system

Abstract

The system according to the embodiment aims to grasp the situation of people who are unable to escape during a disaster and to quickly provide an appropriate rescue plan. [Solution] A system according to an embodiment includes a registration unit, a notification unit, an analysis unit, a generation unit, and a provision unit. The registration unit registers the user's status. The notification unit sends a notification in the event of a disaster. The analysis unit analyzes the notification sent by the notification unit. The generation unit generates a rescue plan based on the information analyzed by the analysis unit. The provision unit provides the rescue plan generated by the generation unit.

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 had the problem of making it difficult to grasp the situation of people who are unable to escape during a disaster and to quickly provide appropriate rescue plans.

[0005] The system according to the embodiment aims to grasp the situation of people who are unable to escape during a disaster and to quickly provide an appropriate rescue plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a notification unit, an analysis unit, a generation unit, and a provision unit. The registration unit registers the user's status. The notification unit transmits a notification in the event of a disaster. The analysis unit analyzes the notification transmitted by the notification unit. The generation unit generates a rescue plan based on the information analyzed by the analysis unit. The provision unit provides the rescue plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the situation of people who cannot escape during a disaster and quickly provide an appropriate rescue plan. [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) A disaster support system according to an embodiment of the present invention is a system for helping people who have difficulty evacuating on their own during a disaster. This system allows users to register their own situation in an app and, when a disaster occurs, sends a notification saying, "Come help me!" This notification is analyzed by a generating AI to generate an optimal rescue plan. The generating AI analyzes the user's location information and surrounding conditions in real time and issues appropriate instructions to nearby residents and rescue teams. For example, it provides nearby residents with the location information of people who are unable to escape, encouraging them to quickly come to their aid. It also suggests efficient rescue routes to rescue teams, supporting a rapid response. This system is expected to promote mutual assistance during disasters and rescue as many people as possible. The generating AI enables flexible responses in emergencies and realizes rapid and effective rescue operations. For example, users can register their location information and health status in the app, enabling a rapid response during a disaster. Furthermore, the generating AI analyzes the user's situation in real time and generates an optimal rescue plan. For example, the generating AI uses the user's location information to provide nearby residents with the location information of people who are unable to escape, encouraging them to quickly come to their aid. In addition, the generative AI will suggest efficient rescue routes to rescue teams and support a rapid response. This is expected to promote mutual assistance during disasters and allow as many people as possible to be rescued. This allows the disaster support system to register users' situations, send notifications during disasters, analyze data, generate and provide rescue plans, enabling rapid and effective rescue operations.

[0029] The disaster support system according to the embodiment includes a registration unit, a notification unit, an analysis unit, a generation unit, and a provision unit. The registration unit registers the user's situation. The user's situation includes, but is not limited to, health status, location information, and urgency level. For example, the registration unit allows the user to register their location information as GPS data. The user can also register more detailed information by inputting their health status. The notification unit transmits a notification to the user requesting help in the event of a disaster. The notification unit can transmit the notification in the form of, for example, a text message, a voice alert, or a push notification. Sending a notification from the user saying, "Come help me!" enables rapid rescue. The analysis unit performs analysis based on the information from the notification unit. The analysis unit can analyze the user's location information and health status using, for example, a data analysis algorithm. The analysis unit uses a generation AI to analyze the user's situation in real time and generate an optimal rescue plan. The generation unit generates a rescue plan based on the analysis results. The generation unit can generate a rescue plan including, for example, a rescue route, required resources, and priorities. The generation unit can generate an efficient rescue plan using the generation AI. The provision unit provides the generated rescue plan to nearby residents and rescue teams. The provision unit can provide the rescue plan in a format such as digital format, paper media, or audio guidance. The provision unit provides nearby residents with location information of people who cannot escape, urging them to quickly go to help. The provision unit also suggests efficient rescue routes to rescue teams, supporting a rapid response. As a result, the disaster support system according to the embodiment enables rapid and effective rescue operations by registering the user's situation, sending and analyzing notifications during a disaster, and generating and providing rescue plans.

[0030] The registration unit can register the user's location information or situation. For example, the registration unit can register the user's location information as GPS data. For example, when the user opens the app and presses a button to obtain the user's current location, the GPS data is automatically registered. The registration unit can also register more detailed information by inputting the user's health condition. For example, when the user inputs their health condition (e.g., chronic illnesses, allergies, etc.) into the app, information needed in an emergency is registered. Furthermore, the registration unit can also input the user's urgency level. For example, the user selects their urgency level (e.g., high, medium, low) in the app, which determines the priority of the rescue plan. By registering the user's location information and situation, a more accurate rescue plan can be generated. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without AI. For example, the registration unit can input the information entered by the user into a generation AI, which analyzes and registers the information.

[0031] The notification unit can send a notification that the user is requesting help. For example, the notification unit can send a notification that the user is requesting help, such as "Come help!". For example, a text message can be sent when the user presses an emergency button on the app. The notification unit can also send a voice alert. For example, a voice alert can be sent when the user presses an voice alert button on the app. The notification unit can also send a push notification. For example, a push notification can be sent when the user presses a push notification button on the app. This allows the user to send a notification requesting help in an emergency, enabling rapid rescue. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input a notification sent by the user into a generation AI, which can analyze and send the notification.

[0032] The analysis unit can perform analysis based on the information from the notification unit. The analysis unit can perform analysis using, for example, a data analysis algorithm based on the information from the notification unit. For example, the analysis unit can analyze the user's location information and health condition and generate an optimal rescue plan. The analysis unit can also analyze the user's situation in real time using a generation AI. For example, the analysis unit inputs the user's location information to the generation AI, which then analyzes the location information and generates an optimal rescue plan. The analysis unit can also analyze the user's level of urgency. For example, the analysis unit analyzes the user's level of urgency and determines the priority of rescue plans. This allows an appropriate rescue plan to be generated by performing analysis based on the information from the notification unit. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's location information to the generation AI, which then analyzes the location information and generates a rescue plan.

[0033] The generation unit can generate a rescue plan based on the analysis results. The generation unit can generate a rescue plan including, for example, a rescue route, required resources, and priorities based on the analysis results. For example, the generation unit generates an optimal rescue route based on the user's location information. The generation unit can also calculate required resources and reflect them in the rescue plan. Furthermore, the generation unit can determine the priority of the rescue plan. For example, the generation unit determines the priority of the rescue plan based on the user's urgency. This enables rapid and effective rescue operations by generating a rescue plan based on the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results into a generation AI, which then generates a rescue plan.

[0034] The providing unit can provide the generated rescue plan to nearby residents or a rescue team. The providing unit can provide the generated rescue plan in a digital format, a paper medium, an audio guide, or other format. For example, the providing unit can provide the generated rescue plan to nearby residents in a digital format to urge them to quickly come to help. The providing unit can also provide the generated rescue plan in a paper medium to a rescue team to suggest an efficient rescue route. Furthermore, the providing unit can provide the generated rescue plan by audio guidance. For example, the providing unit can provide the generated rescue plan to nearby residents by audio guidance to urge them to quickly come to help. In this way, providing the generated rescue plan enables rapid rescue operations. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the generated rescue plan to a generating AI, and the generating AI can provide the rescue plan.

[0035] The providing unit can provide location information of the person who cannot escape to nearby residents and urge them to quickly come to help. The providing unit can, for example, provide the location information of the person who cannot escape as GPS data. For example, the providing unit can provide the location information of the person who cannot escape to nearby residents in digital format and urge them to quickly come to help. The providing unit can also provide the location information of the person who cannot escape in paper form. For example, the providing unit can provide the location information of the person who cannot escape to nearby residents in paper form and urge them to quickly come to help. The providing unit can also provide the location information of the person who cannot escape by audio guidance. For example, the providing unit can provide the location information of the person who cannot escape to nearby residents by audio guidance and urge them to quickly come to help. In this way, providing the location information of the person who cannot escape to nearby residents enables rapid rescue. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the location information of the person who cannot escape to a generating AI, which can analyze and provide the location information.

