System

Abstract

To provide a system which provides support by voice for appropriate actions in the event of a disaster.SOLUTION: A system includes a collection part, a determination part, and a provision part. The collection part collects disaster information. The determination part determines appropriate actions based on the information collected by the collection part. The provision part provides actions determined by the determination part, by voice.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there was a problem that people would panic or get injured during a disaster and be unable to take appropriate action.

[0005] The system according to the embodiment aims to provide voice support for appropriate actions in the event of a disaster. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a determination unit, and a provision unit. The collection unit collects disaster information. The determination unit determines an appropriate action based on the information collected by the collection unit. The provision unit provides the action determined by the determination unit by voice. Effect of the Invention

[0007] The system according to the embodiment can provide voice support for appropriate actions in the event of a disaster. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A disaster response system according to an embodiment of the present invention is a system for solving the problem that a person who has been hit by a disaster (such as an earthquake or fire) panics and is unable to take appropriate action, or is injured and is unable to evacuate or make contact. In this system, AI appropriately analyzes the disaster situation and provides voice support for the action to be taken that suits the person (ensuring safety and evacuation). In addition, by automatically linking the person's damage situation to necessary contacts (such as family, medical institutions, and the country), others can immediately confirm the impact of the disaster and provide appropriate support immediately. For example, when a user launches an application, AI analyzes the current disaster situation. At this time, disaster information such as earthquakes and fires is collected in real time and compared with the user's location information to determine the optimal action. For example, when an earthquake occurs, AI provides specific instructions to the user by voice, such as "Hide under a desk" or "Evacuate to a safe place". Next, AI automatically links the user's damage situation to the contacts. For example, if the user is injured, AI sends that information to family and medical institutions to encourage a quick response. In addition, the user's location information is also sent at the same time, allowing rescue teams to arrive at the scene quickly. In addition, AI continuously monitors the user's condition and provides additional instructions as necessary. For example, if a fire breaks out, the AI ​​will provide instructions such as "Please move in a low position as the area is full of smoke." In addition, if the evacuation route changes or a new danger occurs, the AI ​​will respond immediately and provide the user with the latest information. This application ensures the safety of users in the event of a disaster and enables rapid evacuation and communication. It is particularly helpful for those who are prone to panic or are injured. This allows the disaster response system to ensure the safety of users and enable rapid evacuation and communication.

[0029] The disaster response system according to the embodiment includes a collection unit, a determination unit, and a provision unit. The collection unit collects disaster information. The disaster information includes, for example, earthquakes, fires, floods, and the like, but is not limited to such examples. The collection unit can collect disaster information in real time, for example, by using sensor technology. The collection unit can also collect disaster information on the Internet. For example, the collection unit collects data from seismometers and fire alarms and uses it as disaster information. The determination unit determines the optimal action based on the information collected by the collection unit. The determination unit determines the optimal action by, for example, checking against the user's location information. For example, the determination unit acquires the user's location information as GPS data and combines it with the disaster information to determine the optimal action. The provision unit provides the action determined by the determination unit by voice. The provision unit generates a voice message using, for example, voice synthesis technology and provides it to the user. For example, the provision unit provides specific instructions such as "hide under a desk" or "evacuate to a safe place" by voice. As a result, the disaster response system according to the embodiment can ensure the safety of the user and enable rapid evacuation and communication.

[0030] The collection unit collects disaster information. Examples of disaster information include, but are not limited to, earthquakes, fires, floods, and the like. The collection unit can collect disaster information in real time using, for example, sensor technology. Specifically, a seismometer detects earthquake tremors and transmits the data immediately to the collection unit. A fire alarm detects smoke and heat and collects data informing of the occurrence of a fire. A flood sensor detects rising water levels and collects data informing of the risk of flooding. These sensors are installed inside and outside buildings and constantly monitor changes in the environment. The collection unit can also collect disaster information on the Internet. For example, the collection unit obtains the latest disaster information from the websites of the Japan Meteorological Agency and disaster prevention organizations and incorporates this into the system. In addition, posts and reports about disasters can be collected from SNS and news sites, and the situation can be grasped in real time. This allows the collection unit to collect a wide range of disaster information from various sources and grasp the situation quickly and accurately. The collected data is stored in a central database and made accessible to other departments. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The judgment unit judges the optimal action based on the information collected by the collection unit. The judgment unit judges the optimal action by, for example, checking against the user's location information. Specifically, the judgment unit acquires the user's location information as GPS data and combines it with disaster information to judge the optimal action. For example, in the event of an earthquake, the judgment unit identifies the user's current location, and if the location is inside a building, instructs the user to "hide under a desk." In addition, in the event of a fire, if the user's location is close to the source of the fire, the judgment unit instructs the user to "evacuate immediately" and presents an evacuation route. In the event of a flood, if the user's location is in low-lying land, the judgment unit instructs the user to "evacuate to higher ground." The judgment unit uses AI technology to analyze the collected data and quickly judge the optimal action. AI can predict the most effective action based on past disaster data and simulation results. Furthermore, the judgment unit can also analyze the location information of multiple users at the same time and judge the optimal method for collective evacuation. As a result, the judgment unit can provide appropriate action instructions not only to individual users but also to multiple users, thereby improving overall safety.

[0032] The providing unit provides the action determined by the determining unit by voice. The providing unit generates a voice message using, for example, a voice synthesis technology and provides it to the user. Specifically, the providing unit receives instructions from the determining unit and generates it as a voice message. For example, specific instructions such as "hide under a desk" or "evacuate to a safe place" are provided by voice. The voice message is played through the user's smartphone or a dedicated disaster response device. This allows the user to act according to the voice instructions even in a situation where visual information is not available. Furthermore, the providing unit can generate voice messages in multiple languages, so that multilingual support is possible. This allows appropriate action instructions to be provided to foreign users as well. In addition, the providing unit can provide information using multiple means, such as not only voice messages but also text messages and vibration notifications. For example, by sending text messages to hearing-impaired people and vibrating notifications to visually-impaired people, information can be reliably transmitted to all users. This allows the providing unit to ensure the safety of users and enable rapid evacuation and communication.

[0033] The disaster response system includes a monitoring unit that monitors the user's condition. The monitoring unit continuously monitors the user's condition. The user's condition includes, for example, a health condition, location information, and activity status, but is not limited to such examples. The monitoring unit can monitor the user's health condition, for example, using sensor technology. The monitoring unit can also monitor the user's location information using GPS data. For example, the monitoring unit measures the user's heart rate and body temperature with a sensor to monitor the health condition. Furthermore, the monitoring unit can also monitor the user's activity status. For example, the monitoring unit measures the user's number of steps and travel distance to monitor the activity status. This allows the monitoring unit to grasp the user's condition in detail and provide appropriate support. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's health data to the generation AI and cause the generation AI to execute an analysis of the health condition.

[0034] The disaster response system includes a linking unit that automatically links the damage situation to the contact. The linking unit automatically links the damage situation to the contact. The damage situation includes, for example, the degree of damage, the damaged location, the condition of the victim, and the like, but is not limited to such examples. For example, the linking unit can transmit information to the contact via the Internet. In addition, the linking unit can also transmit information to the contact using a telephone or email. For example, if the user is injured, the linking unit transmits the information to a family member or a medical institution to prompt a quick response. Furthermore, the linking unit can also transmit the user's location information at the same time. For example, the linking unit acquires the user's location information using GPS data and transmits it to the contact. This enables the linking unit to enable a rescue team to arrive at the scene quickly. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the damage situation data to the generation AI and cause the generation AI to execute the transmission of information to the contact.

[0035] The disaster response system includes a route providing unit that provides an evacuation route. The route providing unit provides the evacuation route to the user. The evacuation route includes, for example, guidance to evacuation sites, safety of the route, and the like, but is not limited to such examples. The route providing unit can grasp the current location of the user using, for example, GPS data and provide the optimal evacuation route. In addition, the route providing unit can update the evacuation route in real time. For example, the route providing unit determines an evacuation route based on disaster information and provides it to the user. Furthermore, the route providing unit can provide the evacuation route by voice using a voice synthesis technology. For example, the route providing unit provides specific instructions such as "turn right" or "go straight" by voice. In this way, the route providing unit supports the user to evacuate quickly. A part or all of the above-mentioned processing in the route providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the route providing unit can input the user's location data to the generation AI and cause the generation AI to generate an optimal evacuation route.

[0036] The collection unit can collect disaster information of earthquakes or fires in real time. The collection unit collects disaster information of earthquakes or fires in real time, for example, by using sensor technology. Specific methods for collecting in real time include, for example, methods for collecting data from seismometers and fire alarms. The collection unit can also collect disaster information on the Internet in real time. For example, the collection unit acquires data from websites and applications that provide disaster information and updates the data in real time. This allows the collection unit to provide the latest disaster information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input disaster information data to the generation AI and cause the generation AI to perform data collection in real time.

