system

Abstract

The system according to this embodiment aims to provide safe evacuation routes and optimal evacuation locations in real time during a disaster. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects data. The analysis unit integrates and analyzes the data collected by the data collection unit. The proposal unit proposes evacuation routes based on the analysis results obtained by the analysis unit. The provision unit provides evacuation advice to the user in natural language.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, providing a safe evacuation route and an optimal evacuation place in real time during a disaster has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a safe evacuation route and an optimal evacuation place in real time during a disaster.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects data. The analysis unit integrates and analyzes the data collected by the data collection unit. The proposal unit proposes evacuation routes based on the analysis results obtained by the analysis unit. The provision unit provides evacuation advice to the user in natural language. [Effects of the Invention]

[0007] The system according to this embodiment can provide safe evacuation routes and optimal evacuation locations in real time during a disaster. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​disaster response navigation system according to an embodiment of the present invention is a system that integrates and analyzes multiple data sources during a disaster to provide users with safe evacuation routes and optimal evacuation locations in real time. The AI ​​disaster response navigation system collects the user's location information, weather information, population movement data, etc., and the AI ​​integrates and analyzes this data to provide users with easy-to-understand evacuation advice in natural language in real time. This reduces confusion during disasters and enables a rapid response. For example, the AI ​​disaster response navigation system uses the GPS function of a smartphone to determine the user's current location. Next, it obtains the current weather and forecast from a weather data service. Furthermore, it obtains data to understand the movement of people in a specific area. The AI ​​integrates and analyzes this data to determine safe evacuation routes and optimal evacuation locations during a disaster. For example, in the event of an earthquake, the AI ​​proposes the optimal evacuation route, taking into account the user's current location, the location of evacuation shelters, the safety of evacuation routes, and the congestion status of evacuation shelters. Furthermore, the AI ​​provides users with easy-to-understand evacuation advice in natural language. For example, it provides specific instructions in real time such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." This allows users to evacuate quickly and without hesitation. The AI ​​disaster response navigation system, with the assistance of AI that integrates and analyzes multiple data sources, can determine the optimal evacuation route and location in real time. For example, if an evacuation center is crowded, the AI ​​will suggest an alternative center, enabling evacuation that avoids congestion. Furthermore, by suggesting evacuation routes that take weather information into account, it is possible to evacuate safely even in bad weather. In this way, the AI ​​disaster response navigation system is a groundbreaking mechanism to support rapid and safe evacuation during disasters. By receiving evacuation advice provided in real time, users can reduce confusion during disasters and respond quickly. Thus, the AI ​​disaster response navigation system can support rapid and safe evacuation during disasters.

[0029] The AI ​​disaster response navigation system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects user location information, weather information, and population movement data. User location information includes, but is not limited to, GPS data and Wi-Fi location information. The data collection unit can, for example, use the GPS function of a smartphone to determine the user's current location. The data collection unit can also obtain current weather and forecasts from weather data provision services. For example, it can obtain data from the Japan Meteorological Agency or real-time updated weather information. Furthermore, the data collection unit can obtain data to understand the movement of people in a specific area. For example, it can collect mobile phone location data and traffic data. The analysis unit integrates and analyzes the data collected by the data collection unit. The analysis unit integrates the data using, for example, data normalization and database integration methods. Based on the collected data, the analysis unit identifies safe evacuation routes and optimal evacuation locations in the event of a disaster. For example, in the event of an earthquake, the analysis unit proposes the optimal evacuation route, taking into account the user's current location, the location of evacuation shelters, the safety of evacuation routes, and the congestion status of evacuation shelters. The proposal unit proposes an evacuation route based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes the optimal evacuation route to the user based on the analysis results. For example, it provides specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." The provision unit provides evacuation advice to the user in natural language. For example, the provision unit uses generative AI to provide the user with easy-to-understand evacuation advice in natural language in real time. For example, it provides specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." As a result, the AI ​​disaster response navigation system according to this embodiment can integrate and analyze multiple data sources during a disaster and provide the user with a safe evacuation route and the optimal evacuation location in real time. Some or all of the above-described processes in the collection unit, analysis unit, proposal unit, and provision unit may be performed using AI, for example, or without using AI.For example, the data collection unit acquires the user's location information, the analysis unit integrates and analyzes the collected data, the proposal unit proposes evacuation routes based on the analysis results, and the provision unit provides evacuation advice to the user in natural language.

[0030] The data collection unit collects user location information, weather information, and population movement data. User location information includes, but is not limited to, GPS data and Wi-Fi location information. For example, the data collection unit uses the GPS function of a smartphone to determine the user's current location. Specifically, it uses the GPS function of a smartphone to obtain the user's latitude and longitude information in real time and transmits it to a central server. In addition, by using Wi-Fi location information, the user's location can be determined even indoors or in places where the GPS signal is weak. Furthermore, the data collection unit can obtain current weather and forecasts from services that provide weather data. For example, it obtains data from the Japan Meteorological Agency and real-time updated weather information. This allows the data collection unit to understand current weather conditions and future forecasts and provide basic data for assessing disaster risks. Furthermore, the data collection unit can obtain data to understand people's movement in specific areas. For example, it collects mobile phone location data and traffic data. This allows the data collection unit to understand population density and people's movement in specific areas and predict congestion on evacuation routes. This data is collected in real time and stored in a central database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit integrates and analyzes the data collected by the collection unit. The analysis unit integrates the data using methods such as data normalization and database integration. Specifically, it converts data collected from different formats and sources into a consistent format and stores it in a unified database. This allows the analysis unit to efficiently process the collected data and improve the accuracy of the analysis. Furthermore, based on the collected data, the analysis unit identifies safe evacuation routes and optimal evacuation locations during disasters. For example, in the event of an earthquake, the analysis unit proposes the optimal evacuation route considering the user's current location, the location of evacuation shelters, the safety of evacuation routes, and the congestion level of evacuation shelters. The analysis unit uses AI to analyze this data and simulate multiple scenarios to identify the safest and most efficient evacuation route. For example, it uses image recognition technology to analyze real-time camera footage and detect road conditions and obstacles. It also analyzes population flow data to predict congestion levels on evacuation routes. Additionally, it analyzes weather data to propose evacuation routes that consider future weather changes. This allows the analysis unit to quickly and accurately analyze the collected data and provide information to minimize risks during disasters. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions and time periods based on past disaster data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The proposal unit proposes evacuation routes based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes the optimal evacuation route to the user based on the analysis results. Specifically, it calculates the optimal evacuation route considering the user's current location, the location of the evacuation shelter, the safety of the evacuation route, and the congestion level of the evacuation shelter. For example, it provides specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." The proposal unit uses AI to analyze this data and simulate multiple scenarios to identify the safest and most efficient evacuation route. For example, it calculates the shortest distance evacuation route based on the user's current location and the location of the evacuation shelter. It also evaluates the safety of the evacuation route and proposes a route that avoids obstacles and dangerous areas. Furthermore, it considers the congestion level of the evacuation shelter and proposes an alternative route to avoid congestion. In this way, the proposal unit can provide users with specific and practical evacuation routes, minimizing risks during disasters. In addition, the proposal unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposals. For example, it can revise evacuation routes and improve the proposals based on feedback from users who have received evacuation route proposals. Furthermore, the proposal department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the proposal department to quickly and reliably propose evacuation routes to users, minimizing risks during disasters.

[0033] The service provider will provide evacuation advice to users in natural language. For example, using generative AI, the service provider will provide users with easy-to-understand evacuation advice in natural language in real time. Specifically, the generative AI will generate appropriate evacuation advice for users based on data provided by the analysis and proposal departments. For example, it will provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." The generative AI will generate optimal evacuation advice considering the user's current location, the location of the evacuation shelter, the safety of the evacuation route, and the congestion level of the shelter. Furthermore, the service provider can continuously improve the accuracy and effectiveness of the advice by collecting user feedback and using it as training data for the generative AI. For example, based on feedback from users who have received evacuation advice, the generative AI's algorithm will be adjusted to provide more appropriate advice. The service provider can also reliably transmit information using multiple communication methods. For example, important information will be reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the service provider to provide users with evacuation advice quickly and reliably, minimizing risks during disasters. Furthermore, the service provider can offer individually customized advice tailored to each user's situation and needs. For example, they can provide more detailed and specific advice to users who require special assistance, such as the elderly or people with disabilities. This allows the service provider to provide appropriate evacuation advice to all users and minimize risks during disasters.