[0036] The providing unit can propose an efficient rescue route to the rescue team. The providing unit can, for example, propose an efficient rescue route to the rescue team in a digital format. For example, the providing unit can propose a rescue route to the rescue team that takes into account the shortest distance and traffic conditions. The providing unit can also propose a rescue route to the rescue team that takes into account the presence or absence of obstacles. Furthermore, the providing unit can propose a priority order of rescue routes to the rescue team. For example, the providing unit can preferentially propose routes with a high level of urgency to the rescue team. This enables rapid rescue operations by proposing an efficient rescue route to the rescue team. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the proposed rescue route to a generating AI, which can analyze and propose the rescue route.

[0037] The registration unit can analyze the user's past registration history and select the optimal registration method. For example, the registration unit can store the user's past registration history in a database and select the optimal registration method using an analysis algorithm. For example, the registration unit can automatically display information that the user has frequently registered in the past as candidates. The registration unit can also preferentially suggest registration methods (voice, text, etc.) that the user has used in the past. Furthermore, the registration unit can predict and suggest information that will be used in a specific time period based on the user's past registration history. For example, the registration unit can suggest similar information based on information that the user has previously registered in a specific time period. In this way, the optimal registration method can be provided by analyzing the user's past registration history. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without AI. For example, the registration unit can input the user's past registration history into a generation AI, which can analyze the history and select the optimal registration method.

[0038] The registration unit can perform filtering based on the user's current health condition and special needs at the time of registration. The registration unit can display appropriate registration items by, for example, inputting the user's health condition. For example, when the user inputs their health condition, the registration unit displays appropriate registration items based on the information. Furthermore, when the user inputs special needs (e.g., wheelchair use), the registration unit can also provide registration items according to those needs. Furthermore, when the user inputs allergy information, the registration unit can display appropriate registration items based on that information. For example, when the user inputs allergy information, the registration unit displays appropriate registration items based on that information. This allows for registering information according to the user's health condition and special needs, thereby providing a more appropriate rescue plan. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's health condition and special needs to a generation AI, which can analyze the information and perform filtering.

[0039] During registration, the registration unit can prioritize registering highly relevant information taking into account the user's geographical location information. The registration unit can, for example, acquire the user's geographical location information as GPS data and prioritize registering highly relevant information. For example, if the user is in a specific area, it prioritizes registering information related to that area. Furthermore, if the user is moving, the registration unit can also register optimal information based on the user's current location. Furthermore, if the user is in a specific facility, the registration unit can prioritize registering information related to that facility. For example, if the user is in a specific facility, it prioritizes registering information related to that facility. This allows for registering highly relevant information based on the user's geographical location information, thereby providing a more appropriate rescue plan. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's geographical location information to a generation AI, which can analyze the location information and register highly relevant information.

[0040] The registration unit can analyze the user's social media activity and register related information at the time of registration. For example, the registration unit can store the user's social media activity in a database and register related information using an analysis algorithm. For example, the registration unit can automatically complete the registered information based on location information shared by the user on social media. The registration unit can also prioritize the registered information based on emergency information posted by the user on social media. Furthermore, the registration unit can automatically extract and register related information from the user's social media activity. For example, the registration unit can automatically extract and register related information based on information posted by the user on social media. By registering related information based on the user's social media activity, more appropriate rescue plans can be provided. Some or all of the above-described processing in the registration unit can be performed using, for example, AI, or without AI. For example, the registration unit can input the user's social media activity into a generation AI, which can analyze the activity and register related information.

[0041] The notification unit can adjust the level of detail of the notification based on the type and scale of the disaster when sending a notification. The notification unit can, for example, store the type and scale of the disaster in a database and adjust the level of detail of the notification using an analytical algorithm. For example, in the case of a large-scale disaster, the notification unit transmits a notification including detailed evacuation information. In addition, in the case of a small-scale disaster, the notification unit transmits a notification including concise evacuation information. Furthermore, the notification unit can also transmit a notification including appropriate information depending on the specific disaster (e.g., earthquake, flood). For example, in the case of an earthquake, the notification unit transmits a notification including information on evacuation sites, and in the case of a flood, the notification unit transmits a notification including information on evacuation to higher ground. In this way, by adjusting the level of detail of the notification depending on the type and scale of the disaster, more appropriate notifications can be sent. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input data on the type and scale of the disaster into a generation AI, which can analyze the data and adjust the level of detail of the notification.

[0042] The notification unit can apply different notification algorithms depending on the user's current situation when sending a notification. The notification unit can, for example, store the user's current situation in a database and apply different notification algorithms using an analysis algorithm. For example, if the user is indoors, the notification unit can send a notification regarding indoor evacuation. Also, if the user is outdoors, the notification unit can send a notification regarding outdoor evacuation. Furthermore, if the user is moving, the notification unit can send a notification regarding evacuation while moving. For example, if the user is moving, the notification unit can send a notification including an optimal evacuation route. In this way, by applying different notification algorithms depending on the user's current situation, more appropriate notifications can be sent. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's current situation data into a generation AI, which can analyze the data and apply different notification algorithms.

[0043] The notification unit can prioritize sending highly relevant notifications by taking into account the user's geographical location information when sending notifications. The notification unit can, for example, acquire the user's geographical location information as GPS data and prioritize sending highly relevant notifications. For example, if the user is in a specific area, it prioritizes sending notifications related to that area. Furthermore, if the user is moving, the notification unit can also send the most appropriate notification based on the user's current location. Furthermore, if the user is in a specific facility, the notification unit can prioritize sending notifications related to that facility. For example, if the user is in a specific facility, it prioritizes sending notifications related to that facility. This allows for more appropriate notifications to be sent by sending highly relevant notifications based on the user's geographical location information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's geographical location information to a generation AI, which can analyze the location information and send highly relevant notifications.

[0044] The notification unit can analyze the user's social media activity and send relevant notifications at the time of notification. The notification unit can, for example, store the user's social media activity in a database and send relevant notifications using an analysis algorithm. For example, notifications can be automatically supplemented based on location information shared by the user on social media. The notification unit can also prioritize notifications based on emergency information posted by the user on social media. Furthermore, the notification unit can automatically extract and send relevant notifications from the user's social media activity. For example, the notification unit can automatically extract and send relevant notifications based on information posted by the user on social media. This allows for more appropriate notifications to be sent by sending relevant notifications based on the user's social media activity. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's social media activity into a generation AI, which can analyze the activity and send relevant notifications.

[0045] During analysis, the analysis unit can adjust the level of detail of the analysis based on the type and scale of the disaster. For example, the analysis unit can store the type and scale of the disaster in a database and adjust the level of detail of the analysis using an analysis algorithm. For example, the analysis unit performs a detailed analysis in the case of a large-scale disaster. The analysis unit can also perform a concise analysis in the case of a small-scale disaster. Furthermore, the analysis unit can perform an appropriate analysis depending on the specific disaster (e.g., earthquake, flood). For example, the analysis unit performs an analysis including information on evacuation sites in the case of an earthquake, and information on evacuation to higher ground in the case of a flood. This allows for more appropriate analysis by adjusting the level of detail of the analysis depending on the type and scale of the disaster. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the type and scale of the disaster into the generation AI, which then analyzes the data and adjusts the level of detail of the analysis.

[0046] During analysis, the analysis unit can apply different analysis algorithms depending on the user's current situation. For example, the analysis unit can store the user's current situation in a database and apply different analysis algorithms using an analysis algorithm. For example, when the user is indoors, the analysis unit performs an analysis related to indoor evacuation. Also, when the user is outdoors, the analysis unit can perform an analysis related to outdoor evacuation. Furthermore, when the user is moving, the analysis unit can perform an analysis related to evacuation while moving. For example, when the user is moving, the analysis unit performs an analysis including an optimal evacuation route. This enables more appropriate analysis by applying different analysis algorithms depending on the user's current situation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's current situation data to a generation AI, which analyzes the data and applies different analysis algorithms.

