System

Abstract

An object of a system according to an embodiment is to quickly and accurately provide necessary information at the time of a disaster and support an appropriate evacuation action.SOLUTION: A system includes an information providing part, an evacuation route providing part, a safety confirmation part, and a mind care part. The information providing unit provides necessary information in case of disasters by using the generated AI. The evacuation route providing unit provides an optimal evacuation route based on the information provided by the information providing unit. The safety confirmation unit performs safety confirmation based on the evacuation route provided by the evacuation route providing unit. The mental care unit performs mental care based on the safety information confirmed by the safety confirmation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to provide necessary information quickly and accurately during disasters and support appropriate evacuation actions.

[0005] The system according to the embodiment aims to provide necessary information quickly and accurately in the event of a disaster and to support appropriate evacuation actions. [Means for solving the problem]

[0006] The system according to the embodiment includes an information providing unit, an evacuation route providing unit, a safety confirmation unit, and a mental care unit. The information providing unit provides information necessary in the event of a disaster using a generation AI. The evacuation route providing unit provides an optimal evacuation route based on the information provided by the information providing unit. The safety confirmation unit confirms the safety of individuals based on the evacuation route provided by the evacuation route providing unit. The mental care unit provides mental care based on the safety information confirmed by the safety confirmation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately provide necessary information in the event of a disaster and support appropriate evacuation actions. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 smartphone app according to the embodiment of the present invention is a system that utilizes generative AI to provide necessary information in the event of a disaster and support appropriate actions by disaster victims. As a result, the smartphone app can accurately provide necessary information to disaster victims even in a chaotic situation, enabling them to take appropriate actions.

[0029] A smartphone app according to an embodiment includes an information providing unit, an evacuation route providing unit, a safety confirmation unit, and a mental health care unit. The information providing unit uses a generation AI to provide necessary information in the event of a disaster. For example, the generation AI analyzes data related to the type of disaster and the situation in the affected area and instructs the victim on the next action to take. The information providing unit provides appropriate instructions to the victim based on information collected and analyzed in real time by the generation AI. The evacuation route providing unit provides the optimal evacuation route based on the information provided by the information providing unit. For example, when a victim enters their current location into the app, the generation AI analyzes the information and displays the optimal evacuation route. The app also provides information on the capacity and facilities of evacuation shelters. The safety confirmation unit confirms the safety of the victim based on the evacuation route provided by the evacuation route providing unit. For example, when a victim sends a safety confirmation message through the app, the generation AI analyzes the message and notifies family and friends. The app also provides a chat function with neighbors to promote information sharing and mutual assistance. The mental health care unit provides mental health care based on the safety information confirmed by the safety confirmation unit. For example, if a disaster victim feels stressed or anxious, the AI ​​generator can provide information on relaxation techniques and counseling services. It can also provide contact information for specialized institutions where disaster victims can receive mental health care. This allows the smartphone app according to the embodiment to quickly and accurately provide necessary information in the event of a disaster and support disaster victims in taking appropriate action.

[0030] The information provision unit can analyze the victim's past behavioral history and provide individually optimized behavioral instructions. For example, the information provision unit uses a generation AI to analyze the victim's past evacuation behavior and movement history and propose an individually optimized evacuation route. For example, it provides the optimal evacuation route by taking into account the evacuation shelters and routes used in the past. This makes it possible to provide optimal behavioral instructions based on the victim's past behavioral history.

[0031] The information provision unit monitors the progress of the disaster in real time and can update action instructions according to changes in the situation. For example, the generation AI in the information provision unit monitors the progress of the disaster in real time and updates action instructions according to changes in the situation. For example, evacuation routes are changed based on information on aftershocks of an earthquake or the progress of flooding. This makes it possible to update action instructions according to the progress of the disaster.

[0032] The evacuation route provision unit can analyze traffic conditions during a disaster and provide the optimal evacuation route. For example, the generation AI analyzes traffic conditions during a disaster in real time and provides the optimal evacuation route. For example, it proposes an evacuation route based on road congestion and road closure information. This makes it possible to provide the optimal evacuation route based on traffic conditions during a disaster.

[0033] The evacuation route provision unit can analyze the occupancy status of evacuation shelters in real time and suggest the most suitable evacuation shelter. For example, the generation AI can analyze the occupancy status of evacuation shelters in real time and suggest the most suitable evacuation shelter. For example, it can guide users to available evacuation shelters based on the capacity and equipment status. This makes it possible to suggest the most suitable evacuation shelter based on the occupancy status of the evacuation shelter.

