system

Abstract

Provide a system. A means for receiving voice data from a user terminal; means for analyzing the audio data to identify a siren sound; means for calculating the direction and distance of the siren sound; means for generating a warning message based on the siren sound information; means for transmitting the warning message to a user terminal; means for transferring a warning message from the user terminal to a display device; means for displaying the warning message on the display device; A system including:

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] Currently, automobile driving systems provide limited means for hearing-impaired people to detect approaching emergency vehicles on the road. This can lead to hearing-impaired people being unable to respond appropriately to emergency vehicles, making safe driving difficult. Conventional systems rely solely on visual information, making it difficult to quickly and accurately provide information about the direction and distance of approaching emergency vehicles. A system that can resolve these issues and enhance driving assistance for hearing-impaired people is needed. [Means for solving the problem]

[0005] To address the above-mentioned challenges, the present invention provides a system for receiving audio data from a user terminal and analyzing the audio data to identify a siren sound. The system calculates the direction and distance of the siren sound and generates a warning message based on the calculated direction and distance. The generated warning message is then sent to the user terminal and forwarded to a display device. The display device visually displays the warning message and, in some cases, can also issue an audio or visual alert. This allows even hearing-impaired individuals to recognize the approach of an emergency vehicle and take appropriate action. In particular, audio analysis using a machine learning model enables highly accurate identification of emergency vehicle sirens and provides real-time warnings.

[0006] A "user terminal" is an electronic device that has the capability to communicate with a server, send voice data and receive alert messages.

[0007] "Audio data" is information that represents audio signals including environmental sounds in a digital format.

[0008] "Analysis" is the process of processing received audio data and identifying specific sounds within it.

[0009] A "siren sound" is a warning sound made by emergency vehicles such as ambulances and police vehicles.

[0010] "Direction" refers to the direction from which the sound emitted by the sound source originates.

[0011] "Distance" refers to the physical length from the user terminal or server to the sound source.

[0012] A "warning message" is a text notification that includes information about the siren sound and alerts the user to the approach of an emergency vehicle.

[0013] A "display device" is an electronic device that includes a display for providing visual information to a user.

[0014] A "machine learning model" is software that implements algorithms to learn from large amounts of data and recognize specific patterns and features. [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 relates to a system that receives audio data from a user terminal, analyzes the audio data to identify siren sounds, and generates a warning message based on that information and displays it on a display device.

[0037] server

[0038] The server has the function of receiving real-time voice data sent from the user terminal. The voice data is uploaded to the server using a specific API. The server inputs the received voice data into a voice analysis engine. This engine uses a machine learning model to identify siren sounds and further calculate the direction and distance of the sound source.

[0039] Specifically, the server analyzes the waveforms in the audio data and determines whether a specific sound wave pattern matches the siren of an ambulance or police vehicle. If there is a match, it calculates the direction and distance of the sound source and generates a warning message based on this information. This warning message takes the form of something like "An ambulance is approaching 30 meters behind you to the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is immediately sent to the user's device.

[0040] User terminal

[0041] The user terminal receives the warning message sent from the server. The terminal has the function of transferring the warning message to the vehicle's display device (display audio) via Apple CarPlay (registered trademark) or ANDROID (registered trademark) Auto. The warning message displayed on the terminal is designed to be easily recognizable by the driver.

[0042] For example, when the user device receives a warning message, it immediately transfers the message to the car's display audio via the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so that the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed.

[0043] User

[0044] Before starting to drive, users need to connect their smartphone to the display audio and start Apple CarPlay or Android Auto. While driving, the user's device detects the sound of a siren and generates a warning message to display on the display device. This allows even hearing-impaired drivers to recognize the approach of an emergency vehicle and take appropriate action.

[0045] As a specific example of a driving scenario, if a user is driving and an ambulance approaches 30 meters behind them to the right, a warning message stating "Ambulance approaching 30 meters behind them to the right" will be displayed on the user's display audio. If a message is also displayed stating "There is room to move out of the way about 1 meter to the left," the user can move the car to the left and safely give way to the ambulance.

[0046] In this way, the present invention is a system that realizes safe driving assistance for hearing-impaired people by identifying siren sounds, locating the sound source, and generating and displaying warning messages based on the identified sounds.

[0047] The processing flow will be explained below.