[0047] During analysis, the analysis unit can prioritize analysis of highly relevant information taking into account the user's geographical location information. The analysis unit can, for example, acquire the user's geographical location information as GPS data and prioritize analysis of highly relevant information. For example, if the user is in a specific area, it prioritizes analysis of information related to that area. Furthermore, if the user is moving, the analysis unit can analyze optimal information based on the user's current location. Furthermore, if the user is in a specific facility, the analysis unit can prioritize analysis of information related to that facility. For example, if the user is in a specific facility, it prioritizes analysis of information related to that facility. This enables more appropriate analysis by analyzing highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to a generation AI, which can analyze the location information and analyze highly relevant information.

[0048] During analysis, the analysis unit can analyze the user's social media activities and analyze related information. The analysis unit can, for example, store the user's social media activities in a database and analyze the related information using an analysis algorithm. For example, the analysis unit can automatically supplement the analysis information based on location information shared by the user on social media. The analysis unit can also prioritize the analysis information based on emergency information posted by the user on social media. Furthermore, the analysis unit can automatically extract and analyze related information from the user's social media activities. For example, the analysis unit can automatically extract and analyze related information based on information posted by the user on social media. This enables more appropriate analysis by analyzing related information based on the user's social media activities. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's social media activities into a generation AI, which can analyze the activities and analyze related information.

[0049] The generation unit can adjust the level of detail of the rescue plan based on the type and scale of the disaster when generating the rescue plan. For example, the generation unit can store the type and scale of the disaster in a database and adjust the level of detail of the rescue plan using an analytical algorithm. For example, the generation unit generates a detailed rescue plan in the case of a large-scale disaster. The generation unit can also generate a concise rescue plan in the case of a small-scale disaster. Furthermore, the generation unit can generate an appropriate rescue plan depending on a specific disaster (e.g., earthquake, flood). For example, the generation unit generates a rescue plan that includes information on evacuation locations in the case of an earthquake, and information on evacuation to higher ground in the case of a flood. In this way, by adjusting the level of detail of the rescue plan depending on the type and scale of the disaster, a more appropriate rescue plan can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the type and scale of the disaster into the generation AI, which can analyze the data and adjust the level of detail of the rescue plan.

[0050] The generation unit can apply different generation algorithms depending on the user's current situation during generation. For example, the generation unit can store the user's current situation in a database and apply different generation algorithms using an analysis algorithm. For example, if the user is indoors, the generation unit generates a rescue plan for indoor evacuation. Furthermore, if the user is outdoors, the generation unit can generate a rescue plan for outdoor evacuation. Furthermore, if the user is moving, the generation unit can generate a rescue plan for evacuation while moving. For example, if the user is moving, the generation unit generates a rescue plan including an optimal evacuation route. This allows for the generation of a more appropriate rescue plan by applying different generation algorithms depending on the user's current situation. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's current situation data into a generation AI, which can analyze the data and apply different generation algorithms.

[0051] The generation unit can prioritize generating a highly relevant rescue plan by taking into account the user's geographical location information. For example, the generation unit can acquire the user's geographical location information as GPS data and prioritize generating a highly relevant rescue plan. For example, if the user is in a specific area, the generation unit prioritizes generating a rescue plan related to that area. Furthermore, if the user is moving, the generation unit can also generate an optimal rescue plan based on the user's current location. Furthermore, if the user is in a specific facility, the generation unit can prioritize generating a rescue plan related to that facility. For example, if the user is in a specific facility, the generation unit prioritizes generating a rescue plan related to that facility. This allows for the generation of a highly relevant rescue plan based on the user's geographical location information, thereby generating a more appropriate rescue plan. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information into a generation AI, which can analyze the location information and generate a highly relevant rescue plan.

[0052] The generation unit can analyze the user's social media activities and generate a related rescue plan during generation. The generation unit can, for example, store the user's social media activities in a database and generate a related rescue plan using an analysis algorithm. For example, the generation unit can automatically complete the rescue plan based on location information shared by the user on social media. The generation unit can also prioritize the rescue plan based on emergency information posted by the user on social media. Furthermore, the generation unit can automatically extract relevant information from the user's social media activities and generate a rescue plan. For example, the generation unit can automatically extract relevant information based on information posted by the user on social media and generate a rescue plan. This allows for the generation of a related rescue plan based on the user's social media activities, resulting in a more appropriate rescue plan. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's social media activities into a generation AI, which can analyze the activities and generate a related rescue plan.

[0053] The providing unit can adjust the level of detail of the information to be provided based on the type and scale of the disaster when providing the information. The providing unit can, for example, store the type and scale of the disaster in a database and adjust the level of detail of the information to be provided using an analysis algorithm. For example, the providing unit can provide information including detailed evacuation information in the case of a large-scale disaster. The providing unit can also provide information including concise evacuation information in the case of a small-scale disaster. Furthermore, the providing unit can provide information including appropriate information depending on the specific disaster (e.g., earthquake, flood). For example, the providing unit provides information on evacuation sites in the case of an earthquake, and information on evacuation to higher ground in the case of a flood. In this way, by adjusting the level of detail of the information to be provided depending on the type and scale of the disaster, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the type and scale of the disaster into the generating AI, which can analyze the data and adjust the level of detail of the information to be provided.

[0054] The providing unit can apply different providing algorithms depending on the user's current situation when providing information. The providing unit can, for example, store the user's current situation in a database and apply different providing algorithms using an analysis algorithm. For example, when the user is indoors, the providing unit provides information about indoor evacuation. Also, when the user is outdoors, the providing unit can provide information about outdoor evacuation. Furthermore, when the user is moving, the providing unit can provide information about evacuation while moving. For example, when the user is moving, the providing unit provides information including an optimal evacuation route. This makes it possible to provide more appropriate information by applying different providing algorithms depending on the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data to a generating AI, which can analyze the data and apply different providing algorithms.

[0055] The providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. The providing unit can, for example, acquire the user's geographical location information as GPS data and prioritize providing highly relevant information. For example, if the user is in a specific area, it can prioritize providing information related to that area. Furthermore, if the user is moving, the providing unit can also provide optimal information based on the user's current location. Furthermore, if the user is in a specific facility, it can prioritize providing information related to that facility. For example, if the user is in a specific facility, it can prioritize providing information related to that facility. This allows for more appropriate information to be provided by providing highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to a generation AI, which can analyze the location information and provide highly relevant information.

[0056] The providing unit can analyze the user's social media activity and provide related information at the time of providing the information. The providing unit can, for example, store the user's social media activity in a database and provide related information using an analysis algorithm. For example, the providing unit can automatically supplement the information based on location information shared by the user on social media. The providing unit can also prioritize information based on emergency information posted by the user on social media. Furthermore, the providing unit can automatically extract and provide related information from the user's social media activity. For example, the providing unit can automatically extract and provide related information based on information posted by the user on social media. This allows for more appropriate information to be provided by providing related information based on the user's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's social media activity into a generation AI, which can analyze the activity and provide related information.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The disaster support system can also analyze the user's past evacuation behavior data and suggest the optimal evacuation route. For example, based on past evacuation behavior data, it can evaluate the safety and efficiency of the user's previous evacuation routes and suggest the optimal route. It can also identify obstacles and dangerous areas the user encountered during evacuation based on past evacuation behavior data and suggest routes that should be avoided. Furthermore, based on past evacuation behavior data, it can predict the congestion status of evacuation shelters used by the user during evacuation and suggest alternative evacuation shelters to avoid congestion. In this way, by utilizing the user's past evacuation behavior data, safer and more efficient evacuations are possible.

