System

Abstract

A system is provided.SOLUTION: The system includes means for acquiring voice data, voice recognition means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for generating notification data when the emergency broadcast is detected, means for transmitting the generated notification data to a terminal of a user, and means for displaying the transmitted notification data on the terminal of the user.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] In recent years, emergency broadcasts have become an essential means of quickly conveying important information during disasters and emergencies. However, for the hearing impaired, these broadcasts cannot be received directly, making it difficult for them to respond quickly. For this reason, there is a need for a system that can recognize emergency broadcasts in real time and provide appropriate notifications to the hearing impaired. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system including a means for acquiring voice data, a voice recognition means for converting the acquired voice data into text data, a means for detecting an emergency broadcast from the converted text data, a means for generating notification data when an emergency broadcast is detected, a means for transmitting the generated notification data to a user's terminal, and a means for displaying the transmitted notification data on the user's terminal. This system recognizes the content of voice broadcasts in real time and enables the hearing-impaired to quickly notify the hearing-impaired of necessary information. Furthermore, the means for detecting an emergency broadcast from the converted text data detects an emergency broadcast by analyzing keywords related to the emergency broadcast, and the means for transmitting the generated notification data to the user's terminal notifies the user via push notification or SMS.

[0006] "Audio data" refers to sound data obtained from an audio input device such as a microphone or a streaming service.

[0007] "Speech recognition means" is a technology that analyzes voice data and converts the voice into text data.

[0008] "Text data" refers to character string data converted from voice data by voice recognition means.

[0009] An "emergency broadcast" is a broadcast that conveys important information about disasters and emergencies to the public.

[0010] The "means for detecting emergency broadcasts" is a technology for analyzing specific keywords and patterns related to emergency broadcasts from text data converted by the voice recognition means to determine whether or not emergency information is present.

[0011] "Notification data" is data that is generated when an emergency broadcast is detected and includes warnings and instructions to the user.

[0012] A "user device" is an electronic device used by a user, such as a smartphone, tablet, or PC.

[0013] The "means for sending notifications" refers to the technology used to send the generated notification data to the user's device, and primarily uses push notifications and SMS.

[0014] The "means for displaying a notification" is a technique for visually or audibly notifying the user of the notification data received by the user's terminal. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention provides a system for supporting hearing-impaired people to receive emergency broadcasts in real time and respond appropriately. Specific embodiments of this system are as follows.

[0037] Server Processing

[0038] 1. Acquiring audio data

[0039] The server acquires audio data from a designated audio input device or streaming service, which is capable of receiving television or radio broadcasts and has an interface for transmitting audio data to the server in real time.

[0040] 2. Voice Recognition

[0041] The server passes the acquired voice data to a voice recognition engine, which converts the voice data into text data using existing voice recognition technology (e.g., Google® Speech-to-Text API). The converted text data is used for subsequent processing.

[0042] 3. Emergency broadcast detection

[0043] The server analyzes the text data obtained from the voice recognition engine and detects keywords and specific patterns related to emergency broadcasts. For example, it checks whether keywords such as "Earthquake Early Warning" or "Tsunami Warning" are included. If it is determined to be an emergency broadcast, notification data is generated.

[0044] 4. Notification Data Generation

[0045] The server generates notification data based on the detected emergency broadcast, including the specific content and urgency of the detected emergency broadcast, recommended actions, etc. The notification data is then sent to the user's device.

[0046] 5. Sending notifications

[0047] The server then sends the generated notification data to the user's device using an appropriate communication method, such as push notification or SMS, allowing the user to receive emergency information in real time.

[0048] Terminal handling

[0049] 1. Receiving notifications

[0050] The user's device receives the notification data sent from the server. The device has a dedicated app or notification listener set up to receive notifications.

[0051] 2. Display of notifications

[0052] The device will then display the received notification data to the user visually or audibly, for example, as a pop-up message on the screen, or by vibrating or issuing an audio alert, allowing the user to quickly grasp the emergency information.

[0053] User Action

[0054] 1. Check notifications

[0055] The user checks the emergency notification displayed on the device. The notification contains the specific emergency broadcast content and recommended actions, so the user can take the necessary action immediately.

[0056] 2. Send Feedback (Optional)

[0057] Users can send notification reception status and feedback to the server as needed, which can be used to check and improve the system's operation.

[0058] Specific examples

[0059] For example, the operation of the system when an earthquake occurs and an "Earthquake Early Warning" is broadcast will be described.

[0060] 1. Acquiring audio data

[0061] The server acquires the audio of emergency earthquake alerts broadcast on television and radio.

[0062] 2. Voice Recognition

[0063] The server passes the acquired voice to a voice recognition engine, and obtains text data saying, "An emergency earthquake warning has been issued."

[0064] 3. Emergency broadcast detection

[0065] The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[0066] 4. Notification Data Generation

[0067] The server generates notification data saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately."

[0068] 5. Sending notifications

[0069] The server sends the generated notification data to the user's device as a push notification.

[0070] 6. Receiving Notifications

[0071] The user's terminal receives the notification sent from the server.

[0072] 7. Display of notifications

[0073] The device will display a pop-up message saying, "An emergency earthquake alert has been issued. Please evacuate to a safe location immediately," and will alert the user with vibrations and an audio alarm.

[0074] 8. Checking Notifications

[0075] The user should check the notification and immediately evacuate to a safe location.

[0076] In this way, the present invention can provide a system that enables all users, including the hearing impaired, to quickly receive emergency broadcasts and respond appropriately.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] The server acquires audio data from a designated audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server.

[0080] Step 2:

[0081] The server sends the captured voice data to a speech recognition engine, which uses speech recognition technology such as the Google Speech-to-Text API to convert the voice data into text data for further processing.

[0082] Step 3:

[0083] The server analyzes the text data obtained from the speech recognition engine, detecting keywords and specific patterns related to emergency broadcasts. For example, it checks whether the text contains keywords such as "Earthquake Early Warning" or "Tsunami Warning."

[0084] Step 4:

[0085] When an emergency broadcast is detected, the server generates notification data, which includes information such as the specific content and urgency of the detected emergency broadcast, and recommended actions.

[0086] Step 5:

[0087] The server then sends the generated notification data to the user's device using communication methods such as push notifications or SMS, allowing the user to receive emergency information in real time.

[0088] Step 6:

[0089] The device receives the notification data sent from the server. The device has a dedicated app or notification listener set up to receive notifications.

[0090] Step 7:

[0091] The device displays the received notification data to the user visually or audibly, for example, by displaying a pop-up message on the screen or by emitting a vibration or audio alarm, allowing the user to quickly grasp the emergency information.

[0092] Step 8:

[0093] The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to a safe location.

[0094] Step 9:

[0095] Users can send notification reception status and feedback to the server as needed, which the server can use to check and improve the system's operation.

[0096] Example 1

[0097] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0098] Conventional emergency broadcasts rely on sight and hearing, so there is a problem that information cannot be properly conveyed to the hearing-impaired or visually impaired. This is particularly true for highly urgent information, which can lead to increased human casualties. Furthermore, when real-time information transmission is required, existing notification systems are sometimes unable to adequately respond. This creates a need for a fast and reliable means of transmitting information.

[0099] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0100] In this invention, the server includes means for acquiring voice data, means for storing the acquired voice data in a fixed buffer, means for passing the voice data to a voice recognition engine in real time, means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for identifying keywords based on the converted text data and determining whether the text data is an emergency broadcast, means for generating notification data when an emergency broadcast is detected, means for transmitting the generated notification data by push notification or SMS, means for transmitting the generated notification data to a user's device, and means for displaying the transmitted notification data on the user's device and generating a vibration or voice alert. This enables all users, including those with hearing impairments, to quickly and reliably receive emergency information in real time and respond appropriately.

[0101] 1. "Audio Data" means data that represents in digital form sound wave signals obtained from audio input devices or streaming services.

[0102] 2. "Buffer" is a storage area for temporarily storing audio data in preparation for subsequent processing.

[0103] 3. A "voice recognition engine" is software for converting voice data into text data, and is a program that utilizes existing voice recognition technology.

[0104] 4. "Text data" means character string data converted from voice data by a voice recognition engine.

[0105] 5. An "emergency broadcast" is an official broadcast to inform people of emergencies such as disasters and accidents, and is information that contains specific keywords or patterns.

[0106] 6. "Notification data" means data generated when an emergency broadcast is detected, and includes information such as the content of the emergency broadcast, its urgency, and recommended actions.

[0107] 7. "Push notification" is a method of sending information from a server to a user's device in real time, and is a notification method that does not require any special operation on the user's side.

[0108] 8. "SMS" means a text messaging service for sending short messages to a user's device.

[0109] 9. "Terminal" means a hardware device that receives notification data and displays it to the user, including a smartphone or tablet.

[0110] 10. "Alert" means a visual or audible notification that is used to get the user's attention.

[0111] 11. "Notification listener program" means software that resides on a user terminal to receive notification data sent from a server.

[0112] The present invention is a system for supporting hearing-impaired people in receiving emergency broadcasts in real time and responding appropriately. A specific embodiment of this system will be described.

[0113] Hardware and software used

[0114] 1. Audio Input Device:

[0115] The broadcast audio data is acquired using a device such as a TV tuner card or radio receiver.

[0116] 2. Server:

[0117] The server processes the acquired voice data, detects emergency broadcasts, and generates and sends notification data to the user's device. The software running on the server includes a speech recognition engine (e.g., Google Speech-to-Text API) and a notification system (e.g., Firebase Cloud Messaging).

[0118] 3. Terminal:

[0119] The user's terminal is a smartphone, tablet, or the like, and has installed thereon a dedicated app or notification listener program for visually or audibly displaying the received notification data to the user.

[0120] Data processing and calculation flow

[0121] The server receives voice data in real time from voice input devices and streaming services and stores it in a fixed buffer. This voice data is passed to a speech recognition engine and converted into text data using a method such as the Google Speech-to-Text API. The converted text data is then analyzed again by the server to detect keywords and specific patterns related to emergency broadcasts. This analysis is performed using keyword search and pattern matching technology, and when an emergency broadcast is detected, notification data is generated. The generated notification data is sent to the user's device via push notification or SMS. This notification data includes the specific content of the emergency broadcast, its urgency, and recommended actions.

[0122] Specific Examples

[0123] For example, when an earthquake occurs and an "Earthquake Early Warning" is broadcast, the server obtains the audio of the earthquake early warning from television or radio. This audio is converted into text data using the Google Speech-to-Text API. The server then detects the keyword "Earthquake Early Warning" from the converted text data and generates notification data. This notification data contains the message "An Earthquake Early Warning has been issued. Please evacuate to a safe place immediately," and is sent to the user's device via push notification. The user's device displays this notification as a pop-up message and simultaneously vibrates and generates an audio alert. The user can respond quickly by checking the notification and immediately evacuating to a safe place.

[0124] Prompt Sentence Examples

[0125] "Please give us an overview of the Emergency Alert Notification System."

[0126] "Please explain the operational flow of emergency broadcast notifications for the hearing impaired."

[0127] The above is an embodiment of the invention. This system enables all users, including the hearing impaired, to receive emergency broadcasts in real time and respond appropriately.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1:

[0130] The server acquires audio data. Specifically, it captures audio data in real time through an audio input device or a streaming service and stores this data in a buffer on the server. The input of this step is real-time audio data, and the output is the audio data stored in the buffer.

[0131] Step 2:

[0132] The server passes the voice data stored in the buffer to a voice recognition engine. The voice recognition engine used here uses technology such as the Google Speech-to-Text API and converts the voice data into text data. The input for this step is the voice data stored in the buffer, and the output is text data. Specifically, a voice message such as "An emergency earthquake warning has been issued" is converted into the text "An emergency earthquake warning has been issued."

[0133] Step 3:

[0134] The server analyzes the converted text data to detect keywords and specific patterns related to emergency broadcasts. This analysis is performed on the entire text data using keyword search and pattern matching techniques. The input for this step is the converted text data, and the output is a determination result indicating whether or not an emergency broadcast is present. For example, the keyword "Emergency Earthquake Alert" is detected.

[0135] Step 4:

[0136] When an emergency broadcast is detected, the server generates notification data. This notification data includes the specific content of the emergency broadcast, the urgency level, recommended actions, etc. The input to this step is the detection result of the emergency broadcast, and the output is the generated notification data. Specifically, a notification message is generated stating, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately."

[0137] Step 5:

[0138] The server sends the generated notification data to the user's device. This is done using push notifications or SMS. Services such as Firebase Cloud Messaging are used to deliver the notification data to the device in real time. The input to this step is the generated notification data, and the output is the notification data being sent to the user's device.

[0139] Step 6:

[0140] The user's device receives the notification data sent from the server. The device, on which the dedicated app or notification listener program is installed, temporarily stores the received data and prepares it for the next process. The input of this step is the notification data sent from the server, and the output is the notification data stored on the device.

[0141] Step 7:

[0142] The device displays the received notification data to the user visually or audibly. Specifically, it displays a pop-up message on the screen and generates a vibration or audio alert. For example, a message may be displayed saying, "An emergency earthquake warning has been issued. Please evacuate to a safe location immediately." The input of this step is the notification data stored in the device, and the output is the notification displayed to the user.

[0143] Step 8:

[0144] The user checks the emergency notification displayed on the device. The notification contains the specific emergency broadcast content and recommended actions, allowing the user to take the necessary action immediately. Specifically, the user checks the notification and takes action to evacuate to a safe place. The input of this step is the notification data displayed on the device, and the output is the user's response action.

[0145] (Application example 1)

[0146] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0147] Food delivery workers are required to quickly grasp information and take appropriate action in the event of an emergency. However, with conventional systems, workers with hearing impairments or those working in noisy environments may miss emergency broadcasts. For this reason, a means is needed to ensure that all workers receive emergency information and continue working safely.

[0148] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0149] In this invention, the server includes means for acquiring voice data, means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for generating notification data when an emergency broadcast is detected, means for transmitting the generated notification data to a user terminal, and means for transmitting emergency notifications to food delivery workers, thereby enabling all food delivery workers to receive emergency information in real time and respond quickly and appropriately.

[0150] "Audio data" is digital data that contains audio information and is a record of sound waves as electronic signals.

[0151] "Acquisition means" refers to a device or method for acquiring audio data from an external source.

[0152] "Speech recognition means" refers to technology or equipment that analyzes acquired voice data and converts it into text data.

[0153] "Text data" is digital data containing character information generated by a voice recognition means.

[0154] "Emergency broadcast detection means" refers to technology or devices that analyze text data and identify broadcasts that contain important emergency information.

[0155] The "notification data generating means" refers to a device or method for creating a specific notification for the user when an emergency broadcast is detected.

[0156] "Communication means" refers to the technology or device for transmitting the generated notification data to the user's terminal.

[0157] "Terminal display means" refers to a technique or device for displaying notification data on a user's terminal.

[0158] "Food delivery" refers to the business or service of delivering food and beverages to customers.

[0159] "Worker" refers to a person engaged in food delivery work.

[0160] "Emergency notification" refers to information that is quickly conveyed to users, including the contents of emergency broadcasts.

[0161] This invention relates to a system that supports food delivery workers by receiving emergency broadcasts in real time, enabling them to carry out their work safely. This system operates while playing the roles of a server, a terminal, and a user.

[0162] The server first obtains audio data from radio or TV streaming services. To do this, it uses HTTP streaming to obtain audio data in real time. The obtained audio data is then converted into text data using the Google Speech-to-Text API. The Google Speech-to-Text API provides highly accurate speech recognition and supports a variety of languages. This allows audio data to be converted into text data quickly and accurately.

[0163] The server then detects emergency broadcasts from the converted text data. To do this, it uses an algorithm that analyzes keywords related to emergency broadcasts. For example, it checks whether the text data contains keywords such as "earthquake early warning" or "typhoon warning." By applying natural language processing technology to this analysis, high detection accuracy is achieved.

[0164] When an emergency broadcast is detected, the server immediately generates notification data, which includes the content of the detected emergency broadcast and specific recommended actions, providing workers with the information they need to take appropriate action.

[0165] The server then sends the generated notification data to the devices of food delivery workers via push notification or SMS, using the Twilio API for rapid notification, allowing workers to receive important emergency information in real time.

[0166] The user's device will then display the received notification data appropriately, including displaying a pop-up message, vibrating, or sounding an alarm, ensuring that workers receive emergency information and can respond promptly.

[0167] As a concrete example, we will explain the system's processing when an earthquake occurs and an "Earthquake Early Warning" is broadcast. The server acquires audio data from the radio or television and generates text data saying "An earthquake early warning has been issued" using the Google Speech-to-Text API. Next, it analyzes this text data to detect the keyword "earthquake early warning" and determines that it is an emergency broadcast. It then generates notification data saying "An earthquake early warning has been issued. Please evacuate to a safe location immediately," and sends it to the worker's smartphone via push notification or SMS via the Twilio API. Finally, the worker's device warns them with a pop-up message or vibration.

[0168] Example prompt sentence:

[0169] Create a program that uses a speech recognition engine to convert voice data into text and detect emergency broadcasts. Next, create a program that generates notification data based on the detected emergency broadcast content and sends the notification to a specified smartphone. The speech recognition engine used is the Google Speech-to-Text API. HTTP streaming will be used to obtain the voice data. The Twilio API will be used to send the notification.

[0170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0171] Step 1:

[0172] The server retrieves audio data in real time from a radio or TV streaming service. Specifically, it periodically receives audio data using HTTP streaming and stores it in a buffer. The input is the audio stream URL, and the output is the buffered audio data.

[0173] Step 2:

[0174] The server passes the buffered voice data to the Google Speech-to-Text API to convert it into text data. The voice recognition engine analyzes the voice data and generates corresponding text data. The input is the buffered voice data, and the output is the converted text data. Specific operations include the process of sending voice data to the recognition engine and receiving its response.

[0175] Step 3:

[0176] The server analyzes the converted text data to detect keywords related to emergency broadcasts. It uses natural language processing technology to search for specific keywords (e.g., "earthquake early warning" or "typhoon warning") in the text data. The input is the converted text data, and the output is the detection results of emergency broadcasts and the detected keywords. Specifically, it involves the process of applying a text analysis algorithm to detect keywords.

[0177] Step 4:

[0178] When an emergency broadcast is detected, the server generates notification data including recommended actions. It creates a notification message and includes the content of the emergency broadcast and recommended measures. The input is the emergency broadcast detection result and the detected keywords, and the output is a detailed notification message. Specific operations include a process using a template-based message generation engine.

[0179] Step 5:

[0180] The server uses the Twilio API to send the generated notification data to the food delivery worker's device. The notification data is sent via push notification or SMS. The input is the notification message, and the output is a confirmation that the message was sent. Specific operations include calling the Twilio API and sending the notification message in the appropriate format.

[0181] Step 6:

[0182] The terminal visually and audibly displays the notification data received from the server to the user. Notifications are given using pop-up messages, vibrations, audio alarms, etc. The input is the notification message, and the output is the display of an emergency notification to the user. Specific operations include the process of displaying a message and notifying the user using the terminal's user interface.

[0183] Step 7:

[0184] The user checks the notification displayed on the device and takes the specified recommended action, such as evacuating to a safe place. The input is the displayed notification message, and the output is the user's action. Specific actions include understanding the notification content and taking the recommended action.

[0185] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0186] The present invention combines a system that acquires audio data, recognizes emergency broadcasts in real time, and notifies the hearing impaired quickly with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are as follows:

[0187] Server Processing

[0188] 1. Acquiring audio data

[0189] The server acquires audio data from a designated audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server.

[0190] 2. Voice Recognition

[0191] The server sends the captured voice data to a speech recognition engine, which uses speech recognition technology such as the Google Speech-to-Text API to convert the voice data into text data for further processing.

[0192] 3. Emergency broadcast detection

[0193] The server analyzes the text data obtained from the speech recognition engine, detecting keywords and specific patterns related to emergency broadcasts. For example, it checks whether the text contains keywords such as "Earthquake Early Warning" or "Tsunami Warning."

[0194] 4. Emotion Recognition by Emotion Engine

[0195] The server operates an emotion engine based on the user's voice and facial expression data acquired from the device. The emotion engine recognizes the user's emotions (e.g., fear, anxiety, calmness, etc.) in real time. This information is reflected in the generation of notification data.

[0196] 5. Generating Notification Data

[0197] When an emergency broadcast is detected, the server generates notification data. This notification data includes the specific content and urgency of the detected emergency broadcast, recommended actions, and content based on the user's emotions recognized by the emotion engine. For example, if the user is feeling very anxious, the notification will include additional instructions to remain calm and act accordingly.

[0198] 6. Sending Notifications

[0199] The server then sends the generated notification data to the user's device using an appropriate communication method, such as push notification or SMS, allowing the user to receive emergency information in real time.

[0200] Terminal handling

[0201] 1. Receiving notifications

[0202] The user's device receives the notification data sent from the server. The device has a dedicated app and notification listener for receiving notifications. The device also has an interface for sending the user's voice and facial expression data to the server.

[0203] 2. Display of notifications

[0204] The device then displays the received notification data to the user visually or audibly. The display method is adjusted according to the user's emotion as recognized by the emotion engine. For example, if the user is calm, a standard pop-up message is displayed, while if the user is anxious, a notification is displayed with vibration and additional explanation.

