System

Abstract

An object of a system according to an embodiment is to enable a hearing-impaired person to grasp content of an emergency broadcast in real time.SOLUTION: A system according to an embodiment includes an application activation unit, a voice recognition unit, and a notification unit. The application activation unit receives a user operation. The voice recognition unit recognizes the voice of the emergency broadcast in real time based on the operation received by the application activation unit. The notification unit notifies the hearing-impaired person of the content of the emergency broadcast recognized by the voice recognition unit.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 that it is difficult for hearing-impaired people to understand the content of emergency broadcasts in real time.

[0005] The system according to the embodiment aims to enable hearing-impaired people to understand the contents of emergency broadcasts in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an application launch unit, a voice recognition unit, and a notification unit. The application launch unit accepts user operations. The voice recognition unit recognizes the voice of the emergency broadcast in real time based on the operations accepted by the application launch unit. The notification unit notifies a hearing-impaired person of the content of the emergency broadcast recognized by the voice recognition unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable hearing-impaired people to understand the content of emergency broadcasts in real time. [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 emergency broadcast notification system according to an embodiment of the present invention uses AI to recognize voices in real time and notify hearing-impaired people of an "emergency broadcast" with a single touch via an application. This allows hearing-impaired people to understand the content of the emergency broadcast in real time.

[0029] An emergency broadcast notification system according to an embodiment includes an application launch unit, a voice recognition unit, and a notification unit. The application launch unit accepts user operations. For example, a user launches a dedicated application installed on a smartphone or tablet, and when an emergency broadcast is transmitted, the user touches a button in the application. This operation causes the application launch unit to start voice recognition. The voice recognition unit recognizes the voice of the emergency broadcast in real time based on the operation accepted by the application launch unit. For example, the voice recognition unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze the content of the emergency broadcast and convert it into text data. The generation AI receives the voice data of the emergency broadcast as input and generates text data based on the voice data. The notification unit notifies a hearing-impaired person of the content of the emergency broadcast recognized by the voice recognition unit. For example, the notification unit may notify the hearing-impaired person by displaying the content as a text message, vibrating the device, flashing the screen, or the like. This allows the hearing-impaired person to immediately understand the content of the emergency broadcast. As a result, the emergency broadcast notification system according to an embodiment allows the hearing-impaired person to understand the content of the emergency broadcast in real time.

[0030] The application launch unit can strengthen security by using facial authentication or fingerprint authentication and prevent erroneous operations. For example, when a user launches an application, the application launch unit uses facial authentication to verify the user's identity. This prevents other people from erroneously launching the application. The application launch unit also uses fingerprint authentication to strengthen security when a user launches an application. This makes it possible to prevent erroneous operations and unauthorized access. The application launch unit also combines facial authentication and fingerprint authentication to doubly strengthen security when a user launches an application. This achieves even higher security. This makes it possible to prevent erroneous operations and unauthorized access.

[0031] The application launch unit acquires the user's location information and can respond to emergency broadcasts for each region. For example, when the application is launched, the application launch unit automatically acquires the user's location information and receives emergency broadcasts for that region. This allows emergency information for each region to be provided promptly. Furthermore, when the user launches the application, the application launch unit uses GPS to identify the user's current location and prioritizes notification of emergency broadcasts for that region. This provides information specialized for the region. Furthermore, the application launch unit acquires the location information at the same time as the application is launched and receives emergency broadcasts for the user's region in real time. This allows emergency information for each region to be immediately known. This allows emergency information for each region to be provided promptly.

[0032] The application launch unit can monitor the user's health condition and provide health advice according to the content of the emergency broadcast. For example, when the application is launched, the application launch unit monitors the user's heart rate in cooperation with a smartwatch or fitness tracker and provides health advice according to the content of the emergency broadcast. When the user launches the application, the application launch unit also measures the user's current blood pressure in cooperation with a blood pressure monitor and displays health advice according to the content of the emergency broadcast. When the application is launched, the application launch unit also monitors the user's health condition and provides appropriate health advice according to the content of the emergency broadcast. For example, if the heart rate is high, it suggests ways to relax. This makes it possible to provide advice according to the user's health condition.

[0033] The application launch unit can cooperate with at least one wearable device, such as a smartwatch or smartglasses, to achieve faster notification. The application launch unit, for example, links an application with a smartwatch to quickly receive notifications of emergency broadcasts. For example, notifications are given by vibration or a screen display on the smartwatch. The application launch unit also cooperates with smartglasses to visually display the contents of the emergency broadcast. For example, a text message is displayed on the smartglasses display. The application launch unit also cooperates with a wearable device to build a system for quickly notifying users of emergency broadcasts. For example, notifications are received in real time through the smartwatch or smartglasses. This enables faster notification.