[0059] The disaster support system can also monitor users' health data in real time and provide appropriate medical support in emergencies. For example, it can monitor the user's heart rate and blood pressure and notify medical institutions if any abnormalities are detected. It can also identify necessary medical resources based on the user's health data and provide them to rescue teams. It can also suggest medicines and medical equipment needed during evacuation based on the user's health data. This allows the system to understand the user's health condition in real time and provide appropriate medical support, thereby reducing health risks during disasters.

[0060] The disaster support system can also have a function to help users contact their family and friends. For example, it can provide a function that automatically sends notifications to family and friends in the event of an emergency. It can also share the user's location information with family and friends, allowing them to quickly confirm their safety. It can also provide a chat function that allows users to stay in touch with family and friends while evacuating. This allows users to stay in touch with family and friends in the event of a disaster, providing a sense of security.

[0061] The disaster support system can also register information about the user's pets and provide support for evacuation of the pets. For example, the user can register the type and health condition of the pet and suggest evacuation routes for the pet in the event of a disaster. It can also track the pet's location information and help the pet evacuate safely. It can also suggest supplies necessary for the pet's evacuation (e.g., pet food, medicine, etc.). This helps the user's pet to evacuate safely.

[0062] The disaster support system can also register the user's vehicle information and provide evacuation support using the vehicle. For example, the user can register the vehicle's location information and fuel status, and the system can suggest the optimal evacuation route in the event of a disaster. The system can also analyze the traffic situation during evacuation in real time based on the vehicle's location information and suggest routes that avoid congestion. Furthermore, the system can suggest refueling points that are necessary during evacuation based on the vehicle's fuel status. This allows the system to support the user in evacuating safely using their vehicle.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The registration unit registers the user's status. The user's status includes, for example, health status, location information, and urgency. The user can register their location information as GPS data or enter detailed information about their health status. Step 2: The notification unit sends a notification to the user requesting help in the event of a disaster. The notification can be sent in the form of a text message, voice alert, or push notification. By sending a notification saying "Come help me!", the user can ensure prompt rescue. Step 3: The analysis unit performs analysis based on the information from the notification unit. The analysis unit uses a data analysis algorithm to analyze the user's location information and health condition. Using the generation AI, the unit analyzes the user's situation in real time and generates an optimal rescue plan. Step 4: The generator generates a rescue plan based on the analysis results. The generator generates a rescue plan that includes rescue routes, required resources, priorities, etc. The generator uses a generation AI to generate an efficient rescue plan. Step 5: The provider provides the generated rescue plan to nearby residents and rescue teams. The provider provides the rescue plan in digital format, paper format, audio guide format, etc. It provides nearby residents with the location information of people who are unable to escape, urging them to quickly come to their aid. It also suggests efficient rescue routes to rescue teams, supporting a rapid response.

[0065] (Example 2) A disaster support system according to an embodiment of the present invention is a system for helping people who have difficulty evacuating on their own during a disaster. This system allows users to register their own situation in an app and, when a disaster occurs, sends a notification saying, "Come help me!" This notification is analyzed by a generating AI to generate an optimal rescue plan. The generating AI analyzes the user's location information and surrounding conditions in real time and issues appropriate instructions to nearby residents and rescue teams. For example, it provides nearby residents with the location information of people who are unable to escape, encouraging them to quickly come to their aid. It also suggests efficient rescue routes to rescue teams, supporting a rapid response. This system is expected to promote mutual assistance during disasters and rescue as many people as possible. The generating AI enables flexible responses in emergencies and realizes rapid and effective rescue operations. For example, users can register their location information and health status in the app, enabling a rapid response during a disaster. Furthermore, the generating AI analyzes the user's situation in real time and generates an optimal rescue plan. For example, the generating AI uses the user's location information to provide nearby residents with the location information of people who are unable to escape, encouraging them to quickly come to their aid. In addition, the generative AI will suggest efficient rescue routes to rescue teams and support a rapid response. This is expected to promote mutual assistance during disasters and allow as many people as possible to be rescued. This allows the disaster support system to register users' situations, send notifications during disasters, analyze data, generate and provide rescue plans, enabling rapid and effective rescue operations.

[0066] The disaster support system according to the embodiment includes a registration unit, a notification unit, an analysis unit, a generation unit, and a provision unit. The registration unit registers the user's situation. The user's situation includes, but is not limited to, health status, location information, and urgency level. For example, the registration unit allows the user to register their location information as GPS data. The user can also register more detailed information by inputting their health status. The notification unit transmits a notification to the user requesting help in the event of a disaster. The notification unit can transmit the notification in the form of, for example, a text message, a voice alert, or a push notification. Sending a notification from the user saying, "Come help me!" enables rapid rescue. The analysis unit performs analysis based on the information from the notification unit. The analysis unit can analyze the user's location information and health status using, for example, a data analysis algorithm. The analysis unit uses a generation AI to analyze the user's situation in real time and generate an optimal rescue plan. The generation unit generates a rescue plan based on the analysis results. The generation unit can generate a rescue plan including, for example, a rescue route, required resources, and priorities. The generation unit can generate an efficient rescue plan using the generation AI. The provision unit provides the generated rescue plan to nearby residents and rescue teams. The provision unit can provide the rescue plan in a format such as digital format, paper media, or audio guidance. The provision unit provides nearby residents with location information of people who cannot escape, urging them to quickly go to help. The provision unit also suggests efficient rescue routes to rescue teams, supporting a rapid response. As a result, the disaster support system according to the embodiment enables rapid and effective rescue operations by registering the user's situation, sending and analyzing notifications during a disaster, and generating and providing rescue plans.

[0067] The registration unit can register the user's location information or situation. For example, the registration unit can register the user's location information as GPS data. For example, when the user opens the app and presses a button to obtain the user's current location, the GPS data is automatically registered. The registration unit can also register more detailed information by inputting the user's health condition. For example, when the user inputs their health condition (e.g., chronic illnesses, allergies, etc.) into the app, information needed in an emergency is registered. Furthermore, the registration unit can also input the user's urgency level. For example, the user selects their urgency level (e.g., high, medium, low) in the app, which determines the priority of the rescue plan. By registering the user's location information and situation, a more accurate rescue plan can be generated. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without AI. For example, the registration unit can input the information entered by the user into a generation AI, which analyzes and registers the information.

[0068] The notification unit can send a notification that the user is requesting help. For example, the notification unit can send a notification that the user is requesting help, such as "Come help!". For example, a text message can be sent when the user presses an emergency button on the app. The notification unit can also send a voice alert. For example, a voice alert can be sent when the user presses an voice alert button on the app. The notification unit can also send a push notification. For example, a push notification can be sent when the user presses a push notification button on the app. This allows the user to send a notification requesting help in an emergency, enabling rapid rescue. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit can input a notification sent by the user into a generation AI, which can analyze and send the notification.

[0069] The analysis unit can perform analysis based on the information from the notification unit. The analysis unit can perform analysis using, for example, a data analysis algorithm based on the information from the notification unit. For example, the analysis unit can analyze the user's location information and health condition and generate an optimal rescue plan. The analysis unit can also analyze the user's situation in real time using a generation AI. For example, the analysis unit inputs the user's location information to the generation AI, which then analyzes the location information and generates an optimal rescue plan. The analysis unit can also analyze the user's level of urgency. For example, the analysis unit analyzes the user's level of urgency and determines the priority of rescue plans. This allows an appropriate rescue plan to be generated by performing analysis based on the information from the notification unit. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's location information to the generation AI, which then analyzes the location information and generates a rescue plan.

