user's moving speed and display the shortest route. Furthermore, if the user is walking slowly, the navigation unit can display a detailed route. For example, the navigation unit can use a generation AI to detect the user's moving speed and display the detailed route. Furthermore, if the user is running, the navigation unit can display the safest route. For example, the navigation unit can use a generation AI to detect the user's moving speed and display the safest route. This allows the navigation unit to provide an appropriate route according to the user's moving speed.

[0076] When displaying an evacuation route, the navigation unit can optimize the route based on environmental information about the user's surroundings. For example, if there is an obstacle around the user, the navigation unit can display a detour route. For example, the navigation unit can use a generation AI to detect environmental information about the user's surroundings and display a detour route if there is an obstacle. The navigation unit can also display a route that avoids the congestion when the user's surroundings are congested. For example, the navigation unit can use a generation AI to detect environmental information about the user's surroundings and display a route that avoids the congestion. Furthermore, the navigation unit can display a well-lit route when the user's surroundings are dark. For example, the navigation unit can use a generation AI to detect environmental information about the user's surroundings and display a well-lit route when it is dark. This allows the navigation unit to provide an optimal route according to the environment around the user.

[0077] The voice guidance unit can estimate the user's emotions and adjust the tone and content of the voice guidance based on the estimated user's emotions. For example, if the user is in a panicked state, the voice guidance unit can provide guidance in a calm tone. For example, the voice guidance unit can use a generation AI to detect that the user is in a panicked state and provide guidance in a calm tone. Furthermore, if the user is calm, the voice guidance unit can provide detailed guidance. For example, the voice guidance unit can use a generation AI to detect that the user is calm and provide detailed guidance. Furthermore, if the user is feeling anxious, the voice guidance unit can provide guidance in a tone that gives a sense of security. For example, the voice guidance unit can use a generation AI to detect that the user is feeling anxious and provide guidance in a tone that gives a sense of security. This allows the voice guidance unit to provide appropriate voice guidance according to the user's emotions.

[0078] When providing an audio guide, the audio guide unit can provide the optimal audio guide by referring to the user's past audio guide usage history. The audio guide unit can provide the optimal audio guide based on, for example, the tones of audio guides used by the user in the past. For example, the audio guide unit can use a generation AI to analyze the user's past audio guide usage history and provide the optimal audio guide. The audio guide unit can also provide preferred audio guide content based on the user's past audio guide usage history. For example, the audio guide unit can use a generation AI to analyze the user's past audio guide usage history and provide the preferred audio guide content. The audio guide unit can also analyze the user's past audio guide usage history and provide the most effective audio guide. For example, the audio guide unit can use a generation AI to analyze the user's past audio guide usage history and provide the most effective audio guide. This allows the audio guide unit to provide the optimal audio guide based on the user's past history.

[0079] When providing the audio guidance, the audio guidance unit can update the content of the guidance in real time based on the user's current location information. For example, the audio guidance unit can update the user's current location in real time while the user is moving and provide optimal guidance. For example, the audio guidance unit can use a generation AI to detect the user's current location in real time and provide optimal guidance. The audio guidance unit can also update the current location in real time as the user approaches the destination and provide optimal guidance. For example, the audio guidance unit can use a generation AI to detect the user's current location in real time and provide optimal guidance as the user approaches the destination. Furthermore, if the user gets lost, the audio guidance unit can update the current location in real time and provide guidance again. For example, the audio guidance unit can use a generation AI to detect the user's current location in real time and provide guidance again if the user gets lost. This allows the audio guidance unit to provide optimal audio guidance based on the user's current location.

[0080] The voice guidance unit can estimate the user's emotions and adjust the frequency of voice guidance based on the estimated user's emotions. For example, if the user is in a panicked state, the voice guidance unit can provide guidance more frequently. For example, the voice guidance unit can use a generation AI to detect that the user is in a panicked state and provide guidance more frequently. Furthermore, if the user is calm, the voice guidance unit can provide guidance only when necessary. For example, the voice guidance unit can use a generation AI to detect that the user is calm and provide guidance only when necessary. Furthermore, if the user is feeling anxious, the voice guidance unit can provide guidance at an appropriate frequency to give the user a sense of security. For example, the voice guidance unit can use a generation AI to detect that the user is feeling anxious and provide guidance at an appropriate frequency to give the user a sense of security. This allows the voice guidance unit to provide an appropriate frequency of voice guidance according to the user's emotions.