[0037] The determination unit can determine an appropriate action in reference to the user's location information. The determination unit, for example, acquires the user's location information using GPS data, and determines the optimal action in combination with disaster information. The method of acquiring the user's location information includes, for example, using the GPS function of a smartphone. The determination unit can also update the user's location information in real time and determine the optimal action. For example, the determination unit recommends rapid evacuation when the user's current location is in a high-risk area. Furthermore, the determination unit can determine an evacuation route based on the user's location information and send instructions to the provision unit. This allows the determination unit to determine the optimal action in consideration of the user's location information. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's location data to the generation AI and cause the generation AI to execute the determination of the optimal action.

[0038] When collecting disaster information, the collection unit can analyze the user's past disaster response history and select the optimal collection method. For example, the collection unit acquires and analyzes the past disaster response history from a database. The past disaster response history includes, for example, past evacuation actions, response results, etc., but is not limited to such examples. For example, if the user has evacuated quickly in the past, the collection unit performs similar quick information collection. In addition, if the user has been confused by information overload in the past, the collection unit can also collect only the minimum amount of information necessary. Furthermore, if the user has trusted a specific information source in the past, the collection unit can preferentially collect information from that information source. This allows the collection unit to analyze the past disaster response history and collect optimal information. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past disaster response history data to the generation AI and cause the generation AI to select the optimal collection method.

[0039] The collection unit can collect the user's current location information and the surrounding environment information in combination when collecting disaster information. The collection unit, for example, uses GPS data to acquire the user's current location information, and combines it with the surrounding environment information to collect disaster information. The surrounding environment information includes, for example, weather information, topographical information, and the like, but is not limited to such examples. For example, when the user's current location is in a high-risk area, the collection unit preferentially collects the surrounding environment information. In addition, when the user's current location is close to a shelter, the collection unit can also collect the status of the shelter. Furthermore, when the user's current location is a key transportation point, the collection unit can also collect traffic information. This allows the collection unit to combine the current location information and the surrounding environment information to collect more accurate information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's location data and the environment information data to the generation AI and cause the generation AI to execute information collection.

[0040] When collecting disaster information, the collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user. The collection unit, for example, acquires the geographical location information of the user using GPS data, and preferentially collects highly relevant information. Highly relevant information includes, for example, information based on the user's location and information based on the type of disaster, but is not limited to such examples. For example, when the user's current location is close to the epicenter, the collection unit preferentially collects earthquake information. In addition, when the user's current location is close to a fire outbreak area, the collection unit can also preferentially collect fire information. Furthermore, when the user's current location is close to an evacuation shelter, the collection unit can also preferentially collect the situation of the evacuation shelter. Thereby, the collection unit can preferentially collect highly relevant information by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's location data to the generation AI and cause the generation AI to execute collection of highly relevant information.

[0041] The collection unit can analyze the social media activity of the user and collect related information when collecting disaster information. The collection unit, for example, analyzes the content of posts on social media and collects related disaster information. The social media activity includes, for example, the content of posts, the reactions of followers, and the like, but is not limited to such examples. The collection unit, for example, collects surrounding disaster information based on location information shared by the user on social media. The collection unit can also preferentially collect information from accounts that the user follows on social media. Furthermore, the collection unit can analyze the content posted by the user on social media and collect related disaster information. In this way, the collection unit can collect related information by analyzing social media activities. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media data to the generation AI and cause the generation AI to collect related information.

[0042] The judgment unit can apply different judgment algorithms depending on the type and scale of the disaster at the time of judgment. The judgment unit applies different judgment algorithms depending on the type and scale of the disaster, for example. The judgment algorithm includes, but is not limited to, rule-based and machine learning algorithms. For example, the judgment unit applies an algorithm that prioritizes ensuring safety in the building in the case of an earthquake. The judgment unit can also apply an algorithm that prioritizes rapid evacuation in the case of a fire. Furthermore, the judgment unit can also apply an algorithm that prioritizes evacuation to high ground in the case of a flood. This enables the judgment unit to make a judgment according to the type and scale of the disaster. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input disaster data to the generation AI and cause the generation AI to apply the judgment algorithm.

[0043] At the time of judgment, the judgment unit can judge the optimal behavior by referring to the user's past behavior history. The judgment unit, for example, acquires and refers to the past behavior history from a database. The past behavior history includes, for example, past evacuation behavior, response results, etc., but is not limited to such examples. For example, if the user has evacuated quickly in the past, the judgment unit recommends a similar quick behavior. In addition, if the user has been confused by information overload in the past, the judgment unit can also provide the minimum necessary behavior instructions. Furthermore, if the user has used a specific evacuation route in the past, the judgment unit can also preferentially recommend that route. This allows the judgment unit to judge the optimal behavior by referring to the past behavior history. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input past behavior history data to the generation AI and cause the generation AI to execute the judgment of the optimal behavior.

[0044] The determination unit can determine the optimal behavior by combining the user's location information and the surrounding environmental information at the time of determination. The determination unit acquires the user's location information using, for example, GPS data, and determines the optimal behavior by combining the location information with the surrounding environmental information. The surrounding environmental information includes, for example, weather information, topographical information, and the like, but is not limited to such examples. For example, the determination unit recommends rapid evacuation when the user's current location is in a high-risk area. In addition, the determination unit can also recommend moving to a shelter when the user's current location is close to a shelter. Furthermore, the determination unit can also recommend behavior that takes traffic information into consideration when the user's current location is a transportation hub. Thereby, the determination unit can determine the optimal behavior by combining the location information and the environmental information. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's location data and environmental information data to the generation AI and cause the generation AI to execute the determination of the optimal behavior.

[0045] At the time of judgment, the judgment unit can analyze the social media activity of the user and judge the related behavior. The judgment unit, for example, analyzes the content of posts on social media and judges the related behavior. The social media activity includes, for example, the content of posts, the reaction of followers, and the like, but is not limited to such examples. The judgment unit, for example, judges the optimal behavior based on location information shared by the user on social media. The judgment unit can also judge the behavior based on information from accounts the user follows on social media. Furthermore, the judgment unit can analyze the content posted by the user on social media and judge the related behavior. Thereby, the judgment unit can judge the related behavior by analyzing the social media activity. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input social media data to the generation AI and cause the generation AI to execute the judgment of the related behavior.

[0046] The providing unit can provide different voice instructions according to the type and scale of the disaster at the time of providing. The providing unit provides different voice instructions according to the type and scale of the disaster, for example. The voice instructions include, for example, evacuation instructions, methods of ensuring safety, and the like, but are not limited to such examples. For example, in the case of an earthquake, the providing unit provides instructions that prioritize ensuring safety in a building. In addition, the providing unit can also provide instructions that prioritize rapid evacuation in the case of a fire. Furthermore, the providing unit can also provide instructions that prioritize evacuation to high ground in the case of a flood. This allows the providing unit to provide voice instructions according to the type and scale of the disaster. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input disaster data to the generating AI and cause the generating AI to execute generation of voice instructions.

[0047] The providing unit can provide optimal voice instructions by referring to the user's past behavior history at the time of providing. The providing unit, for example, acquires and refers to the past behavior history from a database. The past behavior history includes, for example, past evacuation behavior, response results, etc., but is not limited to such examples. For example, if the user has evacuated quickly in the past, the providing unit can provide similar quick instructions. In addition, if the user has been confused by too much information in the past, the providing unit can also provide the minimum necessary instructions. Furthermore, if the user has used a specific evacuation route in the past, the providing unit can also give priority to instructing that route. Thereby, the providing unit can provide optimal voice instructions by referring to the past behavior history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input past behavior history data to the generating AI and cause the generating AI to generate optimal voice instructions.

[0048] The providing unit can provide optimal voice instructions by combining the user's location information and the surrounding environmental information at the time of providing. The providing unit, for example, acquires the user's location information using GPS data, and combines the location information with the surrounding environmental information to provide optimal voice instructions. The surrounding environmental information includes, for example, weather information, topographical information, and the like, but is not limited to such examples. For example, when the user's current location is in a high-risk area, the providing unit instructs the user to evacuate quickly. In addition, when the user's current location is close to a shelter, the providing unit can also instruct the user to move to the shelter. Furthermore, when the user's current location is a transportation hub, the providing unit can also provide instructions that take into account traffic information. Thereby, the providing unit can provide optimal voice instructions by combining the location information and the environmental information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location data and environmental information data to the generating AI, and cause the generating AI to generate optimal voice instructions.