[0070] The generation unit can generate a rescue plan based on the analysis results. The generation unit can generate a rescue plan including, for example, a rescue route, required resources, and priorities based on the analysis results. For example, the generation unit generates an optimal rescue route based on the user's location information. The generation unit can also calculate required resources and reflect them in the rescue plan. Furthermore, the generation unit can determine the priority of the rescue plan. For example, the generation unit determines the priority of the rescue plan based on the user's urgency. This enables rapid and effective rescue operations by generating a rescue plan based on the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results into a generation AI, which then generates a rescue plan.

[0071] The providing unit can provide the generated rescue plan to nearby residents or a rescue team. The providing unit can provide the generated rescue plan in a digital format, a paper medium, an audio guide, or other format. For example, the providing unit can provide the generated rescue plan to nearby residents in a digital format to urge them to quickly come to help. The providing unit can also provide the generated rescue plan in a paper medium to a rescue team to suggest an efficient rescue route. Furthermore, the providing unit can provide the generated rescue plan by audio guidance. For example, the providing unit can provide the generated rescue plan to nearby residents by audio guidance to urge them to quickly come to help. In this way, providing the generated rescue plan enables rapid rescue operations. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the generated rescue plan to a generating AI, and the generating AI can provide the rescue plan.

[0072] The providing unit can provide location information of the person who cannot escape to nearby residents and urge them to quickly come to help. The providing unit can, for example, provide the location information of the person who cannot escape as GPS data. For example, the providing unit can provide the location information of the person who cannot escape to nearby residents in digital format and urge them to quickly come to help. The providing unit can also provide the location information of the person who cannot escape in paper form. For example, the providing unit can provide the location information of the person who cannot escape to nearby residents in paper form and urge them to quickly come to help. The providing unit can also provide the location information of the person who cannot escape by audio guidance. For example, the providing unit can provide the location information of the person who cannot escape to nearby residents by audio guidance and urge them to quickly come to help. In this way, providing the location information of the person who cannot escape to nearby residents enables rapid rescue. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the location information of the person who cannot escape to a generating AI, which can analyze and provide the location information.

[0073] The providing unit can propose an efficient rescue route to the rescue team. The providing unit can, for example, propose an efficient rescue route to the rescue team in a digital format. For example, the providing unit can propose a rescue route to the rescue team that takes into account the shortest distance and traffic conditions. The providing unit can also propose a rescue route to the rescue team that takes into account the presence or absence of obstacles. Furthermore, the providing unit can propose a priority order of rescue routes to the rescue team. For example, the providing unit can preferentially propose routes with a high level of urgency to the rescue team. This enables rapid rescue operations by proposing an efficient rescue route to the rescue team. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the proposed rescue route to a generating AI, which can analyze and propose the rescue route.

[0074] The registration unit can estimate the user's emotions and adjust the level of detail of the registration information based on the estimated user's emotions. The registration unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the registration unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The registration unit can also estimate the user's emotions using voice analysis technology. For example, the registration unit can analyze the tone and speed of the user's voice to estimate the emotions. The registration unit can also estimate the user's emotions using text analysis technology. For example, the registration unit can analyze text entered by the user to estimate the emotions. This allows more appropriate information to be registered by adjusting the level of detail of the registration information according to the user's emotions. For example, if the user is feeling anxious, the registration unit can provide a simple and easy-to-understand registration form to minimize input steps. If the user is relaxed, the registration unit can provide detailed registration options and suggest a customizable registration method. If the user is in a hurry, the registration unit can prioritize voice input to quickly register location information and status. Some or all of the above-described processing in the registration unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the registration unit may input the user's emotion data into the generation AI, and the generation AI may analyze the emotion and adjust the level of detail of the registration information.

[0075] The registration unit can analyze the user's past registration history and select the optimal registration method. For example, the registration unit can store the user's past registration history in a database and select the optimal registration method using an analysis algorithm. For example, the registration unit can automatically display information that the user has frequently registered in the past as candidates. The registration unit can also preferentially suggest registration methods (voice, text, etc.) that the user has used in the past. Furthermore, the registration unit can predict and suggest information that will be used in a specific time period based on the user's past registration history. For example, the registration unit can suggest similar information based on information that the user has previously registered in a specific time period. In this way, the optimal registration method can be provided by analyzing the user's past registration history. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without AI. For example, the registration unit can input the user's past registration history into a generation AI, which can analyze the history and select the optimal registration method.

[0076] The registration unit can perform filtering based on the user's current health condition and special needs at the time of registration. The registration unit can display appropriate registration items by, for example, inputting the user's health condition. For example, when the user inputs their health condition, the registration unit displays appropriate registration items based on the information. Furthermore, when the user inputs special needs (e.g., wheelchair use), the registration unit can also provide registration items according to those needs. Furthermore, when the user inputs allergy information, the registration unit can display appropriate registration items based on that information. For example, when the user inputs allergy information, the registration unit displays appropriate registration items based on that information. This allows for registering information according to the user's health condition and special needs, thereby providing a more appropriate rescue plan. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's health condition and special needs to a generation AI, which can analyze the information and perform filtering.

[0077] The registration unit can estimate the user's emotions and prioritize the registration information based on the estimated user's emotions. The registration unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the registration unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The registration unit can also estimate the user's emotions using voice analysis technology. For example, the registration unit can analyze the tone and speed of the user's voice to estimate the emotions. The registration unit can also estimate the user's emotions using text analysis technology. For example, the registration unit can analyze text entered by the user to estimate the emotions. This allows the registration information to be prioritized according to the user's emotions, thereby allowing more important information to be registered preferentially. For example, if the user is feeling anxious, the registration unit can prioritize registering information with high urgency. If the user is relaxed, the registration unit can prioritize registering detailed information. If the user is in a hurry, the registration unit can prioritize registering the most important information. Some or all of the above-described processing in the registration unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the registration unit can input the user's emotional data into the generation AI, which can then analyze the emotions and determine the priority of the registered information.

[0078] During registration, the registration unit can prioritize registering highly relevant information taking into account the user's geographical location information. The registration unit can, for example, acquire the user's geographical location information as GPS data and prioritize registering highly relevant information. For example, if the user is in a specific area, it prioritizes registering information related to that area. Furthermore, if the user is moving, the registration unit can also register optimal information based on the user's current location. Furthermore, if the user is in a specific facility, the registration unit can prioritize registering information related to that facility. For example, if the user is in a specific facility, it prioritizes registering information related to that facility. This allows for registering highly relevant information based on the user's geographical location information, thereby providing a more appropriate rescue plan. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's geographical location information to a generation AI, which can analyze the location information and register highly relevant information.

[0079] The registration unit can analyze the user's social media activity and register related information at the time of registration. For example, the registration unit can store the user's social media activity in a database and register related information using an analysis algorithm. For example, the registration unit can automatically complete the registered information based on location information shared by the user on social media. The registration unit can also prioritize the registered information based on emergency information posted by the user on social media. Furthermore, the registration unit can automatically extract and register related information from the user's social media activity. For example, the registration unit can automatically extract and register related information based on information posted by the user on social media. By registering related information based on the user's social media activity, more appropriate rescue plans can be provided. Some or all of the above-described processing in the registration unit can be performed using, for example, AI, or without AI. For example, the registration unit can input the user's social media activity into a generation AI, which can analyze the activity and register related information.

[0080] The notification unit can estimate the user's emotion and adjust the notification expression method based on the estimated user's emotion. The notification unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the notification unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The notification unit can also estimate the user's emotion using voice analysis technology. For example, the notification unit can analyze the tone and speed of the user's voice to estimate the emotion. The notification unit can also estimate the user's emotion using text analysis technology. For example, the notification unit can analyze text entered by the user to estimate the emotion. This allows the notification expression method to be adjusted according to the user's emotion, thereby enabling more appropriate notifications to be sent. For example, if the user is feeling anxious, the notification unit can send a notification using an expression method that conveys a sense of security. If the user is relaxed, the notification unit can send a notification containing detailed information. If the user is in a hurry, the notification unit can send a concise and prompt notification. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the notification unit can input the user's emotional data into the generation AI, which can then analyze the emotions and adjust the way the notification is expressed.