[0034] The data collection unit can collect user location information, weather information, and population movement data. For example, the data collection unit can collect user location information as GPS data. For example, the data collection unit can use the GPS function of a smartphone to determine the user's current location. The data collection unit can also collect weather information from weather data provision services. For example, the data collection unit can obtain data from the Japan Meteorological Agency or real-time updated weather information. Furthermore, the data collection unit can collect population movement data as mobile phone location information data or traffic data. For example, the data collection unit can obtain data to understand the movement of people in a specific area. As a result, by collecting user location information, weather information, and population movement data, the data collection unit can provide data to identify safe evacuation routes and optimal evacuation locations in the event of a disaster. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can obtain user location information, collect weather information, and collect population movement data.

[0035] The analysis unit can integrate the collected data to identify safe evacuation routes and optimal evacuation locations during a disaster. For example, the analysis unit normalizes the collected data and integrates it into a database. For example, the analysis unit integrates user location information, weather information, and population flow data to identify safe evacuation routes and optimal evacuation locations during a disaster. Furthermore, the analysis unit can identify safe evacuation routes and optimal evacuation locations during a disaster based on the collected data. For example, in the event of an earthquake, the analysis unit proposes the optimal evacuation route considering the user's current location, the location of the evacuation center, the safety of the evacuation route, and the congestion level of the evacuation center. Furthermore, the analysis unit can identify safe evacuation routes and optimal evacuation locations during a disaster based on the collected data. For example, the analysis unit proposes the optimal evacuation route considering the user's current location, the location of the evacuation center, the safety of the evacuation route, and the congestion level of the evacuation center. In this way, the analysis unit can identify safe evacuation routes and optimal evacuation locations during a disaster by integrating the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit integrates the collected data to identify safe evacuation routes and optimal evacuation locations during a disaster.

[0036] The suggestion unit can propose evacuation routes based on the analysis results. For example, the suggestion unit can propose the optimal evacuation route to the user based on the analysis results. For example, the suggestion unit can provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." The suggestion unit can also propose the optimal evacuation route to the user based on the analysis results. For example, the suggestion unit can provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." Furthermore, the suggestion unit can propose the optimal evacuation route to the user based on the analysis results. For example, the suggestion unit can provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." In this way, the suggestion unit can provide the user with the optimal evacuation route by proposing an evacuation route based on the analysis results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the proposal department suggests the optimal evacuation route to the user based on the analysis results.

[0037] The service provider can provide evacuation advice to users in natural language. For example, using generative AI, the service provider can provide users with easy-to-understand evacuation advice in natural language in real time. For instance, the service provider can provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." Furthermore, the service provider can provide users with easy-to-understand evacuation advice in natural language in real time using generative AI. For example, the service provider can provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." In addition, the service provider can provide users with easy-to-understand evacuation advice in natural language in real time using generative AI. For example, the service provider can provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." This allows the service provider to help users evacuate quickly by providing evacuation advice in natural language. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit may use generative AI to provide users with easy-to-understand evacuation advice in natural language in real time.

[0038] The analysis unit can grasp the congestion status of evacuation shelters and identify evacuation destinations that avoid congestion. For example, the analysis unit uses people counting and real-time data to grasp the congestion status of evacuation shelters in real time. For example, the analysis unit uses sensors and cameras installed in evacuation shelters to grasp the congestion status of evacuation shelters. The analysis unit can also grasp the congestion status of evacuation shelters and identify evacuation destinations that avoid congestion. For example, the analysis unit suggests evacuation destinations that avoid congestion to the user based on the congestion status of the evacuation shelters. Furthermore, the analysis unit can grasp the congestion status of evacuation shelters and identify evacuation destinations that avoid congestion. For example, the analysis unit suggests evacuation destinations that avoid congestion to the user based on the congestion status of the evacuation shelters. In this way, the analysis unit can identify evacuation destinations that avoid congestion by grasping the congestion status of evacuation shelters. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit grasps the congestion status of evacuation shelters and identifies evacuation destinations that avoid congestion.

[0039] The analysis unit can identify evacuation routes that take weather information into consideration. For example, the analysis unit collects weather information from a weather data provision service and considers it when identifying evacuation routes. For example, the analysis unit obtains data from the Japan Meteorological Agency or real-time updated weather information and identifies evacuation routes. Furthermore, the analysis unit can identify evacuation routes that take weather information into consideration. For example, the analysis unit proposes a safe evacuation route to the user based on the weather information. In addition, the analysis unit can identify evacuation routes that take weather information into consideration. For example, the analysis unit proposes a safe evacuation route to the user based on the weather information. As a result, by considering weather information, the analysis unit can identify safe evacuation routes even in bad weather. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit considers weather information and identifies evacuation routes.

[0040] The data collection unit can analyze the user's past location history and select the optimal data collection method. For example, the data collection unit can collect and analyze the user's past location history as GPS data. For example, the data collection unit can identify important data collection points based on places the user has frequently visited in the past. The data collection unit can also analyze the user's past location history and select the optimal data collection method. For example, the data collection unit can analyze the user's past movement patterns and set an efficient data collection route. Furthermore, the data collection unit can analyze the user's past location history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data on evacuation routes based on places the user has evacuated to in the past. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past location history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit analyzes the user's past location history and selects the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can collect data on the user's current activities, such as physical activity levels and current location, and then filter the data. For example, if the user is currently evacuating, the data collection unit will prioritize collecting data related to evacuation routes. The data collection unit can also collect data on the user's areas of interest, such as past search history and social media activity, and then filter the data. For example, if the user is interested in a particular region, the data collection unit will prioritize collecting disaster information for that region. Furthermore, the data collection unit can filter data based on the user's current activities and areas of interest. For example, if the user is with their family, the data collection unit will collect data that takes into account the safety of the entire family. In this way, the data collection unit can collect highly relevant data by filtering the data based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit filters data based on the user's current activities and areas of interest.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect the user's geographical location information as GPS data or Wi-Fi location information and prioritize the collection of highly relevant data. For example, the data collection unit prioritizes the collection of disaster information for the area where the user is currently located. The data collection unit can also prioritize the collection of highly relevant data by considering the user's geographical location information. For example, the data collection unit prioritizes the collection of information about the area the user is moving to. Furthermore, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information. For example, the data collection unit prioritizes the collection of information about evacuation shelters around the user's home. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit prioritizes the collection of highly relevant data by considering the user's geographical location information.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect and analyze data such as the content of posts and the trends of followers from a user's social media activity. For example, the data collection unit can collect relevant disaster information based on location information shared by a user on social media. The data collection unit can also analyze a user's social media activity and collect relevant data. For example, the data collection unit can analyze the content of posts from accounts that a user follows and collect relevant data. Furthermore, the data collection unit can analyze a user's social media activity and collect relevant data. For example, the data collection unit can collect relevant data based on the activity status of groups that a user participates in. In this way, the data collection unit can collect relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit analyzes a user's social media activity and collects relevant data.

[0044] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, the analysis unit can analyze the safety of evacuation routes by combining user location information and weather information. For example, the analysis unit evaluates the safety of evacuation routes based on the user's current location and weather information. The analysis unit can also analyze the congestion status of evacuation shelters by combining population flow data and shelter location information. For example, the analysis unit evaluates the congestion status of evacuation shelters based on population flow data and shelter location information. Furthermore, the analysis unit can analyze the optimal evacuation route by combining the user's past movement history and current location information. For example, the analysis unit proposes the optimal evacuation route based on the user's past movement history and current location information. In this way, the analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit improves the accuracy of its analysis by considering the interrelationships between data.