[0049] The providing unit can analyze the social media activity of the user at the time of providing and provide the related voice instructions. The providing unit can, for example, analyze the content of posts on social media and provide the related voice instructions. The social media activities include, for example, the content of posts, the reactions of followers, and the like, but are not limited to such examples. The providing unit can provide optimal instructions, for example, based on location information shared by the user on social media. The providing unit can also provide instructions based on information from accounts the user follows on social media. Furthermore, the providing unit can also analyze the content posted by the user on social media and provide related instructions. Thereby, the providing unit can provide related voice instructions by analyzing the social media activities. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input social media data to a generating AI and cause the generating AI to generate related voice instructions.

[0050] The monitoring unit can select the optimal monitoring method by referring to the user's past health condition and behavior history during monitoring. The monitoring unit, for example, acquires and refers to the past health condition and behavior history from a database. The past health condition includes, for example, medical history, medical examination results, etc., but is not limited to such examples. For example, the monitoring unit performs frequent monitoring when the user's health condition has deteriorated in the past. In addition, when the user has shown a specific behavior pattern in the past, the monitoring unit can also perform monitoring based on the pattern. Furthermore, when the user has had a specific health problem in the past, the monitoring unit can also perform monitoring related to the problem. Thereby, the monitoring unit can perform optimal monitoring by referring to the past health condition and behavior history. Part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past health condition data to the generation AI and cause the generation AI to select the optimal monitoring method.

[0051] The monitoring unit can perform monitoring by combining the user's location information and the surrounding environment information during monitoring. The monitoring unit, for example, acquires the user's location information using GPS data, and performs monitoring by combining the location information with the surrounding environment information. The surrounding environment information includes, for example, weather information, topographical information, and the like, but is not limited to such examples. For example, when the user's current location is in a high-risk area, the monitoring unit monitors the surrounding environment information preferentially. In addition, when the user's current location is close to a shelter, the monitoring unit can also monitor the situation of the shelter. Furthermore, when the user's current location is a transportation hub, the monitoring unit can also monitor traffic information. As a result, the monitoring unit can perform optimal monitoring by combining the location information and the environment information. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's location data and the environment information data to the generation AI and cause the generation AI to perform monitoring.

[0052] The linking unit can select the optimal linking method by referring to the user's past contact history at the time of linking. The linking unit, for example, acquires and refers to the past contact history from a database. The past contact history includes, for example, past contact content, contact frequency, etc., but is not limited to such examples. The linking unit, for example, preferentially selects contacts that the user has frequently contacted in the past. The linking unit can also preferentially select a contact method (such as telephone or email) that the user has used in the past. Furthermore, the linking unit can predict and select a contact method to be used in a specific time period from the user's past contact history. In this way, the linking unit can select the optimal linking method by referring to the past contact history. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input past contact history data to the generation AI and cause the generation AI to select the optimal linking method.

[0053] The linking unit can perform linking by combining the user's location information and the surrounding environment information at the time of linking. The linking unit, for example, uses GPS data to acquire the user's location information, and combines it with the surrounding environment information to perform linking. The surrounding environment information includes, for example, weather information, topographical information, and the like, but is not limited to such examples. For example, when the user's current location is in a high-risk area, the linking unit links the surrounding environment information. In addition, when the user's current location is close to a shelter, the linking unit can also link the situation of the shelter. Furthermore, when the user's current location is a transportation hub, the linking unit can also link traffic information. Thereby, the linking unit can perform optimal linking by combining the location information and the environment information. Part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input the user's location data and the environment information data to the generation AI and cause the generation AI to execute the linking.

[0054] The route providing unit can provide different evacuation routes according to the type and scale of the disaster when providing the route. The route providing unit provides different evacuation routes according to, for example, the type and scale of the disaster. The evacuation route includes, for example, guidance to evacuation sites, safety of the route, and the like, but is not limited to such examples. For example, in the case of an earthquake, the route providing unit provides an evacuation route that prioritizes ensuring safety in the building. In addition, in the case of a fire, the route providing unit can also provide an evacuation route that prioritizes rapid evacuation. Furthermore, in the case of a flood, the route providing unit can also provide an evacuation route that prioritizes evacuation to high ground. This enables the route providing unit to provide an evacuation route according to the type and scale of the disaster. A part or all of the above-mentioned processing in the route providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the route providing unit can input disaster data to the generation AI and cause the generation AI to execute generation of an evacuation route.

[0055] The route providing unit can provide an optimal evacuation route by combining the user's location information and the surrounding environment information when providing the route. The route providing unit acquires the user's location information using, for example, GPS data, and combines it with the surrounding environment information to provide an optimal evacuation route. The surrounding environment information includes, for example, weather information, topographical information, and the like, but is not limited to such examples. For example, when the user's current location is in a high-risk area, the route providing unit can provide a route to the evacuation shelter when the user's current location is close to the evacuation shelter. Furthermore, when the user's current location is a key transportation point, the route providing unit can also provide an evacuation route that takes traffic information into consideration. Thereby, the route providing unit can provide an optimal evacuation route by combining the location information and the environment information. A part or all of the above-mentioned processing in the route providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the route providing unit can input the user's location data and the environment information data to the generation AI and cause the generation AI to generate an evacuation route.

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

[0057] The disaster response system can analyze the user's past evacuation behavior and provide an optimal evacuation route. For example, if the user has used a specific evacuation route in the past, that route can be provided preferentially. Also, if the user's past evacuation behavior was quick, a similar quick evacuation route can be provided. Furthermore, if the user's past evacuation behavior caused confusion, a more concise and clear evacuation route can be provided. This makes it possible to provide an optimal evacuation route that takes into account the user's past behavior history.

[0058] The disaster response system can monitor the user's health condition in real time and automatically contact medical institutions if an abnormality is detected. For example, if the user's heart rate is abnormally high, the system will immediately notify medical institutions. It can also respond in a similar manner if the user's body temperature suddenly rises. Furthermore, if the user loses consciousness, the system can automatically notify emergency contacts to promptly respond. This allows the system to continuously monitor the user's health condition and provide the necessary support.

[0059] The disaster response system can provide an optimal evacuation route by combining the user's location information and surrounding environmental information. For example, if the user's current location is in a high-risk area, a quick evacuation route can be provided. Also, if the user's current location is close to an evacuation shelter, a route to the evacuation shelter can be provided. Furthermore, if the user's current location is a key transportation point, an evacuation route that takes traffic information into consideration can be provided. In this way, by combining location information and environmental information, an optimal evacuation route can be provided.

[0060] The disaster response system can analyze the social media activities of a user and collect related disaster information. For example, surrounding disaster information can be collected based on location information shared by the user on social media. It can also preferentially collect information from accounts that the user follows on social media. Furthermore, it can analyze the content posted by the user on social media and collect related disaster information. In this way, it is possible to collect related information by analyzing social media activities.

[0061] The disaster response system can select the optimal monitoring method by referring to the user's past health condition and behavior history. For example, if the user's health condition has deteriorated in the past, frequent monitoring can be performed. Also, if the user has shown a specific behavior pattern in the past, monitoring can be performed based on that pattern. Furthermore, if the user has had a specific health problem in the past, monitoring related to that problem can be performed. In this way, optimal monitoring can be performed by referring to the user's past health condition and behavior history.

[0062] The disaster response system can select the optimal partner by combining the user's location information with surrounding environmental information. For example, if the user's current location is in a high-risk area, the most reliable contact will be selected as a priority. Also, if the user's current location is close to an evacuation shelter, the shelter's situation can be shared. Furthermore, if the user's current location is a key transportation point, traffic information can also be shared. In this way, by combining location information and environmental information, it becomes possible to select the optimal partner.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The collection unit collects disaster information. Disaster information includes, but is not limited to, earthquakes, fires, floods, etc. The collection unit can collect disaster information in real time using sensor technology. It can also collect disaster information on the Internet. For example, data from seismometers and fire alarms is collected and used as disaster information. Step 2: The determination unit determines the optimal action based on the information collected by the collection unit. The determination unit determines the optimal action by checking against the user's location information. For example, the determination unit obtains the user's location information as GPS data and combines it with disaster information to determine the optimal action. Step 3: The providing unit provides the action determined by the determining unit by voice. The providing unit generates a voice message using voice synthesis technology and provides it to the user. For example, specific instructions such as "Hide under a desk" or "Evacuate to a safe place" are provided by voice.