[0081] The notification unit can adjust the level of detail of the notification based on the type and scale of the disaster when sending a notification. The notification unit can, for example, store the type and scale of the disaster in a database and adjust the level of detail of the notification using an analytical algorithm. For example, in the case of a large-scale disaster, the notification unit transmits a notification including detailed evacuation information. In addition, in the case of a small-scale disaster, the notification unit transmits a notification including concise evacuation information. Furthermore, the notification unit can also transmit a notification including appropriate information depending on the specific disaster (e.g., earthquake, flood). For example, in the case of an earthquake, the notification unit transmits a notification including information on evacuation sites, and in the case of a flood, the notification unit transmits a notification including information on evacuation to higher ground. In this way, by adjusting the level of detail of the notification depending on the type and scale of the disaster, more appropriate notifications can be sent. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input data on the type and scale of the disaster into a generation AI, which can analyze the data and adjust the level of detail of the notification.

[0082] The notification unit can apply different notification algorithms depending on the user's current situation when sending a notification. The notification unit can, for example, store the user's current situation in a database and apply different notification algorithms using an analysis algorithm. For example, if the user is indoors, the notification unit can send a notification regarding indoor evacuation. Also, if the user is outdoors, the notification unit can send a notification regarding outdoor evacuation. Furthermore, if the user is moving, the notification unit can send a notification regarding evacuation while moving. For example, if the user is moving, the notification unit can send a notification including an optimal evacuation route. In this way, by applying different notification algorithms depending on the user's current situation, more appropriate notifications can be sent. Some or all of the above-mentioned processing in the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input the user's current situation data into a generation AI, which can analyze the data and apply different notification algorithms.

[0083] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated user's emotions. The notification unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the notification unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The notification unit can also estimate the user's emotions using voice analysis technology. For example, the notification unit can analyze the tone and speed of the user's voice to estimate the emotions. The notification unit can also estimate the user's emotions using text analysis technology. For example, the notification unit can analyze text entered by the user to estimate the emotions. This allows the notification unit to prioritize notifications based on the user's emotions, thereby allowing more important notifications to be sent preferentially. For example, if the user is feeling anxious, the notification unit can prioritize sending notifications with a high level of urgency. If the user is relaxed, the notification unit can prioritize sending notifications containing detailed information. If the user is in a hurry, the notification unit can prioritize sending the most important notifications. Some or all of the above-described processing in the notification unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the notification unit can input the user's emotional data into the generation AI, which can then analyze the emotions and determine the priority of notifications.

[0084] The notification unit can prioritize sending highly relevant notifications by taking into account the user's geographical location information when sending notifications. The notification unit can, for example, acquire the user's geographical location information as GPS data and prioritize sending highly relevant notifications. For example, if the user is in a specific area, it prioritizes sending notifications related to that area. Furthermore, if the user is moving, the notification unit can also send the most appropriate notification based on the user's current location. Furthermore, if the user is in a specific facility, the notification unit can prioritize sending notifications related to that facility. For example, if the user is in a specific facility, it prioritizes sending notifications related to that facility. This allows for more appropriate notifications to be sent by sending highly relevant notifications based on the user's geographical location information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's geographical location information to a generation AI, which can analyze the location information and send highly relevant notifications.

[0085] The notification unit can analyze the user's social media activity and send relevant notifications at the time of notification. The notification unit can, for example, store the user's social media activity in a database and send relevant notifications using an analysis algorithm. For example, the notification unit can automatically supplement notifications based on location information shared by the user on social media. The notification unit can also prioritize notifications based on emergency information posted by the user on social media. Furthermore, the notification unit can automatically extract and send relevant notifications from the user's social media activity. For example, the notification unit can automatically extract and send relevant notifications based on information posted by the user on social media. This allows for more appropriate notifications to be sent by sending relevant notifications based on the user's social media activity. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input the user's social media activity into a generation AI, which can analyze the activity and send relevant notifications.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit can analyze text entered by the user to estimate the emotions. This allows for more appropriate analysis by adjusting the analysis criteria according to the user's emotions. For example, if the user is feeling anxious, the analysis unit performs a quick and concise analysis. On the other hand, if the user is relaxed, the analysis unit performs a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit prioritizes the analysis of the most important information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then analyze the emotions and adjust the analysis criteria.

[0087] During analysis, the analysis unit can adjust the level of detail of the analysis based on the type and scale of the disaster. For example, the analysis unit can store the type and scale of the disaster in a database and adjust the level of detail of the analysis using an analysis algorithm. For example, the analysis unit performs a detailed analysis in the case of a large-scale disaster. The analysis unit can also perform a concise analysis in the case of a small-scale disaster. Furthermore, the analysis unit can perform an appropriate analysis depending on the specific disaster (e.g., earthquake, flood). For example, the analysis unit performs an analysis including information on evacuation sites in the case of an earthquake, and information on evacuation to higher ground in the case of a flood. This allows for more appropriate analysis by adjusting the level of detail of the analysis depending on the type and scale of the disaster. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the type and scale of the disaster into the generation AI, which then analyzes the data and adjusts the level of detail of the analysis.

[0088] During analysis, the analysis unit can apply different analysis algorithms depending on the user's current situation. For example, the analysis unit can store the user's current situation in a database and apply different analysis algorithms using an analysis algorithm. For example, when the user is indoors, the analysis unit performs an analysis related to indoor evacuation. Also, when the user is outdoors, the analysis unit can perform an analysis related to outdoor evacuation. Furthermore, when the user is moving, the analysis unit can perform an analysis related to evacuation while moving. For example, when the user is moving, the analysis unit performs an analysis including an optimal evacuation route. This enables more appropriate analysis by applying different analysis algorithms depending on the user's current situation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's current situation data to a generation AI, which analyzes the data and applies different analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit can also estimate the user's emotions using text analysis technology. For example, the analysis unit can analyze text entered by the user to estimate the emotions. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions. For example, if the user is feeling anxious, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit provides a display method including detailed information. If the user is in a hurry, the analysis unit provides a display method that focuses on the main points. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's emotional data into the generation AI, which can then analyze the emotions and adjust how the analysis results are displayed.

[0090] During analysis, the analysis unit can prioritize analysis of highly relevant information taking into account the user's geographical location information. The analysis unit can, for example, acquire the user's geographical location information as GPS data and prioritize analysis of highly relevant information. For example, if the user is in a specific area, it prioritizes analysis of information related to that area. Furthermore, if the user is moving, the analysis unit can analyze optimal information based on the user's current location. Furthermore, if the user is in a specific facility, the analysis unit can prioritize analysis of information related to that facility. For example, if the user is in a specific facility, it prioritizes analysis of information related to that facility. This enables more appropriate analysis by analyzing highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to a generation AI, which can analyze the location information and analyze highly relevant information.

[0091] During analysis, the analysis unit can analyze the user's social media activities and analyze related information. The analysis unit can, for example, store the user's social media activities in a database and analyze the related information using an analysis algorithm. For example, the analysis unit can automatically supplement the analysis information based on location information shared by the user on social media. The analysis unit can also prioritize the analysis information based on emergency information posted by the user on social media. Furthermore, the analysis unit can automatically extract and analyze related information from the user's social media activities. For example, the analysis unit can automatically extract and analyze related information based on information posted by the user on social media. This enables more appropriate analysis by analyzing related information based on the user's social media activities. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's social media activities into a generation AI, which can analyze the activities and analyze related information.