[0034] The speech recognition unit supports multiple languages ​​and can recognize emergency broadcasts in different languages ​​in real time. For example, the speech recognition unit adds multiple language support functions to the emergency broadcast speech recognition system, and recognizes emergency broadcasts in different languages ​​in real time. For example, it can support English, Japanese, Chinese, etc. The speech recognition unit also makes the speech recognition engine multilingual, and recognizes emergency broadcasts in real time regardless of the language in which they are transmitted. For example, it is equipped with a language model switching function. The speech recognition unit also trains the emergency broadcast speech recognition system with speech data in different languages ​​and recognizes them in real time. For example, it builds a speech recognition model that supports the language of each region. This allows emergency broadcasts in different languages ​​to be recognized in real time.

[0035] The speech recognition unit can improve the accuracy of speech recognition by introducing filtering technology that automatically removes background noise. For example, the speech recognition unit introduces filtering technology that automatically removes background noise into a speech recognition system for emergency broadcasts to improve the accuracy of speech recognition. For example, a noise canceling algorithm is used. The speech recognition unit also adds a filtering function that removes background noise in real time to the speech recognition engine to improve the recognition accuracy. For example, noise is removed by analyzing environmental sounds. The speech recognition unit also incorporates technology that automatically filters background noise into the speech recognition system for emergency broadcasts to improve the accuracy of speech recognition. For example, a noise reduction filter is used. This improves the accuracy of speech recognition.

[0036] The voice recognition unit can be applied not only to evacuation instructions during disasters, but also to warnings in the event of medical emergencies or crimes. For example, the voice recognition unit applies a voice recognition system for emergency broadcasts to notify users of medical emergencies, as well as evacuation instructions during disasters. For example, it recognizes the voice announcing the arrival of an ambulance. The voice recognition unit also enables the voice recognition engine to also handle crime warnings, recognizing the content of emergency broadcasts in real time. For example, it recognizes emergency calls from the police. The voice recognition unit also enables the voice recognition system for emergency broadcasts to handle various emergencies, such as evacuation instructions during disasters, medical emergencies, and crime warnings. For example, it recognizes the voice of a fire alarm. This makes it possible to respond to a variety of emergencies.

[0037] The voice recognition unit can handle not only emergency broadcasts but also important daily notifications. For example, the voice recognition unit applies the voice recognition system for emergency broadcasts to important daily notifications, notifying the user of the time to take medicine using voice recognition technology. For example, a reminder can be set by voice. The voice recognition unit also makes the voice recognition engine compatible with important daily notifications, notifying the user of not only emergency broadcasts but also the start time of a meeting, for example. The voice recognition unit also makes the voice recognition system for emergency broadcasts compatible with important daily notifications. For example, notifying the user of family schedules by voice. This makes it possible to handle important daily notifications as well.

[0038] The notification unit can learn and personalize the optimal notification method based on the user's past reaction data. The notification unit, for example, analyzes the user's past reaction data and builds a system that learns the optimal notification method. For example, text notifications are given priority for users who have preferred text notifications in the past. The notification unit also personalizes the notification method based on the user's reaction data. For example, vibration notifications are given priority for users who prefer vibration notifications. The notification unit also uses the past reaction data to learn the optimal notification method for each user and provides personalized notifications. For example, notifications are sent during specific time periods for users who are more likely to receive notifications during those time periods. This makes it possible to provide the optimal notification method for each user.

[0039] The notification unit adds a function to customize the vibration pattern of the device, and can set a vibration pattern according to the level of urgency. For example, the notification unit adds a function to customize the vibration pattern of the device to the notification method, and sets a vibration pattern according to the level of urgency. For example, a strong vibration is set when the level of urgency is high. The notification unit also provides a function to allow the user to customize the vibration pattern of the device, and sets a vibration pattern according to the level of urgency. For example, a short vibration is set when the level of urgency is low. The notification unit also adds a function to set a vibration pattern according to the level of urgency to the notification method, and allows the user to customize it. For example, a medium vibration is set when the level of urgency is medium. This makes it possible to set a vibration pattern according to the level of urgency.

[0040] The notification unit can cooperate with smart home devices to enhance notifications within the home. For example, the notification unit cooperates with smart home devices to enhance notifications within the home. For example, the notification unit flashes a smart light when an emergency broadcast is transmitted. The notification unit also cooperates with a smart speaker to notify the content of the emergency broadcast by voice. For example, the smart speaker reads out the content of the emergency broadcast. The notification unit also cooperates with smart home devices to build a system that enhances notifications within the home. For example, the content of the emergency broadcast is notified through a smart light or a smart speaker. This enhances notifications within the home.