[0205] User Action

[0206] 1. Check notifications

[0207] The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to a safe location.

[0208] 2. Send Feedback (Optional)

[0209] Users can send notification reception status and feedback to the server as needed, which the server can use to check and improve the system's operation.

[0210] Specific examples

[0211] For example, the operation of the system when an earthquake occurs and an "Earthquake Early Warning" is broadcast will be described.

[0212] 1. Acquiring audio data

[0213] The server acquires the audio of emergency earthquake alerts broadcast on television and radio.

[0214] 2. Voice Recognition

[0215] The server passes the acquired voice to a voice recognition engine, and obtains text data saying, "An emergency earthquake warning has been issued."

[0216] 3. Emergency broadcast detection

[0217] The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[0218] 4. Emotion Recognition by Emotion Engine

[0219] The server activates an emotion engine based on the user's voice and facial expression data and recognizes that the user is feeling anxious.

[0220] 5. Generating Notification Data

[0221] The server generates notification data saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly."

[0222] 6. Sending Notifications

[0223] The server sends the generated notification data to the user's device as a push notification.

[0224] 7. Receiving Notifications

[0225] The user's terminal receives the notification sent from the server.

[0226] 8. Display of notifications

[0227] The device will display a pop-up message saying, "An emergency earthquake alert has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly," and will alert the user with vibrations and an audio alarm.

[0228] 9. Checking Notifications

[0229] The user should check the notification and immediately evacuate to a safe location.

[0230] In this way, the present invention makes it possible to provide an emergency notification system that takes into account the user's emotions, and enables all users to quickly receive emergency broadcasts and respond appropriately.

[0231] The processing flow will be explained below.

[0232] Step 1:

[0233] The server retrieves audio data from a designated audio input device or streaming service, such as a device that transmits broadcast audio from a television or radio to the server in real time.

[0234] Step 2:

[0235] The server sends the acquired voice data to a speech recognition engine, which converts the voice data into text data using technologies such as the Google Speech-to-Text API, which is then used for further processing.

[0236] Step 3:

[0237] The server analyzes the text data obtained from the speech recognition engine. During the analysis, it detects keywords and specific patterns related to emergency broadcasts. For example, it checks whether the text contains keywords such as "Earthquake Early Warning" or "Tsunami Warning."

[0238] Step 4:

[0239] The server passes the user's voice and facial expression data acquired from the device to the emotion engine, which uses this data to recognize the user's emotions in real time. For example, it can determine whether the user is feeling anxious or calm from the tone of their voice and facial expression.

[0240] Step 5:

[0241] When an emergency broadcast is detected, the server generates notification data based on the user's emotions recognized by the emotion engine. For example, if the server determines that the user is feeling extremely anxious, the notification data will include an additional message such as "Please remain calm and act calmly."

[0242] Step 6:

[0243] The server then sends the generated notification data to the user's device using an appropriate communication method, such as push notification or SMS, allowing the user to receive emergency information in real time.

[0244] Step 7:

[0245] The device receives the notification data sent from the server. The device has a dedicated app and notification listener for receiving notifications. It also has an interface for sending the user's voice and facial expression data to the server.

[0246] Step 8:

[0247] The device displays the received notification data to the user visually or audibly, and adjusts the notification display depending on the user's emotions. For example, if the user is calm, a standard pop-up message will be displayed, whereas if the user is anxious, detailed instructions will be displayed along with vibrations and audio alerts.

[0248] Step 9:

[0249] The user then checks the emergency notification displayed on their device, which includes the specific emergency broadcast content, recommended actions, and additional emotion-based instructions, allowing the user to take appropriate action quickly.

[0250] Step 10:

[0251] Users can send notification reception status and feedback to the server as needed. This helps the server check and improve the system's operation. For example, users can report their impressions after receiving notifications or any bugs.

[0252] Example 2

[0253] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0254] Conventional emergency broadcast notification systems analyze audio data to detect emergency broadcasts, but they lack consideration for users with hearing impairments or who are emotionally upset. As a result, it can be difficult for users to respond quickly and appropriately to an emergency. Furthermore, notifications that do not take the user's emotional state into account can cause further anxiety and confusion. Therefore, there is a need for an emergency broadcast notification system that takes the user's emotional state into account.

[0255] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring voice data, voice recognition means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for acquiring user voice and facial expression data and recognizing the emotion, means for generating notification data taking into account the user's emotion recognition result when an emergency broadcast is detected, means for transmitting the generated notification data to the user's terminal, and means for displaying the transmitted notification data on the user's terminal. This enables emergency notifications that take into account the user's emotional state, allowing all users to quickly receive the emergency broadcast and respond appropriately.

[0256] "Means for acquiring audio data" refers to the function of collecting audio data in real time from various audio input devices and streaming services.

[0257] "Speech recognition means for converting acquired voice data into text data" refers to a function that uses voice recognition technology or an API (e.g., a voice recognition API) to convert collected voice data into text format.

[0258] "Means for detecting emergency broadcasts from converted text data" refers to a function that analyzes the text data converted by the voice recognition means and identifies keywords or specific patterns related to emergency broadcasts.

[0259] "Means for acquiring the user's voice and facial expression data and recognizing their emotions" refers to the function of collecting the user's voice and facial expression data, analyzing it, and recognizing the user's emotional state (for example, fear, anxiety, calmness, etc.) in real time.

[0260] "Means for generating notification data taking into consideration the user's emotion recognition results when an emergency broadcast is detected" refers to a function that automatically generates notification data to provide appropriate information to the user based on the detected emergency broadcast and the user's emotional state.

[0261] "Means for sending generated notification data to the user's device" refers to a function that uses communication means such as push notification or SMS to send the generated notification data to the user's mobile device, computer, etc.

[0262] "Means for displaying the transmitted notification data on the user's terminal" refers to a function for visually or audibly informing the user of the notification data received on the user's terminal and displaying it so that the user can recognize it.

[0263] The present invention combines a system that acquires audio data, recognizes emergency broadcasts in real time, and promptly notifies the hearing impaired with an emotion engine that recognizes the user's emotions. This system includes the following specific components and processes:

[0264] Server Processing

[0265] Acquiring audio data

[0266] The server acquires audio data from a specified audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server. Specifically, the audio input device streams audio data in real time using HTTP requests.

[0267] Voice Recognition

[0268] The server sends the acquired voice data to a speech recognition engine, such as the Google Speech-to-Text API. This engine converts the voice data into text data. The server parses the response from the API and stores the necessary information.

[0269] Emergency broadcast detection

[0270] The server analyzes the text data obtained from the speech recognition engine to detect keywords and specific patterns related to emergency broadcasts, such as "emergency earthquake alert" and "tsunami warning," and flags any that match.

[0271] Emotion recognition by emotion engine

[0272] The server operates the emotion engine based on the user's voice and facial expression data acquired from the device. Emotion recognition uses a facial expression recognition API and a voice emotion analysis API. The server grasps the user's emotional state based on the analyzed information.

[0273] Generate notification data

[0274] The server generates notification data when an emergency broadcast is detected. This notification data is created taking into account the user's emotion recognition results. For example, if the user is feeling anxious, an additional message such as "Please stay calm" may be included.

[0275] Sending notifications

[0276] The server sends the generated notification data to the user's device using push notifications, SMS, etc. Specifically, it uses a notification service API such as Firebase Cloud Messaging.

[0277] Terminal handling

[0278] Receive notifications

[0279] The user's device receives the notification data sent from the server. A dedicated app or notification listener is set up on the device to receive notifications. A background service waits for notifications and stores the received data in the app.

[0280] Viewing notifications

[0281] The user's device displays the received notification data visually or audibly to the user. The display method is adjusted according to the user's emotion recognized by the emotion engine. For example, it may display a pop-up message, and if the user is in an anxious state, it may also use vibration or an audio alarm.

[0282] User Action

[0283] Checking notifications

[0284] The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to a safe location.

[0285] Send feedback (optional)

[0286] Users can send notification reception status and feedback to the server as needed. Through the app's feedback function, users can send their impressions of notifications and problems to the server in text format.

[0287] Specific examples

[0288] For example, when an earthquake occurs and an "Earthquake Early Warning" is broadcast, the system operates as follows.

[0289] 1. Acquiring audio data

[0290] The server receives the audio of emergency earthquake alerts broadcast on television and radio in real time.

[0291] 2. Voice Recognition

[0292] The server passes the acquired voice data to a voice recognition engine, and obtains text data saying, "An emergency earthquake warning has been issued."

[0293] 3. Emergency broadcast detection

[0294] The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[0295] 4. Emotion Recognition by Emotion Engine

[0296] The server activates an emotion engine based on the user's voice and facial expression data and recognizes that the user is feeling anxious.

[0297] 5. Generating Notification Data

[0298] The server generates notification data saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly."

[0299] 6. Sending Notifications

[0300] The server sends the generated notification data to the user's device as a push notification.

[0301] 7. Receiving Notifications

[0302] The user's terminal receives the notification sent from the server.

[0303] 8. Display of notifications

[0304] The device will display a pop-up message saying, "An emergency earthquake alert has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly," and will alert the user with vibrations and an audio alarm.

[0305] 9. Checking Notifications

[0306] The user should check the notification and immediately evacuate to a safe location.

[0307] Prompt Sentence Examples

[0308] Below are some examples of prompts to input to the generative AI model.

[0309] "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm."

[0310] This enables emergency notifications that take into account the emotional state of the user, allowing all users to quickly receive emergency broadcasts and respond appropriately.

[0311] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0312] Step 1:

[0313] Acquiring audio data

[0314] Description: The server retrieves audio data from a specified audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server.

[0315] Input: Real-time audio data from TV or radio.

[0316] Data processing / computation: Audio input devices stream audio data in real time using HTTP requests.

[0317] Output: Real-time audio data sent to the server.

[0318] Step 2:

[0319] Voice Recognition

[0320] Description: The server sends the captured voice data to a speech recognition engine, such as the Google Speech-to-Text API, which converts the voice data into text data.

[0321] Input: The real-time audio data obtained in step 1.

[0322] Data processing / calculation: The server processes the batch of audio data, sends an API request to receive text data, parses the response from the API, and stores the necessary information.

[0323] Output: Text data obtained from the speech recognition engine.

[0324] Step 3:

[0325] Emergency broadcast detection

[0326] Description: The server analyzes the text data obtained from the speech recognition engine to detect keywords and specific patterns related to emergency broadcasts.

[0327] Input: The text data generated in step 2.

[0328] Data processing / calculation: The server searches for keywords such as "earthquake early warning" or "tsunami warning" within the text data and flags any matches. This flag is used for subsequent processing.

[0329] Output: A flag indicating whether an emergency broadcast was detected.

[0330] Step 4:

[0331] Emotion recognition by emotion engine

[0332] Description: The server operates the emotion engine based on the user's voice and facial expression data acquired from the device. The emotion engine recognizes the user's emotional state (e.g., fear, anxiety, calmness, etc.) in real time.

[0333] Input: User's voice and facial expression data.

[0334] Data processing / calculation: The server sends data to the facial expression recognition API and voice emotion analysis API, analyzes the response, and determines the user's emotions.

[0335] Output: Data indicating the user's emotional state.

[0336] Step 5:

[0337] Generate notification data

[0338] Description: The server generates notification data when an emergency broadcast is detected. This notification data is created taking into account the content of the emergency broadcast and the user's emotion recognition results.

[0339] Input: Emergency broadcast flag from step 3, user emotion data from step 4.

[0340] Data processing / calculation: The server generates notification messages using templates and inserts additional messages such as "please stay calm" if the user is feeling anxious.

[0341] Output: The generated notification data.

[0342] Step 6:

[0343] Sending notifications

[0344] Description: The server sends the generated notification data to the user's device using communication methods such as push notification or SMS.

[0345] Input: The notification data generated in step 5.

[0346] Data processing / calculation: The server uses a notification service API (e.g., Firebase Cloud Messaging) to send the generated notification data to the device.

[0347] Output: Notification data received by the user's device.

[0348] Step 7:

[0349] Receive notifications

[0350] Description: The user's device receives notification data sent from the server. The device has a dedicated app or notification listener set up to receive notifications.

[0351] Input: Notification data sent by the server.

[0352] Data processing / calculation: The device's background service waits for notifications and stores the received data within the app.

[0353] Output: Notification data stored in the device.

[0354] Step 8:

[0355] Viewing notifications

[0356] Description: The device displays the received notification data to the user visually or audibly, adjusting the display method according to the user's emotion recognized by the emotion engine.

[0357] Input: Notification data stored on the device.

[0358] Data processing / calculation: The app displays the notification content as a popup, and also vibrates or sounds an alarm if the user is in an anxious state.

[0359] Output: Notification data displayed visually and audibly to the user.

[0360] Step 9:

[0361] Checking notifications

[0362] Description: The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to evacuate to a safe location.

[0363] Input: The notification displayed on the device.

[0364] Data processing / calculation: The user checks the notification and acts according to the instructions.

[0365] Output: User action.

[0366] Step 10:

[0367] Send feedback (optional)

[0368] Description: Users can optionally send notification status and feedback to the server.

[0369] Input: Feedback information from the user.

[0370] Data processing / calculation: Through the app's feedback function, users can send their thoughts and problems about notifications in text format to the server.

[0371] Output: Feedback information sent to the server.

[0372] (Application example 2)

[0373] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0374] Conventional emergency broadcast notification systems lack functionality to accommodate specific users, such as the hearing impaired and elderly. Furthermore, emergency notifications are not adapted to the user's emotions, which can lead to panic or incorrect judgment in an emergency. Furthermore, if notifications are not delivered promptly and appropriately, the user's safety may be threatened. In response to these issues, the present invention aims to provide more accurate emergency notifications by taking the user's emotions into account.

[0375] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring voice data, voice recognition means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, emotion recognition means for recognizing a user's emotion based on the user's voice and facial expression data, means for generating notification data based on the user's emotion when an emergency broadcast is detected, means for transmitting the generated notification data to the user's terminal, and means for displaying the transmitted notification data on the user's terminal. This makes it possible to provide a quick and appropriate emergency notification that takes the user's emotion into consideration, thereby improving the safety of specific users.

[0376] "Audio data" is information obtained by capturing sound waves and converting them into digital form, and is data that includes an audio signal.

[0377] "Speech recognition means" refers to a technical means for converting voice data into text data, such as a device that uses a speech recognition API or speech recognition software.

[0378] "Text data" is data expressed as a character string, and includes character information converted from audio data.

[0379] The "emergency broadcast detection means" is a technical means for detecting information related to emergency broadcasts from the converted text data.

[0380] "Emotion recognition means" refers to a technical means for analyzing the user's voice and facial expression data to recognize the user's emotional state.

[0381] The "notification data generation means" is a technical means for creating notification data that is generated when an emergency broadcast is detected, and includes notification content that is adjusted based on the user's emotion recognition results.

[0382] The "notification data transmission means" is a technical means for transmitting the generated notification data to the user's terminal.

[0383] The "notification data display means" is a technical means for displaying the transmitted notification data on the user terminal, and has the function of notifying the user visually or audibly.

[0384] "Emotion" refers to the user's psychological state, and includes, for example, anxiety, fear, calmness, and the like.

[0385] "User's terminal" refers to an electronic device owned by the user, specifically a smartphone or a head-mounted display.

[0386] MODE FOR CARRYING OUT THE INVENTION

[0387] The present invention is a system that acquires audio data, recognizes emergency broadcasts in real time, and generates and transmits notifications according to the user's emotions. Detailed embodiments for carrying out the present invention are described below.

[0388] Server Processing

[0389] The server uses the following hardware and software to acquire audio data and detect emergency broadcasts in real time.

[0390] Hardware and software used

[0391] Audio input device: Hardware required to receive television and radio audio in real time.

[0392] Google Speech-to-Text API: Speech recognition software that converts acquired voice data into text data.

[0393] Emotion engine: Software for recognizing the user's emotions, analyzing the user's voice and facial expression data.

[0394] Notification sending system: Includes push notification and SMS sending functions for sending generated notification data to the user's device.

[0395] Acquiring audio data

[0396] The server acquires audio data from a designated audio input device, for example, by receiving television or radio broadcasts in real time and periodically transmitting the audio to the server.

[0397] Voice Recognition

[0398] The server sends the acquired audio data to the Google Speech-to-Text API and converts it into text data, which is then used to detect emergency broadcasts.

[0399] Emergency broadcast detection

[0400] The server analyzes the converted text data and detects keywords related to emergency broadcasts (e.g., "Earthquake Early Warning" or "Tsunami Warning"), thereby determining whether an emergency broadcast has been made.

[0401] emotion recognition

[0402] The server acquires the user's voice and facial expression data and inputs it into the emotion engine, which analyzes this data and recognizes the user's emotions (anxiety, fear, calmness, etc.) in real time.

[0403] Generate notification data

[0404] When an emergency broadcast is detected, the server generates notification data based on the user's emotional state. For example, if the user is feeling extremely anxious, the server generates notification data that includes instructions such as "Please remain calm and act calmly."

[0405] Sending notifications

[0406] The server sends the generated notification data to the user's device using push notifications or SMS, allowing the user to receive emergency information in real time.

[0407] Terminal handling

[0408] The user's device (such as a smartphone, head-mounted display, or smart glasses) quickly receives the notification sent from the server and presents it to the user visually or audibly.

[0409] Receiving and Viewing Notifications

[0410] The device receives the notification data sent from the server and displays the notification in a way that corresponds to the user's emotion. For example, if the user is calm, a standard pop-up message is displayed, but if the user is anxious, a notification is displayed with vibration and additional explanation.

[0411] User Action

[0412] The user checks the notification displayed on the device and takes necessary action, for example, by following the instruction "Please evacuate immediately" and evacuating to a safe place.

[0413] Specific examples

[0414] For example, when an earthquake occurs and an "Earthquake Early Warning" is broadcast, the system operates as follows:

[0415] 1. Acquisition of audio data: The server acquires the audio of the emergency earthquake alert broadcast from television or radio.

[0416] 2. Speech recognition: The server passes the acquired speech to the speech recognition engine, and obtains the text data "An emergency earthquake warning has been issued."

[0417] 3. Emergency broadcast detection: The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[0418] 4. Emotion recognition: The server activates an emotion engine based on the user's voice and facial expression data to recognize when the user is feeling anxious.

[0419] 5. Generation of notification data: The server generates notification data stating, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly."

[0420] 6. Sending notification: The server sends the generated notification data to the user's device via push notification.

[0421] 7. Receiving notification: The user's device receives the notification sent from the server.

[0422] 8. Display notification: The device will display a pop-up message saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm." and will notify the user with vibration and an audio alarm.

[0423] 9. Check the notification: The user checks the notification and immediately evacuates to a safe place.

[0424] Example prompts to give to a generative AI model:

[0425] Generate the example application code for an emergency notification and emotion recognition system. Follow these steps:

[0426] 1. Get the audio data from the specified URL.

[0427] 2. Use the Google Speech-to-Text API to convert the audio data into text data.

[0428] 3. Detect urgent keywords in the text data.

[0429] 4. Recognize user emotions using an emotion engine.

[0430] 5. Generate notification data based on emergency broadcast and emotion.

[0431] 6. Send notification data to a smartphone or HMD.

[0432] 7. Implement these functions in Python using specific libraries (e.g. requests, google.cloud.speech, pyttsx3)

[0433] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0434] Step 1:

[0435] The server acquires audio data. Specifically, it receives television or radio broadcast audio in real time from a designated audio input device and acquires the audio data. The input is an audio signal, and the output is audio data converted into a digital format.

[0436] Step 2:

[0437] The server sends the acquired voice data to a voice recognition means and converts it into text data. Specifically, it uses the Google Speech-to-Text API to analyze the voice data and convert it into text data. The input is digital voice data, and the output is text data.

[0438] Step 3:

[0439] The server analyzes the converted text data and detects emergency broadcasts. Specifically, it searches for keywords such as "Earthquake Early Warning" and "Tsunami Warning" to determine whether or not an emergency broadcast is occurring. The input is text data, and the server analyzes its contents to determine whether or not an emergency broadcast is occurring.

[0440] Step 4:

[0441] The server collects the user's voice and facial expression data and inputs it into the emotion recognition means to recognize the user's emotions. Specifically, the emotion engine uses the voice and facial expression data sent from the user terminal to analyze the user's emotional state (anxiety, fear, calmness, etc.). The input is the user's voice and facial expression data, and the output is the recognized emotional state.

[0442] Step 5:

[0443] When an emergency broadcast is detected, the server generates notification data based on the user's emotional state. For example, if the user is feeling anxious, the server generates notification data that includes supplemental information such as "Please remain calm and act calmly." The input is the content of the emergency broadcast and the user's emotional state, and the output is the adjusted notification data.

[0444] Step 6:

[0445] The server sends the generated notification data to the user's device via push notification or SMS. Specifically, it sends notifications to the user's device in real time through a notification sending system. The input is the notification data, and the output is the sent notification.

[0446] Step 7:

[0447] The device receives notification data sent from the server. Specifically, it receives push notifications and SMS using a dedicated application or notification listener. The input is the notification data sent from the server, and the output is the received notification.

[0448] Step 8:

[0449] The device displays the notification data to the user visually or audibly, for example, by using a pop-up message, vibration, or audio alarm. The input is the received notification data, and the output is the displayed notification content.