[0034] The evacuation route provision unit can evaluate the safety of evacuation routes and give instructions to avoid dangerous routes. For example, the generation AI can evaluate the safety of evacuation routes in real time and give instructions to avoid dangerous routes. For example, it can analyze the damage status of roads caused by an earthquake and propose safe routes. This makes it possible to evaluate the safety of evacuation routes and give instructions to avoid dangerous routes.

[0035] The safety confirmation unit can analyze the location information of the victim and notify family or friends in real time. For example, the generation AI can analyze the location information of the victim in real time and notify family or friends. For example, a notification can be sent automatically when the victim arrives at an evacuation shelter. This allows the location information of the victim to be notified to family and friends in real time.

[0036] The safety confirmation unit can analyze the content of messages sent by disaster victims and prioritize notifications according to the level of urgency. For example, the generation AI in the safety confirmation unit can analyze the content of messages sent by disaster victims and prioritize notifications according to the level of urgency. For example, messages with a high level of urgency can be sent immediately to family and friends. This makes it possible to send notifications according to the level of urgency based on the content of messages sent by disaster victims.

[0037] The mental care unit can analyze the stress level of disaster victims and suggest relaxation methods. For example, the generative AI can analyze the stress level of disaster victims and suggest relaxation methods. For example, it can suggest deep breathing or meditation methods. This makes it possible to suggest relaxation methods based on the disaster victim's stress level.

[0038] The mental care department can monitor the psychological state of disaster victims and provide necessary counseling services. For example, the generative AI can monitor the psychological state of disaster victims and provide necessary counseling services. For example, it can support online counseling reservations. This allows necessary counseling services to be provided based on the psychological state of disaster victims.

[0039] The mental care unit can provide an online counseling function to support the mental care of disaster victims. For example, the generative AI can provide an online counseling function to support the mental care of disaster victims. For example, it can support video calls with psychological counselors. This allows for online support of mental care for disaster victims.

[0040] The mental care section can provide relaxation music and videos that match the psychological state of the disaster victim. For example, the generation AI analyzes the psychological state of the disaster victim and provides relaxation music. For example, if stress levels are high, relaxing music can be played. This allows the system to provide relaxation music and videos that match the psychological state of the disaster victim.

[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] Smartphone apps can also monitor the health status of disaster victims and provide necessary medical assistance. For example, if a disaster victim has a chronic illness, they can be directed to an appropriate medical institution based on that information. They can also provide first aid instructions if a disaster victim is injured. Furthermore, they can monitor the disaster victim's health status in real time and send emergency notifications if an abnormality is detected.

[0043] The smartphone app can guide disaster victims to nearby volunteer activities based on their location information. For example, when a disaster victim arrives at an evacuation shelter, the app will provide them with information about nearby volunteer activities. If a disaster victim needs assistance, the app will notify volunteers of this information. Furthermore, by participating in volunteer activities, disaster victims can feel a sense of solidarity as part of the community.

[0044] Smartphone apps can be equipped with a function to check the safety of disaster victims' pets. For example, disaster victims can register their pets' location information, allowing them to check their safety during a disaster. Notifications are also sent to owners when their pets arrive at evacuation shelters. Furthermore, the app can monitor the pets' health and provide necessary medical assistance.

[0045] Smartphone apps can be equipped with features to support disaster victims' dietary management. For example, they can help disaster victims check the nutritional information of meals provided at evacuation centers and choose balanced meals. They can also help disaster victims register allergy information to ensure the food provided at evacuation centers is safe. Furthermore, if disaster victims are evacuating at home, they can be provided with recipes and cooking methods for preserved foods.

[0046] Smartphone apps can be equipped with features to support the care of disaster victims' children. For example, disaster victims can register their location information to check on their children's safety during a disaster. They can also send notifications to parents when their children arrive at evacuation shelters. Furthermore, they can monitor children's psychological state and provide necessary mental health support.