[0065] (Example 2) A disaster response system according to an embodiment of the present invention is a system for solving the problem that a person who has been hit by a disaster (such as an earthquake or fire) panics and is unable to take appropriate action, or is injured and is unable to evacuate or make contact. In this system, AI appropriately analyzes the disaster situation and provides voice support for the action to be taken that suits the person (ensuring safety and evacuation). In addition, by automatically linking the person's damage situation to necessary contacts (such as family, medical institutions, and the country), others can immediately confirm the impact of the disaster and provide appropriate support immediately. For example, when a user launches an application, AI analyzes the current disaster situation. At this time, disaster information such as earthquakes and fires is collected in real time and compared with the user's location information to determine the optimal action. For example, when an earthquake occurs, AI provides specific instructions to the user by voice, such as "Hide under a desk" or "Evacuate to a safe place". Next, AI automatically links the user's damage situation to the contacts. For example, if the user is injured, AI sends that information to family and medical institutions to encourage a quick response. In addition, the user's location information is also sent at the same time, allowing rescue teams to arrive at the scene quickly. In addition, AI continuously monitors the user's condition and provides additional instructions as necessary. For example, if a fire breaks out, the AI ​​will provide instructions such as "Please move in a low position as the area is full of smoke." In addition, if the evacuation route changes or a new danger occurs, the AI ​​will respond immediately and provide the user with the latest information. This application ensures the safety of users in the event of a disaster and enables rapid evacuation and communication. It is particularly helpful for those who are prone to panic or are injured. This allows the disaster response system to ensure the safety of users and enable rapid evacuation and communication.

[0066] The disaster response system according to the embodiment includes a collection unit, a determination unit, and a provision unit. The collection unit collects disaster information. The disaster information includes, for example, earthquakes, fires, floods, and the like, but is not limited to such examples. The collection unit can collect disaster information in real time, for example, by using sensor technology. The collection unit can also collect disaster information on the Internet. For example, the collection unit collects data from seismometers and fire alarms and uses it as disaster information. The determination unit determines the optimal action based on the information collected by the collection unit. The determination unit determines the optimal action by, for example, checking against the user's location information. For example, the determination unit acquires the user's location information as GPS data and combines it with the disaster information to determine the optimal action. The provision unit provides the action determined by the determination unit by voice. The provision unit generates a voice message using, for example, voice synthesis technology and provides it to the user. For example, the provision unit provides specific instructions such as "hide under a desk" or "evacuate to a safe place" by voice. As a result, the disaster response system according to the embodiment can ensure the safety of the user and enable rapid evacuation and communication.

[0067] The collection unit collects disaster information. Examples of disaster information include, but are not limited to, earthquakes, fires, floods, and the like. The collection unit can collect disaster information in real time using, for example, sensor technology. Specifically, a seismometer detects earthquake tremors and transmits the data immediately to the collection unit. A fire alarm detects smoke and heat and collects data informing of the occurrence of a fire. A flood sensor detects rising water levels and collects data informing of the risk of flooding. These sensors are installed inside and outside buildings and constantly monitor changes in the environment. The collection unit can also collect disaster information on the Internet. For example, the collection unit obtains the latest disaster information from the websites of the Japan Meteorological Agency and disaster prevention organizations and incorporates this into the system. In addition, posts and reports about disasters can be collected from SNS and news sites, and the situation can be grasped in real time. This allows the collection unit to collect a wide range of disaster information from various sources and grasp the situation quickly and accurately. The collected data is stored in a central database and made accessible to other departments. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0068] The judgment unit judges the optimal action based on the information collected by the collection unit. The judgment unit judges the optimal action by, for example, checking against the user's location information. Specifically, the judgment unit acquires the user's location information as GPS data and combines it with disaster information to judge the optimal action. For example, in the event of an earthquake, the judgment unit identifies the user's current location, and if the location is inside a building, instructs the user to "hide under a desk." In addition, in the event of a fire, if the user's location is close to the source of the fire, the judgment unit instructs the user to "evacuate immediately" and presents an evacuation route. In the event of a flood, if the user's location is in low-lying land, the judgment unit instructs the user to "evacuate to higher ground." The judgment unit uses AI technology to analyze the collected data and quickly judge the optimal action. AI can predict the most effective action based on past disaster data and simulation results. Furthermore, the judgment unit can also analyze the location information of multiple users at the same time and judge the optimal method for collective evacuation. As a result, the judgment unit can provide appropriate action instructions not only to individual users but also to multiple users, thereby improving overall safety.

[0069] The providing unit provides the action determined by the determining unit by voice. The providing unit generates a voice message using, for example, a voice synthesis technology and provides it to the user. Specifically, the providing unit receives instructions from the determining unit and generates it as a voice message. For example, specific instructions such as "hide under a desk" or "evacuate to a safe place" are provided by voice. The voice message is played through the user's smartphone or a dedicated disaster response device. This allows the user to act according to the voice instructions even in a situation where visual information is not available. Furthermore, the providing unit can generate voice messages in multiple languages, so that multilingual support is possible. This allows appropriate action instructions to be provided to foreign users as well. In addition, the providing unit can provide information using multiple means, such as not only voice messages but also text messages and vibration notifications. For example, by sending text messages to hearing-impaired people and vibrating notifications to visually-impaired people, information can be reliably transmitted to all users. This allows the providing unit to ensure the safety of users and enable rapid evacuation and communication.

[0070] The disaster response system includes a monitoring unit that monitors the user's condition. The monitoring unit continuously monitors the user's condition. The user's condition includes, for example, a health condition, location information, and activity status, but is not limited to such examples. The monitoring unit can monitor the user's health condition, for example, using sensor technology. The monitoring unit can also monitor the user's location information using GPS data. For example, the monitoring unit measures the user's heart rate and body temperature with a sensor to monitor the health condition. Furthermore, the monitoring unit can also monitor the user's activity status. For example, the monitoring unit measures the user's number of steps and travel distance to monitor the activity status. This allows the monitoring unit to grasp the user's condition in detail and provide appropriate support. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's health data to the generation AI and cause the generation AI to execute an analysis of the health condition.

[0071] The disaster response system includes a linking unit that automatically links the damage situation to the contact. The linking unit automatically links the damage situation to the contact. The damage situation includes, for example, the degree of damage, the damaged location, the condition of the victim, and the like, but is not limited to such examples. For example, the linking unit can transmit information to the contact via the Internet. In addition, the linking unit can also transmit information to the contact using a telephone or email. For example, if the user is injured, the linking unit transmits the information to a family member or a medical institution to prompt a quick response. Furthermore, the linking unit can also transmit the user's location information at the same time. For example, the linking unit acquires the user's location information using GPS data and transmits it to the contact. This enables the linking unit to enable a rescue team to arrive at the scene quickly. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the damage situation data to the generation AI and cause the generation AI to execute the transmission of information to the contact.

[0072] The disaster response system includes a route providing unit that provides an evacuation route. The route providing unit provides the evacuation route to the user. The evacuation route includes, for example, guidance to evacuation sites, safety of the route, and the like, but is not limited to such examples. The route providing unit can grasp the current location of the user using, for example, GPS data and provide the optimal evacuation route. In addition, the route providing unit can update the evacuation route in real time. For example, the route providing unit determines an evacuation route based on disaster information and provides it to the user. Furthermore, the route providing unit can provide the evacuation route by voice using a voice synthesis technology. For example, the route providing unit provides specific instructions such as "turn right" or "go straight" by voice. In this way, the route providing unit supports the user to evacuate quickly. A part or all of the above-mentioned processing in the route providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the route providing unit can input the user's location data to the generation AI and cause the generation AI to generate an optimal evacuation route.

[0073] The collection unit can collect disaster information of earthquakes or fires in real time. The collection unit collects disaster information of earthquakes or fires in real time, for example, by using sensor technology. Specific methods for collecting in real time include, for example, methods for collecting data from seismometers and fire alarms. The collection unit can also collect disaster information on the Internet in real time. For example, the collection unit acquires data from websites and applications that provide disaster information and updates the data in real time. This allows the collection unit to provide the latest disaster information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input disaster information data to the generation AI and cause the generation AI to perform data collection in real time.

[0074] The determination unit can determine an appropriate action in reference to the user's location information. The determination unit, for example, acquires the user's location information using GPS data, and determines the optimal action in combination with disaster information. The method of acquiring the user's location information includes, for example, using the GPS function of a smartphone. The determination unit can also update the user's location information in real time and determine the optimal action. For example, the determination unit recommends rapid evacuation when the user's current location is in a high-risk area. Furthermore, the determination unit can determine an evacuation route based on the user's location information and send instructions to the provision unit. This allows the determination unit to determine the optimal action in consideration of the user's location information. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's location data to the generation AI and cause the generation AI to execute the determination of the optimal action.

