system

Abstract

The system according to the embodiment aims to provide optimal evacuation routes in real time in the event of a disaster. [Solution] A system according to an embodiment includes an acquisition unit, a collection unit, an analysis unit, a proposal unit, a recalculation unit, and a provision unit. The acquisition unit acquires the current location of a subject. The collection unit collects disaster information in real time. The analysis unit analyzes the information acquired by the acquisition unit and the collection unit and calculates an evacuation route. The proposal unit proposes the evacuation route calculated by the analysis unit to the subject. The recalculation unit recalculates the route if new disaster information occurs along the evacuation route. The provision unit provides the subject with the evacuation route recalculated by the recalculation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide optimal evacuation routes in response to ever-changing situations during disasters.

[0005] The system according to the embodiment aims to provide optimal evacuation routes in real time in the event of a disaster. [Means for solving the problem]

[0006] The system according to this embodiment comprises an acquisition unit, a collection unit, an analysis unit, a proposal unit, a recalculation unit, and a provision unit. The acquisition unit acquires the current location of the target person. The collection unit collects disaster information in real time. The analysis unit analyzes the information acquired by the acquisition unit and the collection unit and calculates an evacuation route. The proposal unit proposes the evacuation route calculated by the analysis unit to the target person. The recalculation unit recalculates the route if new disaster information occurs along the evacuation route. The provision unit provides the evacuation route recalculated by the recalculation unit to the target person. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal evacuation routes in real time in the event of a disaster. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A guidance system according to an embodiment of the present invention is a system for enabling a target person to safely reach the most appropriate evacuation shelter in the event of a disaster, even in situations that change from moment to moment. This guidance system acquires the target person's current location and collects and analyzes information such as the type, scale, and progress of the disaster in real time to determine the status of evacuation routes and evacuation shelters. Next, based on the collected information, the system proposes an optimal evacuation route for the target person. The system selects the safest and fastest evacuation route from the target person's current location to the evacuation shelter. Furthermore, if new disaster information is generated along the evacuation route, the system recalculates the route in real time and provides the target person with the latest evacuation route. For example, in the event of an earthquake, the system acquires the target person's current location and collects information such as the earthquake's epicenter, seismic intensity, and damage status. Next, the system analyzes the collected information and proposes an optimal evacuation route for the target person. The system selects an evacuation route that is less likely to collapse and that is the shortest distance to the evacuation shelter. Furthermore, if an aftershock occurs along the evacuation route, the system recalculates the route in real time and provides the target person with the latest evacuation route. This system allows individuals to safely reach the most appropriate evacuation shelter without being confused by the rapidly changing disaster situation. This enables the guidance system to effectively guide individuals to the most suitable evacuation shelter.

[0029] The guidance system according to this embodiment comprises an acquisition unit, a collection unit, an analysis unit, a proposal unit, a recalculation unit, and a provision unit. The acquisition unit acquires the current location of the target person. For example, the acquisition unit can acquire the current location of the target person using GPS or mobile phone location information. The collection unit collects disaster information in real time. For example, the collection unit can collect disaster information in real time from databases of the Japan Meteorological Agency or local governments. The analysis unit analyzes the information acquired by the acquisition unit and the collection unit and calculates an evacuation route. For example, the analysis unit can use an algorithm that analyzes the collected information and calculates the optimal evacuation route. The proposal unit proposes the evacuation route calculated by the analysis unit to the target person. For example, the proposal unit can propose the calculated evacuation route to the target person using a method of notifying them of the calculated evacuation route. The recalculation unit recalculates the route when new disaster information occurs along the evacuation route. For example, the recalculation unit can use an algorithm that recalculates the route in real time. The provision unit provides the target person with the latest evacuation route recalculated by the recalculation unit. For example, the providing unit can provide the recalculated evacuation route to the target person using a method of notifying the target person, thereby enabling the guidance system according to the embodiment to guide the target person to safely reach the most appropriate evacuation shelter.

[0030] The acquisition unit can obtain the target person's current location using GPS or mobile phone location information. For example, the acquisition unit can obtain the target person's current location using GPS. GPS can provide highly accurate location information based on the type of satellite used and the frequency of location information updates. The acquisition unit can also obtain the target person's current location using mobile phone location information. Mobile phone location information is obtained using base station location information or Wi-Fi location information. For example, the acquisition unit can determine the target person's current location based on base station location information. The acquisition unit can also determine the target person's current location using Wi-Fi location information. This allows the acquisition unit to accurately obtain the target person's current location.

[0031] The data collection unit can collect disaster information in real time from databases of the Japan Meteorological Agency or local governments. For example, the unit can collect disaster information in real time from the Japan Meteorological Agency's database. Methods for collecting information from the Japan Meteorological Agency include the use of APIs and the frequency of data updates. For example, the unit can obtain the latest weather information using the Japan Meteorological Agency's API. The data collection unit can also collect disaster information in real time from local government databases. Methods for collecting information from local government databases include the use of APIs and the frequency of data updates. For example, the unit can obtain the latest disaster information using local government APIs. This allows the data collection unit to collect disaster information in real time.

[0032] The analysis unit can use algorithms to analyze the collected information and calculate evacuation routes. For example, the analysis unit can use algorithms to analyze the collected information and calculate the optimal evacuation route. Specific algorithms for calculating evacuation routes include Dijkstra's algorithm and the A* algorithm. For example, the analysis unit can calculate the shortest route using Dijkstra's algorithm. The analysis unit can also calculate the optimal evacuation route using the A* algorithm. Thus, the analysis unit can calculate the optimal evacuation route.

[0033] The recalculation unit can use an algorithm to recalculate the route if new disaster information arises along the evacuation route. For example, the recalculation unit can use an algorithm to recalculate the route in real time if new disaster information arises along the evacuation route. Specific algorithms for recalculation include dynamic programming and real-time optimization. For example, the recalculation unit can recalculate the route using dynamic programming. The recalculation unit can also recalculate the route using real-time optimization. This allows the recalculation unit to recalculate the route in real time based on new disaster information.

[0034] The service provider can provide the recalculated evacuation routes to the target individuals. For example, the service provider can provide the recalculated evacuation routes to the target individuals. The method of providing the recalculated evacuation routes includes the notification method and the level of detail in the provided information. For example, the service provider can provide the recalculated evacuation routes to the target individuals using a notification method. The service provider can also provide detailed information about the recalculated evacuation routes. This allows the service provider to provide the target individuals with the most up-to-date evacuation routes.

[0035] The acquisition unit can analyze the subject's past travel history and select an acquisition method. For example, the acquisition unit can select a method for acquiring the current location based on places the subject has frequently visited in the past. It can also analyze the subject's past travel patterns and select the most efficient method for acquiring the current location. Furthermore, the acquisition unit can consider the means of transportation the subject has used in the past (walking, driving, etc.) to select the optimal method for acquiring the current location. In this way, the acquisition unit can select the optimal acquisition method based on the subject's past travel history.