[0092] The generation unit can estimate the user's emotions and adjust the rescue plan generation method based on the estimated user's emotions. The generation unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotions. The generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit can analyze text entered by the user to estimate the emotions. This allows the rescue plan generation method to be adjusted according to the user's emotions, thereby generating a more appropriate rescue plan. For example, if the user is feeling anxious, the generation unit generates a quick and concise rescue plan. If the user is relaxed, the generation unit generates a detailed rescue plan. If the user is in a hurry, the generation unit generates a rescue plan that prioritizes the most important information. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's emotional data into the generation AI, which can then analyze the emotions and adjust the method for generating a rescue plan.

[0093] The generation unit can adjust the level of detail of the rescue plan based on the type and scale of the disaster when generating the rescue plan. For example, the generation unit can store the type and scale of the disaster in a database and adjust the level of detail of the rescue plan using an analytical algorithm. For example, the generation unit generates a detailed rescue plan in the case of a large-scale disaster. The generation unit can also generate a concise rescue plan in the case of a small-scale disaster. Furthermore, the generation unit can generate an appropriate rescue plan depending on a specific disaster (e.g., earthquake, flood). For example, the generation unit generates a rescue plan that includes information on evacuation locations in the case of an earthquake, and information on evacuation to higher ground in the case of a flood. In this way, by adjusting the level of detail of the rescue plan depending on the type and scale of the disaster, a more appropriate rescue plan can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the type and scale of the disaster into the generation AI, which can analyze the data and adjust the level of detail of the rescue plan.

[0094] The generation unit can apply different generation algorithms depending on the user's current situation during generation. For example, the generation unit can store the user's current situation in a database and apply different generation algorithms using an analysis algorithm. For example, if the user is indoors, the generation unit generates a rescue plan for indoor evacuation. Furthermore, if the user is outdoors, the generation unit can generate a rescue plan for outdoor evacuation. Furthermore, if the user is moving, the generation unit can generate a rescue plan for evacuation while moving. For example, if the user is moving, the generation unit generates a rescue plan including an optimal evacuation route. This allows for the generation of a more appropriate rescue plan by applying different generation algorithms depending on the user's current situation. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the user's current situation data into a generation AI, which can analyze the data and apply different generation algorithms.

[0095] The generation unit can estimate the user's emotions and prioritize rescue plans based on the estimated user's emotions. The generation unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the user's voice to estimate the emotions. The generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit analyzes text entered by the user to estimate the emotions. This allows the priority of rescue plans to be determined according to the user's emotions, thereby preferentially generating more important rescue plans. For example, if the user is feeling anxious, the generation unit preferentially generates rescue plans with a high level of urgency. If the user is relaxed, the generation unit preferentially generates rescue plans that include detailed information. If the user is in a hurry, the generation unit preferentially generates rescue plans that include the most important information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI, which may analyze the emotions and determine the priority of rescue plans.

[0096] The generation unit can prioritize generating a highly relevant rescue plan by taking into account the user's geographical location information. For example, the generation unit can acquire the user's geographical location information as GPS data and prioritize generating a highly relevant rescue plan. For example, if the user is in a specific area, the generation unit prioritizes generating a rescue plan related to that area. Furthermore, if the user is moving, the generation unit can also generate an optimal rescue plan based on the user's current location. Furthermore, if the user is in a specific facility, the generation unit can prioritize generating a rescue plan related to that facility. For example, if the user is in a specific facility, the generation unit prioritizes generating a rescue plan related to that facility. This allows for the generation of a highly relevant rescue plan based on the user's geographical location information, thereby generating a more appropriate rescue plan. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information into a generation AI, which can analyze the location information and generate a highly relevant rescue plan.

[0097] The generation unit can analyze the user's social media activities and generate a related rescue plan during generation. The generation unit can, for example, store the user's social media activities in a database and generate a related rescue plan using an analysis algorithm. For example, the generation unit can automatically complete the rescue plan based on location information shared by the user on social media. The generation unit can also prioritize the rescue plan based on emergency information posted by the user on social media. Furthermore, the generation unit can automatically extract relevant information from the user's social media activities and generate a rescue plan. For example, the generation unit can automatically extract relevant information based on information posted by the user on social media and generate a rescue plan. This allows for the generation of a related rescue plan based on the user's social media activities, resulting in a more appropriate rescue plan. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's social media activities into a generation AI, which can analyze the activities and generate a related rescue plan.

[0098] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. The providing unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the user's voice to estimate the emotions. The providing unit can also estimate the user's emotions using text analysis technology. For example, the providing unit analyzes text entered by the user to estimate the emotions. This allows the information to be presented in a way that gives a sense of security, depending on the user's emotions, thereby providing more appropriate information. For example, if the user is feeling anxious, the providing unit provides information in a way that gives a sense of security. If the user is relaxed, the providing unit provides information in a way that includes detailed information. If the user is in a hurry, the providing unit provides information in a concise and quick way. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's emotional data into the generating AI, which can then analyze the emotions and adjust the way the information is presented.

[0099] The providing unit can adjust the level of detail of the information to be provided based on the type and scale of the disaster when providing the information. The providing unit can, for example, store the type and scale of the disaster in a database and adjust the level of detail of the information to be provided using an analysis algorithm. For example, the providing unit can provide information including detailed evacuation information in the case of a large-scale disaster. The providing unit can also provide information including concise evacuation information in the case of a small-scale disaster. Furthermore, the providing unit can provide information including appropriate information depending on the specific disaster (e.g., earthquake, flood). For example, the providing unit provides information on evacuation sites in the case of an earthquake, and information on evacuation to higher ground in the case of a flood. In this way, by adjusting the level of detail of the information to be provided depending on the type and scale of the disaster, more appropriate information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input data on the type and scale of the disaster into the generating AI, which can analyze the data and adjust the level of detail of the information to be provided.

[0100] The providing unit can apply different providing algorithms depending on the user's current situation when providing information. The providing unit can, for example, store the user's current situation in a database and apply different providing algorithms using an analysis algorithm. For example, when the user is indoors, the providing unit provides information about indoor evacuation. Also, when the user is outdoors, the providing unit can provide information about outdoor evacuation. Furthermore, when the user is moving, the providing unit can provide information about evacuation while moving. For example, when the user is moving, the providing unit provides information including an optimal evacuation route. This makes it possible to provide more appropriate information by applying different providing algorithms depending on the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data to a generating AI, which can analyze the data and apply different providing algorithms.

[0101] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. The providing unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The providing unit can also estimate the user's emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotions. The providing unit can also estimate the user's emotions using text analysis technology. For example, the providing unit can analyze text entered by the user to estimate the emotions. This allows the priority of information to be provided based on the user's emotions, thereby providing more important information preferentially. For example, if the user is feeling anxious, the providing unit can prioritize providing information with high urgency. Also, if the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, if the user is in a hurry, the providing unit can prioritize providing the most important information. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's emotional data into the generating AI, which can then analyze the emotions and determine the priority of the information to be provided.

[0102] The providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. The providing unit can, for example, acquire the user's geographical location information as GPS data and prioritize providing highly relevant information. For example, if the user is in a specific area, it can prioritize providing information related to that area. Furthermore, if the user is moving, the providing unit can also provide optimal information based on the user's current location. Furthermore, if the user is in a specific facility, it can prioritize providing information related to that facility. For example, if the user is in a specific facility, it can prioritize providing information related to that facility. This allows for more appropriate information to be provided by providing highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to a generation AI, which can analyze the location information and provide highly relevant information.