[0081] When providing audio guidance, the audio guide unit can customize the content of the guidance based on the user's hearing characteristics. For example, if the user has a hearing impairment, the audio guide unit can adjust the volume of the guidance when providing the guidance. For example, the audio guide unit can use a generation AI to detect the user's hearing characteristics and adjust the volume when providing the guidance. Furthermore, if the user is elderly, the audio guide unit can provide guidance in a tone that is easy to hear. For example, the audio guide unit can use a generation AI to detect that the user is elderly and provide guidance in a tone that is easy to hear. Furthermore, if the user is young, the audio guide unit can provide guidance in a brighter tone. For example, the audio guide unit can use a generation AI to detect that the user is young and provide guidance in a brighter tone. This allows the audio guide unit to provide optimal audio guidance according to the user's hearing characteristics.

[0082] When providing audio guidance, the audio guidance unit can adjust the volume of the guidance based on the noise level around the user. For example, if the user's surroundings are noisy, the audio guidance unit can increase the volume of the guidance. For example, the audio guidance unit can use a generation AI to detect the noise level around the user and increase the volume of the guidance. Furthermore, if the user's surroundings are quiet, the audio guidance unit can decrease the volume of the guidance. For example, the audio guidance unit can use a generation AI to detect the noise level around the user and decrease the volume of the guidance. Furthermore, the audio guidance unit can provide guidance at an appropriate volume depending on the noise level around the user. For example, the audio guidance unit can use a generation AI to detect the noise level around the user and provide guidance at an appropriate volume. This allows the audio guidance unit to provide audio guidance at an optimal volume depending on the noise level around the user.

[0083] The training unit can estimate the user's emotions and adjust the training content based on the estimated user's emotions. For example, if the user is nervous, the training unit provides training to help the user relax. For example, the training unit can use the generation AI to detect that the user is nervous and provide training to help the user relax. The training unit can also provide practical training if the user is relaxed. For example, the training unit can use the generation AI to detect that the user is relaxed and provide practical training. Furthermore, the training unit can also provide training to give the user a sense of security if the user is feeling anxious. For example, the training unit can use the generation AI to detect that the user is feeling anxious and provide training to give the user a sense of security. This allows the training unit to provide appropriate training content according to the user's emotions.

[0084] When providing training, the training unit can provide optimal training by referring to the user's past training history. The training unit can, for example, provide similar training based on the content of training the user has done in the past. For example, the training unit can use a generation AI to analyze the user's past training history and provide similar training. The training unit can also prioritize providing effective training from the user's past training history. For example, the training unit can use a generation AI to analyze the user's past training history and prioritize providing effective training. Furthermore, the training unit can analyze the user's past training history and provide the most effective training. For example, the training unit can use a generation AI to analyze the user's past training history and provide the most effective training. This allows the training unit to provide optimal training based on the user's past history.

[0085] When providing training, the training unit can customize the training content based on the user's current physical condition and health state. For example, if the user is tired, the training unit can provide lighter training. For example, the training unit can use the generation AI to detect the user's physical condition and provide lighter training. Furthermore, the training unit can also provide practical training if the user is in good health. For example, the training unit can use the generation AI to detect the user's health condition and provide practical training. Furthermore, the training unit can also provide training to help the user relax if the user is not feeling well. For example, the training unit can use the generation AI to detect the user's poor physical condition and provide training to help the user relax. This allows the training unit to provide optimal training according to the user's physical condition and health state.