[0036] The location acquisition unit can filter the data based on the subject's current activity status or environment when acquiring their current location. For example, if the subject is indoors, the acquisition unit can acquire their current location using Wi-Fi or Bluetooth®. If the subject is outdoors, the acquisition unit can also acquire their current location using GPS. Furthermore, if the subject is moving, the acquisition unit can acquire their current location considering their speed of movement. This allows the acquisition unit to optimize the acquisition of the subject's current location based on their activity status and environment.

[0037] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the geographical location information of the subject when acquiring their current location. For example, if the subject is in an urban area, the data acquisition unit can prioritize the acquisition of information on nearby evacuation shelters. If the subject is in a suburban area, the data acquisition unit can also prioritize the acquisition of information on the nearest evacuation shelter. Furthermore, if the subject is in a mountainous area, the data acquisition unit can also prioritize the acquisition of information on evacuation routes. In this way, the data acquisition unit can prioritize the acquisition of highly relevant information based on the geographical location information of the subject.

[0038] The acquisition unit can analyze the subject's social media activity and obtain relevant information when acquiring their current location. For example, if the subject is sharing evacuation information on social media, the acquisition unit can acquire their current location based on that information. Furthermore, if the subject is collecting disaster information on social media, the acquisition unit can acquire their current location based on that information. In addition, if the subject is sharing location information on social media, the acquisition unit can acquire their current location based on that information. This allows the acquisition unit to obtain relevant information based on the subject's social media activity.

[0039] The data collection unit can analyze past disaster data and select the optimal collection method. For example, it can select the most effective information collection method based on past disaster data. Furthermore, the data collection unit can analyze past disaster data and determine the priority of the information to be collected. In addition, the data collection unit can define the scope of information to be collected based on past disaster data. This allows the data collection unit to select the optimal collection method based on past disaster data.

[0040] The data collection unit can filter disaster information based on the current environment and circumstances of the target individual. For example, if the target individual is in an urban area, the data collection unit can prioritize collecting disaster information from the surrounding area. Similarly, if the target individual is in a suburban area, the data collection unit can prioritize collecting disaster information from the nearest location. Furthermore, if the target individual is in a mountainous area, the data collection unit can prioritize collecting evacuation route information. This allows the data collection unit to filter disaster information based on the target individual's environment and circumstances.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of the person being collected during the collection of disaster information. For example, if the person is in an urban area, the data collection unit can prioritize the collection of disaster information in the surrounding area. Furthermore, if the person is in a suburban area, the data collection unit can prioritize the collection of disaster information in the nearest area. In addition, if the person is in a mountainous area, the data collection unit can prioritize the collection of evacuation route information. This allows the data collection unit to prioritize the collection of highly relevant information based on the geographical location of the person being collected.

[0042] The data collection unit can analyze the social media activity of individuals when collecting disaster information and collect relevant information. For example, if an individual shares evacuation information on social media, the data collection unit can collect disaster information based on that information. Furthermore, if an individual is collecting disaster information on social media, the data collection unit can collect disaster information based on that information. In addition, if an individual shares location information on social media, the data collection unit can collect disaster information based on that information. This allows the data collection unit to collect relevant information based on the individual's social media activity.

[0043] The analysis unit can calculate the optimal evacuation route by referring to past disaster data during the analysis. For example, the analysis unit can calculate the safest evacuation route based on past disaster data. Furthermore, the analysis unit can determine the priority of evacuation routes by referring to past disaster data. In addition, the analysis unit can assess the risk of evacuation routes based on past disaster data. This allows the analysis unit to calculate the optimal evacuation route based on past disaster data.

[0044] The analysis unit can customize its analysis methods based on the subject's current situation and environment during the analysis. For example, if the subject is in an urban area, the analysis unit can consider the risk of collapse of surrounding buildings. If the subject is in a suburban area, the analysis unit can consider the distance to the nearest evacuation shelter. Furthermore, if the subject is in a mountainous area, the analysis unit can consider the risks of the terrain. In this way, the analysis unit can customize its analysis methods according to the subject's situation and environment.

[0045] The analysis unit can calculate the optimal evacuation route by considering the geographical location information of the subject during analysis. For example, if the subject is in an urban area, the analysis unit can calculate the optimal evacuation route by considering the risk of collapse of surrounding buildings. Furthermore, if the subject is in a suburban area, the analysis unit can calculate the optimal evacuation route by considering the distance to the nearest evacuation shelter. In addition, if the subject is in a mountainous area, the analysis unit can calculate the optimal evacuation route by considering the terrain risks. Thus, the analysis unit can calculate the optimal evacuation route based on the geographical location information of the subject.

[0046] During analysis, the analysis unit can analyze the social media activities of the subject and reflect related information in the analysis. For example, if the subject shares evacuation information on social media, the analysis unit can perform analysis based on that information. In addition, if the subject collects disaster information on social media, the analysis unit can also perform analysis based on that information. Furthermore, if the subject shares location information on social media, the analysis unit can also perform analysis based on that information. This allows the analysis unit to reflect related information based on the subject's social media activities in the analysis.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the evacuation route when making the suggestion. For example, if the evacuation route is very important, the suggestion unit can make a suggestion including detailed information. Also, if the evacuation route is relatively unimportant, the suggestion unit can make a concise suggestion. Furthermore, the suggestion unit can gradually adjust the level of detail of the suggestion depending on the importance of the evacuation route. This allows the suggestion unit to adjust the level of detail of the suggestion depending on the importance of the evacuation route.

[0048] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the category of the evacuation route. For example, if the evacuation route is in an urban area, the suggestion unit can make a proposal that takes into account the risk of building collapse. Furthermore, if the evacuation route is in a suburban area, the suggestion unit can make a proposal that takes into account the distance to the nearest evacuation shelter. Furthermore, if the evacuation route is in a mountainous area, the suggestion unit can make a proposal that takes into account the risk of the terrain. This allows the suggestion unit to apply the optimal proposal algorithm depending on the category of the evacuation route.

[0049] The proposal department can determine the priority of proposals based on the timing of evacuation route submissions. For example, if an evacuation route is submitted early, the proposal department can prioritize that proposal. Conversely, if an evacuation route is submitted late, the proposal department can postpone that proposal. Furthermore, the proposal department can adjust the priority of proposals in stages according to the submission timing. This allows the proposal department to determine the priority of proposals based on the timing of evacuation route submissions.