[0450] Step 9:

[0451] The user checks the notification displayed on the device and takes the necessary action. Specifically, the user follows the instructions in the notification and takes action, such as evacuating to a safe place. The input is the displayed notification content, and the output is the appropriate response action.

[0452] 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.

[0453] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0454] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0455] [Second embodiment]

[0456] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0457] 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.

[0458] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0459] 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.

[0460] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0461] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0462] 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.

[0463] 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.

[0464] 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 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.

[0465] 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.

[0466] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0467] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0468] The present invention provides a system for supporting hearing-impaired people to receive emergency broadcasts in real time and respond appropriately. Specific embodiments of this system are as follows.

[0469] Server Processing

[0470] 1. Acquiring audio data

[0471] The server acquires audio data from a designated audio input device or streaming service, which is capable of receiving television or radio broadcasts and has an interface for transmitting audio data to the server in real time.

[0472] 2. Voice Recognition

[0473] The server passes the acquired voice data to a speech recognition engine, which converts the voice data into text data using existing speech recognition technology (e.g., Google Speech-to-Text API). The converted text data is used for subsequent processing.

[0474] 3. Emergency broadcast detection

[0475] The server analyzes the text data obtained from the voice recognition engine and detects keywords and specific patterns related to emergency broadcasts. For example, it checks whether keywords such as "Earthquake Early Warning" or "Tsunami Warning" are included. If it is determined to be an emergency broadcast, notification data is generated.

[0476] 4. Notification Data Generation

[0477] The server generates notification data based on the detected emergency broadcast, including the specific content and urgency of the detected emergency broadcast, recommended actions, etc. The notification data is then sent to the user's device.

[0478] 5. Sending notifications

[0479] The server then sends the generated notification data to the user's device using an appropriate communication method, such as push notification or SMS, allowing the user to receive emergency information in real time.

[0480] Terminal handling

[0481] 1. Receiving notifications

[0482] The user's device receives the notification data sent from the server. The device has a dedicated app or notification listener set up to receive notifications.

[0483] 2. Display of notifications

[0484] The device will then display the received notification data to the user visually or audibly, for example, as a pop-up message on the screen, or by vibrating or issuing an audio alert, allowing the user to quickly grasp the emergency information.

[0485] User Action

[0486] 1. Check notifications

[0487] The user checks the emergency notification displayed on the device. The notification contains the specific emergency broadcast content and recommended actions, so the user can take the necessary action immediately.

[0488] 2. Send Feedback (Optional)

[0489] Users can send notification reception status and feedback to the server as needed, which can be used to check and improve the system's operation.

[0490] Specific examples

[0491] For example, the operation of the system when an earthquake occurs and an "Earthquake Early Warning" is broadcast will be described.

[0492] 1. Acquiring audio data

[0493] The server acquires the audio of emergency earthquake alerts broadcast on television and radio.

[0494] 2. Voice Recognition

[0495] The server passes the acquired voice to a voice recognition engine, and obtains text data saying, "An emergency earthquake warning has been issued."

[0496] 3. Emergency broadcast detection

[0497] The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[0498] 4. Notification Data Generation

[0499] The server generates notification data saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately."

[0500] 5. Sending notifications

[0501] The server sends the generated notification data to the user's device as a push notification.

[0502] 6. Receiving Notifications

[0503] The user's terminal receives the notification sent from the server.

[0504] 7. Display of notifications

[0505] The device will display a pop-up message saying, "An emergency earthquake alert has been issued. Please evacuate to a safe location immediately," and will alert the user with vibrations and an audio alarm.

[0506] 8. Checking Notifications

[0507] The user should check the notification and immediately evacuate to a safe location.

[0508] In this way, the present invention can provide a system that enables all users, including the hearing impaired, to quickly receive emergency broadcasts and respond appropriately.

[0509] The processing flow will be explained below.

[0510] Step 1:

[0511] The server acquires audio data from a designated audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server.

[0512] Step 2:

[0513] The server sends the captured voice data to a speech recognition engine, which uses speech recognition technology such as the Google Speech-to-Text API to convert the voice data into text data for further processing.

[0514] Step 3:

[0515] The server analyzes the text data obtained from the speech recognition engine, detecting keywords and specific patterns related to emergency broadcasts. For example, it checks whether the text contains keywords such as "Earthquake Early Warning" or "Tsunami Warning."

[0516] Step 4:

[0517] When an emergency broadcast is detected, the server generates notification data, which includes information such as the specific content and urgency of the detected emergency broadcast, and recommended actions.

[0518] Step 5:

[0519] The server then sends the generated notification data to the user's device using communication methods such as push notifications or SMS, allowing the user to receive emergency information in real time.

[0520] Step 6:

[0521] The device receives the notification data sent from the server. The device has a dedicated app or notification listener set up to receive notifications.

[0522] Step 7:

[0523] The device displays the received notification data to the user visually or audibly, for example, by displaying a pop-up message on the screen or by emitting a vibration or audio alarm, allowing the user to quickly grasp the emergency information.

[0524] Step 8:

[0525] The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to a safe location.

[0526] Step 9:

[0527] Users can send notification reception status and feedback to the server as needed, which the server can use to check and improve the system's operation.

[0528] Example 1

[0529] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0530] Conventional emergency broadcasts rely on sight and hearing, so there is a problem that information cannot be properly conveyed to the hearing-impaired or visually impaired. This is particularly true for highly urgent information, which can lead to increased human casualties. Furthermore, when real-time information transmission is required, existing notification systems are sometimes unable to adequately respond. This creates a need for a fast and reliable means of transmitting information.

[0531] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0532] In this invention, the server includes means for acquiring voice data, means for storing the acquired voice data in a fixed buffer, means for passing the voice data to a voice recognition engine in real time, means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for identifying keywords based on the converted text data and determining whether the text data is an emergency broadcast, means for generating notification data when an emergency broadcast is detected, means for transmitting the generated notification data by push notification or SMS, means for transmitting the generated notification data to a user's device, and means for displaying the transmitted notification data on the user's device and generating a vibration or voice alert. This enables all users, including those with hearing impairments, to quickly and reliably receive emergency information in real time and respond appropriately.

[0533] 1. "Audio Data" means data that represents in digital form sound wave signals obtained from audio input devices or streaming services.

[0534] 2. "Buffer" is a storage area for temporarily storing audio data in preparation for subsequent processing.

[0535] 3. A "voice recognition engine" is software for converting voice data into text data, and is a program that utilizes existing voice recognition technology.

[0536] 4. "Text data" means character string data converted from voice data by a voice recognition engine.

[0537] 5. An "emergency broadcast" is an official broadcast to inform people of emergencies such as disasters and accidents, and is information that contains specific keywords or patterns.

[0538] 6. "Notification data" means data generated when an emergency broadcast is detected, and includes information such as the content of the emergency broadcast, its urgency, and recommended actions.

[0539] 7. "Push notification" is a method of sending information from a server to a user's device in real time, and is a notification method that does not require any special operation on the user's side.

[0540] 8. "SMS" means a text messaging service for sending short messages to a user's device.

[0541] 9. "Terminal" means a hardware device that receives notification data and displays it to the user, including a smartphone or tablet.

[0542] 10. "Alert" means a visual or audible notification that is used to get the user's attention.

[0543] 11. "Notification listener program" means software that resides on a user terminal to receive notification data sent from a server.

[0544] The present invention is a system for supporting hearing-impaired people in receiving emergency broadcasts in real time and responding appropriately. A specific embodiment of this system will be described.

[0545] Hardware and software used

[0546] 1. Audio Input Device:

[0547] The broadcast audio data is acquired using a device such as a TV tuner card or radio receiver.

[0548] 2. Server:

[0549] The server processes the acquired voice data, detects emergency broadcasts, and generates and sends notification data to the user's device. The software running on the server includes a speech recognition engine (e.g., Google Speech-to-Text API) and a notification system (e.g., Firebase Cloud Messaging).

[0550] 3. Terminal:

[0551] The user's terminal is a smartphone, tablet, or the like, and has installed thereon a dedicated app or notification listener program for visually or audibly displaying the received notification data to the user.

[0552] Data processing and calculation flow

[0553] The server receives voice data in real time from voice input devices and streaming services and stores it in a fixed buffer. This voice data is passed to a speech recognition engine and converted into text data using a method such as the Google Speech-to-Text API. The converted text data is then analyzed again by the server to detect keywords and specific patterns related to emergency broadcasts. This analysis is performed using keyword search and pattern matching technology, and when an emergency broadcast is detected, notification data is generated. The generated notification data is sent to the user's device via push notification or SMS. This notification data includes the specific content of the emergency broadcast, its urgency, and recommended actions.

[0554] Specific Examples

[0555] For example, when an earthquake occurs and an "Earthquake Early Warning" is broadcast, the server obtains the audio of the earthquake early warning from television or radio. This audio is converted into text data using the Google Speech-to-Text API. The server then detects the keyword "Earthquake Early Warning" from the converted text data and generates notification data. This notification data contains the message "An Earthquake Early Warning has been issued. Please evacuate to a safe place immediately," and is sent to the user's device via push notification. The user's device displays this notification as a pop-up message and simultaneously vibrates and generates an audio alert. The user can respond quickly by checking the notification and immediately evacuating to a safe place.

[0556] Prompt Sentence Examples

[0557] "Please give us an overview of the Emergency Alert Notification System."

[0558] "Please explain the operational flow of emergency broadcast notifications for the hearing impaired."

[0559] The above is an embodiment of the invention. This system enables all users, including the hearing impaired, to receive emergency broadcasts in real time and respond appropriately.

[0560] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0561] Step 1:

[0562] The server acquires audio data. Specifically, it captures audio data in real time through an audio input device or a streaming service and stores this data in a buffer on the server. The input of this step is real-time audio data, and the output is the audio data stored in the buffer.

[0563] Step 2:

[0564] The server passes the voice data stored in the buffer to a voice recognition engine. The voice recognition engine used here uses technology such as the Google Speech-to-Text API and converts the voice data into text data. The input for this step is the voice data stored in the buffer, and the output is text data. Specifically, a voice message such as "An emergency earthquake warning has been issued" is converted into the text "An emergency earthquake warning has been issued."

[0565] Step 3:

[0566] The server analyzes the converted text data to detect keywords and specific patterns related to emergency broadcasts. This analysis is performed on the entire text data using keyword search and pattern matching techniques. The input for this step is the converted text data, and the output is a determination result indicating whether or not an emergency broadcast is present. For example, the keyword "Emergency Earthquake Alert" is detected.

[0567] Step 4:

[0568] When an emergency broadcast is detected, the server generates notification data. This notification data includes the specific content of the emergency broadcast, the urgency level, recommended actions, etc. The input to this step is the detection result of the emergency broadcast, and the output is the generated notification data. Specifically, a notification message is generated stating, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately."

[0569] Step 5:

[0570] The server sends the generated notification data to the user's device. This is done using push notifications or SMS. Services such as Firebase Cloud Messaging are used to deliver the notification data to the device in real time. The input to this step is the generated notification data, and the output is the notification data being sent to the user's device.

[0571] Step 6:

[0572] The user's device receives the notification data sent from the server. The device, on which the dedicated app or notification listener program is installed, temporarily stores the received data and prepares it for the next process. The input of this step is the notification data sent from the server, and the output is the notification data stored on the device.

[0573] Step 7:

[0574] The device displays the received notification data to the user visually or audibly. Specifically, it displays a pop-up message on the screen and generates a vibration or audio alert. For example, a message may be displayed saying, "An emergency earthquake warning has been issued. Please evacuate to a safe location immediately." The input of this step is the notification data stored in the device, and the output is the notification displayed to the user.

[0575] Step 8:

[0576] The user checks the emergency notification displayed on the device. The notification contains the specific emergency broadcast content and recommended actions, allowing the user to take the necessary action immediately. Specifically, the user checks the notification and takes action to evacuate to a safe place. The input of this step is the notification data displayed on the device, and the output is the user's response action.

[0577] (Application example 1)

[0578] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0579] Food delivery workers are required to quickly grasp information and take appropriate action in the event of an emergency. However, with conventional systems, workers with hearing impairments or those working in noisy environments may miss emergency broadcasts. For this reason, a means is needed to ensure that all workers receive emergency information and continue working safely.

[0580] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0581] In this invention, the server includes means for acquiring voice data, means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for generating notification data when an emergency broadcast is detected, means for transmitting the generated notification data to a user terminal, and means for transmitting emergency notifications to food delivery workers, thereby enabling all food delivery workers to receive emergency information in real time and respond quickly and appropriately.

[0582] "Audio data" is digital data that contains audio information and is a record of sound waves as electronic signals.

[0583] "Acquisition means" refers to a device or method for acquiring audio data from an external source.

[0584] "Speech recognition means" refers to technology or equipment that analyzes acquired voice data and converts it into text data.

[0585] "Text data" is digital data containing character information generated by a voice recognition means.

[0586] "Emergency broadcast detection means" refers to technology or devices that analyze text data and identify broadcasts that contain important emergency information.

[0587] The "notification data generating means" refers to a device or method for creating a specific notification for the user when an emergency broadcast is detected.

[0588] "Communication means" refers to the technology or device for transmitting the generated notification data to the user's terminal.

[0589] "Terminal display means" refers to a technique or device for displaying notification data on a user's terminal.

[0590] "Food delivery" refers to the business or service of delivering food and beverages to customers.

[0591] "Worker" refers to a person engaged in food delivery work.

[0592] "Emergency notification" refers to information that is quickly conveyed to users, including the contents of emergency broadcasts.

[0593] This invention relates to a system that supports food delivery workers by receiving emergency broadcasts in real time, enabling them to carry out their work safely. This system operates while playing the roles of a server, a terminal, and a user.

[0594] The server first obtains audio data from radio or TV streaming services. To do this, it uses HTTP streaming to obtain audio data in real time. The obtained audio data is then converted into text data using the Google Speech-to-Text API. The Google Speech-to-Text API provides highly accurate speech recognition and supports a variety of languages. This allows audio data to be converted into text data quickly and accurately.

[0595] The server then detects emergency broadcasts from the converted text data. To do this, it uses an algorithm that analyzes keywords related to emergency broadcasts. For example, it checks whether the text data contains keywords such as "earthquake early warning" or "typhoon warning." By applying natural language processing technology to this analysis, high detection accuracy is achieved.

[0596] When an emergency broadcast is detected, the server immediately generates notification data, which includes the content of the detected emergency broadcast and specific recommended actions, providing workers with the information they need to take appropriate action.

[0597] The server then sends the generated notification data to the devices of food delivery workers via push notification or SMS, using the Twilio API for rapid notification, allowing workers to receive important emergency information in real time.

[0598] The user's device will then display the received notification data appropriately, including displaying a pop-up message, vibrating, or sounding an alarm, ensuring that workers receive emergency information and can respond promptly.

[0599] As a concrete example, we will explain the system's processing when an earthquake occurs and an "Earthquake Early Warning" is broadcast. The server acquires audio data from the radio or television and generates text data saying "An earthquake early warning has been issued" using the Google Speech-to-Text API. Next, it analyzes this text data to detect the keyword "earthquake early warning" and determines that it is an emergency broadcast. It then generates notification data saying "An earthquake early warning has been issued. Please evacuate to a safe location immediately," and sends it to the worker's smartphone via push notification or SMS via the Twilio API. Finally, the worker's device warns them with a pop-up message or vibration.

[0600] Example prompt sentence:

[0601] Create a program that uses a speech recognition engine to convert voice data into text and detect emergency broadcasts. Next, create a program that generates notification data based on the detected emergency broadcast content and sends the notification to a specified smartphone. The speech recognition engine used is the Google Speech-to-Text API. HTTP streaming will be used to obtain the voice data. The Twilio API will be used to send the notification.

[0602] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0603] Step 1:

[0604] The server retrieves audio data in real time from a radio or TV streaming service. Specifically, it periodically receives audio data using HTTP streaming and stores it in a buffer. The input is the audio stream URL, and the output is the buffered audio data.

[0605] Step 2:

[0606] The server passes the buffered voice data to the Google Speech-to-Text API to convert it into text data. The voice recognition engine analyzes the voice data and generates corresponding text data. The input is the buffered voice data, and the output is the converted text data. Specific operations include the process of sending voice data to the recognition engine and receiving its response.

[0607] Step 3:

[0608] The server analyzes the converted text data to detect keywords related to emergency broadcasts. It uses natural language processing technology to search for specific keywords (e.g., "earthquake early warning" or "typhoon warning") in the text data. The input is the converted text data, and the output is the detection results of emergency broadcasts and the detected keywords. Specifically, it involves the process of applying a text analysis algorithm to detect keywords.

[0609] Step 4:

[0610] When an emergency broadcast is detected, the server generates notification data including recommended actions. It creates a notification message and includes the content of the emergency broadcast and recommended measures. The input is the emergency broadcast detection result and the detected keywords, and the output is a detailed notification message. Specific operations include a process using a template-based message generation engine.

[0611] Step 5:

[0612] The server uses the Twilio API to send the generated notification data to the food delivery worker's device. The notification data is sent via push notification or SMS. The input is the notification message, and the output is a confirmation that the message was sent. Specific operations include calling the Twilio API and sending the notification message in the appropriate format.

[0613] Step 6:

[0614] The terminal visually and audibly displays the notification data received from the server to the user. Notifications are given using pop-up messages, vibrations, audio alarms, etc. The input is the notification message, and the output is the display of an emergency notification to the user. Specific operations include the process of displaying a message and notifying the user using the terminal's user interface.

[0615] Step 7:

[0616] The user checks the notification displayed on the device and takes the specified recommended action, such as evacuating to a safe place. The input is the displayed notification message, and the output is the user's action. Specific actions include understanding the notification content and taking the recommended action.

[0617] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0618] The present invention combines a system that acquires audio data, recognizes emergency broadcasts in real time, and notifies the hearing impaired quickly with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are as follows:

[0619] Server Processing

[0620] 1. Acquiring audio data

[0621] The server acquires audio data from a designated audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server.

[0622] 2. Voice Recognition

[0623] The server sends the captured voice data to a speech recognition engine, which uses speech recognition technology such as the Google Speech-to-Text API to convert the voice data into text data for further processing.

[0624] 3. Emergency broadcast detection

[0625] The server analyzes the text data obtained from the speech recognition engine, detecting keywords and specific patterns related to emergency broadcasts. For example, it checks whether the text contains keywords such as "Earthquake Early Warning" or "Tsunami Warning."

[0626] 4. Emotion Recognition by Emotion Engine

[0627] The server operates an emotion engine based on the user's voice and facial expression data acquired from the device. The emotion engine recognizes the user's emotions (e.g., fear, anxiety, calmness, etc.) in real time. This information is reflected in the generation of notification data.

[0628] 5. Generating Notification Data

[0629] When an emergency broadcast is detected, the server generates notification data. This notification data includes the specific content and urgency of the detected emergency broadcast, recommended actions, and content based on the user's emotions recognized by the emotion engine. For example, if the user is feeling very anxious, the notification will include additional instructions to remain calm and act accordingly.

[0630] 6. Sending Notifications

[0631] The server then sends the generated notification data to the user's device using an appropriate communication method, such as push notification or SMS, allowing the user to receive emergency information in real time.

[0632] Terminal handling

[0633] 1. Receiving notifications

[0634] The user's device receives the notification data sent from the server. The device has a dedicated app and notification listener for receiving notifications. The device also has an interface for sending the user's voice and facial expression data to the server.

[0635] 2. Display of notifications

[0636] The device then displays the received notification data to the user visually or audibly. The display method is adjusted according to the user's emotion as recognized by the emotion engine. For example, if the user is calm, a standard pop-up message is displayed, while if the user is anxious, a notification is displayed with vibration and additional explanation.

[0637] User Action

[0638] 1. Check notifications

[0639] The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to a safe location.

[0640] 2. Send Feedback (Optional)

[0641] Users can send notification reception status and feedback to the server as needed, which the server can use to check and improve the system's operation.

[0642] Specific examples

[0643] For example, the operation of the system when an earthquake occurs and an "Earthquake Early Warning" is broadcast will be described.

[0644] 1. Acquiring audio data

[0645] The server acquires the audio of emergency earthquake alerts broadcast on television and radio.

[0646] 2. Voice Recognition

[0647] The server passes the acquired voice to a voice recognition engine, and obtains text data saying, "An emergency earthquake warning has been issued."

[0648] 3. Emergency broadcast detection

[0649] The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[0650] 4. Emotion Recognition by Emotion Engine

[0651] The server activates an emotion engine based on the user's voice and facial expression data and recognizes that the user is feeling anxious.

[0652] 5. Generating Notification Data

[0653] The server generates notification data saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly."

[0654] 6. Sending Notifications

[0655] The server sends the generated notification data to the user's device as a push notification.

[0656] 7. Receiving Notifications

[0657] The user's terminal receives the notification sent from the server.

[0658] 8. Display of notifications

[0659] The device will display a pop-up message saying, "An emergency earthquake alert has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly," and will alert the user with vibrations and an audio alarm.

[0660] 9. Checking Notifications

[0661] The user should check the notification and immediately evacuate to a safe location.

[0662] In this way, the present invention makes it possible to provide an emergency notification system that takes into account the user's emotions, and enables all users to quickly receive emergency broadcasts and respond appropriately.