[0045] The analysis unit can perform analysis while considering the user's attribute information. For example, the analysis unit can analyze the optimal evacuation route by considering the user's age and health condition. For example, the analysis unit can evaluate the safety of the evacuation route based on the user's age and health condition. The analysis unit can also analyze a route that allows all family members to evacuate safely by considering the user's family structure. For example, the analysis unit can propose a route that allows all family members to evacuate safely based on the user's family structure. Furthermore, the analysis unit can analyze an evacuation route from the workplace by considering the user's occupation and workplace. For example, the analysis unit can propose an evacuation route from the workplace based on the user's occupation and workplace. In this way, the analysis unit can provide more appropriate analysis results by considering the user's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit performs analysis while considering the user's attribute information.

[0046] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can prioritize the analysis of disaster information around the user's current location. For example, the analysis unit can analyze disaster information based on map data of the user's current location. The analysis unit can also analyze information about the user's destination region. For example, the analysis unit can analyze disaster information based on map data of the user's destination region. Furthermore, the analysis unit can analyze information about evacuation shelters around the user's home. For example, the analysis unit can analyze evacuation shelter information based on map data of the user's home. By doing so, the analysis unit can provide more appropriate analysis results by considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit performs analysis while considering the geographical distribution of the data.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit performs analysis by referring to the latest disaster response guidelines. For example, the analysis unit evaluates the safety of evacuation routes based on the latest disaster response guidelines. The analysis unit can also perform analysis by referring to past disaster cases. For example, the analysis unit evaluates the safety of evacuation routes based on past disaster cases. Furthermore, the analysis unit can perform analysis by referring to expert opinions. For example, the analysis unit evaluates the safety of evacuation routes based on expert opinions. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit improves the accuracy of its analysis by referring to relevant literature.

[0048] The proposal department can adjust the level of detail in its proposals based on the importance of the evacuation routes. For example, the proposal department evaluates the importance of evacuation routes based on criteria such as risk assessment and route safety, and adjusts the level of detail in its proposals. For example, the proposal department provides detailed proposals for major evacuation routes. The proposal department can also adjust the level of detail in its proposals based on the importance of the evacuation routes. For example, the proposal department provides concise proposals for auxiliary evacuation routes. Furthermore, the proposal department can adjust the level of detail in its proposals based on the importance of the evacuation routes. For example, in an emergency, the proposal department prioritizes proposing the most important evacuation routes. In this way, the proposal department can provide more appropriate proposals by adjusting the level of detail in its proposals based on the importance of the evacuation routes. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department adjusts the level of detail in its proposals based on the importance of the evacuation routes.

[0049] The proposal unit can apply different proposal algorithms depending on the category of the evacuation route when making a proposal. For example, the proposal unit classifies evacuation route categories based on criteria such as urgency, distance, and type of evacuation site, and applies different proposal algorithms. For example, the proposal unit applies an earthquake-response proposal algorithm during an earthquake. Furthermore, the proposal unit can apply different proposal algorithms depending on the category of the evacuation route. For example, the proposal unit applies a flood-response proposal algorithm during a flood. In addition, the proposal unit can apply different proposal algorithms depending on the category of the evacuation route. For example, the proposal unit applies a typhoon-response proposal algorithm during a typhoon. In this way, the proposal unit can provide more appropriate proposals by applying different proposal algorithms depending on the category of the evacuation route. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit applies different proposal algorithms depending on the category of the evacuation route.

[0050] The proposal department can determine the priority of proposals based on the timing of evacuation route submissions. For example, the proposal department evaluates the timing of evacuation route submissions based on criteria such as the time of disaster occurrence or the time of evacuation order issuance, and determines the priority of proposals. For example, in an emergency, the proposal department will prioritize proposing the most important evacuation routes. The proposal department can also determine the priority of proposals based on the timing of evacuation route submissions. For example, in normal circumstances, the proposal department will propose detailed evacuation routes. Furthermore, the proposal department can determine the priority of proposals based on the timing of evacuation route submissions. For example, immediately after a disaster occurs, the proposal department will propose rapid evacuation routes. In this way, the proposal department can provide more appropriate proposals by determining the priority of proposals based on the timing of evacuation route submissions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department determines the priority of proposals based on the timing of evacuation route submissions.

[0051] The proposal unit can adjust the order of proposals based on the relevance of evacuation routes. For example, the proposal unit evaluates the relevance of evacuation routes based on criteria such as the degree of route overlap and the commonality of evacuation locations, and adjusts the order of proposals accordingly. For example, the proposal unit proposes the most relevant evacuation route first. Furthermore, the proposal unit can adjust the order of proposals based on the relevance of evacuation routes. For example, the proposal unit postpones less relevant evacuation routes. In addition, the proposal unit can adjust the order of proposals based on the relevance of evacuation routes. For example, in an emergency, the proposal unit prioritizes proposing the most relevant evacuation route. This allows the proposal unit to provide more appropriate proposals by adjusting the order of proposals based on the relevance of evacuation routes. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit adjusts the order of proposals based on the relevance of evacuation routes.

[0052] The service provider can provide optimal advice by referring to the user's past evacuation history when providing evacuation advice. For example, the service provider collects and refers to the user's past evacuation history as data such as past evacuation routes and evacuation locations. For example, the service provider suggests the optimal evacuation location based on the places the user has evacuated to in the past. Furthermore, the service provider can provide optimal advice by referring to the user's past evacuation history when providing evacuation advice. For example, the service provider suggests the optimal evacuation route by referring to the user's past evacuation routes. In addition, the service provider can provide optimal advice by referring to the user's past evacuation history when providing evacuation advice. For example, the service provider analyzes the user's past evacuation history and provides the most efficient evacuation advice. In this way, the service provider can provide optimal evacuation advice by referring to the user's past evacuation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider provides optimal advice by referring to the user's past evacuation history.

[0053] The service provider can customize the content of evacuation advice based on the user's current situation when providing it. For example, the service provider can collect data on the user's current situation, such as their current location and surrounding conditions, and customize the content of the advice. For example, if the user is currently evacuating, the service provider will suggest a route to the nearest evacuation shelter. The service provider can also customize the content of evacuation advice based on the user's current situation when providing it. For example, if the user is at home, the service provider will suggest an evacuation route from home. Furthermore, the service provider can customize the content of evacuation advice based on the user's current situation when providing it. For example, if the user is using public transportation, the service provider will suggest a route to the nearest evacuation shelter. In this way, the service provider can provide more appropriate evacuation advice by customizing the content of the advice based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider customizes the content of the advice based on the user's current situation.

[0054] The service provider can provide optimal advice when offering evacuation advice, taking into account the user's geographical location information. For example, the service provider can collect the user's geographical location information as GPS data or Wi-Fi location information and provide optimal advice. For example, the service provider can prioritize providing information on evacuation shelters in the area where the user is currently located. Furthermore, the service provider can provide optimal advice when offering evacuation advice, taking into account the user's geographical location information. For example, the service provider can provide evacuation advice regarding the area the user is moving to. In addition, the service provider can provide optimal advice when offering evacuation advice, taking into account the user's geographical location information. For example, the service provider can provide information on evacuation shelters around the user's home. This allows the service provider to provide more appropriate evacuation advice by taking into account the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can provide optimal advice by taking into account the user's geographical location information.

[0055] The service provider can analyze the user's social media activity and adjust the content of the evacuation advice when providing it. For example, the service provider can collect and analyze data on the user's social media activity, such as analysis of posted content and follower trends. For example, the service provider can provide relevant evacuation advice based on location information shared by the user on social media. Furthermore, the service provider can analyze the user's social media activity and adjust the content of the evacuation advice when providing it. For example, the service provider can analyze the content of posts from accounts the user follows and provide relevant evacuation advice. In addition, the service provider can analyze the user's social media activity and adjust the content of the evacuation advice when providing it. For example, the service provider can provide relevant evacuation advice based on the activity status of groups the user participates in. In this way, the service provider can provide more appropriate evacuation advice by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can analyze the user's social media activity and adjust the content of the advice.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The AI ​​disaster response navigation system can also include a health management unit that monitors the user's health status. This unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and provides it to the analysis unit. For example, if a user's heart rate suddenly increases during evacuation, the analysis unit can take this data into consideration and suggest a safer and faster evacuation route. The health management unit can also select evacuation shelters based on the user's health status. For instance, if a user has a pre-existing medical condition, it can prioritize suggesting shelters with nearby medical facilities. Furthermore, the health management unit can monitor the user's health status in real time and contact medical institutions as needed. This allows the AI ​​disaster response navigation system to provide evacuation support that takes the user's health status into consideration.