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

[0048] Step 1: The information provision unit uses the generation AI to provide necessary information in the event of a disaster. For example, the generation AI analyzes data on the type of disaster and the situation in the affected area, and instructs the victim on the next action to take. The generation AI also provides appropriate instructions to the victim based on the information it collects and analyzes in real time. Step 2: The evacuation route provider provides the optimal evacuation route based on the information provided by the information provider. For example, when a disaster victim enters their current location into the app, the AI ​​generator analyzes that information and displays the optimal evacuation route. It also provides information on the capacity and facilities of evacuation shelters. Step 3: The safety confirmation module checks the safety of the affected people based on the evacuation route provided by the evacuation route provision module. For example, when a disaster victim sends a safety confirmation message through the app, the generation AI analyzes the message and notifies family and friends. It also provides a chat function with neighbors to encourage information sharing and mutual assistance. Step 4: The Mental Health Care Department provides mental health care based on the safety information confirmed by the Safety Confirmation Department. For example, if a disaster victim is feeling stressed or anxious, the AI ​​generator will provide information on relaxation techniques and counseling services. It will also provide contact information for specialized institutions where disaster victims can receive mental health care.

[0049] (Example 2) The smartphone app according to the embodiment of the present invention is a system that utilizes generative AI to provide necessary information in the event of a disaster and support appropriate actions by disaster victims. As a result, the smartphone app can accurately provide necessary information to disaster victims even in a chaotic situation, enabling them to take appropriate actions.

[0050] A smartphone app according to an embodiment includes an information providing unit, an evacuation route providing unit, a safety confirmation unit, and a mental health care unit. The information providing unit uses a generation AI to provide necessary information in the event of a disaster. For example, the generation AI analyzes data related to the type of disaster and the situation in the affected area and instructs the victim on the next action to take. The information providing unit provides appropriate instructions to the victim based on information collected and analyzed in real time by the generation AI. The evacuation route providing unit provides the optimal evacuation route based on the information provided by the information providing unit. For example, when a victim enters their current location into the app, the generation AI analyzes the information and displays the optimal evacuation route. The app also provides information on the capacity and facilities of evacuation shelters. The safety confirmation unit confirms the safety of the victim based on the evacuation route provided by the evacuation route providing unit. For example, when a victim sends a safety confirmation message through the app, the generation AI analyzes the message and notifies family and friends. The app also provides a chat function with neighbors to promote information sharing and mutual assistance. The mental health care unit provides mental health care based on the safety information confirmed by the safety confirmation unit. For example, if a disaster victim feels stressed or anxious, the AI ​​generator can provide information on relaxation techniques and counseling services. It can also provide contact information for specialized institutions where disaster victims can receive mental health care. This allows the smartphone app according to the embodiment to quickly and accurately provide necessary information in the event of a disaster and support disaster victims in taking appropriate action.

[0051] The information provision unit can analyze the victim's past behavioral history and provide individually optimized behavioral instructions. For example, the information provision unit uses a generation AI to analyze the victim's past evacuation behavior and movement history and propose an individually optimized evacuation route. For example, it provides the optimal evacuation route by taking into account the evacuation shelters and routes used in the past. This makes it possible to provide optimal behavioral instructions based on the victim's past behavioral history.

[0052] The information provision unit monitors the progress of the disaster in real time and can update action instructions according to changes in the situation. For example, the generation AI in the information provision unit monitors the progress of the disaster in real time and updates action instructions according to changes in the situation. For example, evacuation routes are changed based on information on aftershocks of an earthquake or the progress of flooding. This makes it possible to update action instructions according to the progress of the disaster.

[0053] The information provision unit can use the emotion estimation function to analyze the emotional state of the victim and provide behavioral instructions to reduce stress. For example, the generation AI can use the emotion estimation function to analyze the emotional state of the victim and provide behavioral instructions to reduce stress. For example, it can provide instructions on relaxation techniques or deep breathing. This makes it possible to provide behavioral instructions to reduce stress according to the emotional state of the victim.

[0054] The evacuation route provision unit can analyze traffic conditions during a disaster and provide the optimal evacuation route. For example, the generation AI analyzes traffic conditions during a disaster in real time and provides the optimal evacuation route. For example, it proposes an evacuation route based on road congestion and road closure information. This makes it possible to provide the optimal evacuation route based on traffic conditions during a disaster.

[0055] The evacuation route provision unit can analyze the occupancy status of evacuation shelters in real time and suggest the most suitable evacuation shelter. For example, the generation AI can analyze the occupancy status of evacuation shelters in real time and suggest the most suitable evacuation shelter. For example, it can guide users to available evacuation shelters based on the capacity and equipment status. This makes it possible to suggest the most suitable evacuation shelter based on the occupancy status of the evacuation shelter.