[0663] The processing flow will be explained below.

[0664] Step 1:

[0665] The server retrieves audio data from a designated audio input device or streaming service, such as a device that transmits broadcast audio from a television or radio to the server in real time.

[0666] Step 2:

[0667] The server sends the acquired voice data to a speech recognition engine, which converts the voice data into text data using technologies such as the Google Speech-to-Text API, which is then used for further processing.

[0668] Step 3:

[0669] The server analyzes the text data obtained from the speech recognition engine. During the analysis, it detects keywords and specific patterns related to emergency broadcasts. For example, it checks whether the text contains keywords such as "Earthquake Early Warning" or "Tsunami Warning."

[0670] Step 4:

[0671] The server passes the user's voice and facial expression data acquired from the device to the emotion engine, which uses this data to recognize the user's emotions in real time. For example, it can determine whether the user is feeling anxious or calm from the tone of their voice and facial expression.

[0672] Step 5:

[0673] When an emergency broadcast is detected, the server generates notification data based on the user's emotions recognized by the emotion engine. For example, if the server determines that the user is feeling extremely anxious, the notification data will include an additional message such as "Please remain calm and act calmly."

[0674] Step 6:

[0675] The server then sends the generated notification data to the user's device using an appropriate communication method, such as push notification or SMS, allowing the user to receive emergency information in real time.

[0676] Step 7:

[0677] The device receives the notification data sent from the server. The device has a dedicated app and notification listener for receiving notifications. It also has an interface for sending the user's voice and facial expression data to the server.

[0678] Step 8:

[0679] The device displays the received notification data to the user visually or audibly, and adjusts the notification display depending on the user's emotions. For example, if the user is calm, a standard pop-up message will be displayed, whereas if the user is anxious, detailed instructions will be displayed along with vibrations and audio alerts.

[0680] Step 9:

[0681] The user then checks the emergency notification displayed on their device, which includes the specific emergency broadcast content, recommended actions, and additional emotion-based instructions, allowing the user to take appropriate action quickly.

[0682] Step 10:

[0683] Users can send notification reception status and feedback to the server as needed. This helps the server check and improve the system's operation. For example, users can report their impressions after receiving notifications or any bugs.

[0684] Example 2

[0685] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0686] Conventional emergency broadcast notification systems analyze audio data to detect emergency broadcasts, but they lack consideration for users with hearing impairments or who are emotionally upset. As a result, it can be difficult for users to respond quickly and appropriately to an emergency. Furthermore, notifications that do not take the user's emotional state into account can cause further anxiety and confusion. Therefore, there is a need for an emergency broadcast notification system that takes the user's emotional state into account.

[0687] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring voice data, voice recognition means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for acquiring user voice and facial expression data and recognizing the emotion, means for generating notification data taking into account the user's emotion recognition result when an emergency broadcast is detected, means for transmitting the generated notification data to the user's terminal, and means for displaying the transmitted notification data on the user's terminal. This enables emergency notifications that take into account the user's emotional state, allowing all users to quickly receive the emergency broadcast and respond appropriately.

[0688] "Means for acquiring audio data" refers to the function of collecting audio data in real time from various audio input devices and streaming services.

[0689] "Speech recognition means for converting acquired voice data into text data" refers to a function that uses voice recognition technology or an API (e.g., a voice recognition API) to convert collected voice data into text format.

[0690] "Means for detecting emergency broadcasts from converted text data" refers to a function that analyzes the text data converted by the voice recognition means and identifies keywords or specific patterns related to emergency broadcasts.

[0691] "Means for acquiring the user's voice and facial expression data and recognizing their emotions" refers to the function of collecting the user's voice and facial expression data, analyzing it, and recognizing the user's emotional state (for example, fear, anxiety, calmness, etc.) in real time.

[0692] "Means for generating notification data taking into consideration the user's emotion recognition results when an emergency broadcast is detected" refers to a function that automatically generates notification data to provide appropriate information to the user based on the detected emergency broadcast and the user's emotional state.

[0693] "Means for sending generated notification data to the user's device" refers to a function that uses communication means such as push notification or SMS to send the generated notification data to the user's mobile device, computer, etc.

[0694] "Means for displaying the transmitted notification data on the user's terminal" refers to a function for visually or audibly informing the user of the notification data received on the user's terminal and displaying it so that the user can recognize it.

[0695] The present invention combines a system that acquires audio data, recognizes emergency broadcasts in real time, and promptly notifies the hearing impaired with an emotion engine that recognizes the user's emotions. This system includes the following specific components and processes:

[0696] Server Processing

[0697] Acquiring audio data

[0698] The server acquires audio data from a specified audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server. Specifically, the audio input device streams audio data in real time using HTTP requests.

[0699] Voice Recognition

[0700] The server sends the acquired voice data to a speech recognition engine, such as the Google Speech-to-Text API. This engine converts the voice data into text data. The server parses the response from the API and stores the necessary information.

[0701] Emergency broadcast detection

[0702] The server analyzes the text data obtained from the speech recognition engine to detect keywords and specific patterns related to emergency broadcasts, such as "emergency earthquake alert" and "tsunami warning," and flags any that match.

[0703] Emotion recognition by emotion engine

[0704] The server operates the emotion engine based on the user's voice and facial expression data acquired from the device. Emotion recognition uses a facial expression recognition API and a voice emotion analysis API. The server grasps the user's emotional state based on the analyzed information.

[0705] Generate notification data

[0706] The server generates notification data when an emergency broadcast is detected. This notification data is created taking into account the user's emotion recognition results. For example, if the user is feeling anxious, an additional message such as "Please stay calm" may be included.

[0707] Sending notifications

[0708] The server sends the generated notification data to the user's device using push notifications, SMS, etc. Specifically, it uses a notification service API such as Firebase Cloud Messaging.

[0709] Terminal handling

[0710] Receive notifications

[0711] The user's device receives the notification data sent from the server. A dedicated app or notification listener is set up on the device to receive notifications. A background service waits for notifications and stores the received data in the app.

[0712] Viewing notifications

[0713] The user's device displays the received notification data visually or audibly to the user. The display method is adjusted according to the user's emotion recognized by the emotion engine. For example, it may display a pop-up message, and if the user is in an anxious state, it may also use vibration or an audio alarm.

[0714] User Action

[0715] Checking notifications

[0716] The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to a safe location.

[0717] Send feedback (optional)

[0718] Users can send notification reception status and feedback to the server as needed. Through the app's feedback function, users can send their impressions of notifications and problems to the server in text format.

[0719] Specific examples

[0720] For example, when an earthquake occurs and an "Earthquake Early Warning" is broadcast, the system operates as follows.

[0721] 1. Acquiring audio data

[0722] The server receives the audio of emergency earthquake alerts broadcast on television and radio in real time.

[0723] 2. Voice Recognition

[0724] The server passes the acquired voice data to a voice recognition engine, and obtains text data saying, "An emergency earthquake warning has been issued."

[0725] 3. Emergency broadcast detection

[0726] The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[0727] 4. Emotion Recognition by Emotion Engine

[0728] The server activates an emotion engine based on the user's voice and facial expression data and recognizes that the user is feeling anxious.

[0729] 5. Generating Notification Data

[0730] The server generates notification data saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly."

[0731] 6. Sending Notifications

[0732] The server sends the generated notification data to the user's device as a push notification.

[0733] 7. Receiving Notifications

[0734] The user's terminal receives the notification sent from the server.

[0735] 8. Display of notifications

[0736] The device will display a pop-up message saying, "An emergency earthquake alert has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly," and will alert the user with vibrations and an audio alarm.

[0737] 9. Checking Notifications

[0738] The user should check the notification and immediately evacuate to a safe location.

[0739] Prompt Sentence Examples

[0740] Below are some examples of prompts to input to the generative AI model.

[0741] "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm."

[0742] This enables emergency notifications that take into account the emotional state of the user, allowing all users to quickly receive emergency broadcasts and respond appropriately.

[0743] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0744] Step 1:

[0745] Acquiring audio data

[0746] Description: The server retrieves audio data from a specified audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server.

[0747] Input: Real-time audio data from TV or radio.

[0748] Data processing / computation: Audio input devices stream audio data in real time using HTTP requests.

[0749] Output: Real-time audio data sent to the server.

[0750] Step 2:

[0751] Voice Recognition

[0752] Description: The server sends the captured voice data to a speech recognition engine, such as the Google Speech-to-Text API, which converts the voice data into text data.

[0753] Input: The real-time audio data obtained in step 1.

[0754] Data processing / calculation: The server processes the batch of audio data, sends an API request to receive text data, parses the response from the API, and stores the necessary information.

[0755] Output: Text data obtained from the speech recognition engine.

[0756] Step 3:

[0757] Emergency broadcast detection

[0758] Description: The server analyzes the text data obtained from the speech recognition engine to detect keywords and specific patterns related to emergency broadcasts.

[0759] Input: The text data generated in step 2.

[0760] Data processing / calculation: The server searches for keywords such as "earthquake early warning" or "tsunami warning" within the text data and flags any matches. This flag is used for subsequent processing.

[0761] Output: A flag indicating whether an emergency broadcast was detected.

[0762] Step 4:

[0763] Emotion recognition by emotion engine

[0764] Description: The server operates the emotion engine based on the user's voice and facial expression data acquired from the device. The emotion engine recognizes the user's emotional state (e.g., fear, anxiety, calmness, etc.) in real time.

[0765] Input: User's voice and facial expression data.

[0766] Data processing / calculation: The server sends data to the facial expression recognition API and voice emotion analysis API, analyzes the response, and determines the user's emotions.

[0767] Output: Data indicating the user's emotional state.

[0768] Step 5:

[0769] Generate notification data

[0770] Description: The server generates notification data when an emergency broadcast is detected. This notification data is created taking into account the content of the emergency broadcast and the user's emotion recognition results.

[0771] Input: Emergency broadcast flag from step 3, user emotion data from step 4.

[0772] Data processing / calculation: The server generates notification messages using templates and inserts additional messages such as "please stay calm" if the user is feeling anxious.

[0773] Output: The generated notification data.

[0774] Step 6:

[0775] Sending notifications

[0776] Description: The server sends the generated notification data to the user's device using communication methods such as push notification or SMS.

[0777] Input: The notification data generated in step 5.

[0778] Data processing / calculation: The server uses a notification service API (e.g., Firebase Cloud Messaging) to send the generated notification data to the device.

[0779] Output: Notification data received by the user's device.

[0780] Step 7:

[0781] Receive notifications

[0782] Description: The user's device receives notification data sent from the server. The device has a dedicated app or notification listener set up to receive notifications.

[0783] Input: Notification data sent by the server.

[0784] Data processing / calculation: The device's background service waits for notifications and stores the received data within the app.

[0785] Output: Notification data stored in the device.

[0786] Step 8:

[0787] Viewing notifications

[0788] Description: The device displays the received notification data to the user visually or audibly, adjusting the display method according to the user's emotion recognized by the emotion engine.

[0789] Input: Notification data stored on the device.

[0790] Data processing / calculation: The app displays the notification content as a popup, and also vibrates or sounds an alarm if the user is in an anxious state.

[0791] Output: Notification data displayed visually and audibly to the user.

[0792] Step 9:

[0793] Checking notifications

[0794] Description: The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to evacuate to a safe location.

[0795] Input: The notification displayed on the device.

[0796] Data processing / calculation: The user checks the notification and acts according to the instructions.

[0797] Output: User action.

[0798] Step 10:

[0799] Send feedback (optional)

[0800] Description: Users can optionally send notification status and feedback to the server.

[0801] Input: Feedback information from the user.

[0802] Data processing / calculation: Through the app's feedback function, users can send their thoughts and problems about notifications in text format to the server.

[0803] Output: Feedback information sent to the server.

[0804] (Application example 2)

[0805] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0806] Conventional emergency broadcast notification systems lack functionality to accommodate specific users, such as the hearing impaired and elderly. Furthermore, emergency notifications are not adapted to the user's emotions, which can lead to panic or incorrect judgment in an emergency. Furthermore, if notifications are not delivered promptly and appropriately, the user's safety may be threatened. In response to these issues, the present invention aims to provide more accurate emergency notifications by taking the user's emotions into account.

[0807] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring voice data, voice recognition means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, emotion recognition means for recognizing a user's emotion based on the user's voice and facial expression data, means for generating notification data based on the user's emotion when an emergency broadcast is detected, means for transmitting the generated notification data to the user's terminal, and means for displaying the transmitted notification data on the user's terminal. This makes it possible to provide a quick and appropriate emergency notification that takes the user's emotion into consideration, thereby improving the safety of specific users.

[0808] "Audio data" is information obtained by capturing sound waves and converting them into digital form, and is data that includes an audio signal.

[0809] "Speech recognition means" refers to a technical means for converting voice data into text data, such as a device that uses a speech recognition API or speech recognition software.

[0810] "Text data" is data expressed as a character string, and includes character information converted from audio data.

[0811] The "emergency broadcast detection means" is a technical means for detecting information related to emergency broadcasts from the converted text data.

[0812] "Emotion recognition means" refers to a technical means for analyzing the user's voice and facial expression data to recognize the user's emotional state.

[0813] The "notification data generation means" is a technical means for creating notification data that is generated when an emergency broadcast is detected, and includes notification content that is adjusted based on the user's emotion recognition results.

[0814] The "notification data transmission means" is a technical means for transmitting the generated notification data to the user's terminal.

[0815] The "notification data display means" is a technical means for displaying the transmitted notification data on the user terminal, and has the function of notifying the user visually or audibly.

[0816] "Emotion" refers to the user's psychological state, and includes, for example, anxiety, fear, calmness, and the like.

[0817] "User's terminal" refers to an electronic device owned by the user, specifically a smartphone or a head-mounted display.

[0818] MODE FOR CARRYING OUT THE INVENTION

[0819] The present invention is a system that acquires audio data, recognizes emergency broadcasts in real time, and generates and transmits notifications according to the user's emotions. Detailed embodiments for carrying out the present invention are described below.

[0820] Server Processing

[0821] The server uses the following hardware and software to acquire audio data and detect emergency broadcasts in real time.

[0822] Hardware and software used

[0823] Audio input device: Hardware required to receive television and radio audio in real time.

[0824] Google Speech-to-Text API: Speech recognition software that converts acquired voice data into text data.

[0825] Emotion engine: Software for recognizing the user's emotions, analyzing the user's voice and facial expression data.

[0826] Notification sending system: Includes push notification and SMS sending functions for sending generated notification data to the user's device.

[0827] Acquiring audio data

[0828] The server acquires audio data from a designated audio input device, for example, by receiving television or radio broadcasts in real time and periodically transmitting the audio to the server.

[0829] Voice Recognition

[0830] The server sends the acquired audio data to the Google Speech-to-Text API and converts it into text data, which is then used to detect emergency broadcasts.

[0831] Emergency broadcast detection

[0832] The server analyzes the converted text data and detects keywords related to emergency broadcasts (e.g., "Earthquake Early Warning" or "Tsunami Warning"), thereby determining whether an emergency broadcast has been made.

[0833] emotion recognition

[0834] The server acquires the user's voice and facial expression data and inputs it into the emotion engine, which analyzes this data and recognizes the user's emotions (anxiety, fear, calmness, etc.) in real time.

[0835] Generate notification data

[0836] When an emergency broadcast is detected, the server generates notification data based on the user's emotional state. For example, if the user is feeling extremely anxious, the server generates notification data that includes instructions such as "Please remain calm and act calmly."

[0837] Sending notifications

[0838] The server sends the generated notification data to the user's device using push notifications or SMS, allowing the user to receive emergency information in real time.

[0839] Terminal handling

[0840] The user's device (such as a smartphone, head-mounted display, or smart glasses) quickly receives the notification sent from the server and presents it to the user visually or audibly.

[0841] Receiving and Viewing Notifications

[0842] The device receives the notification data sent from the server and displays the notification in a way that corresponds to the user's emotion. For example, if the user is calm, a standard pop-up message is displayed, but if the user is anxious, a notification is displayed with vibration and additional explanation.

[0843] User Action

[0844] The user checks the notification displayed on the device and takes necessary action, for example, by following the instruction "Please evacuate immediately" and evacuating to a safe place.

[0845] Specific examples

[0846] For example, when an earthquake occurs and an "Earthquake Early Warning" is broadcast, the system operates as follows:

[0847] 1. Acquisition of audio data: The server acquires the audio of the emergency earthquake alert broadcast from television or radio.

[0848] 2. Speech recognition: The server passes the acquired speech to the speech recognition engine, and obtains the text data "An emergency earthquake warning has been issued."

[0849] 3. Emergency broadcast detection: The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[0850] 4. Emotion recognition: The server activates an emotion engine based on the user's voice and facial expression data to recognize when the user is feeling anxious.

[0851] 5. Generation of notification data: The server generates notification data stating, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly."

[0852] 6. Sending notification: The server sends the generated notification data to the user's device via push notification.

[0853] 7. Receiving notification: The user's device receives the notification sent from the server.

[0854] 8. Display notification: The device will display a pop-up message saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm." and will notify the user with vibration and an audio alarm.

[0855] 9. Check the notification: The user checks the notification and immediately evacuates to a safe place.

[0856] Example prompts to give to a generative AI model:

[0857] Generate the example application code for an emergency notification and emotion recognition system. Follow these steps:

[0858] 1. Get the audio data from the specified URL.

[0859] 2. Use the Google Speech-to-Text API to convert the audio data into text data.

[0860] 3. Detect urgent keywords in the text data.

[0861] 4. Recognize user emotions using an emotion engine.

[0862] 5. Generate notification data based on emergency broadcast and emotion.

[0863] 6. Send notification data to a smartphone or HMD.

[0864] 7. Implement these functions in Python using specific libraries (e.g. requests, google.cloud.speech, pyttsx3)

[0865] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0866] Step 1:

[0867] The server acquires audio data. Specifically, it receives television or radio broadcast audio in real time from a designated audio input device and acquires the audio data. The input is an audio signal, and the output is audio data converted into a digital format.

[0868] Step 2:

[0869] The server sends the acquired voice data to a voice recognition means and converts it into text data. Specifically, it uses the Google Speech-to-Text API to analyze the voice data and convert it into text data. The input is digital voice data, and the output is text data.

[0870] Step 3:

[0871] The server analyzes the converted text data and detects emergency broadcasts. Specifically, it searches for keywords such as "Earthquake Early Warning" and "Tsunami Warning" to determine whether or not an emergency broadcast is occurring. The input is text data, and the server analyzes its contents to determine whether or not an emergency broadcast is occurring.

[0872] Step 4:

[0873] The server collects the user's voice and facial expression data and inputs it into the emotion recognition means to recognize the user's emotions. Specifically, the emotion engine uses the voice and facial expression data sent from the user terminal to analyze the user's emotional state (anxiety, fear, calmness, etc.). The input is the user's voice and facial expression data, and the output is the recognized emotional state.

[0874] Step 5:

[0875] When an emergency broadcast is detected, the server generates notification data based on the user's emotional state. For example, if the user is feeling anxious, the server generates notification data that includes supplemental information such as "Please remain calm and act calmly." The input is the content of the emergency broadcast and the user's emotional state, and the output is the adjusted notification data.

[0876] Step 6:

[0877] The server sends the generated notification data to the user's device via push notification or SMS. Specifically, it sends notifications to the user's device in real time through a notification sending system. The input is the notification data, and the output is the sent notification.

[0878] Step 7:

[0879] The device receives notification data sent from the server. Specifically, it receives push notifications and SMS using a dedicated application or notification listener. The input is the notification data sent from the server, and the output is the received notification.

[0880] Step 8:

[0881] The device displays the notification data to the user visually or audibly, for example, by using a pop-up message, vibration, or audio alarm. The input is the received notification data, and the output is the displayed notification content.

[0882] Step 9:

[0883] The user checks the notification displayed on the device and takes the necessary action. Specifically, the user follows the instructions in the notification and takes action, such as evacuating to a safe place. The input is the displayed notification content, and the output is the appropriate response action.

[0884] 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.

[0885] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[0886] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0887] [Third embodiment]

[0888] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0889] 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.

[0890] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0891] 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.

[0892] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0893] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0894] 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.

[0895] 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.

[0896] 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 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.

[0897] 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.

[0898] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0899] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0900] The present invention provides a system for supporting hearing-impaired people to receive emergency broadcasts in real time and respond appropriately. Specific embodiments of this system are as follows.

[0901] Server Processing

[0902] 1. Acquiring audio data

[0903] The server acquires audio data from a designated audio input device or streaming service, which is capable of receiving television or radio broadcasts and has an interface for transmitting audio data to the server in real time.

[0904] 2. Voice Recognition

[0905] The server passes the acquired voice data to a speech recognition engine, which converts the voice data into text data using existing speech recognition technology (e.g., Google Speech-to-Text API). The converted text data is used for subsequent processing.

[0906] 3. Emergency broadcast detection

[0907] The server analyzes the text data obtained from the voice recognition engine and detects keywords and specific patterns related to emergency broadcasts. For example, it checks whether keywords such as "Earthquake Early Warning" or "Tsunami Warning" are included. If it is determined to be an emergency broadcast, notification data is generated.