[0058] The analysis unit can analyze a user's past evacuation history and identify the optimal evacuation route. For example, it can suggest an efficient evacuation route based on the locations and routes the user has previously evacuated to. Furthermore, based on past evacuation history, the analysis unit can predict the congestion level of evacuation shelters and suggest evacuation destinations that avoid congestion. In addition, the analysis unit can analyze the user's past evacuation history and evaluate the safety of evacuation routes. This allows the analysis unit to identify a more appropriate evacuation route by considering the user's past evacuation history.

[0059] The analysis unit can understand the congestion level of evacuation shelters and identify evacuation destinations that avoid congestion. For example, the analysis unit uses sensors and cameras installed in evacuation shelters to understand the congestion level in real time. The analysis unit can also suggest evacuation destinations that avoid congestion to users based on the congestion level of the evacuation shelters. Furthermore, the analysis unit can predict the congestion level of evacuation shelters and identify evacuation destinations that avoid congestion in advance. In this way, the analysis unit can identify evacuation destinations that avoid congestion by understanding the congestion level of evacuation shelters.

[0060] The data collection unit can analyze the user's past location history and select the optimal data collection method. For example, the unit can identify important data collection points based on places the user has frequently visited in the past. It can also analyze the user's past movement patterns and set efficient data collection routes. Furthermore, the unit can prioritize data collection for evacuation routes, referencing places the user has evacuated to in the past. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past location history.

[0061] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, it can combine user location information and weather information to analyze the safety of evacuation routes. It can also combine population flow data and evacuation shelter location information to analyze the congestion status of evacuation shelters. Furthermore, it can combine a user's past travel history with their current location information to analyze the optimal evacuation route. In this way, the analysis unit can improve the accuracy of its analysis by considering the interrelationships between data.

[0062] The proposal department can adjust the level of detail in its proposals based on the importance of the evacuation routes. For example, it can evaluate the importance of evacuation routes based on criteria such as risk assessment and route safety, and adjust the level of detail accordingly. It can also provide detailed proposals for major evacuation routes and concise proposals for secondary routes. Furthermore, in emergencies, it can prioritize proposing the most important evacuation routes. In this way, the proposal department can provide more appropriate proposals by adjusting the level of detail based on the importance of the evacuation routes.

[0063] The service provider can provide optimal evacuation advice by referring to the user's past evacuation history. For example, it can suggest the most suitable evacuation location based on the user's past evacuation routes and locations. It can also suggest the most suitable evacuation route by referring to the user's past evacuation routes. Furthermore, it can analyze the user's past evacuation history to provide the most efficient evacuation advice. In this way, the service provider can provide optimal evacuation advice by referring to the user's past evacuation history.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects user location information, weather information, and population movement data. For example, it uses the smartphone's GPS function to determine the user's current location, obtains current weather and forecasts from weather data services, and collects mobile phone location data and traffic data. Step 2: The analysis unit integrates and analyzes the data collected by the collection unit. For example, it integrates the data using data normalization and database integration methods to identify safe evacuation routes and optimal evacuation locations during disasters. Step 3: The proposal unit proposes an evacuation route based on the analysis results obtained by the analysis unit. For example, it provides specific instructions such as, "The nearest evacuation shelter from your current location is XX. The evacuation route is via XX Street, then turn right at XX Park." Step 4: The service provider will provide evacuation advice to the user in natural language. For example, it will use generative AI to provide the user with easy-to-understand evacuation advice in natural language in real time.

[0066] (Example of form 2) The AI ​​disaster response navigation system according to an embodiment of the present invention is a system that integrates and analyzes multiple data sources during a disaster to provide users with safe evacuation routes and optimal evacuation locations in real time. The AI ​​disaster response navigation system collects the user's location information, weather information, population movement data, etc., and the AI ​​integrates and analyzes this data to provide users with easy-to-understand evacuation advice in natural language in real time. This reduces confusion during disasters and enables a rapid response. For example, the AI ​​disaster response navigation system uses the GPS function of a smartphone to determine the user's current location. Next, it obtains the current weather and forecast from a weather data service. Furthermore, it obtains data to understand the movement of people in a specific area. The AI ​​integrates and analyzes this data to determine safe evacuation routes and optimal evacuation locations during a disaster. For example, in the event of an earthquake, the AI ​​proposes the optimal evacuation route, taking into account the user's current location, the location of evacuation shelters, the safety of evacuation routes, and the congestion status of evacuation shelters. Furthermore, the AI ​​provides users with easy-to-understand evacuation advice in natural language. For example, it provides specific instructions in real time such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." This allows users to evacuate quickly and without hesitation. The AI ​​disaster response navigation system, with the assistance of AI that integrates and analyzes multiple data sources, can determine the optimal evacuation route and location in real time. For example, if an evacuation center is crowded, the AI ​​will suggest an alternative center, enabling evacuation that avoids congestion. Furthermore, by suggesting evacuation routes that take weather information into account, it is possible to evacuate safely even in bad weather. In this way, the AI ​​disaster response navigation system is a groundbreaking mechanism to support rapid and safe evacuation during disasters. By receiving evacuation advice provided in real time, users can reduce confusion during disasters and respond quickly. Thus, the AI ​​disaster response navigation system can support rapid and safe evacuation during disasters.

[0067] The AI ​​disaster response navigation system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a provision unit. The data collection unit collects user location information, weather information, and population movement data. User location information includes, but is not limited to, GPS data and Wi-Fi location information. The data collection unit can, for example, use the GPS function of a smartphone to determine the user's current location. The data collection unit can also obtain current weather and forecasts from weather data provision services. For example, it can obtain data from the Japan Meteorological Agency or real-time updated weather information. Furthermore, the data collection unit can obtain data to understand the movement of people in a specific area. For example, it can collect mobile phone location data and traffic data. The analysis unit integrates and analyzes the data collected by the data collection unit. The analysis unit integrates the data using, for example, data normalization and database integration methods. Based on the collected data, the analysis unit identifies safe evacuation routes and optimal evacuation locations in the event of a disaster. For example, in the event of an earthquake, the analysis unit proposes the optimal evacuation route, taking into account the user's current location, the location of evacuation shelters, the safety of evacuation routes, and the congestion status of evacuation shelters. The proposal unit proposes an evacuation route based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes the optimal evacuation route to the user based on the analysis results. For example, it provides specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." The provision unit provides evacuation advice to the user in natural language. For example, the provision unit uses generative AI to provide the user with easy-to-understand evacuation advice in natural language in real time. For example, it provides specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." As a result, the AI ​​disaster response navigation system according to this embodiment can integrate and analyze multiple data sources during a disaster and provide the user with a safe evacuation route and the optimal evacuation location in real time. Some or all of the above-described processes in the collection unit, analysis unit, proposal unit, and provision unit may be performed using AI, for example, or without using AI.For example, the data collection unit acquires the user's location information, the analysis unit integrates and analyzes the collected data, the proposal unit proposes evacuation routes based on the analysis results, and the provision unit provides evacuation advice to the user in natural language.