[0041] The notification unit can add a function to visually display the contents of the emergency broadcast using AR technology. For example, the notification unit adds a function to visually display the contents of the emergency broadcast using AR technology to the notification method. For example, the contents of the emergency broadcast are displayed through a smartphone camera. The notification unit also uses AR technology to build a system that visually displays the contents of the emergency broadcast. For example, the contents of the emergency broadcast are displayed through AR glasses. The notification unit also introduces AR technology into the notification method and adds a function to visually display the contents of the emergency broadcast. For example, the contents of the emergency broadcast are displayed through an AR app. This makes it possible to visually display the contents of the emergency broadcast.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The application launch unit can monitor the user's health condition and provide health advice according to the content of the emergency broadcast. For example, when the application is launched, it works with a smartwatch or fitness tracker to monitor the user's heart rate and provides health advice according to the content of the emergency broadcast. When the user launches the application, the application launch unit also works with a blood pressure monitor to measure the user's current blood pressure and display health advice according to the content of the emergency broadcast. When the application is launched, the application launch unit also monitors the user's health condition and provides appropriate health advice according to the content of the emergency broadcast. For example, if the heart rate is high, it suggests ways to relax. This makes it possible to provide advice according to the user's health condition.

[0044] The application launch unit can cooperate with at least one wearable device, such as a smartwatch or smartglasses, to achieve faster notifications. For example, the application can be cooperated with a smartwatch to quickly receive notifications of emergency broadcasts. For example, notifications can be sent by vibration or a screen display on the smartwatch. The application launch unit can also cooperate with smartglasses to visually display the contents of the emergency broadcast. For example, a text message can be displayed on the smartglasses display. The application launch unit can also cooperate with a wearable device to build a system for quickly notifying users of emergency broadcasts. For example, notifications can be received in real time through the smartwatch or smartglasses. This allows for faster notifications.

[0045] The application launch unit can acquire the user's location information and respond to emergency broadcasts for each region. For example, when the application is launched, the user's location information is automatically acquired and emergency broadcasts for that region are received. This allows emergency information for that region to be provided promptly. Furthermore, when the user launches the application, the application launch unit uses GPS to identify the user's current location and prioritizes notification of emergency broadcasts for that region. This provides information specific to the region. Furthermore, the application launch unit acquires the location information at the same time as the application is launched and receives emergency broadcasts for the user's region in real time. This allows emergency information for each region to be immediately known. This allows emergency information for each region to be provided promptly.

[0046] The speech recognition unit supports multiple languages ​​and can recognize emergency broadcasts in different languages ​​in real time. For example, a multiple language support function can be added to the emergency broadcast speech recognition system, allowing it to recognize emergency broadcasts in different languages ​​in real time. For example, it can support English, Japanese, Chinese, etc. The speech recognition unit also makes the speech recognition engine multilingual, allowing it to recognize emergency broadcasts in real time regardless of the language in which they are transmitted. For example, it is equipped with a language model switching function. The speech recognition unit also trains the emergency broadcast speech recognition system with speech data in different languages ​​and recognizes them in real time. For example, it builds a speech recognition model that supports the language of each region. This allows it to recognize emergency broadcasts in different languages ​​in real time.

[0047] The speech recognition unit can improve the accuracy of speech recognition by introducing filtering technology that automatically removes background noise. For example, a filtering technology that automatically removes background noise can be introduced into a speech recognition system for emergency broadcasts to improve the accuracy of speech recognition. For example, a noise canceling algorithm can be used. The speech recognition unit can also add a filtering function to the speech recognition engine that removes background noise in real time to improve recognition accuracy. For example, noise can be removed by analyzing environmental sounds. The speech recognition unit can also incorporate technology that automatically filters background noise into a speech recognition system for emergency broadcasts to improve the accuracy of speech recognition. For example, a noise reduction filter can be used. This improves the accuracy of speech recognition.