[0908] 4. Notification Data Generation

[0909] The server generates notification data based on the detected emergency broadcast, including the specific content and urgency of the detected emergency broadcast, recommended actions, etc. The notification data is then sent to the user's device.

[0910] 5. Sending notifications

[0911] The server then sends the generated notification data to the user's device using an appropriate communication method, such as push notification or SMS, allowing the user to receive emergency information in real time.

[0912] Terminal handling

[0913] 1. Receiving notifications

[0914] The user's device receives the notification data sent from the server. The device has a dedicated app or notification listener set up to receive notifications.

[0915] 2. Display of notifications

[0916] The device will then display the received notification data to the user visually or audibly, for example, as a pop-up message on the screen, or by vibrating or issuing an audio alert, allowing the user to quickly grasp the emergency information.

[0917] User Action

[0918] 1. Check notifications

[0919] The user checks the emergency notification displayed on the device. The notification contains the specific emergency broadcast content and recommended actions, so the user can take the necessary action immediately.

[0920] 2. Send Feedback (Optional)

[0921] Users can send notification reception status and feedback to the server as needed, which can be used to check and improve the system's operation.

[0922] Specific examples

[0923] For example, the operation of the system when an earthquake occurs and an "Earthquake Early Warning" is broadcast will be described.

[0924] 1. Acquiring audio data

[0925] The server acquires the audio of emergency earthquake alerts broadcast on television and radio.

[0926] 2. Voice Recognition

[0927] The server passes the acquired voice to a voice recognition engine, and obtains text data saying, "An emergency earthquake warning has been issued."

[0928] 3. Emergency broadcast detection

[0929] The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[0930] 4. Notification Data Generation

[0931] The server generates notification data saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately."

[0932] 5. Sending notifications

[0933] The server sends the generated notification data to the user's device as a push notification.

[0934] 6. Receiving Notifications

[0935] The user's terminal receives the notification sent from the server.

[0936] 7. Display of notifications

[0937] The device will display a pop-up message saying, "An emergency earthquake alert has been issued. Please evacuate to a safe location immediately," and will alert the user with vibrations and an audio alarm.

[0938] 8. Checking Notifications

[0939] The user should check the notification and immediately evacuate to a safe location.

[0940] In this way, the present invention can provide a system that enables all users, including the hearing impaired, to quickly receive emergency broadcasts and respond appropriately.

[0941] The processing flow will be explained below.

[0942] Step 1:

[0943] The server acquires audio data from a designated audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server.

[0944] Step 2:

[0945] The server sends the captured voice data to a speech recognition engine, which uses speech recognition technology such as the Google Speech-to-Text API to convert the voice data into text data for further processing.

[0946] Step 3:

[0947] The server analyzes the text data obtained from the speech recognition engine, detecting keywords and specific patterns related to emergency broadcasts. For example, it checks whether the text contains keywords such as "Earthquake Early Warning" or "Tsunami Warning."

[0948] Step 4:

[0949] When an emergency broadcast is detected, the server generates notification data, which includes information such as the specific content and urgency of the detected emergency broadcast, and recommended actions.

[0950] Step 5:

[0951] The server then sends the generated notification data to the user's device using communication methods such as push notifications or SMS, allowing the user to receive emergency information in real time.

[0952] Step 6:

[0953] The device receives the notification data sent from the server. The device has a dedicated app or notification listener set up to receive notifications.

[0954] Step 7:

[0955] The device displays the received notification data to the user visually or audibly, for example, by displaying a pop-up message on the screen or by emitting a vibration or audio alarm, allowing the user to quickly grasp the emergency information.

[0956] Step 8:

[0957] The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to a safe location.

[0958] Step 9:

[0959] Users can send notification reception status and feedback to the server as needed, which the server can use to check and improve the system's operation.

[0960] Example 1

[0961] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0962] Conventional emergency broadcasts rely on sight and hearing, so there is a problem that information cannot be properly conveyed to the hearing-impaired or visually impaired. This is particularly true for highly urgent information, which can lead to increased human casualties. Furthermore, when real-time information transmission is required, existing notification systems are sometimes unable to adequately respond. This creates a need for a fast and reliable means of transmitting information.

[0963] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0964] In this invention, the server includes means for acquiring voice data, means for storing the acquired voice data in a fixed buffer, means for passing the voice data to a voice recognition engine in real time, means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for identifying keywords based on the converted text data and determining whether the text data is an emergency broadcast, means for generating notification data when an emergency broadcast is detected, means for transmitting the generated notification data by push notification or SMS, means for transmitting the generated notification data to a user's device, and means for displaying the transmitted notification data on the user's device and generating a vibration or voice alert. This enables all users, including those with hearing impairments, to quickly and reliably receive emergency information in real time and respond appropriately.

[0965] 1. "Audio Data" means data that represents in digital form sound wave signals obtained from audio input devices or streaming services.

[0966] 2. "Buffer" is a storage area for temporarily storing audio data in preparation for subsequent processing.

[0967] 3. A "voice recognition engine" is software for converting voice data into text data, and is a program that utilizes existing voice recognition technology.

[0968] 4. "Text data" means character string data converted from voice data by a voice recognition engine.

[0969] 5. An "emergency broadcast" is an official broadcast to inform people of emergencies such as disasters and accidents, and is information that contains specific keywords or patterns.

[0970] 6. "Notification data" means data generated when an emergency broadcast is detected, and includes information such as the content of the emergency broadcast, its urgency, and recommended actions.

[0971] 7. "Push notification" is a method of sending information from a server to a user's device in real time, and is a notification method that does not require any special operation on the user's side.

[0972] 8. "SMS" means a text messaging service for sending short messages to a user's device.

[0973] 9. "Terminal" means a hardware device that receives notification data and displays it to the user, including a smartphone or tablet.

[0974] 10. "Alert" means a visual or audible notification that is used to get the user's attention.

[0975] 11. "Notification listener program" means software that resides on a user terminal to receive notification data sent from a server.

[0976] The present invention is a system for supporting hearing-impaired people in receiving emergency broadcasts in real time and responding appropriately. A specific embodiment of this system will be described.

[0977] Hardware and software used

[0978] 1. Audio Input Device:

[0979] The broadcast audio data is acquired using a device such as a TV tuner card or radio receiver.

[0980] 2. Server:

[0981] The server processes the acquired voice data, detects emergency broadcasts, and generates and sends notification data to the user's device. The software running on the server includes a speech recognition engine (e.g., Google Speech-to-Text API) and a notification system (e.g., Firebase Cloud Messaging).

[0982] 3. Terminal:

[0983] The user's terminal is a smartphone, tablet, or the like, and has installed thereon a dedicated app or notification listener program for visually or audibly displaying the received notification data to the user.

[0984] Data processing and calculation flow

[0985] The server receives voice data in real time from voice input devices and streaming services and stores it in a fixed buffer. This voice data is passed to a speech recognition engine and converted into text data using a method such as the Google Speech-to-Text API. The converted text data is then analyzed again by the server to detect keywords and specific patterns related to emergency broadcasts. This analysis is performed using keyword search and pattern matching technology, and when an emergency broadcast is detected, notification data is generated. The generated notification data is sent to the user's device via push notification or SMS. This notification data includes the specific content of the emergency broadcast, its urgency, and recommended actions.

[0986] Specific Examples

[0987] For example, when an earthquake occurs and an "Earthquake Early Warning" is broadcast, the server obtains the audio of the earthquake early warning from television or radio. This audio is converted into text data using the Google Speech-to-Text API. The server then detects the keyword "Earthquake Early Warning" from the converted text data and generates notification data. This notification data contains the message "An Earthquake Early Warning has been issued. Please evacuate to a safe place immediately," and is sent to the user's device via push notification. The user's device displays this notification as a pop-up message and simultaneously vibrates and generates an audio alert. The user can respond quickly by checking the notification and immediately evacuating to a safe place.

[0988] Prompt Sentence Examples

[0989] "Please give us an overview of the Emergency Alert Notification System."

[0990] "Please explain the operational flow of emergency broadcast notifications for the hearing impaired."

[0991] The above is an embodiment of the invention. This system enables all users, including the hearing impaired, to receive emergency broadcasts in real time and respond appropriately.

[0992] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0993] Step 1:

[0994] The server acquires audio data. Specifically, it captures audio data in real time through an audio input device or a streaming service and stores this data in a buffer on the server. The input of this step is real-time audio data, and the output is the audio data stored in the buffer.

[0995] Step 2:

[0996] The server passes the voice data stored in the buffer to a voice recognition engine. The voice recognition engine used here uses technology such as the Google Speech-to-Text API and converts the voice data into text data. The input for this step is the voice data stored in the buffer, and the output is text data. Specifically, a voice message such as "An emergency earthquake warning has been issued" is converted into the text "An emergency earthquake warning has been issued."

[0997] Step 3:

[0998] The server analyzes the converted text data to detect keywords and specific patterns related to emergency broadcasts. This analysis is performed on the entire text data using keyword search and pattern matching techniques. The input for this step is the converted text data, and the output is a determination result indicating whether or not an emergency broadcast is present. For example, the keyword "Emergency Earthquake Alert" is detected.

[0999] Step 4:

[1000] When an emergency broadcast is detected, the server generates notification data. This notification data includes the specific content of the emergency broadcast, the urgency level, recommended actions, etc. The input to this step is the detection result of the emergency broadcast, and the output is the generated notification data. Specifically, a notification message is generated stating, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately."

[1001] Step 5:

[1002] The server sends the generated notification data to the user's device. This is done using push notifications or SMS. Services such as Firebase Cloud Messaging are used to deliver the notification data to the device in real time. The input to this step is the generated notification data, and the output is the notification data being sent to the user's device.

[1003] Step 6:

[1004] The user's device receives the notification data sent from the server. The device, on which the dedicated app or notification listener program is installed, temporarily stores the received data and prepares it for the next process. The input of this step is the notification data sent from the server, and the output is the notification data stored on the device.

[1005] Step 7:

[1006] The device displays the received notification data to the user visually or audibly. Specifically, it displays a pop-up message on the screen and generates a vibration or audio alert. For example, a message may be displayed saying, "An emergency earthquake warning has been issued. Please evacuate to a safe location immediately." The input of this step is the notification data stored in the device, and the output is the notification displayed to the user.

[1007] Step 8:

[1008] The user checks the emergency notification displayed on the device. The notification contains the specific emergency broadcast content and recommended actions, allowing the user to take the necessary action immediately. Specifically, the user checks the notification and takes action to evacuate to a safe place. The input of this step is the notification data displayed on the device, and the output is the user's response action.

[1009] (Application example 1)

[1010] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1011] Food delivery workers are required to quickly grasp information and take appropriate action in the event of an emergency. However, with conventional systems, workers with hearing impairments or those working in noisy environments may miss emergency broadcasts. For this reason, a means is needed to ensure that all workers receive emergency information and continue working safely.

[1012] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1013] In this invention, the server includes means for acquiring voice data, means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for generating notification data when an emergency broadcast is detected, means for transmitting the generated notification data to a user terminal, and means for transmitting emergency notifications to food delivery workers, thereby enabling all food delivery workers to receive emergency information in real time and respond quickly and appropriately.

[1014] "Audio data" is digital data that contains audio information and is a record of sound waves as electronic signals.

[1015] "Acquisition means" refers to a device or method for acquiring audio data from an external source.

[1016] "Speech recognition means" refers to technology or equipment that analyzes acquired voice data and converts it into text data.

[1017] "Text data" is digital data containing character information generated by a voice recognition means.

[1018] "Emergency broadcast detection means" refers to technology or devices that analyze text data and identify broadcasts that contain important emergency information.

[1019] The "notification data generating means" refers to a device or method for creating a specific notification for the user when an emergency broadcast is detected.

[1020] "Communication means" refers to the technology or device for transmitting the generated notification data to the user's terminal.

[1021] "Terminal display means" refers to a technique or device for displaying notification data on a user's terminal.

[1022] "Food delivery" refers to the business or service of delivering food and beverages to customers.

[1023] "Worker" refers to a person engaged in food delivery work.

[1024] "Emergency notification" refers to information that is quickly conveyed to users, including the contents of emergency broadcasts.

[1025] This invention relates to a system that supports food delivery workers by receiving emergency broadcasts in real time, enabling them to carry out their work safely. This system operates while playing the roles of a server, a terminal, and a user.

[1026] The server first obtains audio data from radio or TV streaming services. To do this, it uses HTTP streaming to obtain audio data in real time. The obtained audio data is then converted into text data using the Google Speech-to-Text API. The Google Speech-to-Text API provides highly accurate speech recognition and supports a variety of languages. This allows audio data to be converted into text data quickly and accurately.

[1027] The server then detects emergency broadcasts from the converted text data. To do this, it uses an algorithm that analyzes keywords related to emergency broadcasts. For example, it checks whether the text data contains keywords such as "earthquake early warning" or "typhoon warning." By applying natural language processing technology to this analysis, high detection accuracy is achieved.

[1028] When an emergency broadcast is detected, the server immediately generates notification data, which includes the content of the detected emergency broadcast and specific recommended actions, providing workers with the information they need to take appropriate action.

[1029] The server then sends the generated notification data to the devices of food delivery workers via push notification or SMS, using the Twilio API for rapid notification, allowing workers to receive important emergency information in real time.

[1030] The user's device will then display the received notification data appropriately, including displaying a pop-up message, vibrating, or sounding an alarm, ensuring that workers receive emergency information and can respond promptly.

[1031] As a concrete example, we will explain the system's processing when an earthquake occurs and an "Earthquake Early Warning" is broadcast. The server acquires audio data from the radio or television and generates text data saying "An earthquake early warning has been issued" using the Google Speech-to-Text API. Next, it analyzes this text data to detect the keyword "earthquake early warning" and determines that it is an emergency broadcast. It then generates notification data saying "An earthquake early warning has been issued. Please evacuate to a safe location immediately," and sends it to the worker's smartphone via push notification or SMS via the Twilio API. Finally, the worker's device warns them with a pop-up message or vibration.

[1032] Example prompt sentence:

[1033] Create a program that uses a speech recognition engine to convert voice data into text and detect emergency broadcasts. Next, create a program that generates notification data based on the detected emergency broadcast content and sends the notification to a specified smartphone. The speech recognition engine used is the Google Speech-to-Text API. HTTP streaming will be used to obtain the voice data. The Twilio API will be used to send the notification.

[1034] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1035] Step 1:

[1036] The server retrieves audio data in real time from a radio or TV streaming service. Specifically, it periodically receives audio data using HTTP streaming and stores it in a buffer. The input is the audio stream URL, and the output is the buffered audio data.

[1037] Step 2:

[1038] The server passes the buffered voice data to the Google Speech-to-Text API to convert it into text data. The voice recognition engine analyzes the voice data and generates corresponding text data. The input is the buffered voice data, and the output is the converted text data. Specific operations include the process of sending voice data to the recognition engine and receiving its response.

[1039] Step 3:

[1040] The server analyzes the converted text data to detect keywords related to emergency broadcasts. It uses natural language processing technology to search for specific keywords (e.g., "earthquake early warning" or "typhoon warning") in the text data. The input is the converted text data, and the output is the detection results of emergency broadcasts and the detected keywords. Specifically, it involves the process of applying a text analysis algorithm to detect keywords.

[1041] Step 4:

[1042] When an emergency broadcast is detected, the server generates notification data including recommended actions. It creates a notification message and includes the content of the emergency broadcast and recommended measures. The input is the emergency broadcast detection result and the detected keywords, and the output is a detailed notification message. Specific operations include a process using a template-based message generation engine.

[1043] Step 5:

[1044] The server uses the Twilio API to send the generated notification data to the food delivery worker's device. The notification data is sent via push notification or SMS. The input is the notification message, and the output is a confirmation that the message was sent. Specific operations include calling the Twilio API and sending the notification message in the appropriate format.

[1045] Step 6:

[1046] The terminal visually and audibly displays the notification data received from the server to the user. Notifications are given using pop-up messages, vibrations, audio alarms, etc. The input is the notification message, and the output is the display of an emergency notification to the user. Specific operations include the process of displaying a message and notifying the user using the terminal's user interface.

[1047] Step 7:

[1048] The user checks the notification displayed on the device and takes the specified recommended action, such as evacuating to a safe place. The input is the displayed notification message, and the output is the user's action. Specific actions include understanding the notification content and taking the recommended action.

[1049] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1050] The present invention combines a system that acquires audio data, recognizes emergency broadcasts in real time, and notifies the hearing impaired quickly with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are as follows:

[1051] Server Processing

[1052] 1. Acquiring audio data

[1053] The server acquires audio data from a designated audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server.

[1054] 2. Voice Recognition

[1055] The server sends the captured voice data to a speech recognition engine, which uses speech recognition technology such as the Google Speech-to-Text API to convert the voice data into text data for further processing.

[1056] 3. Emergency broadcast detection

[1057] The server analyzes the text data obtained from the speech recognition engine, detecting keywords and specific patterns related to emergency broadcasts. For example, it checks whether the text contains keywords such as "Earthquake Early Warning" or "Tsunami Warning."

[1058] 4. Emotion Recognition by Emotion Engine

[1059] The server operates an emotion engine based on the user's voice and facial expression data acquired from the device. The emotion engine recognizes the user's emotions (e.g., fear, anxiety, calmness, etc.) in real time. This information is reflected in the generation of notification data.

[1060] 5. Generating Notification Data

[1061] When an emergency broadcast is detected, the server generates notification data. This notification data includes the specific content and urgency of the detected emergency broadcast, recommended actions, and content based on the user's emotions recognized by the emotion engine. For example, if the user is feeling very anxious, the notification will include additional instructions to remain calm and act accordingly.

[1062] 6. Sending Notifications

[1063] The server then sends the generated notification data to the user's device using an appropriate communication method, such as push notification or SMS, allowing the user to receive emergency information in real time.

[1064] Terminal handling

[1065] 1. Receiving notifications

[1066] The user's device receives the notification data sent from the server. The device has a dedicated app and notification listener for receiving notifications. The device also has an interface for sending the user's voice and facial expression data to the server.

[1067] 2. Display of notifications

[1068] The device then displays the received notification data to the user visually or audibly. The display method is adjusted according to the user's emotion as recognized by the emotion engine. For example, if the user is calm, a standard pop-up message is displayed, while if the user is anxious, a notification is displayed with vibration and additional explanation.

[1069] User Action

[1070] 1. Check notifications

[1071] The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to a safe location.

[1072] 2. Send Feedback (Optional)

[1073] Users can send notification reception status and feedback to the server as needed, which the server can use to check and improve the system's operation.

[1074] Specific examples

[1075] For example, the operation of the system when an earthquake occurs and an "Earthquake Early Warning" is broadcast will be described.

[1076] 1. Acquiring audio data

[1077] The server acquires the audio of emergency earthquake alerts broadcast on television and radio.

[1078] 2. Voice Recognition

[1079] The server passes the acquired voice to a voice recognition engine, and obtains text data saying, "An emergency earthquake warning has been issued."

[1080] 3. Emergency broadcast detection

[1081] The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[1082] 4. Emotion Recognition by Emotion Engine

[1083] The server activates an emotion engine based on the user's voice and facial expression data and recognizes that the user is feeling anxious.

[1084] 5. Generating Notification Data

[1085] The server generates notification data saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly."

[1086] 6. Sending Notifications

[1087] The server sends the generated notification data to the user's device as a push notification.

[1088] 7. Receiving Notifications

[1089] The user's terminal receives the notification sent from the server.

[1090] 8. Display of notifications

[1091] The device will display a pop-up message saying, "An emergency earthquake alert has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly," and will alert the user with vibrations and an audio alarm.

[1092] 9. Checking Notifications

[1093] The user should check the notification and immediately evacuate to a safe location.

[1094] In this way, the present invention makes it possible to provide an emergency notification system that takes into account the user's emotions, and enables all users to quickly receive emergency broadcasts and respond appropriately.

[1095] The processing flow will be explained below.

[1096] Step 1:

[1097] The server retrieves audio data from a designated audio input device or streaming service, such as a device that transmits broadcast audio from a television or radio to the server in real time.

[1098] Step 2:

[1099] The server sends the acquired voice data to a speech recognition engine, which converts the voice data into text data using technologies such as the Google Speech-to-Text API, which is then used for further processing.

[1100] Step 3:

[1101] The server analyzes the text data obtained from the speech recognition engine. During the analysis, it detects keywords and specific patterns related to emergency broadcasts. For example, it checks whether the text contains keywords such as "Earthquake Early Warning" or "Tsunami Warning."

[1102] Step 4:

[1103] The server passes the user's voice and facial expression data acquired from the device to the emotion engine, which uses this data to recognize the user's emotions in real time. For example, it can determine whether the user is feeling anxious or calm from the tone of their voice and facial expression.

[1104] Step 5:

[1105] When an emergency broadcast is detected, the server generates notification data based on the user's emotions recognized by the emotion engine. For example, if the server determines that the user is feeling extremely anxious, the notification data will include an additional message such as "Please remain calm and act calmly."

[1106] Step 6:

[1107] The server then sends the generated notification data to the user's device using an appropriate communication method, such as push notification or SMS, allowing the user to receive emergency information in real time.

[1108] Step 7:

[1109] The device receives the notification data sent from the server. The device has a dedicated app and notification listener for receiving notifications. It also has an interface for sending the user's voice and facial expression data to the server.