[0075] The collection unit can estimate the user's emotion and adjust the collection frequency of disaster information based on the estimated user's emotion. The collection unit estimates the user's emotion using, for example, emotion recognition technology. For emotion estimation, for example, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can analyze the user's text message and estimate the emotion. This allows the collection unit to adjust the collection frequency of disaster information according to the user's emotion. For example, when the user is in a panic state, the collection frequency is increased to quickly obtain the latest disaster information. Also, when the user is in a calm state, the collection frequency is kept normal and only necessary information is obtained. Furthermore, when the user is tired, the collection frequency is reduced to avoid excessive information provision. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the collection frequency.

[0076] When collecting disaster information, the collection unit can analyze the user's past disaster response history and select the optimal collection method. For example, the collection unit acquires and analyzes the past disaster response history from a database. The past disaster response history includes, for example, past evacuation actions, response results, etc., but is not limited to such examples. For example, if the user has evacuated quickly in the past, the collection unit performs similar quick information collection. In addition, if the user has been confused by information overload in the past, the collection unit can also collect only the minimum amount of information necessary. Furthermore, if the user has trusted a specific information source in the past, the collection unit can preferentially collect information from that information source. This allows the collection unit to analyze the past disaster response history and collect optimal information. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past disaster response history data to the generation AI and cause the generation AI to select the optimal collection method.

[0077] The collection unit can collect the user's current location information and the surrounding environment information in combination when collecting disaster information. The collection unit, for example, uses GPS data to acquire the user's current location information, and combines it with the surrounding environment information to collect disaster information. The surrounding environment information includes, for example, weather information, topographical information, and the like, but is not limited to such examples. For example, when the user's current location is in a high-risk area, the collection unit preferentially collects the surrounding environment information. In addition, when the user's current location is close to a shelter, the collection unit can also collect the status of the shelter. Furthermore, when the user's current location is a key transportation point, the collection unit can also collect traffic information. This allows the collection unit to combine the current location information and the surrounding environment information to collect more accurate information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's location data and the environment information data to the generation AI and cause the generation AI to execute information collection.

[0078] The collection unit can estimate the user's emotion and determine the priority of disaster information to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion using, for example, emotion recognition technology. For emotion estimation, for example, facial expression recognition, voice analysis, text analysis, and other technologies are used. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can analyze the user's text message and estimate the emotion. This allows the collection unit to determine the priority of disaster information to be collected according to the user's emotion. For example, when the user is in a panic state, the most important information is collected with priority. Furthermore, when the user is in a calm state, detailed information can be collected. Furthermore, when the user is feeling anxious, information that gives a sense of security can be collected with priority. The emotion estimation is realized using, for example, an emotion engine or a generation AI, etc., using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data to the generation AI and have the generation AI determine the priority order of information.

[0079] When collecting disaster information, the collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user. The collection unit, for example, acquires the geographical location information of the user using GPS data, and preferentially collects highly relevant information. Highly relevant information includes, for example, information based on the user's location and information based on the type of disaster, but is not limited to such examples. For example, when the user's current location is close to the epicenter, the collection unit preferentially collects earthquake information. In addition, when the user's current location is close to a fire outbreak area, the collection unit can also preferentially collect fire information. Furthermore, when the user's current location is close to an evacuation shelter, the collection unit can also preferentially collect the situation of the evacuation shelter. Thereby, the collection unit can preferentially collect highly relevant information by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's location data to the generation AI and cause the generation AI to execute collection of highly relevant information.

[0080] The collection unit can analyze the social media activity of the user and collect related information when collecting disaster information. The collection unit, for example, analyzes the content of posts on social media and collects related disaster information. The social media activity includes, for example, the content of posts, the reactions of followers, and the like, but is not limited to such examples. The collection unit, for example, collects surrounding disaster information based on location information shared by the user on social media. The collection unit can also preferentially collect information from accounts that the user follows on social media. Furthermore, the collection unit can analyze the content posted by the user on social media and collect related disaster information. In this way, the collection unit can collect related information by analyzing social media activities. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media data to the generation AI and cause the generation AI to collect related information.

[0081] The judgment unit can estimate the user's emotion and adjust the judgment criteria of the optimal behavior based on the estimated user's emotion. The judgment unit estimates the user's emotion using, for example, emotion recognition technology. For emotion estimation, for example, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the judgment unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The judgment unit can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the judgment unit can analyze the user's text message and estimate the emotion. This allows the judgment unit to adjust the judgment criteria of the optimal behavior according to the user's emotion. For example, when the user is in a panic state, concise and clear behavior instructions can be provided. Furthermore, when the user is in a calm state, detailed behavior instructions can be provided. Furthermore, when the user feels anxious, behavior instructions that give a sense of security can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the judgment criteria for the behavior.

[0082] The judgment unit can apply different judgment algorithms depending on the type and scale of the disaster at the time of judgment. The judgment unit applies different judgment algorithms depending on the type and scale of the disaster, for example. The judgment algorithm includes, but is not limited to, rule-based and machine learning algorithms. For example, the judgment unit applies an algorithm that prioritizes ensuring safety in the building in the case of an earthquake. The judgment unit can also apply an algorithm that prioritizes rapid evacuation in the case of a fire. Furthermore, the judgment unit can also apply an algorithm that prioritizes evacuation to high ground in the case of a flood. This enables the judgment unit to make a judgment according to the type and scale of the disaster. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input disaster data to the generation AI and cause the generation AI to apply the judgment algorithm.

[0083] At the time of judgment, the judgment unit can judge the optimal behavior by referring to the user's past behavior history. The judgment unit, for example, acquires and refers to the past behavior history from a database. The past behavior history includes, for example, past evacuation behavior, response results, etc., but is not limited to such examples. For example, if the user has evacuated quickly in the past, the judgment unit recommends a similar quick behavior. In addition, if the user has been confused by information overload in the past, the judgment unit can also provide the minimum necessary behavior instructions. Furthermore, if the user has used a specific evacuation route in the past, the judgment unit can also preferentially recommend that route. This allows the judgment unit to judge the optimal behavior by referring to the past behavior history. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input past behavior history data to the generation AI and cause the generation AI to execute the judgment of the optimal behavior.

[0084] The judgment unit can estimate the user's emotion and determine the priority of the judgment result based on the estimated user's emotion. The judgment unit estimates the user's emotion using, for example, emotion recognition technology. For emotion estimation, for example, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the judgment unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The judgment unit can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the judgment unit can analyze the user's text message and estimate the emotion. This allows the judgment unit to determine the priority of the judgment result according to the user's emotion. For example, when the user is in a panic state, the most important action is given priority. Furthermore, when the user is in a calm state, detailed action instructions can be provided. Furthermore, when the user feels anxious, action instructions that give a sense of security can be given priority. The emotion estimation is realized using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of the judgment results.

[0085] The determination unit can determine the optimal behavior by combining the user's location information and the surrounding environmental information at the time of determination. The determination unit acquires the user's location information using, for example, GPS data, and determines the optimal behavior by combining the location information with the surrounding environmental information. The surrounding environmental information includes, for example, weather information, topographical information, and the like, but is not limited to such examples. For example, the determination unit recommends rapid evacuation when the user's current location is in a high-risk area. In addition, the determination unit can also recommend moving to a shelter when the user's current location is close to a shelter. Furthermore, the determination unit can also recommend behavior that takes traffic information into consideration when the user's current location is a transportation hub. Thereby, the determination unit can determine the optimal behavior by combining the location information and the environmental information. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the user's location data and environmental information data to the generation AI and cause the generation AI to execute the determination of the optimal behavior.

[0086] At the time of judgment, the judgment unit can analyze the social media activity of the user and judge the related behavior. The judgment unit, for example, analyzes the content of posts on social media and judges the related behavior. The social media activity includes, for example, the content of posts, the reaction of followers, and the like, but is not limited to such examples. The judgment unit, for example, judges the optimal behavior based on location information shared by the user on social media. The judgment unit can also judge the behavior based on information from accounts the user follows on social media. Furthermore, the judgment unit can analyze the content posted by the user on social media and judge the related behavior. Thereby, the judgment unit can judge the related behavior by analyzing the social media activity. Part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input social media data to the generation AI and cause the generation AI to execute the judgment of the related behavior.

[0087] The providing unit can estimate the user's emotion and adjust the expression method of the voice instruction based on the estimated user's emotion. The providing unit estimates the user's emotion using, for example, emotion recognition technology. For emotion estimation, for example, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the providing unit can analyze the user's text message and estimate the emotion. This allows the providing unit to adjust the expression method of the voice instruction according to the user's emotion. For example, when the user is in a panicked state, instructions can be provided in a calm voice. Furthermore, when the user is in a calm state, detailed instructions can be provided. Furthermore, when the user is feeling anxious, instructions can be provided in a voice that gives a sense of security. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's emotion data to the generating AI and cause the generating AI to adjust the way in which the voice instruction is expressed.

