System

Abstract

An object of a system according to an embodiment is to quickly and accurately inform family members of the contents of disaster prevention wireless communication.SOLUTION: A system includes a voice recognition unit, an analysis unit, a message generation unit, and a transmission unit. The voice recognition part converts the voice of the disaster prevention radio into a text. The analysis unit analyzes the text converted by the voice recognition unit. The message generation unit generates a message based on the content analyzed by the analysis unit. The transmission unit transmits the message generated by the message generation unit to the family.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to quickly and accurately convey the contents of disaster prevention radio broadcasts to family members.

[0005] The system according to the embodiment aims to quickly and accurately convey the contents of disaster prevention radio broadcasts to family members. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice recognition unit, an analysis unit, a message generation unit, and a transmission unit. The voice recognition unit converts the voice of the disaster prevention radio into text. The analysis unit analyzes the text converted by the voice recognition unit. The message generation unit generates a message based on the content analyzed by the analysis unit. The transmission unit transmits the message generated by the message generation unit to family members. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately convey the contents of the disaster prevention radio broadcast to family members. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The disaster prevention information provision system according to the embodiment of the present invention converts the voice of the disaster prevention radio into text, and the content is understood and analyzed by a generation AI, and an appropriate message is generated and sent to the family. This allows the disaster prevention information provision system to quickly and accurately convey the information from the disaster prevention radio to the family.

[0029] A disaster prevention information provision system according to an embodiment includes a voice recognition unit, an analysis unit, a message generation unit, and a transmission unit. The voice recognition unit converts voices transmitted via disaster prevention radio into text. For example, the voice recognition unit uses deep learning-based voice recognition technology to convert the voices transmitted via disaster prevention radio into text with high accuracy. The voice recognition unit can also analyze voices using a hidden Markov model (HMM) and convert them into text. The voice recognition unit can also remove background noise using noise canceling technology to obtain clear voice data. The analysis unit analyzes the text converted by the voice recognition unit. For example, the analysis unit can analyze the content of the text using text mining technology to extract important information. The analysis unit can also understand the meaning of the text using natural language processing technology and propose appropriate countermeasures. The analysis unit can also analyze the emotional nuances of the text using an emotion estimation function to estimate the level of urgency and the intensity of the emotion. The message generation unit generates a message based on the content analyzed by the analysis unit. For example, the message generation unit can use template-based generation technology to generate a message based on a pre-prepared template. The message generation unit can also use a generation AI to generate an appropriate message based on the analyzed content. Furthermore, the message generation unit can also use an emotion estimation function to predict the emotional impact on the recipient and select the most appropriate expression. The sending unit sends the message generated by the message generation unit to family members. For example, the sending unit can send the message using SMS. Furthermore, the sending unit can also send the message using email. Furthermore, the sending unit can also send the message using a messenger app. This allows the disaster prevention information provision system according to the embodiment to quickly and accurately convey information from the disaster prevention radio to family members. For example, when an evacuation order is issued, all family members can quickly take evacuation action. Furthermore, by providing a message including information on the local situation and evacuation shelters, evacuation action can be carried out more safely and efficiently.

[0030] The speech recognition unit converts speech into text in multiple languages, making it possible to accommodate foreign residents as well. For example, the speech recognition unit uses speech recognition technology to translate speech from disaster prevention radio broadcasts into multiple languages ​​in real time and convert it into text. For example, Japanese disaster prevention radio broadcasts can be translated into English, Chinese, Korean, and other languages ​​to provide information to foreign residents. The speech recognition unit also enhances speech recognition technology to accommodate specific dialects and accents. For example, it can recognize regional dialects such as Kansai dialect and Tohoku dialect and convert them into standard Japanese. The speech recognition unit also analyzes speech data in real time and builds a system that simultaneously translates it into multiple languages. For example, Japanese disaster prevention radio broadcasts can be simultaneously translated into English, Chinese, Spanish, and other languages ​​and converted into text. This allows disaster prevention information to be provided to foreign residents as well.

[0031] The voice recognition unit can incorporate filtering technology to remove background noise from the voice and obtain clear voice data. For example, the voice recognition unit can add a noise canceling function to the voice recognition technology to remove background noise from the voice of the disaster prevention radio. For example, environmental noise such as wind noise and traffic noise can be removed to obtain clear voice data. The voice recognition unit can also build a system that filters voice data in real time to remove background noise. For example, it can filter noise in a specific frequency band to improve the accuracy of voice recognition. The voice recognition unit can also incorporate a noise removal algorithm using deep learning to effectively remove background noise from the voice of the disaster prevention radio. For example, it can identify noise patterns based on learning data and remove noise in real time. This allows for the acquisition of clear voice data.

[0032] The voice recognition unit can collect the voices of local residents in real time during a disaster and convert them into text. The voice recognition unit, for example, uses voice recognition technology to build a system that collects the voices of local residents in real time during a disaster and converts them into text. For example, it collects voices from smartphones and radios and converts them into text. The voice recognition unit also develops a dedicated app for collecting the voices of local residents in real time during a disaster and converts voice data into text in real time. For example, residents can send voices through the app and convert the content into text. The voice recognition unit also uses voice recognition technology to develop a system that collects the voices of local residents in real time during a disaster and converts them into text. For example, it can collect voices from microphones installed at the disaster site and convert them into text. This makes it possible to collect the voices of local residents in real time and convert them into text.

[0033] The speech recognition unit can add a function to analyze speech data and automatically identify the type of disaster. For example, the speech recognition unit adds a disaster identification function to speech recognition technology and automatically identifies the type of disaster from speech data. For example, disasters such as earthquakes, floods, and fires can be identified from speech data. The speech recognition unit can also analyze speech data and develop an algorithm to automatically identify the type of disaster. For example, the type of disaster can be identified based on specific keywords or phrases. The speech recognition unit can also integrate the disaster identification function into speech recognition technology and build a system that identifies the type of disaster from speech data in real time. For example, the speech data can be analyzed to identify the type of disaster. This makes it possible to automatically identify the type of disaster.

[0034] The analysis unit can track the progress of a disaster in real time and generate messages appropriate to the situation. The analysis unit, for example, uses text analysis technology to build a system that tracks the progress of a disaster in real time and generates messages appropriate to the situation. For example, it can analyze information such as the seismic intensity of an earthquake and the height of a tsunami and generate appropriate messages. The analysis unit also develops a data collection system for tracking the progress of a disaster in real time and generates messages appropriate to the situation using text analysis technology. For example, it can analyze weather data and traffic information and generate appropriate messages. The analysis unit also uses text analysis technology to develop a system that tracks the progress of a disaster in real time and generates messages appropriate to the situation. For example, it can analyze the progress of a disaster and generate evacuation instructions and warning messages. This makes it possible to generate messages appropriate to the progress of a disaster.