[0068] The data collection unit collects user location information, weather information, and population movement data. User location information includes, but is not limited to, GPS data and Wi-Fi location information. For example, the data collection unit uses the GPS function of a smartphone to determine the user's current location. Specifically, it uses the GPS function of a smartphone to obtain the user's latitude and longitude information in real time and transmits it to a central server. In addition, by using Wi-Fi location information, the user's location can be determined even indoors or in places where the GPS signal is weak. Furthermore, the data collection unit can obtain current weather and forecasts from services that provide weather data. For example, it obtains data from the Japan Meteorological Agency and real-time updated weather information. This allows the data collection unit to understand current weather conditions and future forecasts and provide basic data for assessing disaster risks. Furthermore, the data collection unit can obtain data to understand people's movement in specific areas. For example, it collects mobile phone location data and traffic data. This allows the data collection unit to understand population density and people's movement in specific areas and predict congestion on evacuation routes. This data is collected in real time and stored in a central database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0069] The analysis unit integrates and analyzes the data collected by the collection unit. The analysis unit integrates the data using methods such as data normalization and database integration. Specifically, it converts data collected from different formats and sources into a consistent format and stores it in a unified database. This allows the analysis unit to efficiently process the collected data and improve the accuracy of the analysis. Furthermore, based on the collected data, the analysis unit identifies safe evacuation routes and optimal evacuation locations during disasters. For example, in the event of an earthquake, the analysis unit proposes the optimal evacuation route considering the user's current location, the location of evacuation shelters, the safety of evacuation routes, and the congestion level of evacuation shelters. The analysis unit uses AI to analyze this data and simulate multiple scenarios to identify the safest and most efficient evacuation route. For example, it uses image recognition technology to analyze real-time camera footage and detect road conditions and obstacles. It also analyzes population flow data to predict congestion levels on evacuation routes. Additionally, it analyzes weather data to propose evacuation routes that consider future weather changes. This allows the analysis unit to quickly and accurately analyze the collected data and provide information to minimize risks during disasters. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions and time periods based on past disaster data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0070] The proposal unit proposes evacuation routes based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes the optimal evacuation route to the user based on the analysis results. Specifically, it calculates the optimal evacuation route considering the user's current location, the location of the evacuation shelter, the safety of the evacuation route, and the congestion level of the evacuation shelter. For example, it provides specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." The proposal unit uses AI to analyze this data and simulate multiple scenarios to identify the safest and most efficient evacuation route. For example, it calculates the shortest distance evacuation route based on the user's current location and the location of the evacuation shelter. It also evaluates the safety of the evacuation route and proposes a route that avoids obstacles and dangerous areas. Furthermore, it considers the congestion level of the evacuation shelter and proposes an alternative route to avoid congestion. In this way, the proposal unit can provide users with specific and practical evacuation routes, minimizing risks during disasters. In addition, the proposal unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposals. For example, it can revise evacuation routes and improve the proposals based on feedback from users who have received evacuation route proposals. Furthermore, the proposal department can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. This allows the proposal department to quickly and reliably propose evacuation routes to users, minimizing risks during disasters.

[0071] The service provider will provide evacuation advice to users in natural language. For example, using generative AI, the service provider will provide users with easy-to-understand evacuation advice in natural language in real time. Specifically, the generative AI will generate appropriate evacuation advice for users based on data provided by the analysis and proposal departments. For example, it will provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." The generative AI will generate optimal evacuation advice considering the user's current location, the location of the evacuation shelter, the safety of the evacuation route, and the congestion level of the shelter. Furthermore, the service provider can continuously improve the accuracy and effectiveness of the advice by collecting user feedback and using it as training data for the generative AI. For example, based on feedback from users who have received evacuation advice, the generative AI's algorithm will be adjusted to provide more appropriate advice. The service provider can also reliably transmit information using multiple communication methods. For example, important information will be reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the service provider to provide users with evacuation advice quickly and reliably, minimizing risks during disasters. Furthermore, the service provider can offer individually customized advice tailored to each user's situation and needs. For example, they can provide more detailed and specific advice to users who require special assistance, such as the elderly or people with disabilities. This allows the service provider to provide appropriate evacuation advice to all users and minimize risks during disasters.

[0072] The data collection unit can collect user location information, weather information, and population movement data. For example, the data collection unit can collect user location information as GPS data. For example, the data collection unit can use the GPS function of a smartphone to determine the user's current location. The data collection unit can also collect weather information from weather data provision services. For example, the data collection unit can obtain data from the Japan Meteorological Agency or real-time updated weather information. Furthermore, the data collection unit can collect population movement data as mobile phone location information data or traffic data. For example, the data collection unit can obtain data to understand the movement of people in a specific area. As a result, by collecting user location information, weather information, and population movement data, the data collection unit can provide data to identify safe evacuation routes and optimal evacuation locations in the event of a disaster. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can obtain user location information, collect weather information, and collect population movement data.

[0073] The analysis unit can integrate the collected data to identify safe evacuation routes and optimal evacuation locations during a disaster. For example, the analysis unit normalizes the collected data and integrates it into a database. For example, the analysis unit integrates user location information, weather information, and population flow data to identify safe evacuation routes and optimal evacuation locations during a disaster. Furthermore, the analysis unit can identify safe evacuation routes and optimal evacuation locations during a disaster based on the collected data. For example, in the event of an earthquake, the analysis unit proposes the optimal evacuation route considering the user's current location, the location of the evacuation center, the safety of the evacuation route, and the congestion level of the evacuation center. Furthermore, the analysis unit can identify safe evacuation routes and optimal evacuation locations during a disaster based on the collected data. For example, the analysis unit proposes the optimal evacuation route considering the user's current location, the location of the evacuation center, the safety of the evacuation route, and the congestion level of the evacuation center. In this way, the analysis unit can identify safe evacuation routes and optimal evacuation locations during a disaster by integrating the collected data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit integrates the collected data to identify safe evacuation routes and optimal evacuation locations during a disaster.

[0074] The suggestion unit can propose evacuation routes based on the analysis results. For example, the suggestion unit can propose the optimal evacuation route to the user based on the analysis results. For example, the suggestion unit can provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." The suggestion unit can also propose the optimal evacuation route to the user based on the analysis results. For example, the suggestion unit can provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." Furthermore, the suggestion unit can propose the optimal evacuation route to the user based on the analysis results. For example, the suggestion unit can provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." In this way, the suggestion unit can provide the user with the optimal evacuation route by proposing an evacuation route based on the analysis results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the proposal department suggests the optimal evacuation route to the user based on the analysis results.

[0075] The service provider can provide evacuation advice to users in natural language. For example, using generative AI, the service provider can provide users with easy-to-understand evacuation advice in natural language in real time. For instance, the service provider can provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." Furthermore, the service provider can provide users with easy-to-understand evacuation advice in natural language in real time using generative AI. For example, the service provider can provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." In addition, the service provider can provide users with easy-to-understand evacuation advice in natural language in real time using generative AI. For example, the service provider can provide specific instructions such as, "The nearest evacuation shelter from your current location is XX. Take XX Street and turn right at XX Park." This allows the service provider to help users evacuate quickly by providing evacuation advice in natural language. Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit may use generative AI to provide users with easy-to-understand evacuation advice in natural language in real time.

[0076] The analysis unit can grasp the congestion status of evacuation shelters and identify evacuation destinations that avoid congestion. For example, the analysis unit uses people counting and real-time data to grasp the congestion status of evacuation shelters in real time. For example, the analysis unit uses sensors and cameras installed in evacuation shelters to grasp the congestion status of evacuation shelters. The analysis unit can also grasp the congestion status of evacuation shelters and identify evacuation destinations that avoid congestion. For example, the analysis unit suggests evacuation destinations that avoid congestion to the user based on the congestion status of the evacuation shelters. Furthermore, the analysis unit can grasp the congestion status of evacuation shelters and identify evacuation destinations that avoid congestion. For example, the analysis unit suggests evacuation destinations that avoid congestion to the user based on the congestion status of the evacuation shelters. In this way, the analysis unit can identify evacuation destinations that avoid congestion by grasping the congestion status of evacuation shelters. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit grasps the congestion status of evacuation shelters and identifies evacuation destinations that avoid congestion.