[0088] The providing unit can provide different voice instructions according to the type and scale of the disaster at the time of providing. The providing unit provides different voice instructions according to the type and scale of the disaster, for example. The voice instructions include, for example, evacuation instructions, methods of ensuring safety, and the like, but are not limited to such examples. For example, in the case of an earthquake, the providing unit provides instructions that prioritize ensuring safety in a building. In addition, the providing unit can also provide instructions that prioritize rapid evacuation in the case of a fire. Furthermore, the providing unit can also provide instructions that prioritize evacuation to high ground in the case of a flood. This allows the providing unit to provide voice instructions according to the type and scale of the disaster. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input disaster data to the generating AI and cause the generating AI to execute generation of voice instructions.

[0089] The providing unit can provide optimal voice instructions by referring to the user's past behavior history at the time of providing. The providing unit, for example, acquires and refers to the past behavior history from a database. The past behavior history includes, for example, past evacuation behavior, response results, etc., but is not limited to such examples. For example, if the user has evacuated quickly in the past, the providing unit can provide similar quick instructions. In addition, if the user has been confused by too much information in the past, the providing unit can also provide the minimum necessary instructions. Furthermore, if the user has used a specific evacuation route in the past, the providing unit can also give priority to instructing that route. Thereby, the providing unit can provide optimal voice instructions by referring to the past behavior history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input past behavior history data to the generating AI and cause the generating AI to generate optimal voice instructions.

[0090] The providing unit can estimate the user's emotion and determine the priority of the voice instructions based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion using emotion recognition technology. For emotion estimation, for example, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the providing unit can analyze the user's text message and estimate the emotion. This allows the providing unit to determine the priority of the voice instructions according to the user's emotion. For example, when the user is in a panicked state, the most important instructions can be provided preferentially. Furthermore, when the user is in a calm state, detailed instructions can be provided. Furthermore, when the user is feeling anxious, instructions that give a sense of security can be provided preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's emotion data to the generating AI and cause the generating AI to determine the priority order of the voice instructions.

[0091] The providing unit can provide optimal voice instructions by combining the user's location information and the surrounding environmental information at the time of providing. The providing unit, for example, acquires the user's location information using GPS data, and combines the location information with the surrounding environmental information to provide optimal voice instructions. The surrounding environmental information includes, for example, weather information, topographical information, and the like, but is not limited to such examples. For example, when the user's current location is in a high-risk area, the providing unit instructs the user to evacuate quickly. In addition, when the user's current location is close to a shelter, the providing unit can also instruct the user to move to the shelter. Furthermore, when the user's current location is a transportation hub, the providing unit can also provide instructions that take into account traffic information. Thereby, the providing unit can provide optimal voice instructions by combining the location information and the environmental information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's location data and environmental information data to the generating AI, and cause the generating AI to generate optimal voice instructions.

[0092] The providing unit can analyze the social media activity of the user at the time of providing and provide the related voice instructions. The providing unit can, for example, analyze the content of posts on social media and provide the related voice instructions. The social media activities include, for example, the content of posts, the reactions of followers, and the like, but are not limited to such examples. The providing unit can provide optimal instructions, for example, based on location information shared by the user on social media. The providing unit can also provide instructions based on information from accounts the user follows on social media. Furthermore, the providing unit can also analyze the content posted by the user on social media and provide related instructions. Thereby, the providing unit can provide related voice instructions by analyzing the social media activities. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can input social media data to a generating AI and cause the generating AI to generate related voice instructions.

[0093] The monitoring unit can estimate the user's emotion and adjust the monitoring frequency based on the estimated user's emotion. The monitoring unit estimates the user's emotion using, for example, emotion recognition technology. For emotion estimation, for example, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the monitoring unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The monitoring unit can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the monitoring unit can analyze the user's text message and estimate the emotion. This allows the monitoring unit to adjust the monitoring frequency according to the user's emotion. For example, when the user is in a panicked state, the monitoring frequency is increased to grasp the latest state. Furthermore, when the user is in a calm state, the normal monitoring frequency can be maintained. Furthermore, when the user is tired, the monitoring frequency can be reduced to avoid excessive information provision. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the monitoring frequency.

[0094] The monitoring unit can select the optimal monitoring method by referring to the user's past health condition and behavior history during monitoring. The monitoring unit, for example, acquires and refers to the past health condition and behavior history from a database. The past health condition includes, for example, medical history, medical examination results, etc., but is not limited to such examples. For example, the monitoring unit performs frequent monitoring when the user's health condition has deteriorated in the past. In addition, when the user has shown a specific behavior pattern in the past, the monitoring unit can also perform monitoring based on the pattern. Furthermore, when the user has had a specific health problem in the past, the monitoring unit can also perform monitoring related to the problem. Thereby, the monitoring unit can perform optimal monitoring by referring to the past health condition and behavior history. Part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past health condition data to the generation AI and cause the generation AI to select the optimal monitoring method.

[0095] The monitoring unit can estimate the user's emotion and determine the priority of the monitoring results based on the estimated user's emotion. The monitoring unit estimates the user's emotion using, for example, emotion recognition technology. For emotion estimation, for example, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the monitoring unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The monitoring unit can also record the user's voice and estimate the emotion using a voice analysis technology. Furthermore, the monitoring unit can analyze the user's text message and estimate the emotion. This allows the monitoring unit to determine the priority of the monitoring results according to the user's emotion. For example, when the user is in a panicked state, the most important monitoring results can be displayed preferentially. Furthermore, when the user is in a calm state, detailed monitoring results can be displayed. Furthermore, when the user is feeling anxious, monitoring results that give a sense of security can be displayed preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of the monitoring results.

[0096] The monitoring unit can perform monitoring by combining the user's location information and the surrounding environment information during monitoring. The monitoring unit, for example, acquires the user's location information using GPS data, and performs monitoring by combining the location information with the surrounding environment information. The surrounding environment information includes, for example, weather information, topographical information, and the like, but is not limited to such examples. For example, when the user's current location is in a high-risk area, the monitoring unit monitors the surrounding environment information preferentially. In addition, when the user's current location is close to a shelter, the monitoring unit can also monitor the situation of the shelter. Furthermore, when the user's current location is a transportation hub, the monitoring unit can also monitor traffic information. As a result, the monitoring unit can perform optimal monitoring by combining the location information and the environment information. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the user's location data and the environment information data to the generation AI and cause the generation AI to perform monitoring.

[0097] The linking unit can estimate the user's emotion and select a linking destination based on the estimated user's emotion. The linking unit estimates the user's emotion using, for example, emotion recognition technology. For emotion estimation, for example, facial expression recognition, voice analysis, text analysis, and other technologies are used. For example, the linking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The linking unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the linking unit can analyze the user's text message and estimate the emotion. This allows the linking unit to select a linking destination according to the user's emotion. For example, when the user is in a panicked state, the most reliable contact is selected with priority. Furthermore, when the user is in a calm state, detailed contact information can be provided. Furthermore, when the user is feeling anxious, the contact that gives a sense of security can be selected with priority. The emotion estimation is realized using, for example, an emotion engine or a generation AI, using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collaboration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collaboration unit may input the user's emotion data to the generation AI and cause the generation AI to select a collaboration destination.

[0098] The linking unit can select the optimal linking method by referring to the user's past contact history at the time of linking. The linking unit, for example, acquires and refers to the past contact history from a database. The past contact history includes, for example, past contact content, contact frequency, etc., but is not limited to such examples. The linking unit, for example, preferentially selects contacts that the user has frequently contacted in the past. The linking unit can also preferentially select a contact method (such as telephone or email) that the user has used in the past. Furthermore, the linking unit can predict and select a contact method to be used in a specific time period from the user's past contact history. In this way, the linking unit can select the optimal linking method by referring to the past contact history. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input past contact history data to the generation AI and cause the generation AI to select the optimal linking method.

[0099] The linking unit can estimate the user's emotion and determine the priority of linking based on the estimated user's emotion. The linking unit, for example, estimates the user's emotion using emotion recognition technology. For emotion estimation, for example, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the linking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The linking unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the linking unit can analyze the user's text message and estimate the emotion. This allows the linking unit to determine the priority of linking according to the user's emotion. For example, when the user is in a panicked state, the most important contacts are linked with priority. Furthermore, when the user is in a calm state, detailed contact information can be provided. Furthermore, when the user is feeling anxious, contacts that give a sense of security can be linked with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collaboration unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collaboration unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of collaboration.