[0050] The proposal unit can adjust the order of proposals based on the relevance of evacuation routes. For example, if an evacuation route is highly relevant, the proposal unit can prioritize that route. Conversely, if an evacuation route is relatively less relevant, the proposal unit can postpone that route. Furthermore, the proposal unit can adjust the order of proposals in stages according to their relevance. This allows the proposal unit to adjust the order of proposals according to the relevance of evacuation routes.

[0051] The recalculation unit can optimize the recalculation algorithm by referring to past disaster data during recalculation. For example, the recalculation unit can select the most effective recalculation algorithm based on past disaster data. Furthermore, the recalculation unit can adjust the parameters of the recalculation algorithm by referring to past disaster data. In addition, the recalculation unit can improve the accuracy of the recalculation algorithm by referring to past disaster data. This allows the recalculation unit to optimize the recalculation algorithm based on past disaster data.

[0052] When recalculating, the recalculation unit can customize the recalculation method based on the subject's current situation and environment. For example, if the subject is in an urban area, the recalculation unit can recalculate taking into account the risk of surrounding buildings collapsing. Furthermore, if the subject is in a suburban area, the recalculation unit can recalculate taking into account the distance to the nearest evacuation shelter. Furthermore, if the subject is in a mountainous area, the recalculation unit can recalculate taking into account the risk of the terrain. This allows the recalculation unit to customize the recalculation method according to the subject's situation and environment.

[0053] During recalculation, the recalculation unit can recalculate the optimal evacuation route taking into account the geographical location information of the subject. For example, if the subject is in an urban area, the recalculation unit can recalculate the optimal evacuation route taking into account the risk of collapse of surrounding buildings. Furthermore, if the subject is in a suburban area, the recalculation unit can recalculate the optimal evacuation route taking into account the distance to the nearest evacuation shelter. Furthermore, if the subject is in a mountainous area, the recalculation unit can recalculate the optimal evacuation route taking into account the risk of terrain. This allows the recalculation unit to recalculate the optimal evacuation route based on the geographical location information of the subject.

[0054] When recalculating, the recalculation unit can analyze the social media activity of the subject and reflect related information in the recalculation. For example, if the subject shares evacuation information on social media, the recalculation unit can perform the recalculation based on that information. In addition, if the subject collects disaster information on social media, the recalculation unit can also perform the recalculation based on that information. Furthermore, if the subject shares location information on social media, the recalculation unit can also perform the recalculation based on that information. This allows the recalculation unit to reflect related information based on the subject's social media activity in the recalculation.

[0055] The providing unit can adjust the level of detail of the information to be provided based on the importance of the evacuation route when providing the information. For example, if the evacuation route is very important, the providing unit can provide information including detailed information. Also, if the evacuation route is relatively unimportant, the providing unit can provide concise information. Furthermore, the providing unit can gradually adjust the level of detail of the information to be provided according to the importance of the evacuation route. This allows the providing unit to adjust the level of detail of the information to be provided according to the importance of the evacuation route.

[0056] The providing unit can apply different providing algorithms depending on the category of the evacuation route when providing the information. For example, if the evacuation route is in an urban area, the providing unit can provide information that takes into account the risk of building collapse. Furthermore, if the evacuation route is in a suburban area, the providing unit can also provide information that takes into account the distance to the nearest evacuation shelter. Furthermore, if the evacuation route is in a mountainous area, the providing unit can also provide information that takes into account the risk of the terrain. This allows the providing unit to apply the optimal providing algorithm depending on the category of the evacuation route.

[0057] The providing unit can determine the priority of the information to be provided based on the submission time of the evacuation route at the time of providing the information. For example, if the submission time of the evacuation route is early, the providing unit can provide the information preferentially. Also, if the submission time of the evacuation route is late, the providing unit can provide the information later. Furthermore, the providing unit can gradually adjust the priority of the information to be provided depending on the submission time. In this way, the providing unit can determine the priority of the information to be provided depending on the submission time of the evacuation route.

[0058] The providing unit can adjust the order of information to be provided based on the relevance of the evacuation route when providing the information. For example, if the evacuation route is highly relevant, the providing unit can provide the information preferentially. Also, if the evacuation route is relatively less relevant, the providing unit can provide the information later. Furthermore, the providing unit can gradually adjust the order of information to be provided according to the relevance. This allows the providing unit to adjust the order of information to be provided according to the relevance of the evacuation route.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The guidance system can further include a health monitoring unit that monitors the subject's health condition. The health monitoring unit acquires the subject's vital signs, such as heart rate, blood pressure, and body temperature, in real time and transmits them to the analysis unit. The analysis unit can evaluate the subject's health condition based on the acquired vital signs and reflect this in the selection of an evacuation route. For example, if the heart rate is high, the analysis unit can determine that the subject is feeling stressed and suggest a safer and more relaxing evacuation route. Also, if the body temperature is high, the analysis unit can determine that the subject is at risk of heatstroke and include cooler areas in the evacuation route. This allows the guidance system to provide the optimal evacuation route according to the subject's health condition.

[0061] The acquisition unit can adjust the frequency of acquiring the current location in consideration of the remaining battery level of the device of the subject. For example, when the remaining battery level is low, the acquisition unit can reduce the frequency of acquiring the current location to reduce battery consumption. Also, when the remaining battery level is sufficient, the acquisition unit can increase the frequency of acquiring the current location to update the location information in real time. Furthermore, when the remaining battery level is very low, the acquisition unit can temporarily stop acquiring the current location to conserve battery power for emergencies. In this way, the acquisition unit can adjust the frequency of acquiring the current location in accordance with the remaining battery level of the device.

[0062] The collection unit can analyze the target person's social media activity and reflect it in the collection of disaster information. For example, if the target person shares evacuation information on social media, the collection unit can obtain the latest disaster information based on that information. Also, if the target person posts about disasters on social media, the collection unit can analyze the content of those posts and collect related disaster information. Furthermore, if the target person shares location information on social media, the collection unit can prioritize the collection of surrounding disaster information based on that location information. This allows the collection unit to optimize the collection of disaster information based on the target person's social media activity.

[0063] The analysis unit can calculate the optimal evacuation route by referring to the subject's past evacuation history. For example, if a previously evacuation route was found to be safe, the analysis unit can preferentially suggest that route. Also, if a previously evacuation route was found to be dangerous, the analysis unit can calculate a route to avoid that route. Furthermore, based on the past evacuation history, the analysis unit can learn the subject's evacuation behavior patterns and suggest a more appropriate evacuation route. This allows the analysis unit to calculate the optimal evacuation route based on the subject's past evacuation history.

