System

Abstract

An object of the system according to the embodiment is to provide a custom-made disaster prevention education program in consideration of background information of participants.SOLUTION: A system includes a disaster scenario generation part, a custom-made education program providing part, and an interactive education providing part. The disaster scenario generation part generates a disaster scenario. The custom-made education program providing unit provides a custom-made education program in consideration of the background information of the participant based on the disaster scenario generated by the disaster scenario generation unit. The interactive education providing unit provides an interactive education program based on the education program provided by the custom-made education program providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to provide custom-made disaster prevention education programs that took into account the background information of participants, which resulted in the inability to provide effective disaster prevention education.

[0005] The system according to the embodiment aims to provide a custom-made disaster prevention education program that takes into account the background information of the participants. [Means for solving the problem]

[0006] A system according to an embodiment includes a disaster scenario generation unit, a customized educational program provision unit, and an interactive education provision unit. The disaster scenario generation unit generates a disaster scenario. The customized educational program provision unit provides a customized educational program based on the disaster scenario generated by the disaster scenario generation unit, taking into account background information of participants. The interactive education provision unit provides an interactive educational program based on the educational program provided by the customized educational program provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a custom-made disaster prevention education program that takes into account the background information of the participants. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The disaster prevention education system according to an embodiment of the present invention uses AI to generate various disaster scenarios in real time and provides customized educational programs tailored to the participants' backgrounds. This allows users to acquire the ability to make appropriate decisions by taking into account various factors in situations similar to actual disaster scenes.

[0029] A disaster prevention education system according to an embodiment includes a disaster scenario generation unit, a customized educational program provision unit, and an interactive education provision unit. The disaster scenario generation unit generates a disaster scenario. For example, the generation AI creates different disaster scenarios, such as earthquakes, floods, fires, and typhoons, and provides them to participants. The generation AI generates the scenarios based on prompts containing instructions about the type, scale, and location of the disaster. The customized educational program provision unit provides a customized educational program based on the disaster scenario generated by the disaster scenario generation unit, taking into account participants' background information. For example, a company employee may receive a scenario about responding to a fire in an office, and a school student may receive a scenario about responding to an earthquake in a school. The interactive education provision unit provides an interactive educational program based on the educational program provided by the customized educational program provision unit. For example, if the generation AI asks, "An earthquake has occurred. What should I do first?" and a participant answers, "Hide under a desk," the generation AI evaluates the appropriateness of the action and provides feedback. This allows users to acquire appropriate decision-making skills that take various factors into account in situations similar to actual disaster scenarios.

[0030] The disaster scenario generation unit can combine past disaster data, weather forecast data, and geological data to generate more realistic disaster scenarios. For example, the generation AI combines past earthquake data with the latest weather forecast data to generate a scenario that includes the risk of a tsunami after an earthquake. For example, it creates a scenario in which a tsunami warning is issued after an earthquake, and participants practice choosing an evacuation route. The generation AI also analyzes geological data to generate disaster scenarios for areas at high risk of landslides. For example, it creates a scenario in which a landslide occurs due to heavy rain and residents evacuate, allowing participants to learn appropriate evacuation behavior. The generation AI also uses weather forecast data to generate scenarios based on the predicted path of a typhoon. For example, it creates a scenario in which a typhoon approaches and damage is expected from strong winds and heavy rain, allowing participants to learn what measures to prepare in advance. By generating more realistic disaster scenarios, participants can acquire knowledge and skills that will be useful in actual disasters.

[0031] The disaster scenario generation unit can include the long-term impact of a disaster, allowing participants to learn how to respond from a long-term perspective. For example, the generation AI in the disaster scenario generation unit generates a scenario that includes the period of time required for infrastructure restoration after an earthquake. For example, it simulates the period until power and water supply are restored, allowing participants to learn how to prepare the necessary daily necessities during that time. The generation AI also generates a scenario that includes the economic impact of a flood. For example, it simulates damage to crops and economic losses caused by a flood, allowing participants to learn measures to minimize the impact. The generation AI also generates a scenario that includes the environmental impact of a fire. For example, it simulates air pollution and health damage caused by a fire, allowing participants to learn actions to mitigate the impact. In this way, participants can learn how to respond from a long-term perspective, thereby acquiring knowledge that will be useful for life after a disaster and for recovery.