[0035] The analysis unit can refer to past disaster data and propose countermeasures in the event of a similar disaster. The analysis unit, for example, uses text analysis technology to build a system that refers to past disaster data and proposes countermeasures in the event of a similar disaster. For example, it can propose appropriate countermeasures based on data from past earthquakes and floods. The analysis unit also stores past disaster data in a database and uses text analysis technology to propose countermeasures in the event of a similar disaster. For example, it can analyze countermeasures from past disasters and generate appropriate messages. The analysis unit also uses text analysis technology to develop a system that refers to past disaster data and proposes countermeasures in the event of a similar disaster. For example, it can analyze past disaster data and propose appropriate countermeasures. This makes it possible to propose countermeasures based on past disaster data.

[0036] The analysis unit can collect SNS posts during disasters and analyze them in real time. The analysis unit, for example, uses text analysis technology to build a system that collects SNS posts during disasters and analyzes them in real time. For example, it can collect posts from Twitter (registered trademark) and Facebook (registered trademark) and analyze disaster information. The analysis unit also develops a data collection system for collecting SNS posts during disasters and analyzes them in real time using text analysis technology. For example, it can identify the progress of a disaster and the extent of damage from SNS posts. The analysis unit also uses text analysis technology to develop a system that collects SNS posts during disasters and analyzes them in real time. For example, it can generate evacuation instructions and warning messages from SNS posts. This makes it possible to analyze SNS posts during disasters in real time.

[0037] The analysis unit is able to consider the infrastructure status of the region and propose appropriate countermeasures. The analysis unit, for example, uses text analysis technology to build a system that considers the infrastructure status of the region and proposes appropriate countermeasures. For example, it can analyze the electricity and water supply status and generate an appropriate message. The analysis unit also stores the infrastructure status of the region in a database and proposes appropriate countermeasures using text analysis technology. For example, it can analyze the communication status and generate an appropriate message. The analysis unit also uses text analysis technology to develop a system that considers the infrastructure status of the region and proposes appropriate countermeasures. For example, it can analyze the electricity and water supply status and generate an appropriate message. This makes it possible to propose countermeasures that consider the infrastructure status of the region.

[0038] The message generation unit can generate customized messages that take into consideration the structure and special needs of each family member. The message generation unit, for example, builds a system that generates customized messages that take into consideration the structure and special needs of each family member when generating messages. For example, for a household with elderly or disabled members, it can generate a message that includes points to note when evacuating. The message generation unit also stores the structure and special needs of each family member in a database and references that information when generating messages. For example, for a household with infants and young children, it can generate a message that includes a list of items to bring when evacuating. The message generation unit also develops a system that generates customized messages that take into consideration the structure and special needs of each family member when generating messages. For example, for a household with pets, it can generate a message that includes evacuation methods for pets. This makes it possible to generate customized messages according to the structure and special needs of each family member.

[0039] The sending unit is capable of checking the communication status of the destination in real time and selecting the optimal sending method. For example, the sending unit builds a system that checks the communication status of the destination in real time when sending a message and selects the optimal sending method. For example, if the communication status is poor, SMS can be used preferentially. The sending unit also stores the communication status of the destination in a database and refers to that information when sending a message. For example, if Wi-Fi is not available, mobile data communication can be used. The sending unit also develops a system that checks the communication status of the destination in real time when sending a message and selects the optimal sending method. For example, if the communication status is good, a message including an image or video can be sent. This makes it possible to select the optimal sending method depending on the communication status of the destination.

[0040] The message generation unit can also generate audio messages and video messages and provide visual and audio information. For example, the message generation unit builds a system that also generates audio messages and video messages when generating messages. For example, it can send evacuation instructions as audio messages and provide visual and audio information. The message generation unit also develops tools for generating audio messages and video messages and uses them when generating messages. For example, it can generate video messages showing evacuation routes. The message generation unit also develops a system that also generates audio messages and video messages when generating messages. For example, it can send evacuation instructions as audio messages and provide visual and audio information. This makes it possible to provide visual and audio information.

[0041] The sending unit can send a message by combining multiple communication means. The sending unit builds a system that sends a message by combining multiple communication means, for example. For example, a message can be sent by simultaneously using SMS, email, and SNS. The sending unit also develops a tool for sending a message by combining multiple communication means, and uses the tool when sending a message. For example, SMS and email can be sent simultaneously. The sending unit also develops a system that sends a message by combining multiple communication means, for example. For example, a message can be sent by simultaneously using SMS, email, and SNS. This allows a message to be sent by combining multiple communication means.

[0042] The analysis unit can grasp the situation in a region in real time using drones and satellite images. For example, in analyzing the situation in a region, the analysis unit builds a system that grasps the situation in real time using drones. For example, it can fly a drone to photograph the situation in a disaster-stricken area and analyze it. The analysis unit also develops a system that grasps the situation in a region in real time using satellite images. For example, it can identify the situation in a disaster-stricken area by analyzing satellite images. The analysis unit also builds a system that grasps the situation in a region in real time using drones and satellite images. For example, it can fly a drone to photograph the situation in a disaster-stricken area and analyze it. This makes it possible to grasp the situation in a region in real time using drones and satellite images.

[0043] The analysis unit can monitor the number of people that can be accommodated at an evacuation shelter and the status of supplies in real time. For example, in analyzing information about an evacuation shelter, the analysis unit builds a system that monitors the number of people that can be accommodated at an evacuation shelter and the status of supplies in real time. For example, the analysis unit can measure the number of people that can be accommodated at an evacuation shelter using a sensor and store the status of supplies in a database. The analysis unit also develops a data collection system for monitoring the number of people that can be accommodated at an evacuation shelter and the status of supplies in real time. For example, the analysis unit can measure the number of people that can be accommodated at an evacuation shelter using a sensor and store the status of supplies in a database. The analysis unit also develops a system that monitors the number of people that can be accommodated at an evacuation shelter and the status of supplies in real time in analyzing information about an evacuation shelter. For example, the analysis unit can measure the number of people that can be accommodated at an evacuation shelter using a sensor and store the status of supplies in a database. This makes it possible to monitor the number of people that can be accommodated at an evacuation shelter and the status of supplies in real time.