[0064] The recalculation unit can adjust the frequency of recalculation taking into account the moving speed of the subject. For example, if the subject is moving at high speed, the recalculation unit can increase the frequency of recalculation to update the route in real time. Also, if the subject is moving at low speed, the recalculation unit can reduce the frequency of recalculation to save battery consumption. Furthermore, if the subject is stationary, the recalculation unit can minimize the frequency of recalculation. This allows the recalculation unit to adjust the frequency of recalculation according to the moving speed of the subject.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The acquisition unit acquires the current location of the subject. For example, the acquisition unit can acquire the current location of the subject using GPS or location information from a mobile phone. Step 2: The collection unit collects disaster information in real time. For example, the collection unit can collect disaster information in real time from databases of the Japan Meteorological Agency and local governments. Step 3: The analysis unit analyzes the information acquired by the acquisition unit and the collection unit and calculates an evacuation route. For example, the analysis unit can use an algorithm to analyze the collected information and calculate an optimal evacuation route. Step 4: The suggestion unit suggests the evacuation route calculated by the analysis unit to the subject. For example, the suggestion unit can suggest the calculated evacuation route to the subject using a method of notifying the subject. Step 5: The recalculation unit recalculates the route if new disaster information occurs along the evacuation route. For example, the recalculation unit can use an algorithm to recalculate the route in real time. Step 6: The providing unit provides the latest evacuation route recalculated by the recalculation unit to the subject. For example, the providing unit can provide the recalculated evacuation route to the subject using a method of notifying the subject.

[0067] (Example 2) A guidance system according to an embodiment of the present invention is a system for enabling a target person to safely reach the most appropriate evacuation shelter in the event of a disaster, even in situations that change from moment to moment. This guidance system acquires the target person's current location and collects and analyzes information such as the type, scale, and progress of the disaster in real time to determine the status of evacuation routes and evacuation shelters. Next, based on the collected information, the system proposes an optimal evacuation route for the target person. The system selects the safest and fastest evacuation route from the target person's current location to the evacuation shelter. Furthermore, if new disaster information is generated along the evacuation route, the system recalculates the route in real time and provides the target person with the latest evacuation route. For example, in the event of an earthquake, the system acquires the target person's current location and collects information such as the earthquake's epicenter, seismic intensity, and damage status. Next, the system analyzes the collected information and proposes an optimal evacuation route for the target person. The system selects an evacuation route that is less likely to collapse and that is the shortest distance to the evacuation shelter. Furthermore, if an aftershock occurs along the evacuation route, the system recalculates the route in real time and provides the target person with the latest evacuation route. This system allows individuals to safely reach the most appropriate evacuation shelter without being confused by the rapidly changing disaster situation. This enables the guidance system to effectively guide individuals to the most suitable evacuation shelter.

[0068] The guidance system according to this embodiment comprises an acquisition unit, a collection unit, an analysis unit, a proposal unit, a recalculation unit, and a provision unit. The acquisition unit acquires the current location of the target person. For example, the acquisition unit can acquire the current location of the target person using GPS or mobile phone location information. The collection unit collects disaster information in real time. For example, the collection unit can collect disaster information in real time from databases of the Japan Meteorological Agency or local governments. The analysis unit analyzes the information acquired by the acquisition unit and the collection unit and calculates an evacuation route. For example, the analysis unit can use an algorithm that analyzes the collected information and calculates the optimal evacuation route. The proposal unit proposes the evacuation route calculated by the analysis unit to the target person. For example, the proposal unit can propose the calculated evacuation route to the target person using a method of notifying them of the calculated evacuation route. The recalculation unit recalculates the route when new disaster information occurs along the evacuation route. For example, the recalculation unit can use an algorithm that recalculates the route in real time. The provision unit provides the target person with the latest evacuation route recalculated by the recalculation unit. For example, the providing unit can provide the recalculated evacuation route to the target person using a method of notifying the target person, thereby enabling the guidance system according to the embodiment to guide the target person to safely reach the most appropriate evacuation shelter.

[0069] The acquisition unit can obtain the target person's current location using GPS or mobile phone location information. For example, the acquisition unit can obtain the target person's current location using GPS. GPS can provide highly accurate location information based on the type of satellite used and the frequency of location information updates. The acquisition unit can also obtain the target person's current location using mobile phone location information. Mobile phone location information is obtained using base station location information or Wi-Fi location information. For example, the acquisition unit can determine the target person's current location based on base station location information. The acquisition unit can also determine the target person's current location using Wi-Fi location information. This allows the acquisition unit to accurately obtain the target person's current location.

[0070] The data collection unit can collect disaster information in real time from databases of the Japan Meteorological Agency or local governments. For example, the unit can collect disaster information in real time from the Japan Meteorological Agency's database. Methods for collecting information from the Japan Meteorological Agency include the use of APIs and the frequency of data updates. For example, the unit can obtain the latest weather information using the Japan Meteorological Agency's API. The data collection unit can also collect disaster information in real time from local government databases. Methods for collecting information from local government databases include the use of APIs and the frequency of data updates. For example, the unit can obtain the latest disaster information using local government APIs. This allows the data collection unit to collect disaster information in real time.

[0071] The analysis unit can use algorithms to analyze the collected information and calculate evacuation routes. For example, the analysis unit can use algorithms to analyze the collected information and calculate the optimal evacuation route. Specific algorithms for calculating evacuation routes include Dijkstra's algorithm and the A* algorithm. For example, the analysis unit can calculate the shortest route using Dijkstra's algorithm. The analysis unit can also calculate the optimal evacuation route using the A* algorithm. Thus, the analysis unit can calculate the optimal evacuation route.

[0072] The recalculation unit can use an algorithm to recalculate the route if new disaster information arises along the evacuation route. For example, the recalculation unit can use an algorithm to recalculate the route in real time if new disaster information arises along the evacuation route. Specific algorithms for recalculation include dynamic programming and real-time optimization. For example, the recalculation unit can recalculate the route using dynamic programming. The recalculation unit can also recalculate the route using real-time optimization. This allows the recalculation unit to recalculate the route in real time based on new disaster information.

[0073] The service provider can provide the recalculated evacuation routes to the target individuals. For example, the service provider can provide the recalculated evacuation routes to the target individuals. The method of providing the recalculated evacuation routes includes the notification method and the level of detail in the provided information. For example, the service provider can provide the recalculated evacuation routes to the target individuals using a notification method. The service provider can also provide detailed information about the recalculated evacuation routes. This allows the service provider to provide the target individuals with the most up-to-date evacuation routes.

[0074] The acquisition unit can analyze the subject's emotions and adjust the frequency of current location acquisition based on the subject's analyzed emotions. For example, if the subject is feeling anxious, the acquisition unit can increase the frequency of current location acquisition and update location information in real time. Furthermore, if the subject is calm, the acquisition unit can reduce the frequency of current location acquisition to reduce battery consumption. Furthermore, if the subject is in a panicked state, the acquisition unit can maximize the frequency of current location acquisition to enable a quick response. This allows the acquisition unit to adjust the frequency of current location acquisition according to the subject's emotions.