[0032] The disaster scenario generation unit can provide an international disaster prevention education program by reflecting the characteristics of different cultures and regions. For example, the generation AI generates scenarios that take into account the disaster characteristics of different countries and regions. For example, a Japanese earthquake scenario and an American hurricane scenario are provided, allowing participants to learn how to respond to each region. The generation AI also generates scenarios that take cultural backgrounds into account. For example, it creates scenarios in which religious events or traditional buildings are affected, allowing participants to learn how to respond. The generation AI also generates scenarios that support multiple languages. For example, it provides scenarios in multiple languages, such as English, Spanish, and Chinese, allowing international participants to receive disaster prevention education. This makes it possible to realize international disaster prevention education by reflecting the characteristics of different cultures and regions.

[0033] By including evacuation behaviors of animals and pets, the disaster scenario generation unit can provide an educational program that is also suitable for participants who own pets. In the disaster scenario generation unit, for example, the generation AI generates a scenario that includes evacuation behaviors of pets. For example, it provides a scenario for learning how to safely evacuate pets in the event of an earthquake. The generation AI also generates a scenario that includes the establishment and operation of an animal shelter. For example, it provides a scenario for setting up an animal shelter and evacuating pets in the event of a flood. The generation AI also generates a scenario that includes pet health management. For example, it provides a scenario for checking the health of pets after a disaster occurs and providing the necessary care. In this way, by providing an educational program that is also suitable for participants who own pets, it is possible to ensure the safety of pets in the event of a disaster.

[0034] The customized education program provider can provide special disaster prevention scenarios for the elderly and disabled, taking into consideration the health condition and physical ability of the participants. In the customized education program provider, for example, the generation AI generates disaster prevention scenarios for the elderly. For example, it provides a scenario in which the elderly learn how to evacuate safely in the event of an earthquake. The generation AI also generates disaster prevention scenarios for disabled people. For example, it provides a scenario in which wheelchair users learn precautions to take when evacuating. The generation AI also generates disaster prevention scenarios that take health condition into consideration. For example, it provides a scenario in which participants with chronic illnesses learn how to secure the medication they need in the event of a disaster. In this way, it is possible to meet the diverse needs of participants by providing special disaster prevention scenarios for the elderly and disabled.

[0035] The customized educational program provider can reference the participant's past disaster prevention training history and provide an educational program tailored to the participant's progress based on past learning content. In the customized educational program provider, for example, the generation AI analyzes the participant's past disaster prevention training history and provides scenarios tailored to the participant's progress. For example, starting with scenarios for beginners and gradually increasing the difficulty level. The generation AI also provides scenarios that strengthen the participant's weak points based on past learning content. For example, for a participant who is not good at selecting evacuation routes, it provides a scenario that focuses on learning that part. The generation AI also references the participant's training history and provides feedback tailored to the participant's progress. For example, it provides a scenario that points out areas for improvement based on mistakes made in past training. In this way, the effectiveness of the participant's learning can be improved by providing an educational program tailored to the participant's progress based on their past disaster prevention training history.

[0036] The custom-made educational program providing unit can provide disaster prevention scenarios that the entire family can participate in, taking into consideration the family composition of participants and whether or not they have pets. In the custom-made educational program providing unit, for example, the generation AI generates disaster prevention scenarios that the entire family can participate in. For example, it provides a scenario in which the entire family can learn how to evacuate safely in the event of an earthquake. The generation AI also generates disaster prevention scenarios that take into consideration whether or not the family has pets. For example, it provides a scenario in which the entire family can learn how to evacuate safely in the event of an earthquake. The generation AI also generates disaster prevention scenarios that take into consideration the presence of pets. For example, it provides a scenario in which to learn evacuation plans that include pets. The generation AI also generates disaster prevention scenarios that take into consideration the family composition. For example, it provides a scenario for households with young children or elderly people. In this way, by providing disaster prevention scenarios that the entire family can participate in, it is possible to improve the disaster prevention response capabilities of the entire family.

[0037] The customized educational program providing unit can provide an educational program that includes specific risks related to the participant's occupation. For example, in the customized educational program providing unit, the generation AI generates a chemical disaster scenario for employees of a chemical plant. For example, it provides a scenario for learning appropriate responses in the event of a chemical leak. In addition, the generation AI generates a construction site disaster scenario for employees in the construction industry. For example, it provides a scenario for learning how to respond to fires and collapse accidents at construction sites. In addition, the generation AI generates a medical facility disaster scenario for medical workers. For example, it provides a scenario for learning how to safely evacuate patients in the event of a fire or power outage at a hospital. In this way, by providing an educational program that includes specific risks related to the participant's occupation, it is possible to improve disaster prevention response capabilities according to occupation.