[0086] The training unit can estimate the user's emotions and adjust the frequency of training based on the estimated user's emotions. For example, if the user is tense, the training unit can provide frequent training to relax. For example, the training unit can use the generation AI to detect that the user is tense and provide frequent training to relax. Furthermore, if the user is relaxed, the training unit can provide training at an appropriate frequency. For example, the training unit can use the generation AI to detect that the user is relaxed and provide training at an appropriate frequency. Furthermore, if the user is feeling anxious, the training unit can provide training at an appropriate frequency to give the user a sense of security. For example, the training unit can use the generation AI to detect that the user is feeling anxious and provide training at an appropriate frequency to give the user a sense of security. This allows the training unit to provide an appropriate frequency of training according to the user's emotions.

[0087] When providing training, the training unit can select training content based on the user's interests. For example, the training unit can provide training related to a field in which the user is interested. For example, the training unit can use a generation AI to detect the user's interests and provide training related to the field in which the user is interested. The training unit can also select training content based on a theme in which the user is interested. For example, the training unit can use a generation AI to detect the user's interests and select training content based on the theme in which the user is interested. Furthermore, the training unit can provide training that reflects the user's interests. For example, the training unit can use a generation AI to detect the user's interests and provide training that reflects them. This allows the training unit to provide optimal training according to the user's interests.

[0088] When providing training, the training unit can adjust the timing of the training based on the user's lifestyle rhythm. For example, if the user is a morning person, the training unit can provide training in the morning. For example, the training unit can use the generation AI to detect the user's lifestyle rhythm and provide training in the morning if the user is a morning person. Furthermore, the training unit can also provide training in the evening if the user is a night owl. For example, the training unit can use the generation AI to detect the user's lifestyle rhythm and provide training in the evening if the user is a night owl. Furthermore, the training unit can also provide training at the optimal timing in accordance with the user's lifestyle rhythm. For example, the training unit can use the generation AI to detect the user's lifestyle rhythm and provide training at the optimal timing in accordance with that. This allows the training unit to provide training at the optimal timing according to the user's lifestyle rhythm. === Hard Collateral 1-1 === Each of the multiple elements including the information generation unit, navigation unit, audio guidance unit, and training unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates information corresponding to an emergency scenario using a generation AI. The navigation unit is realized by the control unit 46A of the smart device 14 and displays evacuation routes on the AR glasses. The audio guidance unit is realized by the control unit 46A of the smart device 14 and provides audio guidance. The training unit is realized by the specific processing unit 290 of the data processing device 12 and provides training in a virtual reality space. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned information generation unit, navigation unit, audio guidance unit, and training unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates information corresponding to an emergency scenario using a generation AI. The navigation unit is realized by the control unit 46A of the smart glasses 214 and displays evacuation routes on the AR glasses. The audio guidance unit is realized by the control unit 46A of the smart glasses 214 and provides audio guidance. The training unit is realized by the specific processing unit 290 of the data processing device 12 and provides training in a virtual reality space. === Hard Collateral 1-3 === Each of the multiple elements including the information generation unit, navigation unit, audio guidance unit, and training unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the information generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates information corresponding to an emergency scenario using a generation AI. The navigation unit is realized by the control unit 46A of the headset-type terminal 314 and displays evacuation routes on the AR glasses. The audio guidance unit is realized by the control unit 46A of the headset-type terminal 314 and provides audio guidance. The training unit is realized by the specific processing unit 290 of the data processing device 12 and provides training in a virtual reality space. === Hard Collateral 1-4 === Each of the multiple elements including the information generation unit, navigation unit, audio guidance unit, and training unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates information corresponding to an emergency scenario using a generation AI. The navigation unit is realized by the control unit 46A of the robot 414 and displays evacuation routes on the AR glasses. The audio guidance unit is realized by the control unit 46A of the robot 414 and provides audio guidance. The training unit is realized by the specific processing unit 290 of the data processing device 12 and provides training in a virtual reality space.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The emergency support system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit acquires biometric information, such as the user's heart rate, blood pressure, and oxygen saturation, in real time to monitor the user's health condition in an emergency. For example, if the user is feeling excessive stress, the health monitoring unit acquires that information and transmits it to the information generation unit, thereby generating evacuation instructions according to the user's health condition. Furthermore, if the user has a health problem, the health monitoring unit may notify the audio guidance unit and provide the user with appropriate health management advice. Furthermore, the health monitoring unit may adjust the training content of the training unit based on the user's health condition. This allows the emergency support system to provide optimal support according to the user's health condition.