[1110] Step 8:

[1111] The device displays the received notification data to the user visually or audibly, and adjusts the notification display depending on the user's emotions. For example, if the user is calm, a standard pop-up message will be displayed, whereas if the user is anxious, detailed instructions will be displayed along with vibrations and audio alerts.

[1112] Step 9:

[1113] The user then checks the emergency notification displayed on their device, which includes the specific emergency broadcast content, recommended actions, and additional emotion-based instructions, allowing the user to take appropriate action quickly.

[1114] Step 10:

[1115] Users can send notification reception status and feedback to the server as needed. This helps the server check and improve the system's operation. For example, users can report their impressions after receiving notifications or any bugs.

[1116] Example 2

[1117] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1118] Conventional emergency broadcast notification systems analyze audio data to detect emergency broadcasts, but they lack consideration for users with hearing impairments or who are emotionally upset. As a result, it can be difficult for users to respond quickly and appropriately to an emergency. Furthermore, notifications that do not take the user's emotional state into account can cause further anxiety and confusion. Therefore, there is a need for an emergency broadcast notification system that takes the user's emotional state into account.

[1119] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring voice data, voice recognition means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for acquiring user voice and facial expression data and recognizing the emotion, means for generating notification data taking into account the user's emotion recognition result when an emergency broadcast is detected, means for transmitting the generated notification data to the user's terminal, and means for displaying the transmitted notification data on the user's terminal. This enables emergency notifications that take into account the user's emotional state, allowing all users to quickly receive the emergency broadcast and respond appropriately.

[1120] "Means for acquiring audio data" refers to the function of collecting audio data in real time from various audio input devices and streaming services.

[1121] "Speech recognition means for converting acquired voice data into text data" refers to a function that uses voice recognition technology or an API (e.g., a voice recognition API) to convert collected voice data into text format.

[1122] "Means for detecting emergency broadcasts from converted text data" refers to a function that analyzes the text data converted by the voice recognition means and identifies keywords or specific patterns related to emergency broadcasts.

[1123] "Means for acquiring the user's voice and facial expression data and recognizing their emotions" refers to the function of collecting the user's voice and facial expression data, analyzing it, and recognizing the user's emotional state (for example, fear, anxiety, calmness, etc.) in real time.

[1124] "Means for generating notification data taking into consideration the user's emotion recognition results when an emergency broadcast is detected" refers to a function that automatically generates notification data to provide appropriate information to the user based on the detected emergency broadcast and the user's emotional state.

[1125] "Means for sending generated notification data to the user's device" refers to a function that uses communication means such as push notification or SMS to send the generated notification data to the user's mobile device, computer, etc.

[1126] "Means for displaying the transmitted notification data on the user's terminal" refers to a function for visually or audibly informing the user of the notification data received on the user's terminal and displaying it so that the user can recognize it.

[1127] The present invention combines a system that acquires audio data, recognizes emergency broadcasts in real time, and promptly notifies the hearing impaired with an emotion engine that recognizes the user's emotions. This system includes the following specific components and processes:

[1128] Server Processing

[1129] Acquiring audio data

[1130] The server acquires audio data from a specified audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server. Specifically, the audio input device streams audio data in real time using HTTP requests.

[1131] Voice Recognition

[1132] The server sends the acquired voice data to a speech recognition engine, such as the Google Speech-to-Text API. This engine converts the voice data into text data. The server parses the response from the API and stores the necessary information.

[1133] Emergency broadcast detection

[1134] The server analyzes the text data obtained from the speech recognition engine to detect keywords and specific patterns related to emergency broadcasts, such as "emergency earthquake alert" and "tsunami warning," and flags any that match.

[1135] Emotion recognition by emotion engine

[1136] The server operates the emotion engine based on the user's voice and facial expression data acquired from the device. Emotion recognition uses a facial expression recognition API and a voice emotion analysis API. The server grasps the user's emotional state based on the analyzed information.

[1137] Generate notification data

[1138] The server generates notification data when an emergency broadcast is detected. This notification data is created taking into account the user's emotion recognition results. For example, if the user is feeling anxious, an additional message such as "Please stay calm" may be included.

[1139] Sending notifications

[1140] The server sends the generated notification data to the user's device using push notifications, SMS, etc. Specifically, it uses a notification service API such as Firebase Cloud Messaging.

[1141] Terminal handling

[1142] Receive notifications

[1143] The user's device receives the notification data sent from the server. A dedicated app or notification listener is set up on the device to receive notifications. A background service waits for notifications and stores the received data in the app.

[1144] Viewing notifications

[1145] The user's device displays the received notification data visually or audibly to the user. The display method is adjusted according to the user's emotion recognized by the emotion engine. For example, it may display a pop-up message, and if the user is in an anxious state, it may also use vibration or an audio alarm.

[1146] User Action

[1147] Checking notifications

[1148] The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to a safe location.

[1149] Send feedback (optional)

[1150] Users can send notification reception status and feedback to the server as needed. Through the app's feedback function, users can send their impressions of notifications and problems to the server in text format.

[1151] Specific examples

[1152] For example, when an earthquake occurs and an "Earthquake Early Warning" is broadcast, the system operates as follows.

[1153] 1. Acquiring audio data

[1154] The server receives the audio of emergency earthquake alerts broadcast on television and radio in real time.

[1155] 2. Voice Recognition

[1156] The server passes the acquired voice data to a voice recognition engine, and obtains text data saying, "An emergency earthquake warning has been issued."

[1157] 3. Emergency broadcast detection

[1158] The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[1159] 4. Emotion Recognition by Emotion Engine

[1160] The server activates an emotion engine based on the user's voice and facial expression data and recognizes that the user is feeling anxious.

[1161] 5. Generating Notification Data

[1162] The server generates notification data saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly."

[1163] 6. Sending Notifications

[1164] The server sends the generated notification data to the user's device as a push notification.

[1165] 7. Receiving Notifications

[1166] The user's terminal receives the notification sent from the server.

[1167] 8. Display of notifications

[1168] The device will display a pop-up message saying, "An emergency earthquake alert has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly," and will alert the user with vibrations and an audio alarm.

[1169] 9. Checking Notifications

[1170] The user should check the notification and immediately evacuate to a safe location.

[1171] Prompt Sentence Examples

[1172] Below are some examples of prompts to input to the generative AI model.

[1173] "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm."

[1174] This enables emergency notifications that take into account the emotional state of the user, allowing all users to quickly receive emergency broadcasts and respond appropriately.

[1175] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1176] Step 1:

[1177] Acquiring audio data

[1178] Description: The server retrieves audio data from a specified audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server.

[1179] Input: Real-time audio data from TV or radio.

[1180] Data processing / computation: Audio input devices stream audio data in real time using HTTP requests.

[1181] Output: Real-time audio data sent to the server.

[1182] Step 2:

[1183] Voice Recognition

[1184] Description: The server sends the captured voice data to a speech recognition engine, such as the Google Speech-to-Text API, which converts the voice data into text data.

[1185] Input: The real-time audio data obtained in step 1.

[1186] Data processing / calculation: The server processes the batch of audio data, sends an API request to receive text data, parses the response from the API, and stores the necessary information.

[1187] Output: Text data obtained from the speech recognition engine.

[1188] Step 3:

[1189] Emergency broadcast detection

[1190] Description: The server analyzes the text data obtained from the speech recognition engine to detect keywords and specific patterns related to emergency broadcasts.

[1191] Input: The text data generated in step 2.

[1192] Data processing / calculation: The server searches for keywords such as "earthquake early warning" or "tsunami warning" within the text data and flags any matches. This flag is used for subsequent processing.

[1193] Output: A flag indicating whether an emergency broadcast was detected.

[1194] Step 4:

[1195] Emotion recognition by emotion engine

[1196] Description: The server operates the emotion engine based on the user's voice and facial expression data acquired from the device. The emotion engine recognizes the user's emotional state (e.g., fear, anxiety, calmness, etc.) in real time.

[1197] Input: User's voice and facial expression data.

[1198] Data processing / calculation: The server sends data to the facial expression recognition API and voice emotion analysis API, analyzes the response, and determines the user's emotions.

[1199] Output: Data indicating the user's emotional state.

[1200] Step 5:

[1201] Generate notification data

[1202] Description: The server generates notification data when an emergency broadcast is detected. This notification data is created taking into account the content of the emergency broadcast and the user's emotion recognition results.

[1203] Input: Emergency broadcast flag from step 3, user emotion data from step 4.

[1204] Data processing / calculation: The server generates notification messages using templates and inserts additional messages such as "please stay calm" if the user is feeling anxious.

[1205] Output: The generated notification data.

[1206] Step 6:

[1207] Sending notifications

[1208] Description: The server sends the generated notification data to the user's device using communication methods such as push notification or SMS.

[1209] Input: The notification data generated in step 5.

[1210] Data processing / calculation: The server uses a notification service API (e.g., Firebase Cloud Messaging) to send the generated notification data to the device.

[1211] Output: Notification data received by the user's device.

[1212] Step 7:

[1213] Receive notifications

[1214] Description: The user's device receives notification data sent from the server. The device has a dedicated app or notification listener set up to receive notifications.

[1215] Input: Notification data sent by the server.

[1216] Data processing / calculation: The device's background service waits for notifications and stores the received data within the app.

[1217] Output: Notification data stored in the device.

[1218] Step 8:

[1219] Viewing notifications

[1220] Description: The device displays the received notification data to the user visually or audibly, adjusting the display method according to the user's emotion recognized by the emotion engine.

[1221] Input: Notification data stored on the device.

[1222] Data processing / calculation: The app displays the notification content as a popup, and also vibrates or sounds an alarm if the user is in an anxious state.

[1223] Output: Notification data displayed visually and audibly to the user.

[1224] Step 9:

[1225] Checking notifications

[1226] Description: The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to evacuate to a safe location.

[1227] Input: The notification displayed on the device.

[1228] Data processing / calculation: The user checks the notification and acts according to the instructions.

[1229] Output: User action.

[1230] Step 10:

[1231] Send feedback (optional)

[1232] Description: Users can optionally send notification status and feedback to the server.

[1233] Input: Feedback information from the user.

[1234] Data processing / calculation: Through the app's feedback function, users can send their thoughts and problems about notifications in text format to the server.

[1235] Output: Feedback information sent to the server.

[1236] (Application example 2)

[1237] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1238] Conventional emergency broadcast notification systems lack functionality to accommodate specific users, such as the hearing impaired and elderly. Furthermore, emergency notifications are not adapted to the user's emotions, which can lead to panic or incorrect judgment in an emergency. Furthermore, if notifications are not delivered promptly and appropriately, the user's safety may be threatened. In response to these issues, the present invention aims to provide more accurate emergency notifications by taking the user's emotions into account.

[1239] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring voice data, voice recognition means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, emotion recognition means for recognizing a user's emotion based on the user's voice and facial expression data, means for generating notification data based on the user's emotion when an emergency broadcast is detected, means for transmitting the generated notification data to the user's terminal, and means for displaying the transmitted notification data on the user's terminal. This makes it possible to provide a quick and appropriate emergency notification that takes the user's emotion into consideration, thereby improving the safety of specific users.

[1240] "Audio data" is information obtained by capturing sound waves and converting them into digital form, and is data that includes an audio signal.

[1241] "Speech recognition means" refers to a technical means for converting voice data into text data, such as a device that uses a speech recognition API or speech recognition software.

[1242] "Text data" is data expressed as a character string, and includes character information converted from audio data.

[1243] The "emergency broadcast detection means" is a technical means for detecting information related to emergency broadcasts from the converted text data.

[1244] "Emotion recognition means" refers to a technical means for analyzing the user's voice and facial expression data to recognize the user's emotional state.

[1245] The "notification data generation means" is a technical means for creating notification data that is generated when an emergency broadcast is detected, and includes notification content that is adjusted based on the user's emotion recognition results.

[1246] The "notification data transmission means" is a technical means for transmitting the generated notification data to the user's terminal.

[1247] The "notification data display means" is a technical means for displaying the transmitted notification data on the user terminal, and has the function of notifying the user visually or audibly.

[1248] "Emotion" refers to the user's psychological state, and includes, for example, anxiety, fear, calmness, and the like.

[1249] "User's terminal" refers to an electronic device owned by the user, specifically a smartphone or a head-mounted display.

[1250] MODE FOR CARRYING OUT THE INVENTION

[1251] The present invention is a system that acquires audio data, recognizes emergency broadcasts in real time, and generates and transmits notifications according to the user's emotions. Detailed embodiments for carrying out the present invention are described below.

[1252] Server Processing

[1253] The server uses the following hardware and software to acquire audio data and detect emergency broadcasts in real time.

[1254] Hardware and software used

[1255] Audio input device: Hardware required to receive television and radio audio in real time.

[1256] Google Speech-to-Text API: Speech recognition software that converts acquired voice data into text data.

[1257] Emotion engine: Software for recognizing the user's emotions, analyzing the user's voice and facial expression data.

[1258] Notification sending system: Includes push notification and SMS sending functions for sending generated notification data to the user's device.

[1259] Acquiring audio data

[1260] The server acquires audio data from a designated audio input device, for example, by receiving television or radio broadcasts in real time and periodically transmitting the audio to the server.

[1261] Voice Recognition

[1262] The server sends the acquired audio data to the Google Speech-to-Text API and converts it into text data, which is then used to detect emergency broadcasts.

[1263] Emergency broadcast detection

[1264] The server analyzes the converted text data and detects keywords related to emergency broadcasts (e.g., "Earthquake Early Warning" or "Tsunami Warning"), thereby determining whether an emergency broadcast has been made.

[1265] emotion recognition

[1266] The server acquires the user's voice and facial expression data and inputs it into the emotion engine, which analyzes this data and recognizes the user's emotions (anxiety, fear, calmness, etc.) in real time.

[1267] Generate notification data

[1268] When an emergency broadcast is detected, the server generates notification data based on the user's emotional state. For example, if the user is feeling extremely anxious, the server generates notification data that includes instructions such as "Please remain calm and act calmly."

[1269] Sending notifications

[1270] The server sends the generated notification data to the user's device using push notifications or SMS, allowing the user to receive emergency information in real time.

[1271] Terminal handling

[1272] The user's device (such as a smartphone, head-mounted display, or smart glasses) quickly receives the notification sent from the server and presents it to the user visually or audibly.

[1273] Receiving and Viewing Notifications

[1274] The device receives the notification data sent from the server and displays the notification in a way that corresponds to the user's emotion. For example, if the user is calm, a standard pop-up message is displayed, but if the user is anxious, a notification is displayed with vibration and additional explanation.

[1275] User Action

[1276] The user checks the notification displayed on the device and takes necessary action, for example, by following the instruction "Please evacuate immediately" and evacuating to a safe place.

[1277] Specific examples

[1278] For example, when an earthquake occurs and an "Earthquake Early Warning" is broadcast, the system operates as follows:

[1279] 1. Acquisition of audio data: The server acquires the audio of the emergency earthquake alert broadcast from television or radio.

[1280] 2. Speech recognition: The server passes the acquired speech to the speech recognition engine, and obtains the text data "An emergency earthquake warning has been issued."

[1281] 3. Emergency broadcast detection: The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[1282] 4. Emotion recognition: The server activates an emotion engine based on the user's voice and facial expression data to recognize when the user is feeling anxious.

[1283] 5. Generation of notification data: The server generates notification data stating, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly."

[1284] 6. Sending notification: The server sends the generated notification data to the user's device via push notification.

[1285] 7. Receiving notification: The user's device receives the notification sent from the server.

[1286] 8. Display notification: The device will display a pop-up message saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm." and will notify the user with vibration and an audio alarm.

[1287] 9. Check the notification: The user checks the notification and immediately evacuates to a safe place.

[1288] Example prompts to give to a generative AI model:

[1289] Generate the example application code for an emergency notification and emotion recognition system. Follow these steps:

[1290] 1. Get the audio data from the specified URL.

[1291] 2. Use the Google Speech-to-Text API to convert the audio data into text data.

[1292] 3. Detect urgent keywords in the text data.

[1293] 4. Recognize user emotions using an emotion engine.

[1294] 5. Generate notification data based on emergency broadcast and emotion.

[1295] 6. Send notification data to a smartphone or HMD.

[1296] 7. Implement these functions in Python using specific libraries (e.g. requests, google.cloud.speech, pyttsx3)

[1297] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1298] Step 1:

[1299] The server acquires audio data. Specifically, it receives television or radio broadcast audio in real time from a designated audio input device and acquires the audio data. The input is an audio signal, and the output is audio data converted into a digital format.

[1300] Step 2:

[1301] The server sends the acquired voice data to a voice recognition means and converts it into text data. Specifically, it uses the Google Speech-to-Text API to analyze the voice data and convert it into text data. The input is digital voice data, and the output is text data.

[1302] Step 3:

[1303] The server analyzes the converted text data and detects emergency broadcasts. Specifically, it searches for keywords such as "Earthquake Early Warning" and "Tsunami Warning" to determine whether or not an emergency broadcast is occurring. The input is text data, and the server analyzes its contents to determine whether or not an emergency broadcast is occurring.

[1304] Step 4:

[1305] The server collects the user's voice and facial expression data and inputs it into the emotion recognition means to recognize the user's emotions. Specifically, the emotion engine uses the voice and facial expression data sent from the user terminal to analyze the user's emotional state (anxiety, fear, calmness, etc.). The input is the user's voice and facial expression data, and the output is the recognized emotional state.

[1306] Step 5:

[1307] When an emergency broadcast is detected, the server generates notification data based on the user's emotional state. For example, if the user is feeling anxious, the server generates notification data that includes supplemental information such as "Please remain calm and act calmly." The input is the content of the emergency broadcast and the user's emotional state, and the output is the adjusted notification data.

[1308] Step 6:

[1309] The server sends the generated notification data to the user's device via push notification or SMS. Specifically, it sends notifications to the user's device in real time through a notification sending system. The input is the notification data, and the output is the sent notification.

[1310] Step 7:

[1311] The device receives notification data sent from the server. Specifically, it receives push notifications and SMS using a dedicated application or notification listener. The input is the notification data sent from the server, and the output is the received notification.

[1312] Step 8:

[1313] The device displays the notification data to the user visually or audibly, for example, by using a pop-up message, vibration, or audio alarm. The input is the received notification data, and the output is the displayed notification content.

[1314] Step 9:

[1315] The user checks the notification displayed on the device and takes the necessary action. Specifically, the user follows the instructions in the notification and takes action, such as evacuating to a safe place. The input is the displayed notification content, and the output is the appropriate response action.

[1316] 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.

[1317] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[1318] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1319] [Fourth embodiment]

[1320] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1321] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1322] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1323] 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.

[1324] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1325] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1326] 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.

[1327] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1328] 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.

[1329] 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 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.

[1330] 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.

[1331] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1332] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1333] The present invention provides a system for supporting hearing-impaired people to receive emergency broadcasts in real time and respond appropriately. Specific embodiments of this system are as follows.

[1334] Server Processing

[1335] 1. Acquiring audio data

[1336] The server acquires audio data from a designated audio input device or streaming service, which is capable of receiving television or radio broadcasts and has an interface for transmitting audio data to the server in real time.

[1337] 2. Voice Recognition

[1338] The server passes the acquired voice data to a speech recognition engine, which converts the voice data into text data using existing speech recognition technology (e.g., Google Speech-to-Text API). The converted text data is used for subsequent processing.

[1339] 3. Emergency broadcast detection

[1340] The server analyzes the text data obtained from the voice recognition engine and detects keywords and specific patterns related to emergency broadcasts. For example, it checks whether keywords such as "Earthquake Early Warning" or "Tsunami Warning" are included. If it is determined to be an emergency broadcast, notification data is generated.

[1341] 4. Notification Data Generation

[1342] The server generates notification data based on the detected emergency broadcast, including the specific content and urgency of the detected emergency broadcast, recommended actions, etc. The notification data is then sent to the user's device.

[1343] 5. Sending notifications

[1344] The server then sends the generated notification data to the user's device using an appropriate communication method, such as push notification or SMS, allowing the user to receive emergency information in real time.

[1345] Terminal handling

[1346] 1. Receiving notifications

[1347] The user's device receives the notification data sent from the server. The device has a dedicated app or notification listener set up to receive notifications.

[1348] 2. Display of notifications

[1349] The device will then display the received notification data to the user visually or audibly, for example, as a pop-up message on the screen, or by vibrating or issuing an audio alert, allowing the user to quickly grasp the emergency information.

[1350] User Action

[1351] 1. Check notifications

[1352] The user checks the emergency notification displayed on the device. The notification contains the specific emergency broadcast content and recommended actions, so the user can take the necessary action immediately.

[1353] 2. Send Feedback (Optional)

[1354] Users can send notification reception status and feedback to the server as needed, which can be used to check and improve the system's operation.

[1355] Specific examples

[1356] For example, the operation of the system when an earthquake occurs and an "Earthquake Early Warning" is broadcast will be described.

[1357] 1. Acquiring audio data

[1358] The server acquires the audio of emergency earthquake alerts broadcast on television and radio.

[1359] 2. Voice Recognition

[1360] The server passes the acquired voice to a voice recognition engine, and obtains text data saying, "An emergency earthquake warning has been issued."

[1361] 3. Emergency broadcast detection

[1362] The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[1363] 4. Notification Data Generation

[1364] The server generates notification data saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately."