[0038] The interactive education providing unit can dynamically change the scenario based on the participants' answers, providing a more interactive learning experience. In the interactive education providing unit, for example, the generation AI dynamically changes the scenario based on the participants' answers. For example, when a participant selects an evacuation route, the generation AI provides the next scenario based on that selection. The generation AI also customizes the scenario according to the participants' choices during the dialogue. For example, when a participant selects an evacuation location, the generation AI provides a scenario based on that location. The generation AI also adjusts the progress of the scenario based on the participants' answers. For example, if the participant takes the correct action, it proceeds to the next step, and if the participant makes an error, it provides another explanation. In this way, by dynamically changing the scenario based on the participants' answers, a more interactive learning experience can be provided.

[0039] The interactive education providing unit can provide multi-user scenarios in which multiple participants can participate simultaneously. In the interactive education providing unit, for example, the generation AI generates multi-user scenarios in which multiple participants can participate simultaneously. For example, it provides a disaster prevention training scenario in which the entire family can participate. Furthermore, the generation AI generates a disaster prevention training scenario for corporate employees in which the entire team can participate. For example, it provides a fire response scenario in the office. Furthermore, the generation AI generates a disaster prevention training scenario for school students in which the entire class can participate. For example, it provides an earthquake response scenario at school. In this way, by providing multi-user scenarios in which multiple participants can participate simultaneously, joint disaster prevention training becomes possible.

[0040] The interactive education providing unit supports dialogue in different languages, enabling international disaster prevention education. In the interactive education providing unit, for example, the generating AI provides a multilingual interactive education program. For example, scenarios are provided in multiple languages, such as English, Spanish, and Chinese, allowing international participants to receive disaster prevention education. The generating AI also supports dialogue in different languages, enabling international disaster prevention education. For example, scenarios are provided according to the language selected by the participant. The generating AI also provides a multilingual interactive education program, generating scenarios that participants speaking different languages ​​can participate in simultaneously. For example, international disaster prevention training scenarios are provided. This makes it possible to realize international disaster prevention education by supporting dialogue in different languages.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The disaster prevention education system can also simulate the social impact of a disaster in its disaster scenario generation section. For example, the generation AI generates scenarios that include traffic congestion and the suspension of public transportation after a disaster occurs. This allows participants to learn about selecting transportation methods and evacuation routes in the event of a disaster. The generation AI also generates scenarios that simulate delays and shortages in the supply of supplies after a disaster occurs. This allows participants to learn about stockpiling and supply planning for supplies needed in the event of a disaster. The generation AI also generates scenarios that simulate social chaos and panic after a disaster occurs. This allows participants to learn about the importance of calm response and communication.

[0043] The disaster prevention education system can also simulate the psychological impact of a disaster in its disaster scenario generation section. For example, the generation AI generates a scenario that simulates trauma and stress after a disaster. This allows participants to learn about the importance of psychological care and support after a disaster. The generation AI also generates a scenario that simulates the difficulty of reuniting with family and friends after a disaster. This allows participants to learn about communication methods and reunion plans in the event of a disaster. The generation AI also generates a scenario that simulates community reconstruction and support activities after a disaster. This allows participants to learn about the importance of community recovery and support activities after a disaster.

[0044] The disaster prevention education system can also simulate the economic impact of a disaster in its disaster scenario generation section. For example, the generation AI generates a scenario that simulates the economic losses and recovery costs for businesses after a disaster occurs. This allows participants to learn about the economic risks and importance of insurance during a disaster. The generation AI also generates a scenario that simulates the economic burden on individuals and the increase in living expenses after a disaster occurs. This allows participants to learn about economic preparations and support systems in the event of a disaster. Furthermore, the generation AI generates a scenario that simulates the recovery of the local economy and support activities after a disaster occurs. This allows participants to learn about the importance of rebuilding the local economy and support activities after a disaster.

[0045] The disaster prevention education system can also simulate the environmental impact of a disaster in its disaster scenario generation section. For example, the generation AI generates a scenario that simulates environmental pollution and the impact on the ecosystem after a disaster. This allows participants to learn about the importance of environmental protection and recovery activities during a disaster. The generation AI also generates a scenario that simulates the challenges of waste disposal and recycling after a disaster. This allows participants to learn about the importance of waste management and recycling during a disaster. The generation AI also generates a scenario that simulates secondary disasters caused by natural disasters after a disaster. This allows participants to learn about the risks of secondary disasters and countermeasures during a disaster.