[0103] The providing unit can analyze the user's social media activity and provide related information at the time of providing the information. The providing unit can, for example, store the user's social media activity in a database and provide related information using an analysis algorithm. For example, the providing unit can automatically supplement the information based on location information shared by the user on social media. The providing unit can also prioritize information based on emergency information posted by the user on social media. Furthermore, the providing unit can automatically extract and provide related information from the user's social media activity. For example, the providing unit can automatically extract and provide related information based on information posted by the user on social media. This allows for more appropriate information to be provided by providing related information based on the user's social media activity. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's social media activity into a generation AI, which can analyze the activity and provide related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned registration unit, notification unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14 and registers the user's location information and health condition. The notification unit is realized by the control unit 46A of the smart device 14 and sends a notification to the user requesting help in the event of a disaster. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's location information and health condition. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal rescue plan. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated rescue plan to nearby residents and rescue teams. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned registration unit, notification unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214 and registers the user's location information and health condition. The notification unit is realized by the control unit 46A of the smart glasses 214 and transmits a notification that the user is requesting help in the event of a disaster. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's location information and health condition. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal rescue plan. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated rescue plan to nearby residents and rescue teams. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned registration unit, notification unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset type terminal 314 and registers the user's location information and health condition. The notification unit is realized by the control unit 46A of the headset type terminal 314 and transmits a notification that the user is requesting help in the event of a disaster. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's location information and health condition. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal rescue plan. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the generated rescue plan to nearby residents and rescue teams. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned registration unit, notification unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414 and registers the user's location information and health condition. The notification unit is realized by the control unit 46A of the robot 414 and transmits a notification that the user is requesting help in the event of a disaster. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's location information and health condition. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal rescue plan. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated rescue plan to nearby residents and rescue teams.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The disaster support system can also analyze the user's past evacuation behavior data and suggest the optimal evacuation route. For example, based on past evacuation behavior data, it can evaluate the safety and efficiency of the user's previous evacuation routes and suggest the optimal route. It can also identify obstacles and dangerous areas the user encountered during evacuation based on past evacuation behavior data and suggest routes that should be avoided. Furthermore, based on past evacuation behavior data, it can predict the congestion status of evacuation shelters used by the user during evacuation and suggest alternative evacuation shelters to avoid congestion. In this way, by utilizing the user's past evacuation behavior data, safer and more efficient evacuations are possible.

[0106] The disaster support system can also monitor users' health data in real time and provide appropriate medical support in emergencies. For example, it can monitor the user's heart rate and blood pressure and notify medical institutions if any abnormalities are detected. It can also identify necessary medical resources based on the user's health data and provide them to rescue teams. It can also suggest medicines and medical equipment needed during evacuation based on the user's health data. This allows the system to understand the user's health condition in real time and provide appropriate medical support, thereby reducing health risks during disasters.

[0107] The disaster support system can also have a function to help users contact their family and friends. For example, it can provide a function that automatically sends notifications to family and friends in the event of an emergency. It can also share the user's location information with family and friends, allowing them to quickly confirm their safety. It can also provide a chat function that allows users to stay in touch with family and friends while evacuating. This allows users to stay in touch with family and friends in the event of a disaster, providing a sense of security.

[0108] The disaster support system can also register information about the user's pets and provide support for evacuation of the pets. For example, the user can register the type and health condition of the pet and suggest evacuation routes for the pet in the event of a disaster. It can also track the pet's location information and help the pet evacuate safely. It can also suggest supplies necessary for the pet's evacuation (e.g., pet food, medicine, etc.). This helps the user's pet to evacuate safely.

[0109] The disaster support system can also register the user's vehicle information and provide evacuation support using the vehicle. For example, the user can register the vehicle's location information and fuel status, and the system can suggest the optimal evacuation route in the event of a disaster. The system can also analyze the traffic situation during evacuation in real time based on the vehicle's location information and suggest routes that avoid congestion. Furthermore, the system can suggest refueling points that are necessary during evacuation based on the vehicle's fuel status. This allows the system to support the user in evacuating safely using their vehicle.

[0110] The disaster support system can estimate the user's emotions and adjust evacuation support methods based on the estimated emotions. For example, if the user is feeling anxious, the system can send a reassuring message and briefly explain evacuation procedures. If the user is in a state of panic, the system can also suggest breathing exercises and relaxation techniques to help them calm down. Furthermore, if the user is relaxed, the system can provide detailed evacuation information and explain the evacuation plan in more detail. This enables appropriate evacuation support to be provided according to the user's emotions.

[0111] The disaster support system can estimate a user's emotions and provide support at evacuation shelters based on the estimated emotions. For example, if a user feels anxious at the evacuation shelter, the system can provide relaxing music or meditation guides. If the user feels lonely, the system can also provide event information to encourage interaction with other evacuees. Furthermore, if the user feels stressed, the system can suggest activities to reduce stress (e.g., yoga, stretching, etc.). This provides support according to the user's emotions at the evacuation shelter, making evacuation life more comfortable.

[0112] The disaster support system can estimate the user's emotions and adjust the way it provides evacuation information based on the estimated emotions. For example, if the user is feeling anxious, the system can explain evacuation routes using visually easy-to-understand maps and illustrations. If the user is in a state of panic, the system can also use audio guidance to explain how to evacuate calmly. Furthermore, if the user is relaxed, the system can provide detailed text information and explain the evacuation plan in more detail. This makes it possible to provide appropriate evacuation information according to the user's emotions.

[0113] The disaster support system can estimate the user's emotions and adjust the evacuation shelter layout based on the estimated emotions. For example, if the user is feeling anxious, the system can suggest quiet areas or spaces where they can relax. If the user is feeling sociable, the system can also suggest areas where it is easy to interact with other evacuees. Furthermore, if the user is feeling stressed, the system can suggest areas where stress-reducing activities can be carried out. This makes it possible to appropriately arrange evacuation shelters according to the user's emotions.

[0114] The disaster support system can estimate the user's emotions and adjust the food provided at evacuation shelters based on the estimated emotions. For example, if the user is feeling anxious, the system can suggest meals using ingredients that have a relaxing effect. Also, if the user is feeling stressed, the system can suggest meals using ingredients that have a stress-reducing effect. Furthermore, if the user is relaxed, the system can suggest nutritionally balanced meals. This makes it possible to provide appropriate meals at evacuation shelters according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The registration unit registers the user's status. The user's status includes, for example, health status, location information, and urgency. The user can register their location information as GPS data or enter detailed information about their health status. Step 2: The notification unit sends a notification to the user requesting help in the event of a disaster. The notification can be sent in the form of a text message, voice alert, or push notification. By sending a notification saying "Come help me!", the user can ensure prompt rescue. Step 3: The analysis unit performs analysis based on the information from the notification unit. The analysis unit uses a data analysis algorithm to analyze the user's location information and health condition. Using the generation AI, the unit analyzes the user's situation in real time and generates an optimal rescue plan. Step 4: The generator generates a rescue plan based on the analysis results. The generator generates a rescue plan that includes rescue routes, required resources, priorities, etc. The generator uses a generation AI to generate an efficient rescue plan. Step 5: The provider provides the generated rescue plan to nearby residents and rescue teams. The provider provides the rescue plan in digital format, paper format, audio guide format, etc. It provides nearby residents with the location information of people who are unable to escape, urging them to quickly come to their aid. It also suggests efficient rescue routes to rescue teams, supporting a rapid response.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 registration unit for registering the user's status; a notification unit that sends a notification in the event of a disaster; an analysis unit that analyzes the notification sent by the notification unit; a generation unit that generates a rescue plan based on the information analyzed by the analysis unit; a providing unit that provides the rescue plan generated by the generating unit. A system characterized by:

2. The registration unit Registering the user's location or status 2. The system of claim 1.

3. The notification unit A user sends a notification asking for help 2. The system of claim 1.

4. The analysis unit Analysis is performed based on the information from the notification unit.

2. The system of claim 1.

5. The generation unit Generate a rescue plan based on the analysis results 2. The system of claim 1.

6. The providing unit Provide the generated rescue plan to neighbors or rescue teams 2. The system of claim 1.

7. The providing unit Providing local residents with location information for people who are unable to escape, and urging them to quickly come to their aid 2. The system of claim 1.

8. The providing unit Propose efficient rescue routes to rescue teams 2. The system of claim 1.

Images