[0056] The evacuation route provision unit can evaluate the safety of evacuation routes and give instructions to avoid dangerous routes. For example, the generation AI can evaluate the safety of evacuation routes in real time and give instructions to avoid dangerous routes. For example, it can analyze the damage status of roads caused by an earthquake and propose safe routes. This makes it possible to evaluate the safety of evacuation routes and give instructions to avoid dangerous routes.

[0057] The safety confirmation unit can analyze the location information of the victim and notify family or friends in real time. For example, the generation AI can analyze the location information of the victim in real time and notify family or friends. For example, a notification can be sent automatically when the victim arrives at an evacuation shelter. This allows the location information of the victim to be notified to family and friends in real time.

[0058] The safety confirmation unit can analyze the content of messages sent by disaster victims and prioritize notifications according to the level of urgency. For example, the generation AI in the safety confirmation unit can analyze the content of messages sent by disaster victims and prioritize notifications according to the level of urgency. For example, messages with a high level of urgency can be sent immediately to family and friends. This makes it possible to send notifications according to the level of urgency based on the content of messages sent by disaster victims.

[0059] The mental care unit can analyze the stress level of disaster victims and suggest relaxation methods. For example, the generative AI can analyze the stress level of disaster victims and suggest relaxation methods. For example, it can suggest deep breathing or meditation methods. This makes it possible to suggest relaxation methods based on the disaster victim's stress level.

[0060] The mental care department can monitor the psychological state of disaster victims and provide necessary counseling services. For example, the generative AI can monitor the psychological state of disaster victims and provide necessary counseling services. For example, it can support online counseling reservations. This allows necessary counseling services to be provided based on the psychological state of disaster victims.

[0061] The mental care unit can use the emotion estimation function to analyze the emotional state of the disaster victim and propose appropriate mental care methods. For example, the generation AI can use the emotion estimation function to analyze the emotional state of the disaster victim and propose appropriate mental care methods. For example, it can propose relaxation methods or counseling services. This makes it possible to propose appropriate mental care methods based on the disaster victim's emotional state.

[0062] The mental care unit can provide an online counseling function to support the mental care of disaster victims. For example, the generative AI can provide an online counseling function to support the mental care of disaster victims. For example, it can support video calls with psychological counselors. This allows for online support of mental care for disaster victims.

[0063] The mental care section can provide relaxation music and videos that match the psychological state of the disaster victim. For example, the generation AI analyzes the psychological state of the disaster victim and provides relaxation music. For example, if stress levels are high, relaxing music can be played. This allows the system to provide relaxation music and videos that match the psychological state of the disaster victim.

[0064] The mental care unit uses the emotion estimation function to provide mental care information according to the emotional state of the disaster victim, giving them a sense of security. For example, the generation AI uses the emotion estimation function to analyze the emotional state of the disaster victim and provides mental care information to give them a sense of security. For example, it provides information on relaxation methods and counseling services. This allows the unit to provide mental care information according to the emotional state of the disaster victim, giving them a sense of security.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] Smartphone apps can also monitor the health status of disaster victims and provide necessary medical assistance. For example, if a disaster victim has a chronic illness, they can be directed to an appropriate medical institution based on that information. They can also provide first aid instructions if a disaster victim is injured. Furthermore, they can monitor the disaster victim's health status in real time and send emergency notifications if an abnormality is detected.

[0067] The smartphone app can estimate the emotional state of disaster victims and provide music and videos that correspond to their emotions. For example, if a disaster victim is feeling anxious, it can play relaxing music to reduce stress. If a disaster victim is feeling lonely, it can display encouraging messages. It can also provide entertainment content that corresponds to the disaster victim's emotional state to help them change their mood.

[0068] The smartphone app can guide disaster victims to nearby volunteer activities based on their location information. For example, when a disaster victim arrives at an evacuation shelter, the app will provide them with information about nearby volunteer activities. If a disaster victim needs assistance, the app will notify volunteers of this information. Furthermore, by participating in volunteer activities, disaster victims can feel a sense of solidarity as part of the community.