[0044] The analysis unit can integrate traffic information and infrastructure status to optimize evacuation routes. The analysis unit, for example, builds a system that integrates traffic information and infrastructure status in analyzing regional situations and optimizes evacuation routes. For example, it can analyze traffic congestion and road closure information and propose optimal evacuation routes. The analysis unit also stores traffic information and infrastructure status in a database and references that information when analyzing regional situations. For example, it can analyze the status of electricity and water and propose optimal evacuation routes. The analysis unit also develops a system that integrates traffic information and infrastructure status in analyzing regional situations and optimizes evacuation routes. For example, it can analyze traffic congestion and road closure information and propose optimal evacuation routes. This makes it possible to integrate traffic information and infrastructure status and optimize evacuation routes.

[0045] The analysis unit strengthens cooperation between evacuation shelters and enables efficient distribution of supplies. For example, in analyzing information about evacuation shelters, the analysis unit builds a system that strengthens cooperation between evacuation shelters and enables efficient distribution of supplies. For example, the supply status at each evacuation shelter can be shared in real time, allowing necessary supplies to be distributed quickly. The analysis unit also develops a data sharing system to strengthen cooperation between evacuation shelters and enables efficient distribution of supplies. For example, the capacity of evacuation shelters and the inventory status of supplies can be shared, allowing appropriate distribution to be carried out. The analysis unit also develops a system that strengthens cooperation between evacuation shelters and enables efficient distribution of supplies in analyzing information about evacuation shelters. For example, the supply status at each evacuation shelter can be shared in real time, allowing necessary supplies to be distributed quickly. This strengthens cooperation between evacuation shelters and enables efficient distribution of supplies.

[0046] The message generation unit can reflect the congestion status of evacuation routes and evacuation shelters in real time. The message generation unit, for example, builds a system that reflects the congestion status of evacuation routes and evacuation shelters in real time when generating messages. For example, the message generation unit can analyze the traffic conditions on evacuation routes and the capacity of evacuation shelters in real time and reflect them in messages. The message generation unit also stores the congestion status of evacuation routes and evacuation shelters in a database and references that information when generating messages. For example, the message generation unit can analyze the traffic conditions on evacuation routes and the capacity of evacuation shelters in real time and reflect them in messages. The message generation unit also develops a system that reflects the congestion status of evacuation routes and evacuation shelters in real time when generating messages. For example, the message generation unit can analyze the traffic conditions on evacuation routes and the capacity of evacuation shelters in real time and reflect them in messages. This makes it possible to reflect the congestion status of evacuation routes and evacuation shelters in real time.

[0047] The message generation unit can provide information that takes into account the capacity of the evacuation shelter and the status of supplies. For example, the message generation unit builds a system that provides information that takes into account the capacity of the evacuation shelter and the status of supplies when generating a message. For example, if the evacuation shelter is full, it can generate a message that guides users to another evacuation shelter. The message generation unit also stores the capacity of the evacuation shelter and the status of supplies in a database and references that information when generating a message. For example, if the evacuation shelter is low on supplies, it can generate a message that calls for the donation of supplies. The message generation unit also develops a system that provides information that takes into account the capacity of the evacuation shelter and the status of supplies when generating a message. For example, if the evacuation shelter is full, it can generate a message that guides users to another evacuation shelter. This makes it possible to provide information that takes into account the capacity of the evacuation shelter and the status of supplies.

[0048] The message generation unit generates a message including visual map information, and can visually indicate an evacuation route. The message generation unit, for example, builds a system for generating a message including visual map information when generating a message. For example, a map showing an evacuation route can be included in the message and visually displayed. The message generation unit also stores map information in a database and references the information when generating a message. For example, a map showing an evacuation route can be included in the message and visually displayed. The message generation unit also develops a system for generating a message including visual map information when generating a message. For example, a map showing an evacuation route can be included in the message and visually displayed. This allows a message including visual map information to be generated, and an evacuation route to be visually displayed.

[0049] The message generation unit can generate a message including contact information for an evacuation shelter and information about a support organization. For example, the message generation unit builds a system for generating a message including contact information for an evacuation shelter and information about a support organization when generating a message. For example, the information about the evacuation shelter and the support organization can be included in the message. The message generation unit also stores the information about the evacuation shelter and the support organization in a database and references that information when generating a message. For example, the information about the evacuation shelter and the support organization can be included in the message. The message generation unit also develops a system for generating a message including contact information for an evacuation shelter and information about a support organization when generating a message. For example, the information about the evacuation shelter and the support organization can be included in the message. This makes it possible to generate a message including contact information for an evacuation shelter and information about a support organization.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The disaster prevention information providing system may further include a location information acquiring unit. The location information acquiring unit acquires the user's current location in real time and provides it to the analysis unit. For example, the user's location information can be acquired using GPS technology and used to suggest evacuation routes and guide users to evacuation shelters. The location information acquiring unit can also record the user's movement history and analyze behavioral patterns during a disaster. This makes it possible to provide appropriate evacuation instructions based on the user's current location.

[0052] The disaster prevention information provision system can further include a health condition monitoring unit. The health condition monitoring unit monitors the user's health condition in real time and provides the data to the analysis unit. For example, it can monitor vital signs such as heart rate, body temperature, and blood pressure, and generate an emergency message if an abnormality is detected. The health condition monitoring unit can also analyze the user's health data and suggest precautions to take during evacuation and necessary medical support. This makes it possible to provide appropriate countermeasures according to the user's health condition.

[0053] The disaster prevention information provision system can further include a social media analysis unit. The social media analysis unit collects social media posts during a disaster and analyzes them in real time. For example, it can collect posts from Twitter (registered trademark) and Facebook (registered trademark) and analyze disaster information. The social media analysis unit can also identify the progress of the disaster and the extent of the damage from the social media posts and generate appropriate messages. This allows for real-time analysis of social media posts during a disaster and enables rapid information provision.

[0054] The disaster prevention information provision system can further be equipped with a drone control unit. The drone control unit flies a drone during a disaster to photograph the situation in the affected area and provides the photographs to the analysis unit in real time. For example, a drone can be used to take aerial photographs of the situation in the affected area, which can be used to confirm evacuation routes and grasp the extent of damage. The drone control unit can also automatically set the drone's flight route to efficiently collect information. This enables rapid information collection using drones.