[0075] The acquisition unit can analyze the subject's past travel history and select an acquisition method. For example, the acquisition unit can select a method for acquiring the current location based on places the subject has frequently visited in the past. It can also analyze the subject's past travel patterns and select the most efficient method for acquiring the current location. Furthermore, the acquisition unit can consider the means of transportation the subject has used in the past (walking, driving, etc.) to select the optimal method for acquiring the current location. In this way, the acquisition unit can select the optimal acquisition method based on the subject's past travel history.

[0076] When acquiring the current location, the acquisition unit can perform filtering based on the subject's current activity status or environment. For example, if the subject is indoors, the acquisition unit can acquire the current location using Wi-Fi or Bluetooth. If the subject is outdoors, the acquisition unit can also acquire the current location using GPS. Furthermore, if the subject is moving, the acquisition unit can acquire the current location taking into account the subject's movement speed. This allows the acquisition unit to optimize the acquisition of the current location based on the subject's activity status and environment.

[0077] The acquisition unit can analyze the subject's emotions and adjust the accuracy of the acquired current location based on the analyzed subject's emotions. For example, if the subject is feeling anxious, the acquisition unit can increase the accuracy of the current location to provide detailed location information. Furthermore, if the subject is calm, the acquisition unit can reduce the accuracy of the current location to reduce battery consumption. Furthermore, if the subject is in a panicked state, the acquisition unit can maximize the accuracy of the current location to enable a quick response. This allows the acquisition unit to adjust the accuracy of the current location according to the subject's emotions.

[0078] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the geographical location information of the subject when acquiring their current location. For example, if the subject is in an urban area, the data acquisition unit can prioritize the acquisition of information on nearby evacuation shelters. If the subject is in a suburban area, the data acquisition unit can also prioritize the acquisition of information on the nearest evacuation shelter. Furthermore, if the subject is in a mountainous area, the data acquisition unit can also prioritize the acquisition of information on evacuation routes. In this way, the data acquisition unit can prioritize the acquisition of highly relevant information based on the geographical location information of the subject.

[0079] The acquisition unit can analyze the subject's social media activity and obtain relevant information when acquiring their current location. For example, if the subject is sharing evacuation information on social media, the acquisition unit can acquire their current location based on that information. Furthermore, if the subject is collecting disaster information on social media, the acquisition unit can acquire their current location based on that information. In addition, if the subject is sharing location information on social media, the acquisition unit can acquire their current location based on that information. This allows the acquisition unit to obtain relevant information based on the subject's social media activity.

[0080] The collection unit can estimate the subject's emotions and adjust the frequency of disaster information collection based on the estimated subject's emotions. For example, if the subject is feeling anxious, the collection unit can increase the frequency of disaster information collection and update the information in real time. Also, if the subject is calm, the collection unit can reduce the frequency of disaster information collection to reduce battery consumption. Furthermore, if the subject is in a panic state, the collection unit can maximize the frequency of disaster information collection to enable a rapid response. This allows the collection unit to adjust the frequency of disaster information collection according to the subject's emotions.

[0081] The data collection unit can analyze past disaster data and select the optimal collection method. For example, it can select the most effective information collection method based on past disaster data. Furthermore, the data collection unit can analyze past disaster data and determine the priority of the information to be collected. In addition, the data collection unit can define the scope of information to be collected based on past disaster data. This allows the data collection unit to select the optimal collection method based on past disaster data.

[0082] The data collection unit can filter disaster information based on the current environment and circumstances of the target individual. For example, if the target individual is in an urban area, the data collection unit can prioritize collecting disaster information from the surrounding area. Similarly, if the target individual is in a suburban area, the data collection unit can prioritize collecting disaster information from the nearest location. Furthermore, if the target individual is in a mountainous area, the data collection unit can prioritize collecting evacuation route information. This allows the data collection unit to filter disaster information based on the target individual's environment and circumstances.

[0083] The data collection unit can estimate the emotions of the target individual and determine the priority of disaster information to collect based on those estimated emotions. For example, if the target individual is feeling anxious, the data collection unit can prioritize collecting important disaster information. If the target individual is calm, the data collection unit can also collect detailed disaster information. Furthermore, if the target individual is in a state of panic, the data collection unit can prioritize collecting disaster information that allows for a quick response. In this way, the data collection unit can determine the priority of disaster information according to the emotions of the target individual.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of the person being collected during the collection of disaster information. For example, if the person is in an urban area, the data collection unit can prioritize the collection of disaster information in the surrounding area. Furthermore, if the person is in a suburban area, the data collection unit can prioritize the collection of disaster information in the nearest area. In addition, if the person is in a mountainous area, the data collection unit can prioritize the collection of evacuation route information. This allows the data collection unit to prioritize the collection of highly relevant information based on the geographical location of the person being collected.

[0085] The data collection unit can analyze the social media activity of individuals when collecting disaster information and collect relevant information. For example, if an individual shares evacuation information on social media, the data collection unit can collect disaster information based on that information. Furthermore, if an individual is collecting disaster information on social media, the data collection unit can collect disaster information based on that information. In addition, if an individual shares location information on social media, the data collection unit can collect disaster information based on that information. This allows the data collection unit to collect relevant information based on the individual's social media activity.

[0086] The analysis unit can estimate the subject's emotions and adjust the parameters of the analysis algorithm based on the estimated emotions of the subject. For example, if the subject is feeling anxious, the analysis unit can set parameters that prioritize a route that allows for quick evacuation. Furthermore, if the subject is calm, the analysis unit can set parameters that perform a detailed analysis. Furthermore, if the subject is in a state of panic, the analysis unit can set parameters that prioritize the shortest route. This allows the analysis unit to adjust the parameters of the analysis algorithm according to the subject's emotions.

[0087] The analysis unit can calculate the optimal evacuation route by referring to past disaster data during the analysis. For example, the analysis unit can calculate the safest evacuation route based on past disaster data. Furthermore, the analysis unit can determine the priority of evacuation routes by referring to past disaster data. In addition, the analysis unit can assess the risk of evacuation routes based on past disaster data. This allows the analysis unit to calculate the optimal evacuation route based on past disaster data.

[0088] The analysis unit can customize its analysis methods based on the subject's current situation and environment during the analysis. For example, if the subject is in an urban area, the analysis unit can consider the risk of collapse of surrounding buildings. If the subject is in a suburban area, the analysis unit can consider the distance to the nearest evacuation shelter. Furthermore, if the subject is in a mountainous area, the analysis unit can consider the risks of the terrain. In this way, the analysis unit can customize its analysis methods according to the subject's situation and environment.