[0046] The disaster prevention education system can also simulate medical responses in the event of a disaster in its disaster scenario generation section. For example, the generation AI generates scenarios that simulate the response of medical facilities and shortages of medical resources after a disaster occurs. This allows participants to learn about medical responses and medical resource management in the event of a disaster. The generation AI also generates scenarios that simulate first aid and emergency response after a disaster occurs. This allows participants to learn first aid and emergency response skills in the event of a disaster. Furthermore, the generation AI generates scenarios that simulate the risks of infectious diseases and preventive measures after a disaster occurs. This allows participants to learn about infectious disease countermeasures and preventive measures in the event of a disaster.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The disaster scenario generator generates disaster scenarios. For example, the generator creates different disaster scenarios, such as earthquakes, floods, fires, and typhoons, and provides them to participants. The generator generates scenarios based on prompts that include instructions on the type, scale, and location of the disaster. Step 2: The customized educational program provider provides a customized educational program based on the disaster scenario generated by the disaster scenario generator, taking into consideration the participants' background information. For example, a scenario on how to respond to a fire in an office is provided for company employees, and a scenario on how to respond to an earthquake in a school is provided for school students. Step 3: The interactive education provider provides an interactive educational program based on the educational program provided by the custom-made educational program provider. For example, if the generating AI asks, "An earthquake has occurred. What should you do first?" and the participant answers, "Hide under a desk," the generating AI evaluates the appropriateness of the action and provides feedback.

[0049] (Example 2) The disaster prevention education system according to an embodiment of the present invention uses AI to generate various disaster scenarios in real time and provides customized educational programs tailored to the participants' backgrounds. This allows users to acquire the ability to make appropriate decisions by taking into account various factors in situations similar to actual disaster scenes.

[0050] A disaster prevention education system according to an embodiment includes a disaster scenario generation unit, a customized educational program provision unit, and an interactive education provision unit. The disaster scenario generation unit generates a disaster scenario. For example, the generation AI creates different disaster scenarios, such as earthquakes, floods, fires, and typhoons, and provides them to participants. The generation AI generates the scenarios based on prompts containing instructions about the type, scale, and location of the disaster. The customized educational program provision unit provides a customized educational program based on the disaster scenario generated by the disaster scenario generation unit, taking into account participants' background information. For example, a company employee may receive a scenario about responding to a fire in an office, and a school student may receive a scenario about responding to an earthquake in a school. The interactive education provision unit provides an interactive educational program based on the educational program provided by the customized educational program provision unit. For example, if the generation AI asks, "An earthquake has occurred. What should I do first?" and a participant answers, "Hide under a desk," the generation AI evaluates the appropriateness of the action and provides feedback. This allows users to acquire appropriate decision-making skills that take various factors into account in situations similar to actual disaster scenarios.

[0051] The disaster scenario generation unit can combine past disaster data, weather forecast data, and geological data to generate more realistic disaster scenarios. For example, the generation AI combines past earthquake data with the latest weather forecast data to generate a scenario that includes the risk of a tsunami after an earthquake. For example, it creates a scenario in which a tsunami warning is issued after an earthquake, and participants practice choosing an evacuation route. The generation AI also analyzes geological data to generate disaster scenarios for areas at high risk of landslides. For example, it creates a scenario in which a landslide occurs due to heavy rain and residents evacuate, allowing participants to learn appropriate evacuation behavior. The generation AI also uses weather forecast data to generate scenarios based on the predicted path of a typhoon. For example, it creates a scenario in which a typhoon approaches and damage is expected from strong winds and heavy rain, allowing participants to learn what measures to prepare in advance. By generating more realistic disaster scenarios, participants can acquire knowledge and skills that will be useful in actual disasters.

[0052] The disaster scenario generation unit can include the long-term impact of a disaster, allowing participants to learn how to respond from a long-term perspective. For example, the generation AI in the disaster scenario generation unit generates a scenario that includes the period of time required for infrastructure restoration after an earthquake. For example, it simulates the period until power and water supply are restored, allowing participants to learn how to prepare the necessary daily necessities during that time. The generation AI also generates a scenario that includes the economic impact of a flood. For example, it simulates damage to crops and economic losses caused by a flood, allowing participants to learn measures to minimize the impact. The generation AI also generates a scenario that includes the environmental impact of a fire. For example, it simulates air pollution and health damage caused by a fire, allowing participants to learn actions to mitigate the impact. In this way, participants can learn how to respond from a long-term perspective, thereby acquiring knowledge that will be useful for life after a disaster and for recovery.

[0053] The disaster scenario generation unit uses the emotion estimation function to analyze participants' emotional reactions to disaster scenarios in real time, starting with scenarios that are less emotionally stressful and gradually increasing the difficulty. For example, the disaster scenario generation unit uses the generation AI to analyze participants' facial expressions and voices and monitor their emotional reactions in real time. For example, it provides a less stressful scenario to first-time participants and gradually increases the difficulty. It also uses the emotion estimation function to measure participants' stress levels and select an appropriate scenario. For example, if stress increases, it switches to a relaxing scenario. The generation AI also adjusts the difficulty of the scenario based on the emotional data. For example, if a participant feels stressed, it pauses the scenario and provides a relaxing activity. This makes it possible to provide an effective learning experience by adjusting the difficulty of the scenario based on the participants' emotional reactions.