[0055] The disaster prevention information system can further include a shelter management unit. The shelter management unit monitors the number of people that can be accommodated at shelters and the status of supplies in real time, and provides this information to the analysis unit. For example, the shelter's capacity can be measured using a sensor, and the stock status of supplies can be stored in a database. The shelter management unit can also strengthen cooperation between shelters and allocate supplies efficiently. This allows the number of people that can be accommodated at shelters and the status of supplies to be grasped in real time, and appropriate countermeasures can be provided.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The speech recognition unit converts the speech from the disaster prevention radio into text. For example, the speech recognition unit uses speech recognition technology based on deep learning to convert the speech from the disaster prevention radio into text with high accuracy. The speech recognition unit can also use an HMM (hidden Markov model) to analyze the speech and convert it into text. Furthermore, the speech recognition unit can also use noise canceling technology to remove background noise and obtain clear speech data. Step 2: The analysis unit analyzes the text converted by the speech recognition unit. For example, the analysis unit may use text mining technology to analyze the content of the text and extract important information. The analysis unit may also use natural language processing technology to understand the meaning of the text and propose appropriate countermeasures. Furthermore, the analysis unit may use an emotion estimation function to analyze the emotional nuances of the text and estimate the urgency and intensity of the emotion. Step 3: The message generation unit generates a message based on the content analyzed by the analysis unit. For example, the message generation unit uses template-based generation technology to generate a message based on a pre-prepared template. The message generation unit can also use generation AI to generate an appropriate message based on the analyzed content. Furthermore, the message generation unit can use an emotion estimation function to predict the emotional impact on the recipient and select the most appropriate expression. Step 4: The sending unit sends the message generated by the message generating unit to the family. For example, the sending unit can send the message using SMS. Alternatively, the sending unit can send the message using email. Alternatively, the sending unit can send the message using a messenger app.

[0058] (Example 2) The disaster prevention information provision system according to the embodiment of the present invention converts the voice of the disaster prevention radio into text, and the content is understood and analyzed by a generation AI, and an appropriate message is generated and sent to the family. This allows the disaster prevention information provision system to quickly and accurately convey the information from the disaster prevention radio to the family.

[0059] A disaster prevention information provision system according to an embodiment includes a voice recognition unit, an analysis unit, a message generation unit, and a transmission unit. The voice recognition unit converts voices transmitted via disaster prevention radio into text. For example, the voice recognition unit uses deep learning-based voice recognition technology to convert the voices transmitted via disaster prevention radio into text with high accuracy. The voice recognition unit can also analyze voices using a hidden Markov model (HMM) and convert them into text. The voice recognition unit can also remove background noise using noise canceling technology to obtain clear voice data. The analysis unit analyzes the text converted by the voice recognition unit. For example, the analysis unit can analyze the content of the text using text mining technology to extract important information. The analysis unit can also understand the meaning of the text using natural language processing technology and propose appropriate countermeasures. The analysis unit can also analyze the emotional nuances of the text using an emotion estimation function to estimate the level of urgency and the intensity of the emotion. The message generation unit generates a message based on the content analyzed by the analysis unit. For example, the message generation unit can use template-based generation technology to generate a message based on a pre-prepared template. The message generation unit can also use a generation AI to generate an appropriate message based on the analyzed content. Furthermore, the message generation unit can also use an emotion estimation function to predict the emotional impact on the recipient and select the most appropriate expression. The sending unit sends the message generated by the message generation unit to family members. For example, the sending unit can send the message using SMS. Furthermore, the sending unit can also send the message using email. Furthermore, the sending unit can also send the message using a messenger app. This allows the disaster prevention information provision system according to the embodiment to quickly and accurately convey information from the disaster prevention radio to family members. For example, when an evacuation order is issued, all family members can quickly take evacuation action. Furthermore, by providing a message including information on the local situation and evacuation shelters, evacuation action can be carried out more safely and efficiently.

[0060] The speech recognition unit converts speech into text in multiple languages, making it possible to accommodate foreign residents as well. For example, the speech recognition unit uses speech recognition technology to translate speech from disaster prevention radio broadcasts into multiple languages ​​in real time and convert it into text. For example, Japanese disaster prevention radio broadcasts can be translated into English, Chinese, Korean, and other languages ​​to provide information to foreign residents. The speech recognition unit also enhances speech recognition technology to accommodate specific dialects and accents. For example, it can recognize regional dialects such as Kansai dialect and Tohoku dialect and convert them into standard Japanese. The speech recognition unit also analyzes speech data in real time and builds a system that simultaneously translates it into multiple languages. For example, Japanese disaster prevention radio broadcasts can be simultaneously translated into English, Chinese, Spanish, and other languages ​​and converted into text. This allows disaster prevention information to be provided to foreign residents as well.

[0061] The voice recognition unit can incorporate filtering technology to remove background noise from the voice and obtain clear voice data. For example, the voice recognition unit can add a noise canceling function to the voice recognition technology to remove background noise from the voice of the disaster prevention radio. For example, environmental noise such as wind noise and traffic noise can be removed to obtain clear voice data. The voice recognition unit can also build a system that filters voice data in real time to remove background noise. For example, it can filter noise in a specific frequency band to improve the accuracy of voice recognition. The voice recognition unit can also incorporate a noise removal algorithm using deep learning to effectively remove background noise from the voice of the disaster prevention radio. For example, it can identify noise patterns based on learning data and remove noise in real time. This allows for the acquisition of clear voice data.

[0062] The speech recognition unit can use the emotion estimation function to estimate the level of urgency and the intensity of emotion from the speech transmitted via disaster prevention radio and determine the priority of text conversion. The speech recognition unit, for example, uses the emotion estimation function to estimate the level of urgency from the speech transmitted via disaster prevention radio and prioritizes text conversion of speech with a high level of urgency. For example, it prioritizes processing of speech with a high level of urgency, such as evacuation instructions or warning sounds. The speech recognition unit also integrates the emotion estimation function into speech recognition technology to estimate the intensity of emotion from the speech transmitted via disaster prevention radio. For example, it can prioritize text conversion of speech that expresses strong feelings of tension or impatience. The speech recognition unit also uses the emotion estimation function to identify the type of emotion from the speech transmitted via disaster prevention radio and determine the priority of text conversion. For example, it can prioritize processing of speech that expresses strong emotions such as anger or sadness. This allows information with a high level of urgency to be processed preferentially.

[0063] The voice recognition unit can collect the voices of local residents in real time during a disaster and convert them into text. The voice recognition unit, for example, uses voice recognition technology to build a system that collects the voices of local residents in real time during a disaster and converts them into text. For example, it collects voices from smartphones and radios and converts them into text. The voice recognition unit also develops a dedicated app for collecting the voices of local residents in real time during a disaster and converts voice data into text in real time. For example, residents can send voices through the app and convert the content into text. The voice recognition unit also uses voice recognition technology to develop a system that collects the voices of local residents in real time during a disaster and converts them into text. For example, it can collect voices from microphones installed at the disaster site and convert them into text. This makes it possible to collect the voices of local residents in real time and convert them into text.