[0048] The voice recognition unit can be applied not only to evacuation instructions during disasters, but also to warnings in the event of medical emergencies or crimes. For example, the voice recognition system for emergency broadcasts can be applied not only to evacuation instructions during disasters, but also to notifications of medical emergencies. For example, it can recognize the voice announcing the arrival of an ambulance. The voice recognition unit also enables the voice recognition engine to also handle crime warnings, recognizing the content of emergency broadcasts in real time. For example, it can recognize emergency calls from the police. The voice recognition unit also enables the voice recognition system for emergency broadcasts to handle various emergencies, such as evacuation instructions during disasters, medical emergencies, and crime warnings. For example, it can recognize the voice of a fire alarm. This makes it possible to respond to a variety of emergencies.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The application launcher accepts user operations. For example, a user launches a dedicated application installed on a smartphone or tablet, and when an emergency broadcast is transmitted, touches a button within the application. This operation causes the application launcher to start voice recognition. Step 2: The speech recognition unit recognizes the emergency broadcast audio in real time based on the operation accepted by the application launch unit. For example, the speech recognition unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze the content of the emergency broadcast and convert it into text data. The generation AI receives the emergency broadcast audio data as input and generates text data based on that audio data. Step 3: The notification unit notifies the hearing-impaired person of the content of the emergency broadcast recognized by the voice recognition unit. For example, the notification unit may notify the hearing-impaired person by displaying it as a text message, vibrating the device, flashing the screen, or by other means. This allows the hearing-impaired person to immediately understand the content of the emergency broadcast.

[0051] (Example 2) The emergency broadcast notification system according to an embodiment of the present invention uses AI to recognize voices in real time and notify hearing-impaired people of an "emergency broadcast" with a single touch via an application. This allows hearing-impaired people to understand the content of the emergency broadcast in real time.

[0052] An emergency broadcast notification system according to an embodiment includes an application launch unit, a voice recognition unit, and a notification unit. The application launch unit accepts user operations. For example, a user launches a dedicated application installed on a smartphone or tablet, and when an emergency broadcast is transmitted, the user touches a button in the application. This operation causes the application launch unit to start voice recognition. The voice recognition unit recognizes the voice of the emergency broadcast in real time based on the operation accepted by the application launch unit. For example, the voice recognition unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze the content of the emergency broadcast and convert it into text data. The generation AI receives the voice data of the emergency broadcast as input and generates text data based on the voice data. The notification unit notifies a hearing-impaired person of the content of the emergency broadcast recognized by the voice recognition unit. For example, the notification unit may notify the hearing-impaired person by displaying the content as a text message, vibrating the device, flashing the screen, or the like. This allows the hearing-impaired person to immediately understand the content of the emergency broadcast. As a result, the emergency broadcast notification system according to an embodiment allows the hearing-impaired person to understand the content of the emergency broadcast in real time.

[0053] The application launch unit can strengthen security by using facial authentication or fingerprint authentication and prevent erroneous operations. For example, when a user launches an application, the application launch unit uses facial authentication to verify the user's identity. This prevents other people from erroneously launching the application. The application launch unit also uses fingerprint authentication to strengthen security when a user launches an application. This makes it possible to prevent erroneous operations and unauthorized access. The application launch unit also combines facial authentication and fingerprint authentication to doubly strengthen security when a user launches an application. This achieves even higher security. This makes it possible to prevent erroneous operations and unauthorized access.

[0054] The application launch unit acquires the user's location information and can respond to emergency broadcasts for each region. For example, when the application is launched, the application launch unit automatically acquires the user's location information and receives emergency broadcasts for that region. This allows emergency information for each region to be provided promptly. Furthermore, when the user launches the application, the application launch unit uses GPS to identify the user's current location and prioritizes notification of emergency broadcasts for that region. This provides information specialized for the region. Furthermore, the application launch unit acquires the location information at the same time as the application is launched and receives emergency broadcasts for the user's region in real time. This allows emergency information for each region to be immediately known. This allows emergency information for each region to be provided promptly.

[0055] The application launching unit can use the emotion estimation function to analyze the emotions of the user when receiving an emergency broadcast and suggest relaxation methods to reduce stress. For example, when a user receives an emergency broadcast, the application launching unit uses the emotion estimation function to analyze the stress level and suggest relaxation methods. For example, it may recommend deep breathing or playing relaxing music. The application launching unit also analyzes the user's facial expression and voice when receiving the emergency broadcast to estimate their emotional state. If stress is high, it may suggest relaxation methods. The application launching unit also uses the emotion estimation function to analyze the user's emotions when receiving the emergency broadcast in real time and provide advice to reduce stress. For example, it may suggest simple exercises to help them relax. This can reduce the user's stress.