[0048] server

[0049] Step 1: Receiving audio data

[0050] The server receives real-time voice data from the user terminal, which is uploaded via a specific API, which is transmitted through a constantly connected real-time communication channel.

[0051] Step 2: Audio analysis

[0052] The server then inputs the received audio data into a voice analysis engine for prioritized analysis, which uses machine learning models to analyze the audio data and identify the specific siren sound.

[0053] Step 3: Calculate the direction and distance of the sound source

[0054] If a siren sound is identified, the server runs an algorithm to calculate the direction and distance of the sound source, based on data such as the time difference between the arrival of the sound waves.

[0055] Step 4: Generate a warning message

[0056] The server generates a warning message based on the calculation results. The warning message is generated as text in the form of, for example, "An ambulance is approaching 30 meters behind you on the right" or "There is room to evacuate about 1 meter to the left."

[0057] Step 5: Sending a warning message

[0058] The server sends the generated warning message to the user terminal in real time, also using a specific API.

[0059] User terminal

[0060] Step 1: Receiving the warning message

[0061] The user terminal receives the alert message sent from the server via a real-time communication channel.

[0062] Step 2: Transfer to Display Audio

[0063] The user terminal transfers the received warning message to the car's display audio via Apple CarPlay or Android Auto.

[0064] Display Audio

[0065] Step 1: Message display

[0066] Display Audio receives warning messages sent from the terminal and immediately displays them on the screen in large letters that are easy for the driver to see.

[0067] User

[0068] Step 1: System Setup

[0069] When the user gets into the car, they connect their smartphone to the display audio and start Apple CarPlay or Android Auto.

[0070] Step 2: Check for warnings

[0071] While driving, the user checks the warning message displayed on the display audio, which describes the direction of the approaching emergency vehicle and the recommended evacuation action.

[0072] Step 3: Safe Operation

[0073] The user takes appropriate safety measures based on the displayed warning message. For example, if the message "An ambulance is approaching 30 meters behind and to the right" or "There is room to evacuate about 1 meter to the left" is displayed, the user will take measures such as moving the car to the left.

[0074] Example 1

[0075] 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."

[0076] Conventional driver assistance systems have made it difficult for hearing-impaired people to recognize approaching emergency vehicles, resulting in impaired driving safety. In particular, if they cannot hear the siren, it is difficult for them to take appropriate action, which can prevent them from quickly changing course or evacuating, increasing the risk of an accident.

[0077] 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.

[0078] In this invention, the server includes means for receiving voice data from a user terminal, means for analyzing the voice data to identify a siren sound, means for calculating the direction and distance of the siren sound, means for generating a warning message based on information about the siren sound, means for transmitting the warning message to the user terminal, means for transferring the warning message from the user terminal to a display device, means for displaying the warning message on the display device, and means for visually displaying the warning message so that the driver can easily recognize it, thereby enabling hearing-impaired people to reliably recognize an approaching emergency vehicle and safely evacuate.

[0079] A "user terminal" is an electronic device that has the function of collecting voice data and transmitting it to a server.

[0080] "Audio data" refers to data that records surrounding sounds as digital signals.

[0081] The "server" refers to a computer system that analyzes voice data received from a user terminal, generates a warning message, and transmits the message to the user terminal.

[0082] A "voice analysis engine" is software or algorithms that analyze received voice data and identify siren sounds.

[0083] A "machine learning model" is an algorithm that is optimized to perform a specific task (in this case, identifying siren sounds) using large amounts of training data.

[0084] A "warning message" is an information message that is generated based on the detection of a siren sound and its direction and distance, and that notifies the user of danger.

[0085] A "display device" refers to a device for visually displaying a warning message, such as a user terminal or a car display audio.

[0086] A "visual alert" is a visual signal or message that is displayed on a display device to convey information or a warning to a user.

[0087] The present invention relates to a system that receives audio data from a user terminal, analyzes the audio data to identify siren sounds, and generates a warning message based on that information and displays it on a display device.

[0088] server

[0089] The server has the function of receiving real-time voice data sent from the user terminal. The voice data is uploaded to the server using a specific API. Specifically, a WebSocket API or a RESTful API can be used. The server is equipped with a voice analysis engine for analyzing the received voice data. This voice analysis engine is a model built using a machine learning framework such as TENSORFLOW (registered trademark) or PyTorch.