[1365] 5. Sending notifications

[1366] The server sends the generated notification data to the user's device as a push notification.

[1367] 6. Receiving Notifications

[1368] The user's terminal receives the notification sent from the server.

[1369] 7. Display of notifications

[1370] The device will display a pop-up message saying, "An emergency earthquake alert has been issued. Please evacuate to a safe location immediately," and will alert the user with vibrations and an audio alarm.

[1371] 8. Checking Notifications

[1372] The user should check the notification and immediately evacuate to a safe location.

[1373] In this way, the present invention can provide a system that enables all users, including the hearing impaired, to quickly receive emergency broadcasts and respond appropriately.

[1374] The processing flow will be explained below.

[1375] Step 1:

[1376] The server acquires audio data from a designated audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server.

[1377] Step 2:

[1378] The server sends the captured voice data to a speech recognition engine, which uses speech recognition technology such as the Google Speech-to-Text API to convert the voice data into text data for further processing.

[1379] Step 3:

[1380] The server analyzes the text data obtained from the speech recognition engine, detecting keywords and specific patterns related to emergency broadcasts. For example, it checks whether the text contains keywords such as "Earthquake Early Warning" or "Tsunami Warning."

[1381] Step 4:

[1382] When an emergency broadcast is detected, the server generates notification data, which includes information such as the specific content and urgency of the detected emergency broadcast, and recommended actions.

[1383] Step 5:

[1384] The server then sends the generated notification data to the user's device using communication methods such as push notifications or SMS, allowing the user to receive emergency information in real time.

[1385] Step 6:

[1386] The device receives the notification data sent from the server. The device has a dedicated app or notification listener set up to receive notifications.

[1387] Step 7:

[1388] The device displays the received notification data to the user visually or audibly, for example, by displaying a pop-up message on the screen or by emitting a vibration or audio alarm, allowing the user to quickly grasp the emergency information.

[1389] Step 8:

[1390] The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to a safe location.

[1391] Step 9:

[1392] Users can send notification reception status and feedback to the server as needed, which the server can use to check and improve the system's operation.

[1393] Example 1

[1394] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1395] Conventional emergency broadcasts rely on sight and hearing, so there is a problem that information cannot be properly conveyed to the hearing-impaired or visually impaired. This is particularly true for highly urgent information, which can lead to increased human casualties. Furthermore, when real-time information transmission is required, existing notification systems are sometimes unable to adequately respond. This creates a need for a fast and reliable means of transmitting information.

[1396] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1397] In this invention, the server includes means for acquiring voice data, means for storing the acquired voice data in a fixed buffer, means for passing the voice data to a voice recognition engine in real time, means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for identifying keywords based on the converted text data and determining whether the text data is an emergency broadcast, means for generating notification data when an emergency broadcast is detected, means for transmitting the generated notification data by push notification or SMS, means for transmitting the generated notification data to a user's device, and means for displaying the transmitted notification data on the user's device and generating a vibration or voice alert. This enables all users, including those with hearing impairments, to quickly and reliably receive emergency information in real time and respond appropriately.

[1398] 1. "Audio Data" means data that represents in digital form sound wave signals obtained from audio input devices or streaming services.

[1399] 2. "Buffer" is a storage area for temporarily storing audio data in preparation for subsequent processing.

[1400] 3. A "voice recognition engine" is software for converting voice data into text data, and is a program that utilizes existing voice recognition technology.

[1401] 4. "Text data" means character string data converted from voice data by a voice recognition engine.

[1402] 5. An "emergency broadcast" is an official broadcast to inform people of emergencies such as disasters and accidents, and is information that contains specific keywords or patterns.

[1403] 6. "Notification data" means data generated when an emergency broadcast is detected, and includes information such as the content of the emergency broadcast, its urgency, and recommended actions.

[1404] 7. "Push notification" is a method of sending information from a server to a user's device in real time, and is a notification method that does not require any special operation on the user's side.

[1405] 8. "SMS" means a text messaging service for sending short messages to a user's device.

[1406] 9. "Terminal" means a hardware device that receives notification data and displays it to the user, including a smartphone or tablet.

[1407] 10. "Alert" means a visual or audible notification that is used to get the user's attention.

[1408] 11. "Notification listener program" means software that resides on a user terminal to receive notification data sent from a server.

[1409] The present invention is a system for supporting hearing-impaired people in receiving emergency broadcasts in real time and responding appropriately. A specific embodiment of this system will be described.

[1410] Hardware and software used

[1411] 1. Audio Input Device:

[1412] The broadcast audio data is acquired using a device such as a TV tuner card or radio receiver.

[1413] 2. Server:

[1414] The server processes the acquired voice data, detects emergency broadcasts, and generates and sends notification data to the user's device. The software running on the server includes a speech recognition engine (e.g., Google Speech-to-Text API) and a notification system (e.g., Firebase Cloud Messaging).

[1415] 3. Terminal:

[1416] The user's terminal is a smartphone, tablet, or the like, and has installed thereon a dedicated app or notification listener program for visually or audibly displaying the received notification data to the user.

[1417] Data processing and calculation flow

[1418] The server receives voice data in real time from voice input devices and streaming services and stores it in a fixed buffer. This voice data is passed to a speech recognition engine and converted into text data using a method such as the Google Speech-to-Text API. The converted text data is then analyzed again by the server to detect keywords and specific patterns related to emergency broadcasts. This analysis is performed using keyword search and pattern matching technology, and when an emergency broadcast is detected, notification data is generated. The generated notification data is sent to the user's device via push notification or SMS. This notification data includes the specific content of the emergency broadcast, its urgency, and recommended actions.

[1419] Specific Examples

[1420] For example, when an earthquake occurs and an "Earthquake Early Warning" is broadcast, the server obtains the audio of the earthquake early warning from television or radio. This audio is converted into text data using the Google Speech-to-Text API. The server then detects the keyword "Earthquake Early Warning" from the converted text data and generates notification data. This notification data contains the message "An Earthquake Early Warning has been issued. Please evacuate to a safe place immediately," and is sent to the user's device via push notification. The user's device displays this notification as a pop-up message and simultaneously vibrates and generates an audio alert. The user can respond quickly by checking the notification and immediately evacuating to a safe place.

[1421] Prompt Sentence Examples

[1422] "Please give us an overview of the Emergency Alert Notification System."

[1423] "Please explain the operational flow of emergency broadcast notifications for the hearing impaired."

[1424] The above is an embodiment of the invention. This system enables all users, including the hearing impaired, to receive emergency broadcasts in real time and respond appropriately.

[1425] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1426] Step 1:

[1427] The server acquires audio data. Specifically, it captures audio data in real time through an audio input device or a streaming service and stores this data in a buffer on the server. The input of this step is real-time audio data, and the output is the audio data stored in the buffer.

[1428] Step 2:

[1429] The server passes the voice data stored in the buffer to a voice recognition engine. The voice recognition engine used here uses technology such as the Google Speech-to-Text API and converts the voice data into text data. The input for this step is the voice data stored in the buffer, and the output is text data. Specifically, a voice message such as "An emergency earthquake warning has been issued" is converted into the text "An emergency earthquake warning has been issued."

[1430] Step 3:

[1431] The server analyzes the converted text data to detect keywords and specific patterns related to emergency broadcasts. This analysis is performed on the entire text data using keyword search and pattern matching techniques. The input for this step is the converted text data, and the output is a determination result indicating whether or not an emergency broadcast is present. For example, the keyword "Emergency Earthquake Alert" is detected.

[1432] Step 4:

[1433] When an emergency broadcast is detected, the server generates notification data. This notification data includes the specific content of the emergency broadcast, the urgency level, recommended actions, etc. The input to this step is the detection result of the emergency broadcast, and the output is the generated notification data. Specifically, a notification message is generated stating, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately."

[1434] Step 5:

[1435] The server sends the generated notification data to the user's device. This is done using push notifications or SMS. Services such as Firebase Cloud Messaging are used to deliver the notification data to the device in real time. The input to this step is the generated notification data, and the output is the notification data being sent to the user's device.

[1436] Step 6:

[1437] The user's device receives the notification data sent from the server. The device, on which the dedicated app or notification listener program is installed, temporarily stores the received data and prepares it for the next process. The input of this step is the notification data sent from the server, and the output is the notification data stored on the device.

[1438] Step 7:

[1439] The device displays the received notification data to the user visually or audibly. Specifically, it displays a pop-up message on the screen and generates a vibration or audio alert. For example, a message may be displayed saying, "An emergency earthquake warning has been issued. Please evacuate to a safe location immediately." The input of this step is the notification data stored in the device, and the output is the notification displayed to the user.

[1440] Step 8:

[1441] The user checks the emergency notification displayed on the device. The notification contains the specific emergency broadcast content and recommended actions, allowing the user to take the necessary action immediately. Specifically, the user checks the notification and takes action to evacuate to a safe place. The input of this step is the notification data displayed on the device, and the output is the user's response action.

[1442] (Application example 1)

[1443] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1444] Food delivery workers are required to quickly grasp information and take appropriate action in the event of an emergency. However, with conventional systems, workers with hearing impairments or those working in noisy environments may miss emergency broadcasts. For this reason, a means is needed to ensure that all workers receive emergency information and continue working safely.

[1445] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1446] In this invention, the server includes means for acquiring voice data, means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for generating notification data when an emergency broadcast is detected, means for transmitting the generated notification data to a user terminal, and means for transmitting emergency notifications to food delivery workers, thereby enabling all food delivery workers to receive emergency information in real time and respond quickly and appropriately.

[1447] "Audio data" is digital data that contains audio information and is a record of sound waves as electronic signals.

[1448] "Acquisition means" refers to a device or method for acquiring audio data from an external source.

[1449] "Speech recognition means" refers to technology or equipment that analyzes acquired voice data and converts it into text data.

[1450] "Text data" is digital data containing character information generated by a voice recognition means.

[1451] "Emergency broadcast detection means" refers to technology or devices that analyze text data and identify broadcasts that contain important emergency information.

[1452] The "notification data generating means" refers to a device or method for creating a specific notification for the user when an emergency broadcast is detected.

[1453] "Communication means" refers to the technology or device for transmitting the generated notification data to the user's terminal.

[1454] "Terminal display means" refers to a technique or device for displaying notification data on a user's terminal.

[1455] "Food delivery" refers to the business or service of delivering food and beverages to customers.

[1456] "Worker" refers to a person engaged in food delivery work.

[1457] "Emergency notification" refers to information that is quickly conveyed to users, including the contents of emergency broadcasts.

[1458] This invention relates to a system that supports food delivery workers by receiving emergency broadcasts in real time, enabling them to carry out their work safely. This system operates while playing the roles of a server, a terminal, and a user.

[1459] The server first obtains audio data from radio or TV streaming services. To do this, it uses HTTP streaming to obtain audio data in real time. The obtained audio data is then converted into text data using the Google Speech-to-Text API. The Google Speech-to-Text API provides highly accurate speech recognition and supports a variety of languages. This allows audio data to be converted into text data quickly and accurately.

[1460] The server then detects emergency broadcasts from the converted text data. To do this, it uses an algorithm that analyzes keywords related to emergency broadcasts. For example, it checks whether the text data contains keywords such as "earthquake early warning" or "typhoon warning." By applying natural language processing technology to this analysis, high detection accuracy is achieved.

[1461] When an emergency broadcast is detected, the server immediately generates notification data, which includes the content of the detected emergency broadcast and specific recommended actions, providing workers with the information they need to take appropriate action.

[1462] The server then sends the generated notification data to the devices of food delivery workers via push notification or SMS, using the Twilio API for rapid notification, allowing workers to receive important emergency information in real time.

[1463] The user's device will then display the received notification data appropriately, including displaying a pop-up message, vibrating, or sounding an alarm, ensuring that workers receive emergency information and can respond promptly.

[1464] As a concrete example, we will explain the system's processing when an earthquake occurs and an "Earthquake Early Warning" is broadcast. The server acquires audio data from the radio or television and generates text data saying "An earthquake early warning has been issued" using the Google Speech-to-Text API. Next, it analyzes this text data to detect the keyword "earthquake early warning" and determines that it is an emergency broadcast. It then generates notification data saying "An earthquake early warning has been issued. Please evacuate to a safe location immediately," and sends it to the worker's smartphone via push notification or SMS via the Twilio API. Finally, the worker's device warns them with a pop-up message or vibration.

[1465] Example prompt sentence:

[1466] Create a program that uses a speech recognition engine to convert voice data into text and detect emergency broadcasts. Next, create a program that generates notification data based on the detected emergency broadcast content and sends the notification to a specified smartphone. The speech recognition engine used is the Google Speech-to-Text API. HTTP streaming will be used to obtain the voice data. The Twilio API will be used to send the notification.

[1467] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1468] Step 1:

[1469] The server retrieves audio data in real time from a radio or TV streaming service. Specifically, it periodically receives audio data using HTTP streaming and stores it in a buffer. The input is the audio stream URL, and the output is the buffered audio data.

[1470] Step 2:

[1471] The server passes the buffered voice data to the Google Speech-to-Text API to convert it into text data. The voice recognition engine analyzes the voice data and generates corresponding text data. The input is the buffered voice data, and the output is the converted text data. Specific operations include the process of sending voice data to the recognition engine and receiving its response.

[1472] Step 3:

[1473] The server analyzes the converted text data to detect keywords related to emergency broadcasts. It uses natural language processing technology to search for specific keywords (e.g., "earthquake early warning" or "typhoon warning") in the text data. The input is the converted text data, and the output is the detection results of emergency broadcasts and the detected keywords. Specifically, it involves the process of applying a text analysis algorithm to detect keywords.

[1474] Step 4:

[1475] When an emergency broadcast is detected, the server generates notification data including recommended actions. It creates a notification message and includes the content of the emergency broadcast and recommended measures. The input is the emergency broadcast detection result and the detected keywords, and the output is a detailed notification message. Specific operations include a process using a template-based message generation engine.

[1476] Step 5:

[1477] The server uses the Twilio API to send the generated notification data to the food delivery worker's device. The notification data is sent via push notification or SMS. The input is the notification message, and the output is a confirmation that the message was sent. Specific operations include calling the Twilio API and sending the notification message in the appropriate format.

[1478] Step 6:

[1479] The terminal visually and audibly displays the notification data received from the server to the user. Notifications are given using pop-up messages, vibrations, audio alarms, etc. The input is the notification message, and the output is the display of an emergency notification to the user. Specific operations include the process of displaying a message and notifying the user using the terminal's user interface.

[1480] Step 7:

[1481] The user checks the notification displayed on the device and takes the specified recommended action, such as evacuating to a safe place. The input is the displayed notification message, and the output is the user's action. Specific actions include understanding the notification content and taking the recommended action.

[1482] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1483] The present invention combines a system that acquires audio data, recognizes emergency broadcasts in real time, and notifies the hearing impaired quickly with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are as follows:

[1484] Server Processing

[1485] 1. Acquiring audio data

[1486] The server acquires audio data from a designated audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server.

[1487] 2. Voice Recognition

[1488] The server sends the captured voice data to a speech recognition engine, which uses speech recognition technology such as the Google Speech-to-Text API to convert the voice data into text data for further processing.

[1489] 3. Emergency broadcast detection

[1490] The server analyzes the text data obtained from the speech recognition engine, detecting keywords and specific patterns related to emergency broadcasts. For example, it checks whether the text contains keywords such as "Earthquake Early Warning" or "Tsunami Warning."

[1491] 4. Emotion Recognition by Emotion Engine

[1492] The server operates an emotion engine based on the user's voice and facial expression data acquired from the device. The emotion engine recognizes the user's emotions (e.g., fear, anxiety, calmness, etc.) in real time. This information is reflected in the generation of notification data.

[1493] 5. Generating Notification Data

[1494] When an emergency broadcast is detected, the server generates notification data. This notification data includes the specific content and urgency of the detected emergency broadcast, recommended actions, and content based on the user's emotions recognized by the emotion engine. For example, if the user is feeling very anxious, the notification will include additional instructions to remain calm and act accordingly.

[1495] 6. Sending Notifications

[1496] The server then sends the generated notification data to the user's device using an appropriate communication method, such as push notification or SMS, allowing the user to receive emergency information in real time.

[1497] Terminal handling

[1498] 1. Receiving notifications

[1499] The user's device receives the notification data sent from the server. The device has a dedicated app and notification listener for receiving notifications. The device also has an interface for sending the user's voice and facial expression data to the server.

[1500] 2. Display of notifications

[1501] The device then displays the received notification data to the user visually or audibly. The display method is adjusted according to the user's emotion as recognized by the emotion engine. For example, if the user is calm, a standard pop-up message is displayed, while if the user is anxious, a notification is displayed with vibration and additional explanation.

[1502] User Action

[1503] 1. Check notifications

[1504] The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to a safe location.

[1505] 2. Send Feedback (Optional)

[1506] Users can send notification reception status and feedback to the server as needed, which the server can use to check and improve the system's operation.

[1507] Specific examples

[1508] For example, the operation of the system when an earthquake occurs and an "Earthquake Early Warning" is broadcast will be described.

[1509] 1. Acquiring audio data

[1510] The server acquires the audio of emergency earthquake alerts broadcast on television and radio.

[1511] 2. Voice Recognition

[1512] The server passes the acquired voice to a voice recognition engine, and obtains text data saying, "An emergency earthquake warning has been issued."

[1513] 3. Emergency broadcast detection

[1514] The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[1515] 4. Emotion Recognition by Emotion Engine

[1516] The server activates an emotion engine based on the user's voice and facial expression data and recognizes that the user is feeling anxious.

[1517] 5. Generating Notification Data

[1518] The server generates notification data saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly."

[1519] 6. Sending Notifications

[1520] The server sends the generated notification data to the user's device as a push notification.

[1521] 7. Receiving Notifications

[1522] The user's terminal receives the notification sent from the server.

[1523] 8. Display of notifications

[1524] The device will display a pop-up message saying, "An emergency earthquake alert has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly," and will alert the user with vibrations and an audio alarm.

[1525] 9. Checking Notifications

[1526] The user should check the notification and immediately evacuate to a safe location.

[1527] In this way, the present invention makes it possible to provide an emergency notification system that takes into account the user's emotions, and enables all users to quickly receive emergency broadcasts and respond appropriately.

[1528] The processing flow will be explained below.

[1529] Step 1:

[1530] The server retrieves audio data from a designated audio input device or streaming service, such as a device that transmits broadcast audio from a television or radio to the server in real time.

[1531] Step 2:

[1532] The server sends the acquired voice data to a speech recognition engine, which converts the voice data into text data using technologies such as the Google Speech-to-Text API, which is then used for further processing.

[1533] Step 3:

[1534] The server analyzes the text data obtained from the speech recognition engine. During the analysis, it detects keywords and specific patterns related to emergency broadcasts. For example, it checks whether the text contains keywords such as "Earthquake Early Warning" or "Tsunami Warning."

[1535] Step 4:

[1536] The server passes the user's voice and facial expression data acquired from the device to the emotion engine, which uses this data to recognize the user's emotions in real time. For example, it can determine whether the user is feeling anxious or calm from the tone of their voice and facial expression.

[1537] Step 5:

[1538] When an emergency broadcast is detected, the server generates notification data based on the user's emotions recognized by the emotion engine. For example, if the server determines that the user is feeling extremely anxious, the notification data will include an additional message such as "Please remain calm and act calmly."

[1539] Step 6:

[1540] The server then sends the generated notification data to the user's device using an appropriate communication method, such as push notification or SMS, allowing the user to receive emergency information in real time.

[1541] Step 7:

[1542] The device receives the notification data sent from the server. The device has a dedicated app and notification listener for receiving notifications. It also has an interface for sending the user's voice and facial expression data to the server.

[1543] Step 8:

[1544] The device displays the received notification data to the user visually or audibly, and adjusts the notification display depending on the user's emotions. For example, if the user is calm, a standard pop-up message will be displayed, whereas if the user is anxious, detailed instructions will be displayed along with vibrations and audio alerts.

[1545] Step 9:

[1546] The user then checks the emergency notification displayed on their device, which includes the specific emergency broadcast content, recommended actions, and additional emotion-based instructions, allowing the user to take appropriate action quickly.

[1547] Step 10:

[1548] Users can send notification reception status and feedback to the server as needed. This helps the server check and improve the system's operation. For example, users can report their impressions after receiving notifications or any bugs.

[1549] Example 2

[1550] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1551] Conventional emergency broadcast notification systems analyze audio data to detect emergency broadcasts, but they lack consideration for users with hearing impairments or who are emotionally upset. As a result, it can be difficult for users to respond quickly and appropriately to an emergency. Furthermore, notifications that do not take the user's emotional state into account can cause further anxiety and confusion. Therefore, there is a need for an emergency broadcast notification system that takes the user's emotional state into account.

[1552] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for acquiring voice data, voice recognition means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, means for acquiring user voice and facial expression data and recognizing the emotion, means for generating notification data taking into account the user's emotion recognition result when an emergency broadcast is detected, means for transmitting the generated notification data to the user's terminal, and means for displaying the transmitted notification data on the user's terminal. This enables emergency notifications that take into account the user's emotional state, allowing all users to quickly receive the emergency broadcast and respond appropriately.