[0056] The application launch unit can monitor the user's health condition and provide health advice according to the content of the emergency broadcast. For example, when the application is launched, the application launch unit monitors the user's heart rate in cooperation with a smartwatch or fitness tracker and provides health advice according to the content of the emergency broadcast. When the user launches the application, the application launch unit also measures the user's current blood pressure in cooperation with a blood pressure monitor and displays health advice according to the content of the emergency broadcast. When the application is launched, the application launch unit also monitors the user's health condition and provides appropriate health advice according to the content of the emergency broadcast. For example, if the heart rate is high, it suggests ways to relax. This makes it possible to provide advice according to the user's health condition.

[0057] The application launch unit can cooperate with at least one wearable device, such as a smartwatch or smartglasses, to achieve faster notification. The application launch unit, for example, links an application with a smartwatch to quickly receive notifications of emergency broadcasts. For example, notifications are given by vibration or a screen display on the smartwatch. The application launch unit also cooperates with smartglasses to visually display the contents of the emergency broadcast. For example, a text message is displayed on the smartglasses display. The application launch unit also cooperates with a wearable device to build a system for quickly notifying users of emergency broadcasts. For example, notifications are received in real time through the smartwatch or smartglasses. This enables faster notification.

[0058] The application launch unit can use the emotion estimation function to analyze the emotion a user has when launching an application and provide an interface design that elicits positive emotions. For example, the application launch unit can use the emotion estimation function to analyze the user's emotional state when launching an application and provide an interface design that elicits positive emotions. For example, it can display bright colors or encouraging messages. The application launch unit can also analyze the user's facial expressions and voice when launching an application and provide an interface design that corresponds to the user's emotional state. For example, it can play relaxing background images or music. The application launch unit can also use the emotion estimation function to analyze the user's emotion when launching an application in real time and provide a design that elicits positive emotions. For example, it can provide a customizable interface that matches the user's preferences. This can elicit positive emotions from the user.

[0059] The speech recognition unit supports multiple languages ​​and can recognize emergency broadcasts in different languages ​​in real time. For example, the speech recognition unit adds multiple language support functions to the emergency broadcast speech recognition system, and recognizes emergency broadcasts in different languages ​​in real time. For example, it can support English, Japanese, Chinese, etc. The speech recognition unit also makes the speech recognition engine multilingual, and recognizes emergency broadcasts in real time regardless of the language in which they are transmitted. For example, it is equipped with a language model switching function. The speech recognition unit also trains the emergency broadcast speech recognition system with speech data in different languages ​​and recognizes them in real time. For example, it builds a speech recognition model that supports the language of each region. This allows emergency broadcasts in different languages ​​to be recognized in real time.

[0060] The speech recognition unit can improve the accuracy of speech recognition by introducing filtering technology that automatically removes background noise. For example, the speech recognition unit introduces filtering technology that automatically removes background noise into a speech recognition system for emergency broadcasts to improve the accuracy of speech recognition. For example, a noise canceling algorithm is used. The speech recognition unit also adds a filtering function that removes background noise in real time to the speech recognition engine to improve the recognition accuracy. For example, noise is removed by analyzing environmental sounds. The speech recognition unit also incorporates technology that automatically filters background noise into the speech recognition system for emergency broadcasts to improve the accuracy of speech recognition. For example, a noise reduction filter is used. This improves the accuracy of speech recognition.

[0061] The voice recognition unit can use the emotion estimation function to analyze the emotion of the caller from the voice of the emergency broadcast and determine the level of urgency. For example, the voice recognition unit adds the emotion estimation function to a voice recognition system for emergency broadcasts and analyzes the emotion of the caller to determine the level of urgency. For example, it analyzes the tone and speed of the caller's voice. The voice recognition unit also incorporates the emotion estimation function into a voice recognition engine and analyzes the emotion of the caller from the voice of the emergency broadcast in real time. For example, it detects emotions with a high level of urgency. The voice recognition unit also introduces the emotion estimation function into the voice recognition system for emergency broadcasts and analyzes the emotion of the caller to determine the level of urgency. For example, it analyzes the strength and intonation of the caller's voice. This makes it possible to determine the level of urgency.

[0062] The voice recognition unit can be applied not only to evacuation instructions during disasters, but also to warnings in the event of medical emergencies or crimes. For example, the voice recognition unit applies a voice recognition system for emergency broadcasts to notify users of medical emergencies, as well as evacuation instructions during disasters. For example, it recognizes the voice announcing the arrival of an ambulance. The voice recognition unit also enables the voice recognition engine to also handle crime warnings, recognizing the content of emergency broadcasts in real time. For example, it recognizes emergency calls from the police. The voice recognition unit also enables the voice recognition system for emergency broadcasts to handle various emergencies, such as evacuation instructions during disasters, medical emergencies, and crime warnings. For example, it recognizes the voice of a fire alarm. This makes it possible to respond to a variety of emergencies.