[0054] The disaster scenario generation unit can provide an international disaster prevention education program by reflecting the characteristics of different cultures and regions. For example, the generation AI generates scenarios that take into account the disaster characteristics of different countries and regions. For example, a Japanese earthquake scenario and an American hurricane scenario are provided, allowing participants to learn how to respond to each region. The generation AI also generates scenarios that take cultural backgrounds into account. For example, it creates scenarios in which religious events or traditional buildings are affected, allowing participants to learn how to respond. The generation AI also generates scenarios that support multiple languages. For example, it provides scenarios in multiple languages, such as English, Spanish, and Chinese, allowing international participants to receive disaster prevention education. This makes it possible to realize international disaster prevention education by reflecting the characteristics of different cultures and regions.

[0055] By including evacuation behaviors of animals and pets, the disaster scenario generation unit can provide an educational program that is also suitable for participants who own pets. In the disaster scenario generation unit, for example, the generation AI generates a scenario that includes evacuation behaviors of pets. For example, it provides a scenario for learning how to safely evacuate pets in the event of an earthquake. The generation AI also generates a scenario that includes the establishment and operation of an animal shelter. For example, it provides a scenario for setting up an animal shelter and evacuating pets in the event of a flood. The generation AI also generates a scenario that includes pet health management. For example, it provides a scenario for checking the health of pets after a disaster occurs and providing the necessary care. In this way, by providing an educational program that is also suitable for participants who own pets, it is possible to ensure the safety of pets in the event of a disaster.

[0056] The disaster scenario generation unit can use the emotion estimation function to customize the difficulty and content of the scenario based on the participants' emotional reactions to the disaster scenario. For example, the generation AI in the disaster scenario generation unit analyzes the participants' emotional reactions in real time and adjusts the difficulty of the scenario. For example, it lowers the difficulty if stress levels increase. The emotion estimation function also provides a scenario that matches the participants' emotional state. For example, it selects a scenario that allows participants to learn in a relaxed state. The generation AI also customizes the content of the scenario based on the emotional data. For example, it provides a scenario that incorporates themes that interest participants. This makes it possible to provide an effective learning experience by customizing the difficulty and content of the scenario based on the participants' emotional reactions.

[0057] The customized education program provider can provide special disaster prevention scenarios for the elderly and disabled, taking into consideration the health condition and physical ability of the participants. In the customized education program provider, for example, the generation AI generates disaster prevention scenarios for the elderly. For example, it provides a scenario in which the elderly learn how to evacuate safely in the event of an earthquake. The generation AI also generates disaster prevention scenarios for disabled people. For example, it provides a scenario in which wheelchair users learn precautions to take when evacuating. The generation AI also generates disaster prevention scenarios that take health condition into consideration. For example, it provides a scenario in which participants with chronic illnesses learn how to secure the medication they need in the event of a disaster. In this way, it is possible to meet the diverse needs of participants by providing special disaster prevention scenarios for the elderly and disabled.

[0058] The customized educational program provider can reference the participant's past disaster prevention training history and provide an educational program tailored to the participant's progress based on past learning content. In the customized educational program provider, for example, the generation AI analyzes the participant's past disaster prevention training history and provides scenarios tailored to the participant's progress. For example, starting with scenarios for beginners and gradually increasing the difficulty level. The generation AI also provides scenarios that strengthen the participant's weak points based on past learning content. For example, for a participant who is not good at selecting evacuation routes, it provides a scenario that focuses on learning that part. The generation AI also references the participant's training history and provides feedback tailored to the participant's progress. For example, it provides a scenario that points out areas for improvement based on mistakes made in past training. In this way, the effectiveness of the participant's learning can be improved by providing an educational program tailored to the participant's progress based on their past disaster prevention training history.

[0059] The custom-made educational program providing unit can provide disaster prevention scenarios that the entire family can participate in, taking into consideration the family composition of participants and whether or not they have pets. In the custom-made educational program providing unit, for example, the generation AI generates disaster prevention scenarios that the entire family can participate in. For example, it provides a scenario in which the entire family can learn how to evacuate safely in the event of an earthquake. The generation AI also generates disaster prevention scenarios that take into consideration whether or not the family has pets. For example, it provides a scenario in which the entire family can learn how to evacuate safely in the event of an earthquake. The generation AI also generates disaster prevention scenarios that take into consideration the presence of pets. For example, it provides a scenario in which to learn evacuation plans that include pets. The generation AI also generates disaster prevention scenarios that take into consideration the family composition. For example, it provides a scenario for households with young children or elderly people. In this way, by providing disaster prevention scenarios that the entire family can participate in, it is possible to improve the disaster prevention response capabilities of the entire family.