[0090] The server analyzes the waveforms in the audio data and determines whether a particular sound wave pattern matches the siren of an ambulance or police vehicle. Once a siren is identified, it then uses a Python algorithm to calculate the direction and distance of the sound source, using techniques such as Time Difference of Arrival (TDOA). Based on the results of this calculation, the server generates a warning message. This warning message is generated in the form of, for example, "An ambulance is approaching 30 meters behind you to the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is sent to the user's device in real time.

[0091] User terminal

[0092] The user terminal has the function of receiving the warning message sent from the server, and then forwards this message to the vehicle's display device (display audio) via Apple CarPlay or Android Auto. The warning message is visually displayed in large letters so that the driver can easily recognize it.

[0093] For example, when a user device receives a warning message, it immediately transmits the message to the car's display audio via the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so that the driver can see it at a glance. For example, if the message "Ambulance approaching 30 meters behind to the right" is displayed, the driver can immediately recognize the information and take action.

[0094] User

[0095] Before starting to drive, users must connect their smartphone to the Display Audio and activate Apple CarPlay or Android Auto. While driving, the system identifies siren sounds and generates and displays a warning message, allowing even hearing-impaired drivers to recognize approaching emergency vehicles and respond appropriately.

[0096] As a specific example of a driving scenario, if a user is driving and an ambulance approaches 30 meters behind them to the right, the user's display audio will display "Ambulance approaching 30 meters behind them to the right." If it also displays "Room for evacuation approximately 1 meter to the left," the user can move the car to the left and safely give way to the ambulance.

[0097] Prompt Sentence Examples

[0098] Imagine a situation where a user is driving and hears the sound of an ambulance siren 30 meters behind them to the right. In this case, the server inputs the received audio data into the TensorFlow audio analysis engine, identifies the siren sound, calculates the direction and distance of the sound source, generates a warning message and sends it to the user's device. The user's device then transmits the warning message to the car's display audio via Apple CarPlay, and the display device displays something like "Ambulance approaching 30 meters behind the right." Please explain the specific processing steps.

[0099] In this way, the present invention is a system that helps hearing-impaired people drive safely by identifying the siren sound, locating the sound source, and generating and displaying a warning message based on that information.

[0100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0101] Step 1:

[0102] The user gets into the car and connects the smartphone to the display audio.

[0103] Input: Smartphone, Display Audio

[0104] How it works: Connect your smartphone to the Display Audio using a USB cable and launch Apple CarPlay or Android Auto.

[0105] Output: Smartphone and Display Audio are connected

[0106] Step 2:

[0107] The user terminal captures the surrounding sounds in real time and transmits the sound data to the server.

[0108] Input: Ambient audio

[0109] How it works: It uses a smartphone's microphone to capture audio in real time, and then streams the captured audio data to a server via a WebSocket API.

[0110] Output: Audio data sent to the server

[0111] Step 3:

[0112] The server analyzes the received voice data using a machine learning model.

[0113] Input: Audio data sent to the server

[0114] How it works: The received audio data is input into the audio analysis engine, and the presence or absence of a siren is analyzed using a machine learning model using TensorFlow or PyTorch.

[0115] Output: Analysis result regarding the presence or absence of siren sound

[0116] Step 4:

[0117] The server identifies the siren sound and calculates the direction and distance of the sound source.

[0118] Input: Analysis result regarding the presence or absence of siren sound

[0119] How it works: If a siren sound is identified, a Python algorithm is used to calculate the direction and distance of the sound source using the TDOA (Time Difference of Arrival) method.

[0120] Output: Information about the direction and distance of the sound source

[0121] Step 5:

[0122] The server generates a warning message.

[0123] Input: Information about the direction and distance of the sound source

[0124] What it does: Based on the calculated direction and distance information, it creates a warning message such as "Ambulance approaching 30 meters behind and to the right." It also adds information about room to retreat if necessary.

[0125] Output: Generated warning message

[0126] Step 6:

[0127] The server sends a warning message to the user terminal.

[0128] Input: The generated warning message

[0129] Operation: Generated warning messages are sent in real time to the user's terminal via HTTP or WebSocket.

[0130] Output: The warning message delivered to the user's terminal.