[0089] The analysis unit can estimate the subject's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the subject is feeling anxious, the analysis unit can provide a simple and highly visible display method. If the subject is calm, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the subject is in a state of panic, the analysis unit can provide a display method that focuses on the essential points. In this way, the analysis unit can adjust the display method of the analysis results according to the subject's emotions.

[0090] The analysis unit can calculate the optimal evacuation route by considering the geographical location information of the subject during analysis. For example, if the subject is in an urban area, the analysis unit can calculate the optimal evacuation route by considering the risk of collapse of surrounding buildings. Furthermore, if the subject is in a suburban area, the analysis unit can calculate the optimal evacuation route by considering the distance to the nearest evacuation shelter. In addition, if the subject is in a mountainous area, the analysis unit can calculate the optimal evacuation route by considering the terrain risks. Thus, the analysis unit can calculate the optimal evacuation route based on the geographical location information of the subject.

[0091] During analysis, the analysis unit can analyze the social media activities of the subject and reflect related information in the analysis. For example, if the subject shares evacuation information on social media, the analysis unit can perform analysis based on that information. In addition, if the subject collects disaster information on social media, the analysis unit can also perform analysis based on that information. Furthermore, if the subject shares location information on social media, the analysis unit can also perform analysis based on that information. This allows the analysis unit to reflect related information based on the subject's social media activities in the analysis.

[0092] The suggestion unit can estimate the emotion of the target person and adjust the way in which the suggestion is expressed based on the estimated emotion of the target person. For example, if the target person is feeling anxious, the suggestion unit can make a simple and highly visible suggestion. Furthermore, if the target person is calm, the suggestion unit can make a suggestion that includes detailed information. Furthermore, if the target person is in a panicked state, the suggestion unit can make a suggestion that focuses on the main points. This allows the suggestion unit to adjust the way in which the suggestion is expressed depending on the emotion of the target person.

[0093] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the evacuation route when making the suggestion. For example, if the evacuation route is very important, the suggestion unit can make a suggestion including detailed information. Also, if the evacuation route is relatively unimportant, the suggestion unit can make a concise suggestion. Furthermore, the suggestion unit can gradually adjust the level of detail of the suggestion depending on the importance of the evacuation route. This allows the suggestion unit to adjust the level of detail of the suggestion depending on the importance of the evacuation route.

[0094] When making a proposal, the suggestion unit can apply different proposal algorithms depending on the category of the evacuation route. For example, if the evacuation route is in an urban area, the suggestion unit can make a proposal that takes into account the risk of building collapse. Furthermore, if the evacuation route is in a suburban area, the suggestion unit can make a proposal that takes into account the distance to the nearest evacuation shelter. Furthermore, if the evacuation route is in a mountainous area, the suggestion unit can make a proposal that takes into account the risk of the terrain. This allows the suggestion unit to apply the optimal proposal algorithm depending on the category of the evacuation route.

[0095] The suggestion function can estimate the subject's emotions and adjust the length of the suggestion based on those emotions. For example, if the subject is feeling anxious, the suggestion function can provide a short, concise suggestion. If the subject is calm, the suggestion function can provide a longer suggestion with more detailed information. Furthermore, if the subject is in a state of panic, the suggestion function can provide a short, easily understandable suggestion. In this way, the suggestion function can adjust the length of the suggestion according to the subject's emotions.

[0096] The proposal department can determine the priority of proposals based on the timing of evacuation route submissions. For example, if an evacuation route is submitted early, the proposal department can prioritize that proposal. Conversely, if an evacuation route is submitted late, the proposal department can postpone that proposal. Furthermore, the proposal department can adjust the priority of proposals in stages according to the submission timing. This allows the proposal department to determine the priority of proposals based on the timing of evacuation route submissions.

[0097] The proposal unit can adjust the order of proposals based on the relevance of evacuation routes. For example, if an evacuation route is highly relevant, the proposal unit can prioritize that route. Conversely, if an evacuation route is relatively less relevant, the proposal unit can postpone that route. Furthermore, the proposal unit can adjust the order of proposals in stages according to their relevance. This allows the proposal unit to adjust the order of proposals according to the relevance of evacuation routes.

[0098] The recalculation unit can estimate the subject's emotions and adjust the frequency of recalculations based on the estimated emotions. For example, if the subject is feeling anxious, the recalculation unit can increase the frequency of recalculations to update the route in real time. Conversely, if the subject is calm, the recalculation unit can decrease the frequency of recalculations to conserve battery power. Furthermore, if the subject is in a state of panic, the recalculation unit can maximize the frequency of recalculations to enable a rapid response. In this way, the recalculation unit can adjust the frequency of recalculations according to the subject's emotions.

[0099] The recalculation unit can optimize the recalculation algorithm by referring to past disaster data during recalculation. For example, the recalculation unit can select the most effective recalculation algorithm based on past disaster data. Furthermore, the recalculation unit can adjust the parameters of the recalculation algorithm by referring to past disaster data. In addition, the recalculation unit can improve the accuracy of the recalculation algorithm by referring to past disaster data. This allows the recalculation unit to optimize the recalculation algorithm based on past disaster data.

[0100] When recalculating, the recalculation unit can customize the recalculation method based on the subject's current situation and environment. For example, if the subject is in an urban area, the recalculation unit can recalculate taking into account the risk of surrounding buildings collapsing. Furthermore, if the subject is in a suburban area, the recalculation unit can recalculate taking into account the distance to the nearest evacuation shelter. Furthermore, if the subject is in a mountainous area, the recalculation unit can recalculate taking into account the risk of the terrain. This allows the recalculation unit to customize the recalculation method according to the subject's situation and environment.

[0101] The recalculation unit can estimate the subject's emotions and adjust the display method of the recalculation results based on the estimated subject's emotions. For example, if the subject is feeling anxious, the recalculation unit can provide a simple, highly visible display method. If the subject is calm, the recalculation unit can also provide a display method including detailed information. Furthermore, if the subject is in a panicked state, the recalculation unit can also provide a display method that focuses on the main points. In this way, the recalculation unit can adjust the display method of the recalculation results according to the subject's emotions.

[0102] During recalculation, the recalculation unit can recalculate the optimal evacuation route taking into account the geographical location information of the subject. For example, if the subject is in an urban area, the recalculation unit can recalculate the optimal evacuation route taking into account the risk of collapse of surrounding buildings. Furthermore, if the subject is in a suburban area, the recalculation unit can recalculate the optimal evacuation route taking into account the distance to the nearest evacuation shelter. Furthermore, if the subject is in a mountainous area, the recalculation unit can recalculate the optimal evacuation route taking into account the risk of terrain. This allows the recalculation unit to recalculate the optimal evacuation route based on the geographical location information of the subject.