[0060] The customized educational program providing unit can provide an educational program that includes specific risks related to the participant's occupation. For example, in the customized educational program providing unit, the generation AI generates a chemical disaster scenario for employees of a chemical plant. For example, it provides a scenario for learning appropriate responses in the event of a chemical leak. In addition, the generation AI generates a construction site disaster scenario for employees in the construction industry. For example, it provides a scenario for learning how to respond to fires and collapse accidents at construction sites. In addition, the generation AI generates a medical facility disaster scenario for medical workers. For example, it provides a scenario for learning how to safely evacuate patients in the event of a fire or power outage at a hospital. In this way, by providing an educational program that includes specific risks related to the participant's occupation, it is possible to improve disaster prevention response capabilities according to occupation.

[0061] The customized educational program providing unit can use the emotion estimation function to provide feedback according to the emotional state of participants and design an educational program that elicits positive emotions. For example, the customized educational program providing unit uses a generation AI to monitor the emotional state of participants in real time and provide feedback that elicits positive emotions. For example, it displays a message of praise when a participant behaves correctly. The emotion estimation function also provides a relaxing scenario according to the participant's emotional state. For example, it provides relaxing music or videos when stress levels rise. The generation AI also customizes an educational program based on the emotional data and provides a scenario that elicits positive emotions. For example, it provides a scenario that incorporates a theme that interests the participant. This makes it possible to provide a positive learning experience by providing feedback according to the participant's emotional state.

[0062] The interactive education providing unit can dynamically change the scenario based on the participants' answers, providing a more interactive learning experience. In the interactive education providing unit, for example, the generation AI dynamically changes the scenario based on the participants' answers. For example, when a participant selects an evacuation route, the generation AI provides the next scenario based on that selection. The generation AI also customizes the scenario according to the participants' choices during the dialogue. For example, when a participant selects an evacuation location, the generation AI provides a scenario based on that location. The generation AI also adjusts the progress of the scenario based on the participants' answers. For example, if the participant takes the correct action, it proceeds to the next step, and if the participant makes an error, it provides another explanation. In this way, by dynamically changing the scenario based on the participants' answers, a more interactive learning experience can be provided.

[0063] The interactive education providing unit can analyze the non-verbal responses of participants and provide more appropriate feedback. In the interactive education providing unit, for example, the generation AI analyzes the facial expressions of participants and provides feedback based on their non-verbal responses. For example, if a participant looks anxious, a reassuring message is displayed. The generation AI also analyzes the tone of the participant's voice and provides feedback based on their non-verbal responses. For example, if a participant sounds nervous, advice on how to relax is provided. The generation AI also analyzes the non-verbal responses of participants in real time and provides appropriate feedback. For example, if a participant seems confused, additional explanation is provided. In this way, more appropriate feedback can be provided by analyzing the non-verbal responses of participants.

[0064] The interactive education providing unit uses the emotion estimation function to analyze the emotional state of participants during a dialogue in real time and provide emotionally less burdensome feedback. For example, in the interactive education providing unit, the generation AI analyzes the emotional state of participants during a dialogue in real time and provides emotionally less burdensome feedback. For example, if a participant is feeling stressed, it provides advice to help them relax. The emotion estimation function also monitors the emotional state of participants during a dialogue and provides appropriate feedback. For example, if a participant is feeling anxious, it displays a reassuring message. The generation AI also adjusts the progress of the dialogue based on the emotion data and provides emotionally less burdensome feedback. For example, if a participant is confused, it provides additional explanation. In this way, by analyzing the emotional state of participants during a dialogue in real time, it is possible to provide emotionally less burdensome feedback.

[0065] The interactive education providing unit can provide multi-user scenarios in which multiple participants can participate simultaneously. In the interactive education providing unit, for example, the generation AI generates multi-user scenarios in which multiple participants can participate simultaneously. For example, it provides a disaster prevention training scenario in which the entire family can participate. Furthermore, the generation AI generates a disaster prevention training scenario for corporate employees in which the entire team can participate. For example, it provides a fire response scenario in the office. Furthermore, the generation AI generates a disaster prevention training scenario for school students in which the entire class can participate. For example, it provides an earthquake response scenario at school. In this way, by providing multi-user scenarios in which multiple participants can participate simultaneously, joint disaster prevention training becomes possible.