[0064] The speech recognition unit can add a function to analyze speech data and automatically identify the type of disaster. For example, the speech recognition unit adds a disaster identification function to speech recognition technology and automatically identifies the type of disaster from speech data. For example, disasters such as earthquakes, floods, and fires can be identified from speech data. The speech recognition unit can also analyze speech data and develop an algorithm to automatically identify the type of disaster. For example, the type of disaster can be identified based on specific keywords or phrases. The speech recognition unit can also integrate the disaster identification function into speech recognition technology and build a system that identifies the type of disaster from speech data in real time. For example, the speech data can be analyzed to identify the type of disaster. This makes it possible to automatically identify the type of disaster.

[0065] The speech recognition unit can use the emotion estimation function to estimate emotions from the voices of evacuees and generate information for providing psychological support at evacuation shelters. The speech recognition unit, for example, uses the emotion estimation function to estimate emotions from the voices of evacuees and build a system that generates information for providing psychological support at evacuation shelters. For example, emotions such as anxiety and fear can be identified from the voices of evacuees and appropriate support can be provided. The speech recognition unit also analyzes the voices of evacuees in real time and identifies emotions using the emotion estimation function. For example, emotions such as stress and fatigue can be identified from the voices of evacuees and psychological support can be provided. The speech recognition unit also uses the emotion estimation function to estimate emotions from the voices of evacuees and develop a system that generates information for providing psychological support at evacuation shelters. For example, emotions can be identified from the voices of evacuees and appropriate support can be provided. This makes it possible to estimate the emotions of evacuees and provide psychological support.

[0066] The analysis unit can track the progress of a disaster in real time and generate messages appropriate to the situation. The analysis unit, for example, uses text analysis technology to build a system that tracks the progress of a disaster in real time and generates messages appropriate to the situation. For example, it can analyze information such as the seismic intensity of an earthquake and the height of a tsunami and generate appropriate messages. The analysis unit also develops a data collection system for tracking the progress of a disaster in real time and generates messages appropriate to the situation using text analysis technology. For example, it can analyze weather data and traffic information and generate appropriate messages. The analysis unit also uses text analysis technology to develop a system that tracks the progress of a disaster in real time and generates messages appropriate to the situation. For example, it can analyze the progress of a disaster and generate evacuation instructions and warning messages. This makes it possible to generate messages appropriate to the progress of a disaster.

[0067] The analysis unit can refer to past disaster data and propose countermeasures in the event of a similar disaster. The analysis unit, for example, uses text analysis technology to build a system that refers to past disaster data and proposes countermeasures in the event of a similar disaster. For example, it can propose appropriate countermeasures based on data from past earthquakes and floods. The analysis unit also stores past disaster data in a database and uses text analysis technology to propose countermeasures in the event of a similar disaster. For example, it can analyze countermeasures from past disasters and generate appropriate messages. The analysis unit also uses text analysis technology to develop a system that refers to past disaster data and proposes countermeasures in the event of a similar disaster. For example, it can analyze past disaster data and propose appropriate countermeasures. This makes it possible to propose countermeasures based on past disaster data.

[0068] The analysis unit can use the emotion estimation function to infer residents' anxiety and fear from the text content and generate a message that provides a sense of security accordingly. The analysis unit, for example, uses the emotion estimation function to build a system that infers residents' anxiety and fear from the text content and generates a message that provides a sense of security accordingly. For example, it can identify residents' anxiety from evacuation instruction text and generate a message that provides a sense of security accordingly. The analysis unit also integrates the emotion estimation function into text analysis technology to infer residents' anxiety and fear from the text content. For example, it can identify residents' fear from evacuation instruction text and generate a message that provides a sense of security accordingly. The analysis unit also develops a system that uses the emotion estimation function to infer residents' anxiety and fear from the text content and generate a message that provides a sense of security accordingly. For example, it can identify residents' anxiety from evacuation instruction text and generate a message that provides a sense of security accordingly. This makes it possible to generate a message that provides a sense of security according to residents' anxiety and fear.

[0069] The analysis unit can collect SNS posts during disasters and analyze them in real time. The analysis unit, for example, uses text analysis technology to build a system that collects SNS posts during disasters and analyzes them in real time. For example, it can collect posts from Twitter (registered trademark) and Facebook (registered trademark) and analyze disaster information. The analysis unit also develops a data collection system for collecting SNS posts during disasters and analyzes them in real time using text analysis technology. For example, it can identify the progress of a disaster and the extent of damage from SNS posts. The analysis unit also uses text analysis technology to develop a system that collects SNS posts during disasters and analyzes them in real time. For example, it can generate evacuation instructions and warning messages from SNS posts. This makes it possible to analyze SNS posts during disasters in real time.

[0070] The analysis unit is able to consider the infrastructure status of the region and propose appropriate countermeasures. The analysis unit, for example, uses text analysis technology to build a system that considers the infrastructure status of the region and proposes appropriate countermeasures. For example, it can analyze the electricity and water supply status and generate an appropriate message. The analysis unit also stores the infrastructure status of the region in a database and proposes appropriate countermeasures using text analysis technology. For example, it can analyze the communication status and generate an appropriate message. The analysis unit also uses text analysis technology to develop a system that considers the infrastructure status of the region and proposes appropriate countermeasures. For example, it can analyze the electricity and water supply status and generate an appropriate message. This makes it possible to propose countermeasures that consider the infrastructure status of the region.

[0071] The analysis unit can use the emotion estimation function to monitor residents' emotions during a disaster in real time and generate information for providing psychological support. The analysis unit, for example, uses the emotion estimation function to build a system that monitors residents' emotions during a disaster in real time and generates information for providing psychological support. For example, emotions can be identified from residents' social media posts and voice data, and appropriate support can be provided. The analysis unit also integrates the emotion estimation function with text analysis technology to monitor residents' emotions during a disaster in real time. For example, emotions can be identified from residents' social media posts and voices at evacuation shelters, and psychological support can be provided. The analysis unit also uses the emotion estimation function to develop a system that monitors residents' emotions during a disaster in real time and generates information for providing psychological support. For example, emotions can be identified from residents' social media posts and voice data, and appropriate support can be provided. This makes it possible to monitor residents' emotions during a disaster in real time and provide psychological support.

[0072] The message generation unit can generate customized messages that take into consideration the structure and special needs of each family member. The message generation unit, for example, builds a system that generates customized messages that take into consideration the structure and special needs of each family member when generating messages. For example, for a household with elderly or disabled members, it can generate a message that includes points to note when evacuating. The message generation unit also stores the structure and special needs of each family member in a database and references that information when generating messages. For example, for a household with infants and young children, it can generate a message that includes a list of items to bring when evacuating. The message generation unit also develops a system that generates customized messages that take into consideration the structure and special needs of each family member when generating messages. For example, for a household with pets, it can generate a message that includes evacuation methods for pets. This makes it possible to generate customized messages according to the structure and special needs of each family member.