[0103] When recalculating, the recalculation unit can analyze the social media activity of the subject and reflect related information in the recalculation. For example, if the subject shares evacuation information on social media, the recalculation unit can perform the recalculation based on that information. In addition, if the subject collects disaster information on social media, the recalculation unit can also perform the recalculation based on that information. Furthermore, if the subject shares location information on social media, the recalculation unit can also perform the recalculation based on that information. This allows the recalculation unit to reflect related information based on the subject's social media activity in the recalculation.

[0104] The providing unit can estimate the emotion of the subject and adjust the way in which the information to be provided is presented based on the estimated emotion of the subject. For example, if the subject is feeling anxious, the providing unit can provide simple, highly visible information. Furthermore, if the subject is calm, the providing unit can provide information that includes detailed information. Furthermore, if the subject is in a panicked state, the providing unit can provide information that focuses on the main points. This allows the providing unit to adjust the way in which the information to be provided is presented according to the emotion of the subject.

[0105] The providing unit can adjust the level of detail of the information to be provided based on the importance of the evacuation route when providing the information. For example, if the evacuation route is very important, the providing unit can provide information including detailed information. Also, if the evacuation route is relatively unimportant, the providing unit can provide concise information. Furthermore, the providing unit can gradually adjust the level of detail of the information to be provided according to the importance of the evacuation route. This allows the providing unit to adjust the level of detail of the information to be provided according to the importance of the evacuation route.

[0106] The providing unit can apply different providing algorithms depending on the category of the evacuation route when providing the information. For example, if the evacuation route is in an urban area, the providing unit can provide information that takes into account the risk of building collapse. Furthermore, if the evacuation route is in a suburban area, the providing unit can also provide information that takes into account the distance to the nearest evacuation shelter. Furthermore, if the evacuation route is in a mountainous area, the providing unit can also provide information that takes into account the risk of the terrain. This allows the providing unit to apply the optimal providing algorithm depending on the category of the evacuation route.

[0107] The providing unit can estimate the subject's emotions and adjust the length of the information to be provided based on the estimated subject's emotions. For example, if the subject is feeling anxious, the providing unit can provide short, to-the-point information. Furthermore, if the subject is calm, the providing unit can provide longer information including detailed information. Furthermore, if the subject is in a panicked state, the providing unit can provide short information that can be quickly understood. This allows the providing unit to adjust the length of the information to be provided according to the subject's emotions.

[0108] The providing unit can determine the priority of the information to be provided based on the submission time of the evacuation route at the time of providing the information. For example, if the submission time of the evacuation route is early, the providing unit can provide the information preferentially. Also, if the submission time of the evacuation route is late, the providing unit can provide the information later. Furthermore, the providing unit can gradually adjust the priority of the information to be provided depending on the submission time. In this way, the providing unit can determine the priority of the information to be provided depending on the submission time of the evacuation route.

[0109] The providing unit can adjust the order of information to be provided based on the relevance of the evacuation route when providing the information. For example, if the evacuation route is highly relevant, the providing unit can provide the information preferentially. Also, if the evacuation route is relatively less relevant, the providing unit can provide the information later. Furthermore, the providing unit can gradually adjust the order of information to be provided according to the relevance. This allows the providing unit to adjust the order of information to be provided according to the relevance of the evacuation route. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, proposal unit, recalculation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires the current location of the subject using the GPS of the smart device 14 or the location information of a mobile phone. The collection unit collects disaster information in real time from databases of the Japan Meteorological Agency and local governments via the communication I / F 26 of the data processing unit 12. The analysis unit analyzes the information acquired by the specific processing unit 290 of the data processing unit 12 and calculates the optimal evacuation route. The proposal unit proposes the evacuation route calculated by the control unit 46A of the smart device 14 to the subject. The recalculation unit recalculates the route in real time using the specific processing unit 290 of the data processing unit 12. The provision unit provides the subject with the latest evacuation route recalculated by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, collection unit, analysis unit, proposal unit, recalculation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires the subject's current location using the GPS of the smart glasses 214 or location information from a mobile phone. The collection unit collects disaster information in real time from databases of the Japan Meteorological Agency and local governments via the communication I / F 26 of the data processing unit 12. The analysis unit analyzes the information acquired by the specific processing unit 290 of the data processing unit 12 and calculates the optimal evacuation route. The proposal unit proposes the evacuation route calculated by the control unit 46A of the smart glasses 214 to the subject. The recalculation unit recalculates the route in real time using the specific processing unit 290 of the data processing unit 12. The provision unit provides the subject with the latest evacuation route recalculated by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, collection unit, analysis unit, proposal unit, recalculation unit, and provision unit, described above, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires the current location of the target person using the GPS of the headset type terminal 314 or location information from a mobile phone. The collection unit collects disaster information in real time from databases of the Japan Meteorological Agency and local governments via the communication I / F 26 of the data processing device 12. The analysis unit analyzes the information acquired by the identification processing unit 290 of the data processing device 12 and calculates an optimal evacuation route. The proposal unit proposes the evacuation route calculated by the control unit 46A of the headset type terminal 314 to the target person. The recalculation unit recalculates the route in real time using the identification processing unit 290 of the data processing device 12. The provision unit provides the target person with the latest evacuation route recalculated by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, collection unit, analysis unit, proposal unit, recalculation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires the current location of the target person using the GPS of the robot 414 or location information from a mobile phone. The collection unit collects disaster information in real time from databases of the Japan Meteorological Agency and local governments via the communication I / F 26 of the data processing device 12. The analysis unit analyzes the information acquired by the identification processing unit 290 of the data processing device 12 and calculates an optimal evacuation route. The proposal unit proposes the evacuation route calculated by the control unit 46A of the robot 414 to the target person. The recalculation unit recalculates the route in real time using the identification processing unit 290 of the data processing device 12. The provision unit provides the target person with the latest evacuation route recalculated by the control unit 46A of the robot 414.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The guidance system can further include a health monitoring unit that monitors the subject's health condition. The health monitoring unit acquires the subject's vital signs, such as heart rate, blood pressure, and body temperature, in real time and transmits them to the analysis unit. The analysis unit can evaluate the subject's health condition based on the acquired vital signs and reflect this in the selection of an evacuation route. For example, if the heart rate is high, the analysis unit can determine that the subject is feeling stressed and suggest a safer and more relaxing evacuation route. Also, if the body temperature is high, the analysis unit can determine that the subject is at risk of heatstroke and include cooler areas in the evacuation route. This allows the guidance system to provide the optimal evacuation route according to the subject's health condition.