[0066] The interactive education providing unit supports dialogue in different languages, enabling international disaster prevention education. In the interactive education providing unit, for example, the generating AI provides a multilingual interactive education program. For example, scenarios are provided in multiple languages, such as English, Spanish, and Chinese, allowing international participants to receive disaster prevention education. The generating AI also supports dialogue in different languages, enabling international disaster prevention education. For example, scenarios are provided according to the language selected by the participant. The generating AI also provides a multilingual interactive education program, generating scenarios that participants speaking different languages ​​can participate in simultaneously. For example, international disaster prevention training scenarios are provided. This makes it possible to realize international disaster prevention education by supporting dialogue in different languages.

[0067] The interactive education providing unit can use the emotion estimation function to adjust the progress of the dialogue based on the emotional state of the participants during the dialogue, thereby providing a positive learning experience. For example, the generation AI in the interactive education providing unit analyzes the emotional state of the participants during the dialogue in real time and adjusts the progress of the dialogue. For example, if a participant is feeling stressed, it provides advice to help them relax. The emotion estimation function can also be used to monitor the emotional state of the participants during the dialogue and provide a positive learning experience. For example, it can display a message of praise when a participant behaves correctly. The generation AI can also adjust the progress of the dialogue based on the emotional data and provide a positive learning experience. For example, if a participant is confused, it can provide additional explanation. In this way, a positive learning experience can be provided by adjusting the progress of the dialogue based on the emotional state of the participants during the dialogue.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The disaster prevention education system can also simulate the social impact of a disaster in its disaster scenario generation section. For example, the generation AI generates scenarios that include traffic congestion and the suspension of public transportation after a disaster occurs. This allows participants to learn about selecting transportation methods and evacuation routes in the event of a disaster. The generation AI also generates scenarios that simulate delays and shortages in the supply of supplies after a disaster occurs. This allows participants to learn about stockpiling and supply planning for supplies needed in the event of a disaster. The generation AI also generates scenarios that simulate social chaos and panic after a disaster occurs. This allows participants to learn about the importance of calm response and communication.

[0070] The disaster prevention education system can also simulate the psychological impact of a disaster in its disaster scenario generation section. For example, the generation AI generates a scenario that simulates trauma and stress after a disaster. This allows participants to learn about the importance of psychological care and support after a disaster. The generation AI also generates a scenario that simulates the difficulty of reuniting with family and friends after a disaster. This allows participants to learn about communication methods and reunion plans in the event of a disaster. The generation AI also generates a scenario that simulates community reconstruction and support activities after a disaster. This allows participants to learn about the importance of community recovery and support activities after a disaster.

[0071] The disaster prevention education system can also simulate the economic impact of a disaster in its disaster scenario generation section. For example, the generation AI generates a scenario that simulates the economic losses and recovery costs for businesses after a disaster occurs. This allows participants to learn about the economic risks and importance of insurance during a disaster. The generation AI also generates a scenario that simulates the economic burden on individuals and the increase in living expenses after a disaster occurs. This allows participants to learn about economic preparations and support systems in the event of a disaster. Furthermore, the generation AI generates a scenario that simulates the recovery of the local economy and support activities after a disaster occurs. This allows participants to learn about the importance of rebuilding the local economy and support activities after a disaster.

[0072] The disaster prevention education system can also simulate the environmental impact of a disaster in its disaster scenario generation section. For example, the generation AI generates a scenario that simulates environmental pollution and the impact on the ecosystem after a disaster. This allows participants to learn about the importance of environmental protection and recovery activities during a disaster. The generation AI also generates a scenario that simulates the challenges of waste disposal and recycling after a disaster. This allows participants to learn about the importance of waste management and recycling during a disaster. The generation AI also generates a scenario that simulates secondary disasters caused by natural disasters after a disaster. This allows participants to learn about the risks of secondary disasters and countermeasures during a disaster.

[0073] The disaster prevention education system can also simulate medical responses in the event of a disaster in its disaster scenario generation section. For example, the generation AI generates scenarios that simulate the response of medical facilities and shortages of medical resources after a disaster occurs. This allows participants to learn about medical responses and medical resource management in the event of a disaster. The generation AI also generates scenarios that simulate first aid and emergency response after a disaster occurs. This allows participants to learn first aid and emergency response skills in the event of a disaster. Furthermore, the generation AI generates scenarios that simulate the risks of infectious diseases and preventive measures after a disaster occurs. This allows participants to learn about infectious disease countermeasures and preventive measures in the event of a disaster.