[0063] The voice recognition unit can handle not only emergency broadcasts but also important daily notifications. For example, the voice recognition unit applies the voice recognition system for emergency broadcasts to important daily notifications, notifying the user of the time to take medicine using voice recognition technology. For example, a reminder can be set by voice. The voice recognition unit also makes the voice recognition engine compatible with important daily notifications, notifying the user of not only emergency broadcasts but also the start time of a meeting, for example. The voice recognition unit also makes the voice recognition system for emergency broadcasts compatible with important daily notifications. For example, notifying the user of family schedules by voice. This makes it possible to handle important daily notifications as well.

[0064] The voice recognition unit can use the emotion estimation function to analyze the user's emotional response to the voice recognition results of the emergency broadcast and adjust the notification content. The voice recognition unit, for example, analyzes the user's emotional response to the voice recognition results of the emergency broadcast in real time and adjusts the notification content. For example, if the user feels stressed, the voice recognition unit provides advice on how to relax. The voice recognition unit also uses the emotion estimation function to analyze the user's emotion to the voice recognition results of the emergency broadcast and adjusts the notification content. For example, if the user feels anxious, the voice recognition unit displays a reassuring message. The voice recognition unit also builds a system that analyzes the user's emotional response to the voice recognition results of the emergency broadcast and adjusts the notification content. For example, the priority of the notification is changed depending on the user's emotional state. This makes it possible to adjust the notification content depending on the user's emotional response.

[0065] The notification unit can learn and personalize the optimal notification method based on the user's past reaction data. The notification unit, for example, analyzes the user's past reaction data and builds a system that learns the optimal notification method. For example, text notifications are given priority for users who have preferred text notifications in the past. The notification unit also personalizes the notification method based on the user's reaction data. For example, vibration notifications are given priority for users who prefer vibration notifications. The notification unit also uses the past reaction data to learn the optimal notification method for each user and provides personalized notifications. For example, notifications are sent during specific time periods for users who are more likely to receive notifications during those time periods. This makes it possible to provide the optimal notification method for each user.

[0066] The notification unit adds a function to customize the vibration pattern of the device, and can set a vibration pattern according to the level of urgency. For example, the notification unit adds a function to customize the vibration pattern of the device to the notification method, and sets a vibration pattern according to the level of urgency. For example, a strong vibration is set when the level of urgency is high. The notification unit also provides a function to allow the user to customize the vibration pattern of the device, and sets a vibration pattern according to the level of urgency. For example, a short vibration is set when the level of urgency is low. The notification unit also adds a function to set a vibration pattern according to the level of urgency to the notification method, and allows the user to customize it. For example, a medium vibration is set when the level of urgency is medium. This makes it possible to set a vibration pattern according to the level of urgency.

[0067] The notification unit uses the emotion estimation function to select a notification method according to the user's emotional state, thereby reducing stress. The notification unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and select a notification method for reducing stress. For example, if the user is feeling stressed, a gentle notification method is selected. The notification unit also selects a notification method according to the user's emotional state, thereby building a system for reducing stress. For example, if the user is relaxed, notifications are made more moderately. The notification unit also uses the emotion estimation function to analyze the user's emotional state and select a notification method for reducing stress. For example, if the user is nervous, notifications are delayed. This reduces the user's stress.

[0068] The notification unit can cooperate with smart home devices to enhance notifications within the home. For example, the notification unit cooperates with smart home devices to enhance notifications within the home. For example, the notification unit flashes a smart light when an emergency broadcast is transmitted. The notification unit also cooperates with a smart speaker to notify the content of the emergency broadcast by voice. For example, the smart speaker reads out the content of the emergency broadcast. The notification unit also cooperates with smart home devices to build a system that enhances notifications within the home. For example, the content of the emergency broadcast is notified through a smart light or a smart speaker. This enhances notifications within the home.

[0069] The notification unit can add a function to visually display the contents of the emergency broadcast using AR technology. For example, the notification unit adds a function to visually display the contents of the emergency broadcast using AR technology to the notification method. For example, the contents of the emergency broadcast are displayed through a smartphone camera. The notification unit also uses AR technology to build a system that visually displays the contents of the emergency broadcast. For example, the contents of the emergency broadcast are displayed through AR glasses. The notification unit also introduces AR technology into the notification method and adds a function to visually display the contents of the emergency broadcast. For example, the contents of the emergency broadcast are displayed through an AR app. This makes it possible to visually display the contents of the emergency broadcast.