[0069] The smartphone app can estimate the emotional state of disaster victims and provide appropriate mental health support. For example, if a disaster victim is feeling highly stressed, it can suggest deep breathing or meditation techniques. If a disaster victim is feeling anxious, it can display messages to reassure them. It can also provide information about counseling services tailored to the disaster victim's emotional state and support them in seeking professional advice if necessary.

[0070] Smartphone apps can be equipped with a function to check the safety of disaster victims' pets. For example, disaster victims can register their pets' location information, allowing them to check their safety during a disaster. Notifications are also sent to owners when their pets arrive at evacuation shelters. Furthermore, the app can monitor the pets' health and provide necessary medical assistance.

[0071] Smartphone apps can estimate the emotional state of disaster victims and provide communication support tailored to their emotions. For example, if a disaster victim feels lonely, they can use the chat function to encourage communication with neighbors. If a disaster victim feels anxious, they can send encouraging messages. They can also provide information about support groups tailored to the disaster victim's emotional state and support interaction with people in the same situation.

[0072] Smartphone apps can be equipped with features to support disaster victims' dietary management. For example, they can help disaster victims check the nutritional information of meals provided at evacuation centers and choose balanced meals. They can also help disaster victims register allergy information to ensure the food provided at evacuation centers is safe. Furthermore, if disaster victims are evacuating at home, they can be provided with recipes and cooking methods for preserved foods.

[0073] The smartphone app can estimate the emotional state of disaster victims and provide exercise programs tailored to their emotions. For example, if a disaster victim is feeling stressed, it can suggest relaxing yoga or stretching exercises. If a disaster victim wants to release energy, it can provide light exercise or workout programs. It can also suggest fitness activities tailored to the disaster victim's emotional state to support their physical and mental health.

[0074] Smartphone apps can be equipped with features to support the care of disaster victims' children. For example, disaster victims can register their location information to check on their children's safety during a disaster. They can also send notifications to parents when their children arrive at evacuation shelters. Furthermore, they can monitor children's psychological state and provide necessary mental health support.

[0075] The smartphone app can estimate the emotional state of disaster victims and suggest relaxation methods according to their emotions. For example, if a disaster victim is feeling highly stressed, it can suggest deep breathing or meditation. If a disaster victim is feeling anxious, it can provide relaxing music or videos. It can also suggest relaxation methods according to the disaster victim's emotional state and support mental care.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The information provision unit uses the generation AI to provide necessary information in the event of a disaster. For example, the generation AI analyzes data on the type of disaster and the situation in the affected area, and instructs the victim on the next action to take. The generation AI also provides appropriate instructions to the victim based on the information it collects and analyzes in real time. Step 2: The evacuation route provider provides the optimal evacuation route based on the information provided by the information provider. For example, when a disaster victim enters their current location into the app, the AI ​​generator analyzes that information and displays the optimal evacuation route. It also provides information on the capacity and facilities of evacuation shelters. Step 3: The safety confirmation module checks the safety of the affected people based on the evacuation route provided by the evacuation route provision module. For example, when a disaster victim sends a safety confirmation message through the app, the generation AI analyzes the message and notifies family and friends. It also provides a chat function with neighbors to encourage information sharing and mutual assistance. Step 4: The Mental Health Care Department provides mental health care based on the safety information confirmed by the Safety Confirmation Department. For example, if a disaster victim is feeling stressed or anxious, the AI ​​generator will provide information on relaxation techniques and counseling services. It will also provide contact information for specialized institutions where disaster victims can receive mental health care.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0091] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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]

[0145] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An information provision department that uses generation AI to provide necessary information in the event of a disaster; an evacuation route providing unit that provides an optimal evacuation route based on the information provided by the information providing unit; a safety confirmation unit that confirms safety based on the evacuation route provided by the evacuation route providing unit; a mental care unit that provides mental care based on the safety information confirmed by the safety confirmation unit. A system characterized by:

2. The evacuation route providing unit Analyzing traffic conditions during disasters and providing optimal evacuation routes 2. The system of claim 1.

3. The safety confirmation unit Analyze the location information of victims and notify their family or friends in real time 2. The system of claim 1.

4. The mental care department Analyzing the stress levels of disaster victims and suggesting relaxation methods 2. The system of claim 1.

5. The information providing unit Analyze the emotional state of disaster victims using emotion estimation functionality and provide behavioral instructions to reduce stress 2. The system of claim 1.

Images