[0100] The linking unit can perform linking by combining the user's location information and the surrounding environment information at the time of linking. The linking unit, for example, uses GPS data to acquire the user's location information, and combines it with the surrounding environment information to perform linking. The surrounding environment information includes, for example, weather information, topographical information, and the like, but is not limited to such examples. For example, when the user's current location is in a high-risk area, the linking unit links the surrounding environment information. In addition, when the user's current location is close to a shelter, the linking unit can also link the situation of the shelter. Furthermore, when the user's current location is a transportation hub, the linking unit can also link traffic information. Thereby, the linking unit can perform optimal linking by combining the location information and the environment information. Part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input the user's location data and the environment information data to the generation AI and cause the generation AI to execute the linking.

[0101] The route providing unit can estimate the user's emotion and adjust the method of providing an evacuation route based on the estimated user's emotion. The route providing unit estimates the user's emotion using, for example, emotion recognition technology. For emotion estimation, for example, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the route providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The route providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the route providing unit can analyze the user's text message and estimate the emotion. This allows the route providing unit to adjust the method of providing an evacuation route according to the user's emotion. For example, when the user is in a panic state, a concise and clear evacuation route can be provided. Furthermore, when the user is in a calm state, a detailed evacuation route can be provided. Furthermore, when the user is feeling anxious, an evacuation route that gives a sense of security can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the route providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the route providing unit may input user emotion data to the generation AI and cause the generation AI to adjust the method of providing an evacuation route.

[0102] The route providing unit can provide different evacuation routes according to the type and scale of the disaster when providing the route. The route providing unit provides different evacuation routes according to, for example, the type and scale of the disaster. The evacuation route includes, for example, guidance to evacuation sites, safety of the route, and the like, but is not limited to such examples. For example, in the case of an earthquake, the route providing unit provides an evacuation route that prioritizes ensuring safety in the building. In addition, in the case of a fire, the route providing unit can also provide an evacuation route that prioritizes rapid evacuation. Furthermore, in the case of a flood, the route providing unit can also provide an evacuation route that prioritizes evacuation to high ground. This enables the route providing unit to provide an evacuation route according to the type and scale of the disaster. A part or all of the above-mentioned processing in the route providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the route providing unit can input disaster data to the generation AI and cause the generation AI to execute generation of an evacuation route.

[0103] The route providing unit can estimate the user's emotion and determine the priority of evacuation routes based on the estimated user's emotion. The route providing unit estimates the user's emotion using, for example, emotion recognition technology. For emotion estimation, for example, technologies such as facial expression recognition, voice analysis, and text analysis are used. For example, the route providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The route providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the route providing unit can analyze the user's text message and estimate the emotion. This allows the route providing unit to determine the priority of evacuation routes according to the user's emotion. For example, when the user is in a panic state, the most important evacuation route can be provided preferentially. Furthermore, when the user is in a calm state, a detailed evacuation route can be provided. Furthermore, when the user is feeling anxious, an evacuation route that gives a sense of security can be provided preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the route providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the route providing unit may input user emotion data to the generation AI and cause the generation AI to determine the priority order of evacuation routes.

[0104] The route providing unit can provide an optimal evacuation route by combining the user's location information and the surrounding environment information when providing the route. The route providing unit acquires the user's location information using, for example, GPS data, and combines it with the surrounding environment information to provide an optimal evacuation route. The surrounding environment information includes, for example, weather information, topographical information, and the like, but is not limited to such examples. For example, when the user's current location is in a high-risk area, the route providing unit can provide a route to the evacuation shelter when the user's current location is close to the evacuation shelter. Furthermore, when the user's current location is a key transportation point, the route providing unit can also provide an evacuation route that takes traffic information into consideration. Thereby, the route providing unit can provide an optimal evacuation route by combining the location information and the environment information. A part or all of the above-mentioned processing in the route providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the route providing unit can input the user's location data and the environment information data to the generation AI and cause the generation AI to generate an evacuation route.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The disaster response system can analyze the user's past evacuation behavior and provide an optimal evacuation route. For example, if the user has used a specific evacuation route in the past, that route can be provided preferentially. Also, if the user's past evacuation behavior was quick, a similar quick evacuation route can be provided. Furthermore, if the user's past evacuation behavior caused confusion, a more concise and clear evacuation route can be provided. This makes it possible to provide an optimal evacuation route that takes into account the user's past behavior history.

[0107] The disaster response system can monitor the user's health condition in real time and automatically contact medical institutions if an abnormality is detected. For example, if the user's heart rate is abnormally high, the system will immediately notify medical institutions. It can also respond in a similar manner if the user's body temperature suddenly rises. Furthermore, if the user loses consciousness, the system can automatically notify emergency contacts to promptly respond. This allows the system to continuously monitor the user's health condition and provide the necessary support.

[0108] The disaster response system can estimate the user's emotions and adjust the content of notifications to contacts based on the estimated emotions. For example, if the user is in a panicked state, an emergency notification requesting a quick response can be sent to the contacts. If the user is calm, a notification including a detailed explanation of the situation can be sent. Furthermore, if the user is feeling anxious, a notification including reassuring content can be sent. In this way, it is possible to provide appropriate notification content according to the user's emotions.

[0109] The disaster response system can provide an optimal evacuation route by combining the user's location information and surrounding environmental information. For example, if the user's current location is in a high-risk area, a quick evacuation route can be provided. Also, if the user's current location is close to an evacuation shelter, a route to the evacuation shelter can be provided. Furthermore, if the user's current location is a key transportation point, an evacuation route that takes traffic information into consideration can be provided. In this way, by combining location information and environmental information, an optimal evacuation route can be provided.

[0110] The disaster response system can estimate the user's emotions and adjust the method of providing an evacuation route based on the estimated emotions. For example, if the user is in a panicked state, a concise and clear evacuation route can be provided. If the user is calm, a detailed evacuation route can be provided. Furthermore, if the user is feeling anxious, a reassuring evacuation route can be provided. This makes it possible to provide an optimal evacuation route according to the user's emotions.

[0111] The disaster response system can analyze the social media activities of a user and collect related disaster information. For example, surrounding disaster information can be collected based on location information shared by the user on social media. It can also preferentially collect information from accounts that the user follows on social media. Furthermore, it can analyze the content posted by the user on social media and collect related disaster information. In this way, it is possible to collect related information by analyzing social media activities.

[0112] The disaster response system can estimate the user's emotions and adjust the way voice instructions are expressed based on the estimated emotions. For example, if the user is in a panicked state, instructions can be provided in a calm voice. If the user is calm, detailed instructions can be provided. Furthermore, if the user is feeling anxious, instructions can be provided in a reassuring voice. This makes it possible to provide optimal voice instructions according to the user's emotions.

[0113] The disaster response system can select the optimal monitoring method by referring to the user's past health condition and behavior history. For example, if the user's health condition has deteriorated in the past, frequent monitoring can be performed. Also, if the user has shown a specific behavior pattern in the past, monitoring can be performed based on that pattern. Furthermore, if the user has had a specific health problem in the past, monitoring related to that problem can be performed. In this way, optimal monitoring can be performed by referring to the user's past health condition and behavior history.

[0114] The disaster response system can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is in a panicked state, the monitoring frequency is increased to grasp the latest situation. If the user is calm, the normal monitoring frequency can be maintained. Furthermore, if the user is tired, the monitoring frequency can be reduced to avoid providing excessive information. This makes it possible to adjust the optimal monitoring frequency according to the user's emotions.

[0115] The disaster response system can select the optimal partner by combining the user's location information with surrounding environmental information. For example, if the user's current location is in a high-risk area, the most reliable contact will be selected as a priority. Also, if the user's current location is close to an evacuation shelter, the shelter's situation can be shared. Furthermore, if the user's current location is a key transportation point, traffic information can also be shared. In this way, by combining location information and environmental information, it becomes possible to select the optimal partner.

[0116] The process flow of the second embodiment will be briefly described below.

[0117] Step 1: The collection unit collects disaster information. Disaster information includes, but is not limited to, earthquakes, fires, floods, etc. The collection unit can collect disaster information in real time using sensor technology. It can also collect disaster information on the Internet. For example, data from seismometers and fire alarms is collected and used as disaster information. Step 2: The determination unit determines the optimal action based on the information collected by the collection unit. The determination unit determines the optimal action by checking against the user's location information. For example, the determination unit obtains the user's location information as GPS data and combines it with disaster information to determine the optimal action. Step 3: The providing unit provides the action determined by the determining unit by voice. The providing unit generates a voice message using voice synthesis technology and provides it to the user. For example, specific instructions such as "Hide under a desk" or "Evacuate to a safe place" are provided by voice.