[1553] "Means for acquiring audio data" refers to the function of collecting audio data in real time from various audio input devices and streaming services.

[1554] "Speech recognition means for converting acquired voice data into text data" refers to a function that uses voice recognition technology or an API (e.g., a voice recognition API) to convert collected voice data into text format.

[1555] "Means for detecting emergency broadcasts from converted text data" refers to a function that analyzes the text data converted by the voice recognition means and identifies keywords or specific patterns related to emergency broadcasts.

[1556] "Means for acquiring the user's voice and facial expression data and recognizing their emotions" refers to the function of collecting the user's voice and facial expression data, analyzing it, and recognizing the user's emotional state (for example, fear, anxiety, calmness, etc.) in real time.

[1557] "Means for generating notification data taking into consideration the user's emotion recognition results when an emergency broadcast is detected" refers to a function that automatically generates notification data to provide appropriate information to the user based on the detected emergency broadcast and the user's emotional state.

[1558] "Means for sending generated notification data to the user's device" refers to a function that uses communication means such as push notification or SMS to send the generated notification data to the user's mobile device, computer, etc.

[1559] "Means for displaying the transmitted notification data on the user's terminal" refers to a function for visually or audibly informing the user of the notification data received on the user's terminal and displaying it so that the user can recognize it.

[1560] The present invention combines a system that acquires audio data, recognizes emergency broadcasts in real time, and promptly notifies the hearing impaired with an emotion engine that recognizes the user's emotions. This system includes the following specific components and processes:

[1561] Server Processing

[1562] Acquiring audio data

[1563] The server acquires audio data from a specified audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server. Specifically, the audio input device streams audio data in real time using HTTP requests.

[1564] Voice Recognition

[1565] The server sends the acquired voice data to a speech recognition engine, such as the Google Speech-to-Text API. This engine converts the voice data into text data. The server parses the response from the API and stores the necessary information.

[1566] Emergency broadcast detection

[1567] The server analyzes the text data obtained from the speech recognition engine to detect keywords and specific patterns related to emergency broadcasts, such as "emergency earthquake alert" and "tsunami warning," and flags any that match.

[1568] Emotion recognition by emotion engine

[1569] The server operates the emotion engine based on the user's voice and facial expression data acquired from the device. Emotion recognition uses a facial expression recognition API and a voice emotion analysis API. The server grasps the user's emotional state based on the analyzed information.

[1570] Generate notification data

[1571] The server generates notification data when an emergency broadcast is detected. This notification data is created taking into account the user's emotion recognition results. For example, if the user is feeling anxious, an additional message such as "Please stay calm" may be included.

[1572] Sending notifications

[1573] The server sends the generated notification data to the user's device using push notifications, SMS, etc. Specifically, it uses a notification service API such as Firebase Cloud Messaging.

[1574] Terminal handling

[1575] Receive notifications

[1576] The user's device receives the notification data sent from the server. A dedicated app or notification listener is set up on the device to receive notifications. A background service waits for notifications and stores the received data in the app.

[1577] Viewing notifications

[1578] The user's device displays the received notification data visually or audibly to the user. The display method is adjusted according to the user's emotion recognized by the emotion engine. For example, it may display a pop-up message, and if the user is in an anxious state, it may also use vibration or an audio alarm.

[1579] User Action

[1580] Checking notifications

[1581] The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to a safe location.

[1582] Send feedback (optional)

[1583] Users can send notification reception status and feedback to the server as needed. Through the app's feedback function, users can send their impressions of notifications and problems to the server in text format.

[1584] Specific examples

[1585] For example, when an earthquake occurs and an "Earthquake Early Warning" is broadcast, the system operates as follows.

[1586] 1. Acquiring audio data

[1587] The server receives the audio of emergency earthquake alerts broadcast on television and radio in real time.

[1588] 2. Voice Recognition

[1589] The server passes the acquired voice data to a voice recognition engine, and obtains text data saying, "An emergency earthquake warning has been issued."

[1590] 3. Emergency broadcast detection

[1591] The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[1592] 4. Emotion Recognition by Emotion Engine

[1593] The server activates an emotion engine based on the user's voice and facial expression data and recognizes that the user is feeling anxious.

[1594] 5. Generating Notification Data

[1595] The server generates notification data saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly."

[1596] 6. Sending Notifications

[1597] The server sends the generated notification data to the user's device as a push notification.

[1598] 7. Receiving Notifications

[1599] The user's terminal receives the notification sent from the server.

[1600] 8. Display of notifications

[1601] The device will display a pop-up message saying, "An emergency earthquake alert has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly," and will alert the user with vibrations and an audio alarm.

[1602] 9. Checking Notifications

[1603] The user should check the notification and immediately evacuate to a safe location.

[1604] Prompt Sentence Examples

[1605] Below are some examples of prompts to input to the generative AI model.

[1606] "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm."

[1607] This enables emergency notifications that take into account the emotional state of the user, allowing all users to quickly receive emergency broadcasts and respond appropriately.

[1608] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1609] Step 1:

[1610] Acquiring audio data

[1611] Description: The server retrieves audio data from a specified audio input device or streaming service. This device has an interface that receives television or radio broadcasts in real time and transmits the audio to the server.

[1612] Input: Real-time audio data from TV or radio.

[1613] Data processing / computation: Audio input devices stream audio data in real time using HTTP requests.

[1614] Output: Real-time audio data sent to the server.

[1615] Step 2:

[1616] Voice Recognition

[1617] Description: The server sends the captured voice data to a speech recognition engine, such as the Google Speech-to-Text API, which converts the voice data into text data.

[1618] Input: The real-time audio data obtained in step 1.

[1619] Data processing / calculation: The server processes the batch of audio data, sends an API request to receive text data, parses the response from the API, and stores the necessary information.

[1620] Output: Text data obtained from the speech recognition engine.

[1621] Step 3:

[1622] Emergency broadcast detection

[1623] Description: The server analyzes the text data obtained from the speech recognition engine to detect keywords and specific patterns related to emergency broadcasts.

[1624] Input: The text data generated in step 2.

[1625] Data processing / calculation: The server searches for keywords such as "earthquake early warning" or "tsunami warning" within the text data and flags any matches. This flag is used for subsequent processing.

[1626] Output: A flag indicating whether an emergency broadcast was detected.

[1627] Step 4:

[1628] Emotion recognition by emotion engine

[1629] Description: The server operates the emotion engine based on the user's voice and facial expression data acquired from the device. The emotion engine recognizes the user's emotional state (e.g., fear, anxiety, calmness, etc.) in real time.

[1630] Input: User's voice and facial expression data.

[1631] Data processing / calculation: The server sends data to the facial expression recognition API and voice emotion analysis API, analyzes the response, and determines the user's emotions.

[1632] Output: Data indicating the user's emotional state.

[1633] Step 5:

[1634] Generate notification data

[1635] Description: The server generates notification data when an emergency broadcast is detected. This notification data is created taking into account the content of the emergency broadcast and the user's emotion recognition results.

[1636] Input: Emergency broadcast flag from step 3, user emotion data from step 4.

[1637] Data processing / calculation: The server generates notification messages using templates and inserts additional messages such as "please stay calm" if the user is feeling anxious.

[1638] Output: The generated notification data.

[1639] Step 6:

[1640] Sending notifications

[1641] Description: The server sends the generated notification data to the user's device using communication methods such as push notification or SMS.

[1642] Input: The notification data generated in step 5.

[1643] Data processing / calculation: The server uses a notification service API (e.g., Firebase Cloud Messaging) to send the generated notification data to the device.

[1644] Output: Notification data received by the user's device.

[1645] Step 7:

[1646] Receive notifications

[1647] Description: The user's device receives notification data sent from the server. The device has a dedicated app or notification listener set up to receive notifications.

[1648] Input: Notification data sent by the server.

[1649] Data processing / calculation: The device's background service waits for notifications and stores the received data within the app.

[1650] Output: Notification data stored in the device.

[1651] Step 8:

[1652] Viewing notifications

[1653] Description: The device displays the received notification data to the user visually or audibly, adjusting the display method according to the user's emotion recognized by the emotion engine.

[1654] Input: Notification data stored on the device.

[1655] Data processing / calculation: The app displays the notification content as a popup, and also vibrates or sounds an alarm if the user is in an anxious state.

[1656] Output: Notification data displayed visually and audibly to the user.

[1657] Step 9:

[1658] Checking notifications

[1659] Description: The user checks the emergency notification displayed on the device and takes necessary action, such as following instructions such as "Evacuate immediately" to evacuate to a safe location.

[1660] Input: The notification displayed on the device.

[1661] Data processing / calculation: The user checks the notification and acts according to the instructions.

[1662] Output: User action.

[1663] Step 10:

[1664] Send feedback (optional)

[1665] Description: Users can optionally send notification status and feedback to the server.

[1666] Input: Feedback information from the user.

[1667] Data processing / calculation: Through the app's feedback function, users can send their thoughts and problems about notifications in text format to the server.

[1668] Output: Feedback information sent to the server.

[1669] (Application example 2)

[1670] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1671] Conventional emergency broadcast notification systems lack functionality to accommodate specific users, such as the hearing impaired and elderly. Furthermore, emergency notifications are not adapted to the user's emotions, which can lead to panic or incorrect judgment in an emergency. Furthermore, if notifications are not delivered promptly and appropriately, the user's safety may be threatened. In response to these issues, the present invention aims to provide more accurate emergency notifications by taking the user's emotions into account.

[1672] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring voice data, voice recognition means for converting the acquired voice data into text data, means for detecting an emergency broadcast from the converted text data, emotion recognition means for recognizing a user's emotion based on the user's voice and facial expression data, means for generating notification data based on the user's emotion when an emergency broadcast is detected, means for transmitting the generated notification data to the user's terminal, and means for displaying the transmitted notification data on the user's terminal. This makes it possible to provide a quick and appropriate emergency notification that takes the user's emotion into consideration, thereby improving the safety of specific users.

[1673] "Audio data" is information obtained by capturing sound waves and converting them into digital form, and is data that includes an audio signal.

[1674] "Speech recognition means" refers to a technical means for converting voice data into text data, such as a device that uses a speech recognition API or speech recognition software.

[1675] "Text data" is data expressed as a character string, and includes character information converted from audio data.

[1676] The "emergency broadcast detection means" is a technical means for detecting information related to emergency broadcasts from the converted text data.

[1677] "Emotion recognition means" refers to a technical means for analyzing the user's voice and facial expression data to recognize the user's emotional state.

[1678] The "notification data generation means" is a technical means for creating notification data that is generated when an emergency broadcast is detected, and includes notification content that is adjusted based on the user's emotion recognition results.

[1679] The "notification data transmission means" is a technical means for transmitting the generated notification data to the user's terminal.

[1680] The "notification data display means" is a technical means for displaying the transmitted notification data on the user terminal, and has the function of notifying the user visually or audibly.

[1681] "Emotion" refers to the user's psychological state, and includes, for example, anxiety, fear, calmness, and the like.

[1682] "User's terminal" refers to an electronic device owned by the user, specifically a smartphone or a head-mounted display.

[1683] MODE FOR CARRYING OUT THE INVENTION

[1684] The present invention is a system that acquires audio data, recognizes emergency broadcasts in real time, and generates and transmits notifications according to the user's emotions. Detailed embodiments for carrying out the present invention are described below.

[1685] Server Processing

[1686] The server uses the following hardware and software to acquire audio data and detect emergency broadcasts in real time.

[1687] Hardware and software used

[1688] Audio input device: Hardware required to receive television and radio audio in real time.

[1689] Google Speech-to-Text API: Speech recognition software that converts acquired voice data into text data.

[1690] Emotion engine: Software for recognizing the user's emotions, analyzing the user's voice and facial expression data.

[1691] Notification sending system: Includes push notification and SMS sending functions for sending generated notification data to the user's device.

[1692] Acquiring audio data

[1693] The server acquires audio data from a designated audio input device, for example, by receiving television or radio broadcasts in real time and periodically transmitting the audio to the server.

[1694] Voice Recognition

[1695] The server sends the acquired audio data to the Google Speech-to-Text API and converts it into text data, which is then used to detect emergency broadcasts.

[1696] Emergency broadcast detection

[1697] The server analyzes the converted text data and detects keywords related to emergency broadcasts (e.g., "Earthquake Early Warning" or "Tsunami Warning"), thereby determining whether an emergency broadcast has been made.

[1698] emotion recognition

[1699] The server acquires the user's voice and facial expression data and inputs it into the emotion engine, which analyzes this data and recognizes the user's emotions (anxiety, fear, calmness, etc.) in real time.

[1700] Generate notification data

[1701] When an emergency broadcast is detected, the server generates notification data based on the user's emotional state. For example, if the user is feeling extremely anxious, the server generates notification data that includes instructions such as "Please remain calm and act calmly."

[1702] Sending notifications

[1703] The server sends the generated notification data to the user's device using push notifications or SMS, allowing the user to receive emergency information in real time.

[1704] Terminal handling

[1705] The user's device (such as a smartphone, head-mounted display, or smart glasses) quickly receives the notification sent from the server and presents it to the user visually or audibly.

[1706] Receiving and Viewing Notifications

[1707] The device receives the notification data sent from the server and displays the notification in a way that corresponds to the user's emotion. For example, if the user is calm, a standard pop-up message is displayed, but if the user is anxious, a notification is displayed with vibration and additional explanation.

[1708] User Action

[1709] The user checks the notification displayed on the device and takes necessary action, for example, by following the instruction "Please evacuate immediately" and evacuating to a safe place.

[1710] Specific examples

[1711] For example, when an earthquake occurs and an "Earthquake Early Warning" is broadcast, the system operates as follows:

[1712] 1. Acquisition of audio data: The server acquires the audio of the emergency earthquake alert broadcast from television or radio.

[1713] 2. Speech recognition: The server passes the acquired speech to the speech recognition engine, and obtains the text data "An emergency earthquake warning has been issued."

[1714] 3. Emergency broadcast detection: The server detects the keyword "Earthquake Early Warning" from the text data and determines that it is an emergency broadcast.

[1715] 4. Emotion recognition: The server activates an emotion engine based on the user's voice and facial expression data to recognize when the user is feeling anxious.

[1716] 5. Generation of notification data: The server generates notification data stating, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm and act accordingly."

[1717] 6. Sending notification: The server sends the generated notification data to the user's device via push notification.

[1718] 7. Receiving notification: The user's device receives the notification sent from the server.

[1719] 8. Display notification: The device will display a pop-up message saying, "An emergency earthquake warning has been issued. Please evacuate to a safe place immediately. Please remain calm." and will notify the user with vibration and an audio alarm.

[1720] 9. Check the notification: The user checks the notification and immediately evacuates to a safe place.

[1721] Example prompts to give to a generative AI model:

[1722] Generate the example application code for an emergency notification and emotion recognition system. Follow these steps:

[1723] 1. Get the audio data from the specified URL.

[1724] 2. Use the Google Speech-to-Text API to convert the audio data into text data.

[1725] 3. Detect urgent keywords in the text data.

[1726] 4. Recognize user emotions using an emotion engine.

[1727] 5. Generate notification data based on emergency broadcast and emotion.

[1728] 6. Send notification data to a smartphone or HMD.

[1729] 7. Implement these functions in Python using specific libraries (e.g. requests, google.cloud.speech, pyttsx3)

[1730] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1731] Step 1:

[1732] The server acquires audio data. Specifically, it receives television or radio broadcast audio in real time from a designated audio input device and acquires the audio data. The input is an audio signal, and the output is audio data converted into a digital format.

[1733] Step 2:

[1734] The server sends the acquired voice data to a voice recognition means and converts it into text data. Specifically, it uses the Google Speech-to-Text API to analyze the voice data and convert it into text data. The input is digital voice data, and the output is text data.

[1735] Step 3:

[1736] The server analyzes the converted text data and detects emergency broadcasts. Specifically, it searches for keywords such as "Earthquake Early Warning" and "Tsunami Warning" to determine whether or not an emergency broadcast is occurring. The input is text data, and the server analyzes its contents to determine whether or not an emergency broadcast is occurring.

[1737] Step 4:

[1738] The server collects the user's voice and facial expression data and inputs it into the emotion recognition means to recognize the user's emotions. Specifically, the emotion engine uses the voice and facial expression data sent from the user terminal to analyze the user's emotional state (anxiety, fear, calmness, etc.). The input is the user's voice and facial expression data, and the output is the recognized emotional state.

[1739] Step 5:

[1740] When an emergency broadcast is detected, the server generates notification data based on the user's emotional state. For example, if the user is feeling anxious, the server generates notification data that includes supplemental information such as "Please remain calm and act calmly." The input is the content of the emergency broadcast and the user's emotional state, and the output is the adjusted notification data.

[1741] Step 6:

[1742] The server sends the generated notification data to the user's device via push notification or SMS. Specifically, it sends notifications to the user's device in real time through a notification sending system. The input is the notification data, and the output is the sent notification.

[1743] Step 7:

[1744] The device receives notification data sent from the server. Specifically, it receives push notifications and SMS using a dedicated application or notification listener. The input is the notification data sent from the server, and the output is the received notification.

[1745] Step 8:

[1746] The device displays the notification data to the user visually or audibly, for example, by using a pop-up message, vibration, or audio alarm. The input is the received notification data, and the output is the displayed notification content.

[1747] Step 9:

[1748] The user checks the notification displayed on the device and takes the necessary action. Specifically, the user follows the instructions in the notification and takes action, such as evacuating to a safe place. The input is the displayed notification content, and the output is the appropriate response action.

[1749] 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.

[1750] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. 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.

[1751] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1752] 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.

[1753] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1754] 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.

[1755] 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).

[1756] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1757] 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."

[1758] 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.

[1759] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1760] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1761] 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.

[1762] 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.

[1763] 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.

[1764] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1765] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1766] 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.

[1767] 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.

[1768] 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.

[1769] 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.

[1770] The following is further disclosed regarding the above embodiment.

[1771] (Claim 1)

[1772] means for acquiring audio data;

[1773] a speech recognition means for converting the acquired speech data into text data;

[1774] means for detecting an emergency broadcast from the converted text data;

[1775] means for generating notification data when an emergency broadcast is detected;

[1776] means for transmitting the generated notification data to a user terminal;

[1777] means for displaying the transmitted notification data on a user's terminal;

[1778] A system including:

[1779] (Claim 2)

[1780] 2. The system according to claim 1, wherein the means for detecting an emergency broadcast from the converted text data detects the emergency broadcast by analyzing keywords related to the emergency broadcast.

[1781] (Claim 3)

[1782] 2. The system according to claim 1, wherein the means for transmitting the generated notification data to the user's terminal transmits the notification by push notification or SMS.

[1783] "Example 1"

[1784] (Claim 1)

[1785] means for acquiring audio data;

[1786] a speech recognition means for converting the acquired speech data into text data;

[1787] means for detecting an emergency broadcast from the converted text data;

[1788] means for generating notification data when an emergency broadcast is detected;

[1789] means for transmitting the generated notification data to a user terminal;

[1790] means for displaying the transmitted notification data on a user's terminal;

[1791] A means for storing the acquired audio data in a certain buffer;

[1792] A means for passing voice data to a voice recognition engine in real time;

[1793] A means for determining keywords based on the converted text data and judging whether the broadcast is an emergency broadcast;

[1794] A means for sending the generated notification data via push notification or SMS;

[1795] means for displaying notification data on a user's device and generating vibration and / or audio alerts;

[1796] A system including:

[1797] (Claim 2)

[1798] 2. The system according to claim 1, wherein the means for detecting an emergency broadcast from the converted text data detects the emergency broadcast by analyzing keywords related to the emergency broadcast.

[1799] (Claim 3)

[1800] 2. The system according to claim 1, wherein the means for transmitting the generated notification data to the user's terminal transmits the notification by push notification or SMS.

[1801] "Application Example 1"

[1802] (Claim 1)

[1803] means for acquiring audio data;

[1804] a speech recognition means for converting the acquired speech data into text data;

[1805] means for detecting an emergency broadcast from the converted text data;

[1806] means for generating notification data when an emergency broadcast is detected;

[1807] means for transmitting the generated notification data to a user terminal;

[1808] means for displaying the transmitted notification data on a user's terminal;

[1809] A system including a means for sending emergency notifications to workers engaged in food delivery operations.

[1810] (Claim 2)

[1811] 2. The system according to claim 1, wherein the means for detecting an emergency broadcast from the converted text data detects the emergency broadcast by analyzing keywords related to the emergency broadcast.

[1812] (Claim 3)

[1813] 2. The system according to claim 1, wherein the means for transmitting the generated notification data to the user's terminal transmits the notification by push notification or SMS.

[1814] "Example 2: Combining Emotion Engines"

[1815] (Claim 1)

[1816] m...

Claims

1. means for acquiring audio data; a speech recognition means for converting the acquired speech data into text data; means for detecting an emergency broadcast from the converted text data; means for generating notification data when an emergency broadcast is detected; means for transmitting the generated notification data to a user terminal; means for displaying the transmitted notification data on a user's terminal; A system including:

2. 2. The system according to claim 1, wherein the means for detecting an emergency broadcast from the converted text data detects the emergency broadcast by analyzing keywords related to the emergency broadcast.

3. The system according to claim 1, characterized in that the means for transmitting the generated notification data to the user's terminal transmits the notification by push notification or SMS.

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