[0112] The acquisition unit can adjust the frequency of acquiring the current location in consideration of the remaining battery level of the device of the subject. For example, when the remaining battery level is low, the acquisition unit can reduce the frequency of acquiring the current location to reduce battery consumption. Also, when the remaining battery level is sufficient, the acquisition unit can increase the frequency of acquiring the current location to update the location information in real time. Furthermore, when the remaining battery level is very low, the acquisition unit can temporarily stop acquiring the current location to conserve battery power for emergencies. In this way, the acquisition unit can adjust the frequency of acquiring the current location in accordance with the remaining battery level of the device.

[0113] The collection unit can analyze the target person's social media activity and reflect it in the collection of disaster information. For example, if the target person shares evacuation information on social media, the collection unit can obtain the latest disaster information based on that information. Also, if the target person posts about disasters on social media, the collection unit can analyze the content of those posts and collect related disaster information. Furthermore, if the target person shares location information on social media, the collection unit can prioritize the collection of surrounding disaster information based on that location information. This allows the collection unit to optimize the collection of disaster information based on the target person's social media activity.

[0114] The analysis unit can calculate the optimal evacuation route by referring to the subject's past evacuation history. For example, if a previously evacuation route was found to be safe, the analysis unit can preferentially suggest that route. Also, if a previously evacuation route was found to be dangerous, the analysis unit can calculate a route to avoid that route. Furthermore, based on the past evacuation history, the analysis unit can learn the subject's evacuation behavior patterns and suggest a more appropriate evacuation route. This allows the analysis unit to calculate the optimal evacuation route based on the subject's past evacuation history.

[0115] The recalculation unit can adjust the frequency of recalculation taking into account the moving speed of the subject. For example, if the subject is moving at high speed, the recalculation unit can increase the frequency of recalculation to update the route in real time. Also, if the subject is moving at low speed, the recalculation unit can reduce the frequency of recalculation to save battery consumption. Furthermore, if the subject is stationary, the recalculation unit can minimize the frequency of recalculation. This allows the recalculation unit to adjust the frequency of recalculation according to the moving speed of the subject.

[0116] The information provider can estimate the emotions of the target individual and adjust the way the information is presented based on those estimated emotions. For example, if the target individual is feeling anxious, the provider can provide simple, easily understandable information. If the target individual is calm, the provider can provide information that includes details. Furthermore, if the target individual is in a state of panic, the provider can provide concise information. In this way, the provider can adjust the way the information is presented according to the emotions of the target individual.

[0117] The acquisition unit can analyze the subject's emotions and adjust the frequency of current location acquisition based on the subject's analyzed emotions. For example, if the subject is feeling anxious, the acquisition unit can increase the frequency of current location acquisition and update location information in real time. In addition, if the subject is calm, the acquisition unit can reduce the frequency of current location acquisition to reduce battery consumption. Furthermore, if the subject is in a panicked state, the acquisition unit can maximize the frequency of current location acquisition to enable a quick response. In this way, the acquisition unit can adjust the frequency of current location acquisition according to the subject's emotions.

[0118] The collection unit can estimate the emotions of the subject and adjust the frequency of disaster information collection based on the estimated emotions of the subject. For example, if the subject is feeling anxious, the collection unit can increase the frequency of disaster information collection and update the information in real time. Also, if the subject is calm, the collection unit can reduce the frequency of disaster information collection to save battery consumption. Furthermore, if the subject is in a state of panic, the collection unit can maximize the frequency of disaster information collection to enable a quick response. In this way, the collection unit can adjust the frequency of disaster information collection according to the emotions of the subject.

[0119] The analysis unit can estimate the subject's emotions and adjust the parameters of the analysis algorithm based on the estimated emotions of the subject. For example, if the subject is feeling anxious, the analysis unit can set parameters that prioritize a route that allows for quick evacuation. Alternatively, if the subject is calm, the analysis unit can set parameters that perform a detailed analysis. Furthermore, if the subject is in a state of panic, the analysis unit can set parameters that prioritize the shortest route. This allows the analysis unit to adjust the parameters of the analysis algorithm according to the subject's emotions.

[0120] The recalculation unit can estimate the subject's emotions and adjust the frequency of recalculation based on the estimated subject's emotions. For example, if the subject is feeling anxious, the recalculation unit can increase the frequency of recalculation and update the route in real time. Also, if the subject is calm, the recalculation unit can reduce the frequency of recalculation to save battery consumption. Furthermore, if the subject is in a panicked state, the recalculation unit can maximize the frequency of recalculation to enable a quick response. Thus, the recalculation unit can adjust the frequency of recalculation according to the subject's emotions.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The acquisition unit acquires the current location of the subject. For example, the acquisition unit can acquire the current location of the subject using GPS or location information from a mobile phone. Step 2: The collection unit collects disaster information in real time. For example, the collection unit can collect disaster information in real time from databases of the Japan Meteorological Agency and local governments. Step 3: The analysis unit analyzes the information acquired by the acquisition unit and the collection unit and calculates an evacuation route. For example, the analysis unit can use an algorithm to analyze the collected information and calculate an optimal evacuation route. Step 4: The suggestion unit suggests the evacuation route calculated by the analysis unit to the subject. For example, the suggestion unit can suggest the calculated evacuation route to the subject using a method of notifying the subject. Step 5: The recalculation unit recalculates the route if new disaster information occurs along the evacuation route. For example, the recalculation unit can use an algorithm to recalculate the route in real time. Step 6: The providing unit provides the latest evacuation route recalculated by the recalculation unit to the subject. For example, the providing unit can provide the recalculated evacuation route to the subject using a method of notifying the subject.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0125] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0167] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0168] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0173] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0179] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0180] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0184] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0185] 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.

[0186] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0187] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0188] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0189] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0192] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires the current location of the target person; a collection unit that collects disaster information in real time; an analysis unit that analyzes the information acquired by the acquisition unit and the collection unit and calculates an evacuation route; a suggestion unit that suggests the evacuation route calculated by the analysis unit to a target person; a recalculation unit that recalculates the evacuation route when new disaster information is generated along the route; a providing unit that provides the evacuation route recalculated by the recalculation unit to the target person. A system characterized by:

2. The acquisition unit Use GPS or mobile phone location information to obtain the target person's current location 2. The system of claim 1.

3. The collecting unit Collect disaster information in real time from the Japan Meteorological Agency or local government databases 2. The system of claim 1.

4. The analysis unit Use algorithms to analyze the collected information and calculate evacuation routes 2. The system of claim 1.

5. The recalculation unit If new disaster information occurs along the evacuation route, an algorithm is used to recalculate the route.

2. The system of claim 1.

6. The providing unit Provide the target person with the recalculated evacuation route 2. The system of claim 1.

7. The acquisition unit Analyze the subject's emotions and adjust the frequency of location acquisition based on the analyzed subject's emotions.

2. The system of claim 1.

8. The acquisition unit Analyze the target person's past movement history and select the acquisition method 2. The system of claim 1.

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