[0070] The notification unit can use the emotion estimation function to monitor the user's emotional response in real time when selecting a notification method and continuously adjust the optimal notification method. The notification unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional response in real time when selecting a notification method and continuously adjusts the optimal notification method. For example, the notification method is changed according to the user's emotional state. The notification unit also monitors the user's emotional response in real time and continuously adjusts the optimal notification method. For example, the notification method is changed when the user feels stressed. The notification unit also uses the emotion estimation function to analyze the user's emotional response when selecting a notification method and continuously adjusts the optimal notification method. For example, the notification frequency is adjusted according to the user's emotional state. This makes it possible to continuously adjust the notification method according to the user's emotional response.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The application launch unit can monitor the user's health condition and provide health advice according to the content of the emergency broadcast. For example, when the application is launched, it works with a smartwatch or fitness tracker to monitor the user's heart rate and provides health advice according to the content of the emergency broadcast. When the user launches the application, the application launch unit also works with a blood pressure monitor to measure the user's current blood pressure and display health advice according to the content of the emergency broadcast. When the application is launched, the application launch unit also monitors the user's health condition and provides appropriate health advice according to the content of the emergency broadcast. For example, if the heart rate is high, it suggests ways to relax. This makes it possible to provide advice according to the user's health condition.

[0073] The application launch unit can cooperate with at least one wearable device, such as a smartwatch or smartglasses, to achieve faster notifications. For example, the application can be cooperated with a smartwatch to quickly receive notifications of emergency broadcasts. For example, notifications can be sent by vibration or a screen display on the smartwatch. The application launch unit can also cooperate with smartglasses to visually display the contents of the emergency broadcast. For example, a text message can be displayed on the smartglasses display. The application launch unit can also cooperate with a wearable device to build a system for quickly notifying users of emergency broadcasts. For example, notifications can be received in real time through the smartwatch or smartglasses. This allows for faster notifications.

[0074] The application launching unit can use the emotion estimation function to analyze the user's emotions when receiving an emergency broadcast and suggest relaxation methods to reduce stress. For example, when a user receives an emergency broadcast, the emotion estimation function can be used to analyze the user's stress level and suggest relaxation methods. For example, deep breathing or playing relaxing music can be recommended. The application launching unit can also analyze the user's facial expressions and voice when receiving the emergency broadcast to estimate their emotional state. If stress is high, relaxation methods can be suggested. The application launching unit can also use the emotion estimation function to analyze the user's emotions when receiving the emergency broadcast in real time and provide advice to reduce stress. For example, simple relaxation exercises can be suggested. This can reduce the user's stress.

[0075] The application launch unit can acquire the user's location information and respond to emergency broadcasts for each region. For example, when the application is launched, the user's location information is automatically acquired and emergency broadcasts for that region are received. This allows emergency information for that region to be provided promptly. Furthermore, when the user launches the application, the application launch unit uses GPS to identify the user's current location and prioritizes notification of emergency broadcasts for that region. This provides information specific to the region. Furthermore, the application launch unit acquires the location information at the same time as the application is launched and receives emergency broadcasts for the user's region in real time. This allows emergency information for each region to be immediately known. This allows emergency information for each region to be provided promptly.

[0076] The application launch unit can use the emotion estimation function to analyze the emotion a user has when launching an application and provide an interface design that elicits positive emotions. For example, when a user launches an application, the emotion estimation function is used to analyze the user's emotional state and provide an interface design that elicits positive emotions. For example, bright colors or encouraging messages can be displayed. The application launch unit can also analyze the user's facial expressions and voice when launching an application and provide an interface design that corresponds to the user's emotional state. For example, relaxing background images or music can be played. The application launch unit can also use the emotion estimation function to analyze the user's emotion when launching an application in real time and provide a design that elicits positive emotions. For example, a customizable interface can be provided to suit the user's preferences. This can elicit positive emotions from the user.

[0077] The speech recognition unit supports multiple languages ​​and can recognize emergency broadcasts in different languages ​​in real time. For example, a multiple language support function can be added to the emergency broadcast speech recognition system, allowing it to recognize emergency broadcasts in different languages ​​in real time. For example, it can support English, Japanese, Chinese, etc. The speech recognition unit also makes the speech recognition engine multilingual, allowing it to recognize emergency broadcasts in real time regardless of the language in which they are transmitted. For example, it is equipped with a language model switching function. The speech recognition unit also trains the emergency broadcast speech recognition system with speech data in different languages ​​and recognizes them in real time. For example, it builds a speech recognition model that supports the language of each region. This allows it to recognize emergency broadcasts in different languages ​​in real time.