[0074] The disaster prevention education system also uses an emotion estimation function in the disaster scenario generation section to adjust the progress of the scenario based on participants' emotional reactions to the disaster scenario. For example, the generation AI analyzes participants' emotional reactions in real time and pauses the progress of the scenario. This allows participants time to take a breather and reduce stress. The generation AI can also change the content of the scenario based on emotional data. For example, if participants are feeling anxious, it can switch to a relaxing scenario. Furthermore, the generation AI uses the emotion estimation function to provide feedback according to the participants' emotional state. This allows participants to have a learning experience that is less emotionally taxing.

[0075] The disaster prevention education system also uses an emotion estimation function in the disaster scenario generation section to adjust the difficulty of the scenario based on participants' emotional reactions to the disaster scenario. For example, the generation AI analyzes participants' emotional reactions in real time and lowers the difficulty of the scenario. This allows participants to progress with their learning while reducing stress. The generation AI also adjusts the progress of the scenario based on emotional data. For example, if a participant is relaxed, it increases the difficulty of the scenario. Furthermore, the generation AI uses the emotion estimation function to provide a scenario that corresponds to the participant's emotional state. This allows participants to progress with their learning at a difficulty level that is emotionally appropriate.

[0076] The disaster prevention education system also uses an emotion estimation function in the disaster scenario generation section to customize the content of the scenario based on participants' emotional reactions to the disaster scenario. For example, the generation AI analyzes participants' emotional reactions in real time and changes the content of the scenario. This allows participants to progress through their learning with topics that interest them or content that helps them relax. The generation AI also adjusts the progress of the scenario based on emotional data. For example, if a participant is feeling stressed, it provides them with a relaxing activity. Furthermore, the generation AI uses the emotion estimation function to provide feedback according to the participant's emotional state. This allows participants to progress through their learning with content that is emotionally appropriate.

[0077] The disaster prevention education system also uses an emotion estimation function in the disaster scenario generation section to adjust the speed at which the scenario progresses based on participants' emotional reactions to the disaster scenario. For example, the generation AI analyzes participants' emotional reactions in real time and slows down the speed at which the scenario progresses. This allows participants to proceed with their learning at a more relaxed pace. The generation AI also speeds up the speed of the scenario based on the emotional data. For example, if a participant is relaxed, the scenario progresses faster. Furthermore, the generation AI uses the emotion estimation function to provide feedback according to the participant's emotional state. This allows participants to proceed with their learning at an emotionally appropriate pace.

[0078] The disaster prevention education system also uses an emotion estimation function in the disaster scenario generation section to customize scenario feedback based on participants' emotional reactions to the disaster scenario. For example, the generation AI analyzes participants' emotional reactions in real time and changes the content of the feedback. This allows participants to receive emotionally appropriate feedback. The generation AI also adjusts the timing of feedback based on emotional data. For example, if a participant is feeling stressed, it delays the feedback. Furthermore, the generation AI uses the emotion estimation function to provide feedback that corresponds to the participant's emotional state. This allows participants to receive emotionally appropriate feedback.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The disaster scenario generator generates disaster scenarios. For example, the generator creates different disaster scenarios, such as earthquakes, floods, fires, and typhoons, and provides them to participants. The generator generates scenarios based on prompts that include instructions on the type, scale, and location of the disaster. Step 2: The customized educational program provider provides a customized educational program based on the disaster scenario generated by the disaster scenario generator, taking into consideration the participants' background information. For example, a scenario on how to respond to a fire in an office is provided for company employees, and a scenario on how to respond to an earthquake in a school is provided for school students. Step 3: The interactive education provider provides an interactive educational program based on the educational program provided by the custom-made educational program provider. For example, if the generating AI asks, "An earthquake has occurred. What should you do first?" and the participant answers, "Hide under a desk," the generating AI evaluates the appropriateness of the action and provides feedback.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a disaster scenario generation unit that generates a disaster scenario; a customized educational program providing unit that provides a customized educational program based on the disaster scenario generated by the disaster scenario generating unit and taking into consideration background information of participants; an interactive education providing unit that provides an interactive education program based on the education program provided by the customized education program providing unit; A system characterized by:

2. The disaster scenario generation unit Combining historical disaster data, weather forecast data, and geological data to generate more realistic disaster scenarios 2. The system of claim 1.

3. The disaster scenario generation unit Include the long-term impact of disasters so participants can learn to respond in the long term.

2. The system of claim 1.

4. The disaster scenario generation unit Analyze participants' emotional responses to disaster scenarios in real time, starting with less emotionally stressful scenarios and gradually increasing their difficulty.

2. The system of claim 1.

5. The disaster scenario generation unit Providing international disaster prevention education programs that reflect the characteristics of different cultures and regions 2. The system of claim 1.

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