[0077] The analysis unit can identify evacuation routes that take weather information into consideration. For example, the analysis unit collects weather information from a weather data provision service and considers it when identifying evacuation routes. For example, the analysis unit obtains data from the Japan Meteorological Agency or real-time updated weather information and identifies evacuation routes. Furthermore, the analysis unit can identify evacuation routes that take weather information into consideration. For example, the analysis unit proposes a safe evacuation route to the user based on the weather information. In addition, the analysis unit can identify evacuation routes that take weather information into consideration. For example, the analysis unit proposes a safe evacuation route to the user based on the weather information. As a result, by considering weather information, the analysis unit can identify safe evacuation routes even in bad weather. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit considers weather information and identifies evacuation routes.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using facial recognition or voice analysis. For instance, it can analyze facial data captured by a camera to estimate emotions. Furthermore, the data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can immediately begin data collection and quickly provide evacuation information. Additionally, the data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can collect data at regular intervals and provide necessary information. This allows the data collection unit to collect data at a more appropriate time by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions.

[0079] The data collection unit can analyze the user's past location history and select the optimal data collection method. For example, the data collection unit can collect and analyze the user's past location history as GPS data. For example, the data collection unit can identify important data collection points based on places the user has frequently visited in the past. The data collection unit can also analyze the user's past location history and select the optimal data collection method. For example, the data collection unit can analyze the user's past movement patterns and set an efficient data collection route. Furthermore, the data collection unit can analyze the user's past location history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data on evacuation routes based on places the user has evacuated to in the past. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past location history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit analyzes the user's past location history and selects the optimal data collection method.

[0080] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can collect data on the user's current activities, such as physical activity levels and current location, and then filter the data. For example, if the user is currently evacuating, the data collection unit will prioritize collecting data related to evacuation routes. The data collection unit can also collect data on the user's areas of interest, such as past search history and social media activity, and then filter the data. For example, if the user is interested in a particular region, the data collection unit will prioritize collecting disaster information for that region. Furthermore, the data collection unit can filter data based on the user's current activities and areas of interest. For example, if the user is with their family, the data collection unit will collect data that takes into account the safety of the entire family. In this way, the data collection unit can collect highly relevant data by filtering the data based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit filters data based on the user's current activities and areas of interest.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using facial recognition or voice analysis. For instance, it can analyze facial data captured by a camera to estimate emotions. Furthermore, the data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting evacuation route information. Additionally, the data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting weather information and population movement data. This allows the data collection unit to prioritize collecting more important data by determining the priority of data based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated user emotions.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect the user's geographical location information as GPS data or Wi-Fi location information and prioritize the collection of highly relevant data. For example, the data collection unit prioritizes the collection of disaster information for the area where the user is currently located. The data collection unit can also prioritize the collection of highly relevant data by considering the user's geographical location information. For example, the data collection unit prioritizes the collection of information about the area the user is moving to. Furthermore, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information. For example, the data collection unit prioritizes the collection of information about evacuation shelters around the user's home. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit prioritizes the collection of highly relevant data by considering the user's geographical location information.

[0083] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect and analyze data such as the content of posts and the trends of followers from a user's social media activity. For example, the data collection unit can collect relevant disaster information based on location information shared by a user on social media. The data collection unit can also analyze a user's social media activity and collect relevant data. For example, the data collection unit can analyze the content of posts from accounts that a user follows and collect relevant data. Furthermore, the data collection unit can analyze a user's social media activity and collect relevant data. For example, the data collection unit can collect relevant data based on the activity status of groups that a user participates in. In this way, the data collection unit can collect relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit analyzes a user's social media activity and collects relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition or voice analysis. For instance, it can analyze facial data of the user captured by a camera to estimate emotions. Furthermore, the analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can quickly perform an analysis and provide evacuation information. Additionally, the analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide comprehensive information. This allows the analysis unit to provide more appropriate analysis results by adjusting the analysis criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit estimates the user's emotions and adjusts the analysis criteria based on the estimated user emotions.

[0085] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, the analysis unit can analyze the safety of evacuation routes by combining user location information and weather information. For example, the analysis unit evaluates the safety of evacuation routes based on the user's current location and weather information. The analysis unit can also analyze the congestion status of evacuation shelters by combining population flow data and shelter location information. For example, the analysis unit evaluates the congestion status of evacuation shelters based on population flow data and shelter location information. Furthermore, the analysis unit can analyze the optimal evacuation route by combining the user's past movement history and current location information. For example, the analysis unit proposes the optimal evacuation route based on the user's past movement history and current location information. In this way, the analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit improves the accuracy of its analysis by considering the interrelationships between data.

[0086] The analysis unit can perform analysis while considering the user's attribute information. For example, the analysis unit can analyze the optimal evacuation route by considering the user's age and health condition. For example, the analysis unit can evaluate the safety of the evacuation route based on the user's age and health condition. The analysis unit can also analyze a route that allows all family members to evacuate safely by considering the user's family structure. For example, the analysis unit can propose a route that allows all family members to evacuate safely based on the user's family structure. Furthermore, the analysis unit can analyze an evacuation route from the workplace by considering the user's occupation and workplace. For example, the analysis unit can propose an evacuation route from the workplace based on the user's occupation and workplace. In this way, the analysis unit can provide more appropriate analysis results by considering the user's attribute information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit performs analysis while considering the user's attribute information.

[0087] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, the analysis unit estimates the user's emotions using facial recognition or voice analysis. For instance, it analyzes facial data captured by a camera to estimate emotions. Furthermore, the analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will display the most important information first. Additionally, the analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit will display detailed information sequentially. This allows the analysis unit to prioritize the display of more important information by adjusting the display order of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions.

[0088] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can prioritize the analysis of disaster information around the user's current location. For example, the analysis unit can analyze disaster information based on map data of the user's current location. The analysis unit can also analyze information about the user's destination region. For example, the analysis unit can analyze disaster information based on map data of the user's destination region. Furthermore, the analysis unit can analyze information about evacuation shelters around the user's home. For example, the analysis unit can analyze evacuation shelter information based on map data of the user's home. By doing so, the analysis unit can provide more appropriate analysis results by considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit performs analysis while considering the geographical distribution of the data.

[0089] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit performs analysis by referring to the latest disaster response guidelines. For example, the analysis unit evaluates the safety of evacuation routes based on the latest disaster response guidelines. The analysis unit can also perform analysis by referring to past disaster cases. For example, the analysis unit evaluates the safety of evacuation routes based on past disaster cases. Furthermore, the analysis unit can perform analysis by referring to expert opinions. For example, the analysis unit evaluates the safety of evacuation routes based on expert opinions. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit improves the accuracy of its analysis by referring to relevant literature.

[0090] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, the suggestion unit can estimate the user's emotions using facial recognition or voice analysis. For instance, it can analyze facial data captured by a camera to estimate emotions. Furthermore, the suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is feeling anxious, the suggestion unit will use a concise and clear presentation. Additionally, the suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will use a presentation that includes detailed explanations. This allows the suggestion unit to provide more appropriate suggestions by adjusting the presentation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal section may be performed using AI, for example, or without AI. For example, the proposal section estimates the user's emotions and adjusts the way the proposal is presented based on the estimated user emotions.

[0091] The proposal department can adjust the level of detail in its proposals based on the importance of the evacuation routes. For example, the proposal department evaluates the importance of evacuation routes based on criteria such as risk assessment and route safety, and adjusts the level of detail in its proposals. For example, the proposal department provides detailed proposals for major evacuation routes. The proposal department can also adjust the level of detail in its proposals based on the importance of the evacuation routes. For example, the proposal department provides concise proposals for auxiliary evacuation routes. Furthermore, the proposal department can adjust the level of detail in its proposals based on the importance of the evacuation routes. For example, in an emergency, the proposal department prioritizes proposing the most important evacuation routes. In this way, the proposal department can provide more appropriate proposals by adjusting the level of detail in its proposals based on the importance of the evacuation routes. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department adjusts the level of detail in its proposals based on the importance of the evacuation routes.