[0073] The sending unit is capable of checking the communication status of the destination in real time and selecting the optimal sending method. For example, the sending unit builds a system that checks the communication status of the destination in real time when sending a message and selects the optimal sending method. For example, if the communication status is poor, SMS can be used preferentially. The sending unit also stores the communication status of the destination in a database and refers to that information when sending a message. For example, if Wi-Fi is not available, mobile data communication can be used. The sending unit also develops a system that checks the communication status of the destination in real time when sending a message and selects the optimal sending method. For example, if the communication status is good, a message including an image or video can be sent. This makes it possible to select the optimal sending method depending on the communication status of the destination.

[0074] The message generation unit can use the emotion estimation function to predict the emotional impact that the content of a message will have on a recipient and select the optimal expression. The message generation unit, for example, uses the emotion estimation function to build a system that predicts the emotional impact that the content of a message will have on a recipient and selects the optimal expression. For example, in a message of evacuation instructions, it is possible to select an expression that gives a sense of security. The message generation unit also integrates the emotion estimation function when generating a message to predict the emotional response of the recipient. For example, in a message of high urgency, it is possible to select an expression that helps the recipient to remain calm. The message generation unit also uses the emotion estimation function to develop a system that predicts the emotional impact that the content of a message will have on a recipient and select the optimal expression. For example, in a message of evacuation instructions, it is possible to select an expression that will ease fear. This makes it possible to predict the emotional impact on the recipient and select the optimal expression.

[0075] The message generation unit can also generate audio messages and video messages and provide visual and audio information. For example, the message generation unit builds a system that also generates audio messages and video messages when generating messages. For example, it can send evacuation instructions as audio messages and provide visual and audio information. The message generation unit also develops tools for generating audio messages and video messages and uses them when generating messages. For example, it can generate video messages showing evacuation routes. The message generation unit also develops a system that also generates audio messages and video messages when generating messages. For example, it can send evacuation instructions as audio messages and provide visual and audio information. This makes it possible to provide visual and audio information.

[0076] The sending unit can send a message by combining multiple communication means. The sending unit builds a system that sends a message by combining multiple communication means, for example. For example, a message can be sent by simultaneously using SMS, email, and SNS. The sending unit also develops a tool for sending a message by combining multiple communication means, and uses the tool when sending a message. For example, SMS and email can be sent simultaneously. The sending unit also develops a system that sends a message by combining multiple communication means, for example. For example, a message can be sent by simultaneously using SMS, email, and SNS. This allows a message to be sent by combining multiple communication means.

[0077] The transmission unit can use the emotion estimation function to monitor the emotional reactions of residents after receiving a message and send a follow-up message as necessary. The transmission unit, for example, uses the emotion estimation function to monitor the emotional reactions of residents after receiving a message and send a follow-up message as necessary. For example, if residents feel strong anxiety after receiving an evacuation order message, a follow-up message that provides a sense of security can be sent. The transmission unit also analyzes residents' emotional reactions after receiving a message in real time and generates a follow-up message using the emotion estimation function. For example, if residents feel strong fear after receiving an evacuation order message, a follow-up message to help them stay calm can be sent. The transmission unit also develops a system that uses the emotion estimation function to monitor residents' emotional reactions after receiving a message and send a follow-up message as necessary. For example, if residents feel strong anxiety after receiving an evacuation order message, a follow-up message that provides a sense of security can be sent. This makes it possible to monitor emotional reactions after receiving a message and send a follow-up message.

[0078] The analysis unit can grasp the situation in a region in real time using drones and satellite images. For example, in analyzing the situation in a region, the analysis unit builds a system that grasps the situation in real time using drones. For example, it can fly a drone to photograph the situation in a disaster-stricken area and analyze it. The analysis unit also develops a system that grasps the situation in a region in real time using satellite images. For example, it can identify the situation in a disaster-stricken area by analyzing satellite images. The analysis unit also builds a system that grasps the situation in a region in real time using drones and satellite images. For example, it can fly a drone to photograph the situation in a disaster-stricken area and analyze it. This makes it possible to grasp the situation in a region in real time using drones and satellite images.

[0079] The analysis unit can monitor the number of people that can be accommodated at an evacuation shelter and the status of supplies in real time. For example, in analyzing information about an evacuation shelter, the analysis unit builds a system that monitors the number of people that can be accommodated at an evacuation shelter and the status of supplies in real time. For example, the analysis unit can measure the number of people that can be accommodated at an evacuation shelter using a sensor and store the status of supplies in a database. The analysis unit also develops a data collection system for monitoring the number of people that can be accommodated at an evacuation shelter and the status of supplies in real time. For example, the analysis unit can measure the number of people that can be accommodated at an evacuation shelter using a sensor and store the status of supplies in a database. The analysis unit also develops a system that monitors the number of people that can be accommodated at an evacuation shelter and the status of supplies in real time in analyzing information about an evacuation shelter. For example, the analysis unit can measure the number of people that can be accommodated at an evacuation shelter using a sensor and store the status of supplies in a database. This makes it possible to monitor the number of people that can be accommodated at an evacuation shelter and the status of supplies in real time.

[0080] The analysis unit can use the emotion estimation function to analyze the emotions of residents at evacuation shelters and generate information for providing psychological support. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of residents at evacuation shelters and generates information for providing psychological support. For example, it can identify emotions from the voices and facial expressions of residents at evacuation shelters and provide appropriate support. The analysis unit also analyzes the emotions of residents at evacuation shelters in real time and generates information for providing psychological support using the emotion estimation function. For example, it can identify emotions from the voices and facial expressions of residents at evacuation shelters and provide appropriate support. The analysis unit also uses the emotion estimation function to develop a system that analyzes the emotions of residents at evacuation shelters and generates information for providing psychological support. For example, it can identify emotions from the voices and facial expressions of residents at evacuation shelters and provide appropriate support. This makes it possible to analyze the emotions of residents at evacuation shelters and generate information for providing psychological support.

[0081] The analysis unit can integrate traffic information and infrastructure status to optimize evacuation routes. The analysis unit, for example, builds a system that integrates traffic information and infrastructure status in analyzing regional situations and optimizes evacuation routes. For example, it can analyze traffic congestion and road closure information and propose optimal evacuation routes. The analysis unit also stores traffic information and infrastructure status in a database and references that information when analyzing regional situations. For example, it can analyze the status of electricity and water and propose optimal evacuation routes. The analysis unit also develops a system that integrates traffic information and infrastructure status in analyzing regional situations and optimizes evacuation routes. For example, it can analyze traffic congestion and road closure information and propose optimal evacuation routes. This makes it possible to integrate traffic information and infrastructure status and optimize evacuation routes.