[0091] The emergency support system may further include an environmental information acquisition unit that acquires surrounding environmental information based on the user's location information. The environmental information acquisition unit collects weather information, traffic information, disaster information, and the like around the user's current location in real time and provides the information to the information generation unit. For example, if the user is in an area at risk of flooding, the environmental information acquisition unit acquires the information and transmits it to the information generation unit to generate evacuation instructions for the flood. Also, if the user is stuck in traffic congestion, the environmental information acquisition unit acquires the information and transmits it to the navigation unit to display the optimal evacuation route to avoid the congestion. Furthermore, the environmental information acquisition unit may detect the noise level around the user and notify the audio guidance unit to provide audio guidance at an appropriate volume. This allows the emergency support system to provide optimal support according to the user's surrounding environment.

[0092] The emergency support system may further include an emotional training unit that estimates the user's emotions and adjusts the training content based on the estimated emotions. The emotional training unit monitors the user's emotional state in real time and provides the information to the training unit. For example, if the user is nervous, the emotional training unit can obtain that information and provide training to help the user relax. If the user is relaxed, the emotional training unit can also provide practical training. Furthermore, if the user is feeling anxious, the emotional training unit can also provide training that gives the user a sense of security. This allows the emergency support system to provide optimal training according to the user's emotional state.

[0093] The emergency support system may further include a history reference unit that references the user's past emergency response history and generates appropriate information. The history reference unit analyzes the history of emergencies the user has experienced in the past and provides the information to the information generation unit. For example, if the user has experienced a fire in the past, the history reference unit can obtain that information and generate evacuation instructions for fires with priority. Also, if the user has experienced an earthquake in the past, the history reference unit can obtain that information and generate evacuation instructions for earthquakes with priority. Furthermore, if the user has experienced a flood in the past, the history reference unit can obtain that information and generate evacuation instructions for floods with priority. This allows the emergency support system to provide optimal information based on the user's past history.

[0094] The emergency support system may further include a family structure reference unit that generates information based on the user's family structure. The family structure reference unit acquires the user's family structure information and provides it to the information generation unit. For example, if the user is with children, the family structure reference unit can acquire the information and generate evacuation route information suitable for children. Also, if the user is accompanied by a pet, the family structure reference unit can acquire the information and generate route information that allows the user to evacuate with the pet. Furthermore, if the user is with elderly family members, the family structure reference unit can acquire the information and generate evacuation route information suitable for elderly people. This allows the emergency support system to provide optimal information according to the user's family structure.

[0095] The emergency support system can further include an emotion navigation unit that estimates the user's emotion and adjusts the display method of the evacuation route based on the estimated emotion. The emotion navigation unit monitors the user's emotional state in real time and provides it to the navigation unit. For example, if the user is in a panic, the emotion navigation unit can provide a simple, highly visible display method. If the user is calm, the emotion navigation unit can also display a detailed evacuation route. Furthermore, if the user is feeling anxious, the emotion navigation unit can also provide a display method that gives a sense of security. This allows the emergency support system to provide an optimal display method according to the user's emotional state.

[0096] The emergency support system may further include a location information customization unit that customizes information based on the user's current location information. The location information customization unit acquires the user's current location in real time and provides it to the information generation unit. For example, if the user's current location is in a high-rise building, the location information customization unit can generate evacuation route information within the building. Also, if the user's current location is a subway station, the location information customization unit can generate evacuation route information from the subway station. Furthermore, if the user's current location is a park, the location information customization unit can generate evacuation route information within the park. This allows the emergency support system to provide optimal information based on the user's current location.