[0131] Step 7:

[0132] The user terminal receives the alert message and forwards it to the display audio.

[0133] Input: Warning message delivered to the user's device

[0134] What it does: Transmits warning messages to the car's display audio via Apple CarPlay or Android Auto.

[0135] Output: Warning messages forwarded to Display Audio

[0136] Step 8:

[0137] The Display Audio will visually display a warning message.

[0138] Input: Warning message forwarded to Display Audio

[0139] What it does: A warning message is displayed on the screen in large letters so that the driver can see it at a glance. For example, it may say, "Ambulance approaching 30 meters behind and to the right."

[0140] Output: Warning message presented to the driver

[0141] (Application example 1)

[0142] 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."

[0143] In autonomous vehicles, there is a lack of support for drivers to quickly and reliably recognize approaching emergency vehicles and take appropriate driving action. Therefore, there is a need for a system that can accurately recognize approaching emergency vehicles and safely give way to them, especially for people with hearing impairments or who have difficulty distinguishing environmental sounds.

[0144] 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.

[0145] In this invention, the server includes means for receiving voice data from the user terminal, means for analyzing the voice data to identify a siren sound, means for calculating the direction and distance of the siren sound, means for generating a warning message based on information about the siren sound, means for transmitting the warning message to the user terminal, means for transferring the warning message from the user terminal to a display device, means for displaying the warning message on the display device, means for transmitting the voice data from the user terminal to a control system installed in the vehicle and notifying the vehicle control system of an approaching emergency vehicle, and means for the control system to perform an appropriate driving operation based on the warning message. This enables the autonomously driven vehicle to accurately recognize an approaching emergency vehicle and safely give way to it.

[0146] A "user terminal" is a device that collects voice data and transmits it to a server.

[0147] "Audio data" is a recording of sound collected from a user terminal.

[0148] A "siren" is a specific sound made by emergency vehicles.

[0149] The "warning message" is the content of a warning generated based on the direction and distance of the siren sound.

[0150] A "display device" is a device that visually or audibly conveys a warning message to a user.

[0151] A "control system" is a computer system installed in a vehicle that controls automatic driving.

[0152] A "machine learning model" is an algorithm trained to identify siren sounds based on large amounts of data.

[0153] An "autonomous vehicle" is a vehicle that navigates autonomously to a destination without the intervention of a human driver.

[0154] A system for implementing the present invention is configured as a driver assistance application for an autonomous vehicle, and includes a user terminal, a server, a control system, and a display device.

[0155] server

[0156] The server has the function of receiving real-time voice data sent from the user terminal. The voice data is uploaded to the server using a specific API. The server inputs the received voice data into a voice analysis engine. This engine uses a machine learning model to identify siren sounds and further calculate the direction and distance of the sound source. This machine learning model is an algorithm trained on a large amount of data.

[0157] Specifically, the server analyzes the waveforms in the audio data to determine whether a particular sound wave pattern matches the sound of an ambulance or police siren. If there is a match, it calculates the direction and distance of the sound source and uses this information to generate a warning message. This warning message is then transmitted to the car's control system, alerting the driver of the approaching emergency vehicle.

[0158] User terminal

[0159] The user terminal receives the warning message sent from the server. The terminal has a communication function for transmitting voice data and warning messages to the vehicle's control system. Furthermore, the terminal can also transfer warning messages to the vehicle's display device via Apple CarPlay or Android Auto.

[0160] For example, when the user device receives a warning message, it immediately transfers the message to the vehicle's display device via the Apple CarPlay or Android Auto application. The display device then displays the received message in large letters on the screen so that the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed.

[0161] Control Systems and Displays

[0162] The control system is a computer system installed in a vehicle that controls autonomous driving. The control system executes appropriate driving actions based on warning messages transmitted from a user terminal. For example, if the control system detects an approaching emergency vehicle, it can reduce the vehicle's speed and execute appropriate evacuation actions.

[0163] The display device is a display installed inside the autonomous vehicle that displays the warning message transferred from the user terminal, allowing the driver to visually confirm the approach of an emergency vehicle.