[0082] The analysis unit strengthens cooperation between evacuation shelters and enables efficient distribution of supplies. For example, in analyzing information about evacuation shelters, the analysis unit builds a system that strengthens cooperation between evacuation shelters and enables efficient distribution of supplies. For example, the supply status at each evacuation shelter can be shared in real time, allowing necessary supplies to be distributed quickly. The analysis unit also develops a data sharing system to strengthen cooperation between evacuation shelters and enables efficient distribution of supplies. For example, the capacity of evacuation shelters and the inventory status of supplies can be shared, allowing appropriate distribution to be carried out. The analysis unit also develops a system that strengthens cooperation between evacuation shelters and enables efficient distribution of supplies in analyzing information about evacuation shelters. For example, the supply status at each evacuation shelter can be shared in real time, allowing necessary supplies to be distributed quickly. This strengthens cooperation between evacuation shelters and enables efficient distribution of supplies.

[0083] The analysis unit can use the emotion estimation function to monitor the emotions of evacuation shelter residents in real time and provide psychological support as needed. The analysis unit, for example, uses the emotion estimation function to build a system that monitors the emotions of evacuation shelter residents in real time and provides psychological support as needed. For example, it can identify emotions from the voices and facial expressions of evacuation shelter residents and provide appropriate support. The analysis unit also analyzes the emotions of evacuation shelter residents in real time and generates information for providing psychological support using the emotion estimation function. For example, it can identify emotions from the voices and facial expressions of evacuation shelter residents and provide appropriate support. The analysis unit also uses the emotion estimation function to develop a system that monitors the emotions of evacuation shelter residents in real time and provides psychological support as needed. For example, it can identify emotions from the voices and facial expressions of evacuation shelter residents and provide appropriate support. This makes it possible to monitor the emotions of evacuation shelter residents in real time and provide psychological support as needed.

[0084] The message generation unit can reflect the congestion status of evacuation routes and evacuation shelters in real time. The message generation unit, for example, builds a system that reflects the congestion status of evacuation routes and evacuation shelters in real time when generating messages. For example, the message generation unit can analyze the traffic conditions on evacuation routes and the capacity of evacuation shelters in real time and reflect them in messages. The message generation unit also stores the congestion status of evacuation routes and evacuation shelters in a database and references that information when generating messages. For example, the message generation unit can analyze the traffic conditions on evacuation routes and the capacity of evacuation shelters in real time and reflect them in messages. The message generation unit also develops a system that reflects the congestion status of evacuation routes and evacuation shelters in real time when generating messages. For example, the message generation unit can analyze the traffic conditions on evacuation routes and the capacity of evacuation shelters in real time and reflect them in messages. This makes it possible to reflect the congestion status of evacuation routes and evacuation shelters in real time.

[0085] The message generation unit can provide information that takes into account the capacity of the evacuation shelter and the status of supplies. For example, the message generation unit builds a system that provides information that takes into account the capacity of the evacuation shelter and the status of supplies when generating a message. For example, if the evacuation shelter is full, it can generate a message that guides users to another evacuation shelter. The message generation unit also stores the capacity of the evacuation shelter and the status of supplies in a database and references that information when generating a message. For example, if the evacuation shelter is low on supplies, it can generate a message that calls for the donation of supplies. The message generation unit also develops a system that provides information that takes into account the capacity of the evacuation shelter and the status of supplies when generating a message. For example, if the evacuation shelter is full, it can generate a message that guides users to another evacuation shelter. This makes it possible to provide information that takes into account the capacity of the evacuation shelter and the status of supplies.

[0086] The message generation unit uses the emotion estimation function to generate messages that reflect the emotions of evacuation shelter residents and provide psychological support. The message generation unit, for example, uses the emotion estimation function to generate messages that reflect the emotions of evacuation shelter residents and build a system for providing psychological support. For example, it can identify emotions from the voices and facial expressions of evacuation shelter residents and generate messages that give a sense of security. The message generation unit also analyzes the emotions of evacuation shelter residents in real time and generates messages for providing psychological support using the emotion estimation function. For example, it can identify emotions from the voices and facial expressions of evacuation shelter residents and generate messages that give a sense of security. The message generation unit also uses the emotion estimation function to develop a system for generating messages that reflect the emotions of evacuation shelter residents and provide psychological support. For example, it can identify emotions from the voices and facial expressions of evacuation shelter residents and generate messages that give a sense of security. This makes it possible to generate messages that reflect the emotions of evacuation shelter residents and provide psychological support.

[0087] The message generation unit generates a message including visual map information, and can visually indicate an evacuation route. The message generation unit, for example, builds a system for generating a message including visual map information when generating a message. For example, a map showing an evacuation route can be included in the message and visually displayed. The message generation unit also stores map information in a database and references the information when generating a message. For example, a map showing an evacuation route can be included in the message and visually displayed. The message generation unit also develops a system for generating a message including visual map information when generating a message. For example, a map showing an evacuation route can be included in the message and visually displayed. This allows a message including visual map information to be generated, and an evacuation route to be visually displayed.

[0088] The message generation unit can generate a message including contact information for an evacuation shelter and information about a support organization. For example, the message generation unit builds a system for generating a message including contact information for an evacuation shelter and information about a support organization when generating a message. For example, the information about the evacuation shelter and the support organization can be included in the message. The message generation unit also stores the information about the evacuation shelter and the support organization in a database and references that information when generating a message. For example, the information about the evacuation shelter and the support organization can be included in the message. The message generation unit also develops a system for generating a message including contact information for an evacuation shelter and information about a support organization when generating a message. For example, the information about the evacuation shelter and the support organization can be included in the message. This makes it possible to generate a message including contact information for an evacuation shelter and information about a support organization.