[0097] The emergency support system can further include an emotional voice guide unit that estimates the user's emotions and adjusts the tone and content of the voice guide based on the estimated emotions. The emotional voice guide unit monitors the user's emotional state in real time and provides the information to the voice guide unit. For example, if the user is in a panic, the emotional voice guide unit can provide guidance in a calm tone. If the user is calm, the emotional voice guide unit can provide detailed guidance. Furthermore, if the user is feeling anxious, the emotional voice guide unit can provide guidance in a tone that gives a sense of security. This allows the emergency support system to provide optimal voice guidance according to the user's emotional state.

[0098] The emergency support system can further include a historical voice guide unit that provides the optimal guide by referring to the user's past voice guide usage history. The historical voice guide unit analyzes the history of voice guides used by the user in the past and provides it to the voice guide unit. For example, the historical voice guide unit can provide the optimal guide based on the tone of the voice guide used by the user in the past. It can also provide preferred guide content based on the user's past voice guide usage history. Furthermore, it can analyze the user's past voice guide usage history and provide the most effective guide. This allows the emergency support system to provide the optimal voice guide based on the user's past history.

[0099] The emergency support system can further include an emotion frequency adjustment unit that estimates the user's emotion and adjusts the frequency of audio guidance based on the estimated emotion. The emotion frequency adjustment unit monitors the user's emotional state in real time and provides the result to the audio guidance unit. For example, if the user is in a panicked state, the emotion frequency adjustment unit can provide frequent guidance. Alternatively, if the user is calm, the emotion frequency adjustment unit can provide guidance only when necessary. Furthermore, if the user is feeling anxious, the emotion frequency adjustment unit can provide guidance at an appropriate frequency to provide a sense of security. This allows the emergency support system to provide an optimal frequency of audio guidance according to the user's emotional state.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The information generation unit generates information corresponding to emergency scenarios. For example, it generates information corresponding to emergency scenarios such as fires, earthquakes, floods, and terrorist attacks. The information generation unit uses generation AI to collect and analyze data related to emergencies. The generation AI can learn from past emergency data and generate new information in real time. Step 2: The navigation unit displays an evacuation route on the AR glasses based on the information generated by the information generation unit. The navigation unit uses generation AI to identify the user's current location and display the optimal evacuation route. AR technology can be used to superimpose the evacuation route on the user's field of vision. Step 3: The voice guidance unit provides voice guidance based on the evacuation route displayed by the navigation unit. The voice guidance unit uses generation AI to provide appropriate instructions to the user by voice. The content of the voice guidance can be adjusted depending on the user's situation. Step 4: The training department provides training in a virtual reality space to proactively improve the user's ability to respond to emergencies. The training department uses generative AI to conduct simulations in a virtual reality space and train the user on how to respond to emergencies. The training history of the user can be recorded and an individual training plan can be provided.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the 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.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information generating unit that generates information corresponding to an emergency scenario; a navigation unit that displays an evacuation route based on the information generated by the information generation unit; a voice guide unit that provides voice guidance based on the evacuation route displayed by the navigation unit; a training unit that provides training in a virtual reality space; A system characterized by:

2. The information generation unit Generate information to respond to fire, earthquake, flood, and terrorism emergency scenarios 2. The system of claim 1.

3. The navigation unit An evacuation route is displayed on the AR glasses based on the information generated by the information generation unit.

2. The system of claim 1.

4. The voice guide unit Provides voice guidance based on the evacuation route displayed by the navigation unit.

2. The system of claim 1.

5. The training section Providing virtual reality training to proactively improve users' ability to respond to emergencies 2. The system of claim 1.

6. The information generation unit Estimating user emotions and adjusting information generation for emergency scenarios based on the estimated user emotions 2. The system of claim 1.

7. The information generation unit When generating emergency scenario information, appropriate information is generated by referencing the user's past emergency response history.

2. The system of claim 1.

8. The information generation unit Customize information generation for emergency scenarios based on the user's current location 2. The system of claim 1.

9. The information generation unit Estimate the user's emotions and determine the priority of the information to be generated based on the estimated user emotions.

2. The system of claim 1.

10. The information generation unit Generate information based on the user's health status when generating information for emergency scenarios 2. The system of claim 1.

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