[0118] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0119] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0120] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0121] For example, the collection unit can collect disaster information using the camera 42 and sensors of the smart device 14. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the optimal action based on the collected disaster information and the user's location information. The provision unit is realized by the control unit 46A of the smart device 14, and provides specific instructions to the user by voice using voice synthesis technology. The monitoring unit continuously monitors the user's health condition and location information using the sensor of the smart device 14. The linking unit is realized by the specific processing unit 290 of the data processing device 12, and automatically links the disaster situation to the contacts. The route provision unit is realized by the control unit 46A of the smart device 14, and provides evacuation routes by voice using voice synthesis technology. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0125] 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.

[0126] 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 the voice according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0128] 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 performed in a secure state.

[0129] Fig. 4 shows an example of 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0134] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0135] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0136] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0137] For example, the collection unit can collect disaster information using the camera 42 and sensors of the smart glasses 214. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the optimal action based on the collected disaster information and the user's location information. The provision unit is realized by the control unit 46A of the smart glasses 214, and provides specific instructions to the user by voice using voice synthesis technology. The monitoring unit continuously monitors the user's health condition and location information using the sensors of the smart glasses 214. The linking unit is realized by the specific processing unit 290 of the data processing device 12, and automatically links the disaster situation to the contacts. The route provision unit is realized by the control unit 46A of the smart glasses 214, and provides evacuation routes by voice using voice synthesis technology. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0141] 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.

[0142] 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 the voice according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0144] 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 performed in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0148] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0149] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0150] 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 voice indicating a user input for 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.

[0151] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0152] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0153] For example, the collection unit can collect disaster information using the camera 42 and sensors of the headset terminal 314. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the optimal action based on the collected disaster information and the user's location information. The provision unit is realized by the control unit 46A of the headset terminal 314, and provides specific instructions to the user by voice using voice synthesis technology. The monitoring unit continuously monitors the user's health condition and location information using the sensor of the headset terminal 314. The linking unit is realized by the specific processing unit 290 of the data processing device 12, and automatically links the disaster situation to the contacts. The route provision unit is realized by the control unit 46A of the headset terminal 314, and provides evacuation routes by voice using voice synthesis technology. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0157] 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.

[0158] 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 the voice according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0160] 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 performed in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0165] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0166] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0167] 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 a voice indicating a user input for 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.

[0168] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0169] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0170] For example, the collection unit can collect disaster information using the camera 42 and sensors of the robot 414. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the optimal action based on the collected disaster information and the user's location information. The provision unit is realized by the control unit 46A of the robot 414, and provides specific instructions to the user by voice using voice synthesis technology. The monitoring unit continuously monitors the user's health condition and location information using the sensor of the robot 414. The linking unit is realized by the specific processing unit 290 of the data processing device 12, and automatically links the disaster situation to the contact information. The route provision unit is realized by the control unit 46A of the robot 414, and provides evacuation routes by voice using voice synthesis technology. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0172] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0174] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0176] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0178] 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.

[0179] 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 Universal Serial Bus (USB) 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.

[0180] In addition, 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 upon request from the data processing device 12.

[0181] 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.

[0182] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0183] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0184] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0185] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0187] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0189] (Appendix 1) A collection department that collects disaster information; A determination unit that determines an appropriate action based on the information collected by the collection unit; a providing unit that provides the action determined by the determining unit by voice; Equipped A system characterized by: (Appendix 2) Equipped with a monitoring unit that monitors the user's condition 2. The system of claim 1, (Appendix 3) Equipped with a linking section that automatically links the damage status to contacts 2. The system of claim 1, (Appendix 4) Equipped with a route provision unit that provides evacuation routes 2. The system of claim 1, (Appendix 5) The collecting unit includes: Collect disaster information on earthquakes or fires in real time 2. The system of claim 1, (Appendix 6) The determination unit is Determine appropriate actions based on user location information 2. The system of claim 1, (Appendix 7) The collecting unit includes: Estimating user emotions and adjusting the frequency of disaster information collection based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting disaster information, the system analyzes the user's past disaster response history and selects the optimal collection method. 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting disaster information, the system collects information by combining the user's current location and surrounding environmental information. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate user emotions and prioritize disaster information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting disaster information, the system prioritizes collection of relevant information by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting disaster information, analyze users' social media activities and collect related information. 2. The system of claim 1, (Appendix 13) The determination unit is Inferring user emotions and adjusting optimal behavior criteria based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The determination unit is When making a judgment, different judgment algorithms are applied depending on the type and scale of the disaster. 2. The system of claim 1, (Appendix 15) The determination unit is When making a decision, the system refers to the user's past behavior history to determine the optimal behavior. 2. The system of claim 1, (Appendix 16) The determination unit is Estimate the user's emotions and prioritize the judgment results based on the estimated user's emotions. 2. The system of claim 1, (Appendix 17) The determination unit is When making a decision, the system combines the user's location information with information about the surrounding environment to determine the optimal course of action. 2. The system of claim 1, (Appendix 18) The determination unit is At the time of decision, analyze the user's social media activity to determine relevant actions. 2. The system of claim 1, (Appendix 19) The providing unit is Estimating a user's emotion and adjusting the way voice instructions are expressed based on the estimated user's emotion 2. The system of claim 1, (Appendix 20) The providing unit is When providing information, different voice instructions are provided depending on the type and scale of the disaster. 2. The system of claim 1, (Appendix 21) The providing unit is When providing a service, the system provides optimal voice instructions by referring to the user's past behavior history. 2. The system of claim 1, (Appendix 22) The providing unit is Estimating a user's emotion and determining the priority of voice instructions based on the estimated user's emotion 2. The system of claim 1, (Appendix 23) The providing unit is When providing the service, the system combines the user's location information and surrounding environmental information to provide optimal voice instructions. 2. The system of claim 1, (Appendix 24) The providing unit is When provided, analyze your social media activity and provide relevant voice prompts 2. The system of claim 1, (Appendix 25) The monitoring unit is Inferring a user's emotions and adjusting the monitoring frequency based on the estimated user's emotions 3. The system of claim 2, (Appendix 26) The monitoring unit is When monitoring, the optimal monitoring method is selected by referring to the user's past health condition and behavioral history. 3. The system of claim 2, (Appendix 27) The monitoring unit is Estimate user emotions and prioritize monitoring results based on the estimated user emotions. 3. The system of claim 2, (Appendix 28) The monitoring unit is When monitoring, the user's location information and surrounding environmental information are combined to perform monitoring. 3. The system of claim 2, (Appendix 29) The linking portion is Estimate the user's emotions and select a partner based on the estimated user's emotions 4. The system of claim 3. (Appendix 30) The linking portion is When linking, the optimal linking method is selected by referring to the user's past contact history. 4. The system of claim 3. (Appendix 31) The linking portion is Estimate user emotions and determine collaboration priorities based on the estimated user emotions 4. The system of claim 3. (Appendix 32) The linking portion is When linking, the user's location information is combined with surrounding environmental information to link the devices. 4. The system of claim 3. (Appendix 33) The route providing unit, Estimating user emotions and adjusting evacuation route provision method based on the estimated user emotions 5. The system of claim 4. (Appendix 34) The route providing unit, When providing routes, different evacuation routes are provided depending on the type and scale of the disaster. 5. The system of claim 4. (Appendix 35) The route providing unit, Estimating user emotions and prioritizing evacuation routes based on the estimated user emotions 5. The system of claim 4. (Appendix 36) The route providing unit, When providing a route, the system combines the user's location information with information about the surrounding environment to provide the optimal evacuation route. 5. The system of claim 4. [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects disaster information; A determination unit that determines an appropriate action based on the information collected by the collection unit; a providing unit that provides the action determined by the determining unit by voice; Equipped A system characterized by:

2. Equipped with a monitoring unit that monitors the user's condition 2. The system of claim 1 .

3. Equipped with a linking section that automatically links the damage status to contacts 2. The system of claim 1 .

4. Equipped with a route provision unit that provides evacuation routes 2. The system of claim 1 .

5. The collecting unit includes: Collect disaster information on earthquakes or fires in real time 2. The system of claim 1 .

6. The determination unit is Determine appropriate actions based on user location information 2. The system of claim 1 .

7. The collecting unit includes: Estimating user emotions and adjusting the frequency of disaster information collection based on the estimated user emotions 2. The system of claim 1 .

8. The collecting unit includes: When collecting disaster information, the system analyzes the user's past disaster response history and selects the optimal collection method.

2. The system of claim 1 .

9. The collecting unit includes: When collecting disaster information, the system collects information by combining the user's current location and surrounding environmental information.

2. The system of claim 1 .

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