[0092] The proposal unit can apply different proposal algorithms depending on the category of the evacuation route when making a proposal. For example, the proposal unit classifies evacuation route categories based on criteria such as urgency, distance, and type of evacuation site, and applies different proposal algorithms. For example, the proposal unit applies an earthquake-response proposal algorithm during an earthquake. Furthermore, the proposal unit can apply different proposal algorithms depending on the category of the evacuation route. For example, the proposal unit applies a flood-response proposal algorithm during a flood. In addition, the proposal unit can apply different proposal algorithms depending on the category of the evacuation route. For example, the proposal unit applies a typhoon-response proposal algorithm during a typhoon. In this way, the proposal unit can provide more appropriate proposals by applying different proposal algorithms depending on the category of the evacuation route. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit applies different proposal algorithms depending on the category of the evacuation route.

[0093] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. The suggestion unit can estimate the user's emotions using, for example, facial recognition or voice analysis. For example, the suggestion unit can analyze the user's facial expression data captured by a camera and estimate their emotions. The suggestion unit can also estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is feeling anxious, the suggestion unit will provide a short, concise suggestion. Furthermore, the suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide a detailed suggestion. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the length of the suggestion based on the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion section estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions.

[0094] The proposal department can determine the priority of proposals based on the timing of evacuation route submissions. For example, the proposal department evaluates the timing of evacuation route submissions based on criteria such as the time of disaster occurrence or the time of evacuation order issuance, and determines the priority of proposals. For example, in an emergency, the proposal department will prioritize proposing the most important evacuation routes. The proposal department can also determine the priority of proposals based on the timing of evacuation route submissions. For example, in normal circumstances, the proposal department will propose detailed evacuation routes. Furthermore, the proposal department can determine the priority of proposals based on the timing of evacuation route submissions. For example, immediately after a disaster occurs, the proposal department will propose rapid evacuation routes. In this way, the proposal department can provide more appropriate proposals by determining the priority of proposals based on the timing of evacuation route submissions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department determines the priority of proposals based on the timing of evacuation route submissions.

[0095] The proposal unit can adjust the order of proposals based on the relevance of evacuation routes. For example, the proposal unit evaluates the relevance of evacuation routes based on criteria such as the degree of route overlap and the commonality of evacuation locations, and adjusts the order of proposals accordingly. For example, the proposal unit proposes the most relevant evacuation route first. Furthermore, the proposal unit can adjust the order of proposals based on the relevance of evacuation routes. For example, the proposal unit postpones less relevant evacuation routes. In addition, the proposal unit can adjust the order of proposals based on the relevance of evacuation routes. For example, in an emergency, the proposal unit prioritizes proposing the most relevant evacuation route. This allows the proposal unit to provide more appropriate proposals by adjusting the order of proposals based on the relevance of evacuation routes. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit adjusts the order of proposals based on the relevance of evacuation routes.

[0096] The service provider can estimate the user's emotions and adjust the way evacuation advice is expressed based on the estimated emotions. For example, the service provider can estimate the user's emotions using facial recognition or voice analysis. For instance, it can analyze facial data of the user captured by a camera to estimate emotions. Furthermore, the service provider can estimate the user's emotions and adjust the way evacuation advice is expressed based on the estimated emotions. For example, if the user is feeling anxious, the service provider will use a concise and clear expression. In addition, the service provider can estimate the user's emotions and adjust the way evacuation advice is expressed based on the estimated emotions. For example, if the user is relaxed, the service provider will use an expression that includes detailed explanations. This allows the service provider to provide more appropriate advice by adjusting the way evacuation advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit estimates the user's emotions and adjusts the way the evacuation advice is expressed based on the estimated user emotions.

[0097] The service provider can provide optimal advice by referring to the user's past evacuation history when providing evacuation advice. For example, the service provider collects and refers to the user's past evacuation history as data such as past evacuation routes and evacuation locations. For example, the service provider suggests the optimal evacuation location based on the places the user has evacuated to in the past. Furthermore, the service provider can provide optimal advice by referring to the user's past evacuation history when providing evacuation advice. For example, the service provider suggests the optimal evacuation route by referring to the user's past evacuation routes. In addition, the service provider can provide optimal advice by referring to the user's past evacuation history when providing evacuation advice. For example, the service provider analyzes the user's past evacuation history and provides the most efficient evacuation advice. In this way, the service provider can provide optimal evacuation advice by referring to the user's past evacuation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider provides optimal advice by referring to the user's past evacuation history.

[0098] The service provider can customize the content of evacuation advice based on the user's current situation when providing it. For example, the service provider can collect data on the user's current situation, such as their current location and surrounding conditions, and customize the content of the advice. For example, if the user is currently evacuating, the service provider will suggest a route to the nearest evacuation shelter. The service provider can also customize the content of evacuation advice based on the user's current situation when providing it. For example, if the user is at home, the service provider will suggest an evacuation route from home. Furthermore, the service provider can customize the content of evacuation advice based on the user's current situation when providing it. For example, if the user is using public transportation, the service provider will suggest a route to the nearest evacuation shelter. In this way, the service provider can provide more appropriate evacuation advice by customizing the content of the advice based on the user's current situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider customizes the content of the advice based on the user's current situation.

[0099] The service provider can estimate the user's emotions and determine the priority of evacuation advice based on the estimated emotions. For example, the service provider can estimate the user's emotions using facial recognition or voice analysis. For instance, it can analyze facial data of the user captured by a camera to estimate emotions. Furthermore, the service provider can estimate the user's emotions and determine the priority of evacuation advice based on the estimated emotions. For example, if the user is feeling anxious, the service provider will prioritize providing the most important evacuation advice. Additionally, the service provider can estimate the user's emotions and determine the priority of evacuation advice based on the estimated emotions. For example, if the user is relaxed, the service provider will sequentially provide detailed evacuation advice. This allows the service provider to prioritize more important advice by determining the priority of evacuation advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit estimates the user's emotions and determines the priority of evacuation advice based on the estimated user emotions.

[0100] The service provider can provide optimal advice when offering evacuation advice, taking into account the user's geographical location information. For example, the service provider can collect the user's geographical location information as GPS data or Wi-Fi location information and provide optimal advice. For example, the service provider can prioritize providing information on evacuation shelters in the area where the user is currently located. Furthermore, the service provider can provide optimal advice when offering evacuation advice, taking into account the user's geographical location information. For example, the service provider can provide evacuation advice regarding the area the user is moving to. In addition, the service provider can provide optimal advice when offering evacuation advice, taking into account the user's geographical location information. For example, the service provider can provide information on evacuation shelters around the user's home. This allows the service provider to provide more appropriate evacuation advice by taking into account the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can provide optimal advice by taking into account the user's geographical location information.

[0101] The service provider can analyze the user's social media activity and adjust the content of the evacuation advice when providing it. For example, the service provider can collect and analyze data on the user's social media activity, such as analysis of posted content and follower trends. For example, the service provider can provide relevant evacuation advice based on location information shared by the user on social media. Furthermore, the service provider can analyze the user's social media activity and adjust the content of the evacuation advice when providing it. For example, the service provider can analyze the content of posts from accounts the user follows and provide relevant evacuation advice. In addition, the service provider can analyze the user's social media activity and adjust the content of the evacuation advice when providing it. For example, the service provider can provide relevant evacuation advice based on the activity status of groups the user participates in. In this way, the service provider can provide more appropriate evacuation advice by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can analyze the user's social media activity and adjust the content of the advice.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The AI ​​disaster response navigation system can also include a health management unit that monitors the user's health status. This unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and provides it to the analysis unit. For example, if a user's heart rate suddenly increases during evacuation, the analysis unit can take this data into consideration and suggest a safer and faster evacuation route. The health management unit can also select evacuation shelters based on the user's health status. For instance, if a user has a pre-existing medical condition, it can prioritize suggesting shelters with nearby medical facilities. Furthermore, the health management unit can monitor the user's health status in real time and contact medical institutions as needed. This allows the AI ​​disaster response navigation system to provide evacuation support that takes the user's health status into consideration.