[0089] The message generation unit can use the emotion estimation function to monitor the emotions of evacuation shelter residents in real time and send follow-up messages as needed. The message generation unit, for example, uses the emotion estimation function to build a system that monitors the emotions of evacuation shelter residents in real time and sends follow-up messages as needed. For example, it can identify emotions from the voices and facial expressions of evacuation shelter residents and send follow-up messages that give a sense of security. The message generation unit also analyzes the emotions of evacuation shelter residents in real time and generates follow-up messages using the emotion estimation function. For example, it can identify emotions from the voices and facial expressions of evacuation shelter residents and send follow-up messages that give a sense of security. The message generation unit also uses the emotion estimation function to develop a system that monitors the emotions of evacuation shelter residents in real time and sends follow-up messages as needed. For example, it can identify emotions from the voices and facial expressions of evacuation shelter residents and send follow-up messages that give a sense of security. This makes it possible to monitor the emotions of evacuation shelter residents in real time and send follow-up messages as needed.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The disaster prevention information providing system may further include a location information acquiring unit. The location information acquiring unit acquires the user's current location in real time and provides it to the analysis unit. For example, the user's location information can be acquired using GPS technology and used to suggest evacuation routes and guide users to evacuation shelters. The location information acquiring unit can also record the user's movement history and analyze behavioral patterns during a disaster. This makes it possible to provide appropriate evacuation instructions based on the user's current location.

[0092] The disaster prevention information provision system can further include a health condition monitoring unit. The health condition monitoring unit monitors the user's health condition in real time and provides the data to the analysis unit. For example, it can monitor vital signs such as heart rate, body temperature, and blood pressure, and generate an emergency message if an abnormality is detected. The health condition monitoring unit can also analyze the user's health data and suggest precautions to take during evacuation and necessary medical support. This makes it possible to provide appropriate countermeasures according to the user's health condition.

[0093] The disaster prevention information provision system can further include a social media analysis unit. The social media analysis unit collects social media posts during a disaster and analyzes them in real time. For example, it can collect posts from Twitter (registered trademark) and Facebook (registered trademark) and analyze disaster information. The social media analysis unit can also identify the progress of the disaster and the extent of the damage from the social media posts and generate appropriate messages. This allows for real-time analysis of social media posts during a disaster and enables rapid information provision.

[0094] The disaster prevention information provision system can further be equipped with a drone control unit. The drone control unit flies a drone during a disaster to photograph the situation in the affected area and provides the photographs to the analysis unit in real time. For example, a drone can be used to take aerial photographs of the situation in the affected area, which can be used to confirm evacuation routes and grasp the extent of damage. The drone control unit can also automatically set the drone's flight route to efficiently collect information. This enables rapid information collection using drones.

[0095] The disaster prevention information system can further include a shelter management unit. The shelter management unit monitors the number of people that can be accommodated at shelters and the status of supplies in real time, and provides this information to the analysis unit. For example, the shelter's capacity can be measured using a sensor, and the stock status of supplies can be stored in a database. The shelter management unit can also strengthen cooperation between shelters and allocate supplies efficiently. This allows the number of people that can be accommodated at shelters and the status of supplies to be grasped in real time, and appropriate countermeasures can be provided.

[0096] The analysis unit can estimate the user's emotions and generate an evacuation instruction message based on the estimated user's emotions. For example, it can identify the user's feelings of anxiety or fear and generate a message that provides a sense of security. The analysis unit can also monitor the user's emotions in real time and send follow-up messages according to changes in emotions. This makes it possible to provide an appropriate message according to the user's emotions.

[0097] The message generation unit can estimate the user's emotions and customize the content of the message based on the estimated user emotions. For example, it can identify the user's feelings of tension or impatience and select expressions to help the user stay calm. The message generation unit can also monitor the user's emotions in real time and adjust the content of the message according to changes in emotions. This makes it possible to provide the user with the optimal message based on their emotions.

[0098] The sending unit can estimate the user's emotions and adjust the timing of message transmission based on the estimated user emotions. For example, if the user's anxiety is increasing, a message that provides reassurance can be sent quickly. The sending unit can also monitor the user's emotions in real time and send follow-up messages according to changes in emotions. This allows messages to be sent at appropriate times according to the user's emotions.

[0099] The analysis unit can estimate the user's emotions and suggest evacuation routes based on the estimated user emotions. For example, it can identify the user's emotions of fear or anxiety and suggest evacuation routes that give a sense of security. The analysis unit can also monitor the user's emotions in real time and adjust the suggested evacuation routes according to changes in emotions. This makes it possible to provide the optimal evacuation route according to the user's emotions.

[0100] The message generation unit can estimate the user's emotions and generate a message for providing psychological support at the evacuation shelter based on the estimated user's emotions. For example, it can identify the user's feelings of anxiety or fear at the evacuation shelter and generate a message that provides a sense of security. The message generation unit can also monitor the user's emotions in real time and send follow-up messages according to changes in the user's emotions. This makes it possible to generate an appropriate message for providing psychological support at the evacuation shelter.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The speech recognition unit converts the speech from the disaster prevention radio into text. For example, the speech recognition unit uses speech recognition technology based on deep learning to convert the speech from the disaster prevention radio into text with high accuracy. The speech recognition unit can also use an HMM (hidden Markov model) to analyze the speech and convert it into text. Furthermore, the speech recognition unit can also use noise canceling technology to remove background noise and obtain clear speech data. Step 2: The analysis unit analyzes the text converted by the speech recognition unit. For example, the analysis unit may use text mining technology to analyze the content of the text and extract important information. The analysis unit may also use natural language processing technology to understand the meaning of the text and propose appropriate countermeasures. Furthermore, the analysis unit may use an emotion estimation function to analyze the emotional nuances of the text and estimate the urgency and intensity of the emotion. Step 3: The message generation unit generates a message based on the content analyzed by the analysis unit. For example, the message generation unit uses template-based generation technology to generate a message based on a pre-prepared template. The message generation unit can also use generation AI to generate an appropriate message based on the analyzed content. Furthermore, the message generation unit can use an emotion estimation function to predict the emotional impact on the recipient and select the most appropriate expression. Step 4: The sending unit sends the message generated by the message generating unit to the family. For example, the sending unit can send the message using SMS. Alternatively, the sending unit can send the message using email. Alternatively, the sending unit can send the message using a messenger app.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] 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.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] 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.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] 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.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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 voice recognition unit that converts the voice of the disaster prevention radio into text; an analysis unit that analyzes the text converted by the speech recognition unit; a message generation unit that generates a message based on the content analyzed by the analysis unit; a sending unit that sends the message generated by the message generating unit to a family member. A system characterized by:

2. The voice recognition unit Text conversion in multiple languages ​​to accommodate foreign residents 2. The system of claim 1.

3. The voice recognition unit Introduce filtering technology to remove background noise and obtain clear audio data.

2. The system of claim 1.

4. The voice recognition unit Estimating the level of urgency and emotional intensity from emergency radio broadcasts and determining the priority of text conversion 2. The system of claim 1.

5. The voice recognition unit Collecting and converting the voices of local residents into text in real time during disasters 2. The system of claim 1.

6. The voice recognition unit Add a function to analyze voice data and automatically identify the type of disaster.

2. The system of claim 1.

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