[0078] The speech recognition unit can improve the accuracy of speech recognition by introducing filtering technology that automatically removes background noise. For example, a filtering technology that automatically removes background noise can be introduced into a speech recognition system for emergency broadcasts to improve the accuracy of speech recognition. For example, a noise canceling algorithm can be used. The speech recognition unit can also add a filtering function to the speech recognition engine that removes background noise in real time to improve recognition accuracy. For example, noise can be removed by analyzing environmental sounds. The speech recognition unit can also incorporate technology that automatically filters background noise into a speech recognition system for emergency broadcasts to improve the accuracy of speech recognition. For example, a noise reduction filter can be used. This improves the accuracy of speech recognition.

[0079] The speech recognition unit can use an emotion estimation function to analyze the emotion of the caller from the voice of the emergency broadcast and determine the level of urgency. For example, an emotion estimation function can be added to the speech recognition system for emergency broadcasts to analyze the emotion of the caller and determine the level of urgency. For example, the tone and speed of the caller's voice can be analyzed. The speech recognition unit can also incorporate an emotion estimation function into the speech recognition engine to analyze the emotion of the caller from the voice of the emergency broadcast in real time. For example, emotions with a high level of urgency can be detected. The speech recognition unit can also introduce an emotion estimation function into the speech recognition system for emergency broadcasts to analyze the emotion of the caller and determine the level of urgency. For example, the strength and intonation of the caller's voice can be analyzed. This makes it possible to determine the level of urgency.

[0080] The voice recognition unit can be applied not only to evacuation instructions during disasters, but also to warnings in the event of medical emergencies or crimes. For example, the voice recognition system for emergency broadcasts can be applied not only to evacuation instructions during disasters, but also to notifications of medical emergencies. For example, it can recognize the voice announcing the arrival of an ambulance. The voice recognition unit also enables the voice recognition engine to also handle crime warnings, recognizing the content of emergency broadcasts in real time. For example, it can recognize emergency calls from the police. The voice recognition unit also enables the voice recognition system for emergency broadcasts to handle various emergencies, such as evacuation instructions during disasters, medical emergencies, and crime warnings. For example, it can recognize the voice of a fire alarm. This makes it possible to respond to a variety of emergencies.

[0081] The speech recognition unit can use the emotion estimation function to analyze the user's emotional response to the speech recognition results of the emergency broadcast and adjust the notification content. For example, the speech recognition unit can analyze the user's emotional response to the speech recognition results of the emergency broadcast in real time and adjust the notification content. For example, if the user feels stressed, the speech recognition unit can provide advice on how to relax. The speech recognition unit can also use the emotion estimation function to analyze the user's emotion to the speech recognition results of the emergency broadcast and adjust the notification content. For example, if the user feels anxious, the speech recognition unit can display a reassuring message. The speech recognition unit can also build a system that analyzes the user's emotional response to the speech recognition results of the emergency broadcast and adjust the notification content. For example, the priority of the notification can be changed depending on the user's emotional state. This makes it possible to adjust the notification content depending on the user's emotional response.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The application launcher accepts user operations. For example, a user launches a dedicated application installed on a smartphone or tablet, and when an emergency broadcast is transmitted, touches a button within the application. This operation causes the application launcher to start voice recognition. Step 2: The speech recognition unit recognizes the emergency broadcast audio in real time based on the operation accepted by the application launch unit. For example, the speech recognition unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to analyze the content of the emergency broadcast and convert it into text data. The generation AI receives the emergency broadcast audio data as input and generates text data based on that audio data. Step 3: The notification unit notifies the hearing-impaired person of the content of the emergency broadcast recognized by the voice recognition unit. For example, the notification unit may notify the hearing-impaired person by displaying it as a text message, vibrating the device, flashing the screen, or by other means. This allows the hearing-impaired person to immediately understand the content of the emergency broadcast.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an application launch unit that accepts user operations; a voice recognition unit that recognizes the voice of an emergency broadcast in real time based on the operation accepted by the application launch unit; a notification unit that notifies a hearing-impaired person of the content of the emergency broadcast recognized by the voice recognition unit. A system characterized by:

2. The application launch unit Enhance security and prevent accidental operation by using facial or fingerprint authentication 2. The system of claim 1.

3. The application launch unit Obtaining user location information and responding to local emergency broadcasts 2. The system of claim 1.

4. The application launch unit Analyzing users' emotions when receiving emergency broadcasts and suggesting relaxation methods to reduce stress 2. The system of claim 1.

5. The application launch unit Monitors the user's health status and provides health advice according to the content of emergency broadcasts 2. The system of claim 1.

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