[0104] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is feeling anxious, the unit will immediately begin data collection and quickly provide evacuation information. If the user is relaxed, it will collect data at regular intervals and provide the necessary information. Furthermore, the data collection unit can also determine the priority of data collection based on the user's emotions. For example, if the user is feeling fear, it will prioritize the collection of evacuation route information. In this way, the data collection unit can collect data at a more appropriate time by adjusting the timing and priority of data collection based on the user's emotions.

[0105] The analysis unit can analyze a user's past evacuation history and identify the optimal evacuation route. For example, it can suggest an efficient evacuation route based on the locations and routes the user has previously evacuated to. Furthermore, based on past evacuation history, the analysis unit can predict the congestion level of evacuation shelters and suggest evacuation destinations that avoid congestion. In addition, the analysis unit can analyze the user's past evacuation history and evaluate the safety of evacuation routes. This allows the analysis unit to identify a more appropriate evacuation route by considering the user's past evacuation history.

[0106] The suggestion function can estimate the user's emotions and adjust the way it presents the suggestion based on those emotions. For example, if the user is feeling anxious, it will use a concise and clear presentation. Conversely, if the user is relaxed, it will use a presentation that includes detailed explanations. Furthermore, the suggestion function can also adjust the length of the suggestion based on the user's emotions. For example, if the user is feeling frightened, it will present a short and to-the-point suggestion. In this way, the suggestion function can provide more appropriate suggestions by adjusting the presentation style and length based on the user's emotions.

[0107] The system can estimate the user's emotions and prioritize evacuation advice based on those emotions. For example, if the user is feeling anxious, the most important evacuation advice will be provided first. If the user is relaxed, more detailed evacuation advice will be provided sequentially. Furthermore, the system can adjust the way the evacuation advice is presented based on the user's emotions. For example, if the user is feeling frightened, a concise and clear presentation will be used. This allows the system to prioritize and provide more important advice by adjusting the priority and presentation of evacuation advice based on the user's emotions.

[0108] The analysis unit can understand the congestion level of evacuation shelters and identify evacuation destinations that avoid congestion. For example, the analysis unit uses sensors and cameras installed in evacuation shelters to understand the congestion level in real time. The analysis unit can also suggest evacuation destinations that avoid congestion to users based on the congestion level of the evacuation shelters. Furthermore, the analysis unit can predict the congestion level of evacuation shelters and identify evacuation destinations that avoid congestion in advance. In this way, the analysis unit can identify evacuation destinations that avoid congestion by understanding the congestion level of evacuation shelters.

[0109] The data collection unit can analyze the user's past location history and select the optimal data collection method. For example, the unit can identify important data collection points based on places the user has frequently visited in the past. It can also analyze the user's past movement patterns and set efficient data collection routes. Furthermore, the unit can prioritize data collection for evacuation routes, referencing places the user has evacuated to in the past. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past location history.

[0110] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, it can combine user location information and weather information to analyze the safety of evacuation routes. It can also combine population flow data and evacuation shelter location information to analyze the congestion status of evacuation shelters. Furthermore, it can combine a user's past travel history with their current location information to analyze the optimal evacuation route. In this way, the analysis unit can improve the accuracy of its analysis by considering the interrelationships between data.

[0111] The proposal department can adjust the level of detail in its proposals based on the importance of the evacuation routes. For example, it can evaluate the importance of evacuation routes based on criteria such as risk assessment and route safety, and adjust the level of detail accordingly. It can also provide detailed proposals for major evacuation routes and concise proposals for secondary routes. Furthermore, in emergencies, it can prioritize proposing the most important evacuation routes. In this way, the proposal department can provide more appropriate proposals by adjusting the level of detail based on the importance of the evacuation routes.

[0112] The service provider can provide optimal evacuation advice by referring to the user's past evacuation history. For example, it can suggest the most suitable evacuation location based on the user's past evacuation routes and locations. It can also suggest the most suitable evacuation route by referring to the user's past evacuation routes. Furthermore, it can analyze the user's past evacuation history to provide the most efficient evacuation advice. In this way, the service provider can provide optimal evacuation advice by referring to the user's past evacuation history.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects user location information, weather information, and population movement data. For example, it uses the smartphone's GPS function to determine the user's current location, obtains current weather and forecasts from weather data services, and collects mobile phone location data and traffic data. Step 2: The analysis unit integrates and analyzes the data collected by the collection unit. For example, it integrates the data using data normalization and database integration methods to identify safe evacuation routes and optimal evacuation locations during disasters. Step 3: The proposal unit proposes an evacuation route based on the analysis results obtained by the analysis unit. For example, it provides specific instructions such as, "The nearest evacuation shelter from your current location is XX. The evacuation route is via XX Street, then turn right at XX Park." Step 4: The service provider will provide evacuation advice to the user in natural language. For example, it will use generative AI to provide the user with easy-to-understand evacuation advice in natural language in real time.

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0118] For example, the collection unit can collect user location information, weather information, and population movement data using the computer 36, camera 42, and communication interface 44 of the smart device 14. The analysis unit integrates and analyzes the collected data using the processor 28 and specific processing unit 290 of the data processing device 12. The proposal unit proposes evacuation routes based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit provides evacuation advice to the user in natural language using the control unit 46A and output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] For example, the data collection unit can collect user location information, weather information, and population movement data using the computer 36, camera 42, and communication interface 44 of the smart glasses 214. The analysis unit integrates and analyzes the collected data using the processor 28 and specific processing unit 290 of the data processing device 12. The suggestion unit proposes an evacuation route based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit provides evacuation advice to the user in natural language using the control unit 46A and speaker 240 of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] For example, the collection unit can collect user location information, weather information, and population movement data using the computer 36, camera 42, and communication interface 44 of the headset terminal 314. The analysis unit integrates and analyzes the collected data using the processor 28 and specific processing unit 290 of the data processing device 12. The proposal unit proposes evacuation routes based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit provides evacuation advice to the user in natural language using the control unit 46A and display 343 of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] For example, the collection unit can collect user location information, weather information, and population flow data using the robot 414's computer 36, camera 42, and communication I / F 44. The analysis unit integrates and analyzes the collected data using the processor 28 and specific processing unit 290 of the data processing device 12. The proposal unit proposes an evacuation route based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit provides evacuation advice to the user in natural language using the robot 414's control unit 46A and speaker 240. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0168] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A data collection unit that collects data, An analysis unit integrates and analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an evacuation route. It includes a service that provides evacuation advice to users in natural language. A system characterized by the following features. (Note 2) The aforementioned collection unit is A system that collects user location information, weather information, and population movement data. (Note 3) The aforementioned analysis unit, A system that integrates collected data to identify safe evacuation routes and shelters during disasters. (Note 4) The aforementioned proposal section is, A system that proposes evacuation routes based on analysis results. (Note 5) The aforementioned supply unit is, A system that provides evacuation advice to users in natural language. (Note 6) The aforementioned analysis unit, Assess the congestion level at evacuation centers and identify alternative evacuation locations that avoid congestion. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Identify evacuation routes that take weather information into account. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past location history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When performing analysis, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the evacuation route. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the evacuation route. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, the priority of proposals will be determined based on the timing of submission of evacuation routes. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of evacuation routes. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way evacuation advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing evacuation advice, the system refers to the user's past evacuation history to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing evacuation advice, customize the advice based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes evacuation advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing evacuation advice, we will provide the most appropriate advice by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing evacuation advice, we analyze the user's social media activity and adjust the content of the advice accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit integrates and analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an evacuation route. It includes a service that provides evacuation advice to users in natural language. A system characterized by the following features.

2. The aforementioned collection unit is Collects user location information, weather information, and population movement data. The system according to feature 1.

3. The aforementioned analysis unit, The collected data will be integrated to identify safe evacuation routes and shelters during disasters. The system according to feature 1.

4. The aforementioned analysis unit, Assess the congestion level at evacuation centers and identify alternative evacuation locations that avoid congestion. The system according to feature 1.

5. The aforementioned analysis unit, Identify evacuation routes that take weather information into account. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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