[0164] Adding specific examples

[0165] As a concrete example, when an autonomous vehicle is driving on a highway, this system monitors the surrounding environmental sounds. For example, if an ambulance is approaching from 150 meters behind the vehicle, the user device will identify the siren and immediately send the audio data to the server. The server will analyze the audio data, generate a warning message, and send it to the user device and the vehicle's control system. The control system will then take appropriate driving action based on the warning message, "An ambulance is approaching 150 meters behind. Please give way." The same message will also be displayed on the display device, allowing the driver to check the situation.

[0166] Example prompts for generative AI models

[0167] Examples of prompts that use generative AI models include the following format:

[0168] Analyze surrounding audio data and identify siren sounds. Calculate the direction and distance of the siren sound and generate a warning message in the following format:

[0169] "Emergency vehicle approaching Y meters in X direction"

[0170] Example: Audio data: "WAV format audio data"

[0171] Using this prompt sentence, the siren sound can be accurately distinguished from the ambient sound and a warning message can be generated based on the appropriate distance and direction.

[0172] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0173] Step 1:

[0174] The user terminal uses a built-in microphone to collect surrounding audio data in real time, which is recorded at a fixed sampling rate and continuously transmitted to the server.

[0175] Step 2:

[0176] The server receives the voice data sent from the user terminal. The voice data received by the server is uploaded through a predefined API. The input data is saved in a buffer.

[0177] Step 3:

[0178] The server inputs the received audio data into a voice analysis engine, which uses a generative AI model to analyze the audio data. Specifically, it analyzes the waveform of the input data and determines whether a particular sound wave pattern matches the sound of a siren.

[0179] Step 4:

[0180] The server calculates the direction and distance of the siren sound based on the output of the sound analysis engine. This calculation is performed using the difference in sound intensity and arrival time. The input data is waveform data containing the characteristics of the siren sound, and the output data is information on direction and distance.

[0181] Step 5:

[0182] The server generates a warning message based on the siren sound identification result and the direction and distance information. The warning message format is "Emergency vehicle approaching in X direction, Y meters." The input data is the direction and distance information, and the output data is the generated warning message.

[0183] Step 6:

[0184] The server sends the generated warning message to the user terminal, and the sent warning message is received through the communication module of the user terminal. The input data is the warning message, and the output data is a notification to the user terminal.

[0185] Step 7:

[0186] The user terminal transmits the received warning message to the vehicle's control system, which analyzes the message and takes appropriate driving action, such as reducing speed and giving way.

[0187] Step 8:

[0188] At the same time, the user device transmits a warning message to the vehicle's display device via Apple CarPlay or Android Auto. The display device then displays the warning message on the screen so that the driver can visually confirm it. Specifically, the message is displayed in large letters to alert the driver.

[0189] 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.

[0190] The present invention combines a system that receives voice data from a user terminal, analyzes the voice data to identify siren sounds, generates a warning message based on that information, and displays it on a display device with an emotion engine that recognizes the user's emotions.

[0191] server

[0192] The server has the function of receiving real-time voice data transmitted from the user terminal. The voice data is uploaded to the server using a specific API, which is transmitted through a constantly connected real-time communication channel. The server inputs the received voice data into a voice analysis engine. The engine analyzes the voice data using a machine learning model and identifies the specific siren sound.

[0193] Specifically, the server analyzes the waveforms in the audio data and determines whether a specific sound wave pattern matches the siren of an ambulance or police vehicle. If there is a match, it calculates the direction and distance of the sound source and generates a warning message based on this information. This warning message is generated as text in a format such as "An ambulance is approaching 30 meters behind you to the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is immediately sent to the user's device.

[0194] Emotion Engine

[0195] The emotion engine recognizes the user's emotional state and performs analysis using biometric data acquired through sensors on the user's device, including heart rate, galvanic skin response, and facial expressions, to assess the user's stress level and other emotional states.

[0196] Specifically, the emotion engine analyzes biometric information acquired in real time to determine whether the user is in a specific emotional state. For example, if a high stress level is detected, the server can generate an additional comforting message for the user and send it along with the warning message. This comforting message can be displayed in a format such as "Please drive safely. Don't rush."

[0197] User terminal

[0198] The user terminal receives the warning message and the comfort message sent from the server. The terminal has the function of transmitting the warning message and the comfort message to the display device (display audio) of the vehicle via Apple CarPlay or Android Auto. The warning message displayed on the terminal is designed to be easily recognizable by the driver.

[0199] For example, when the user device receives a warning message, it immediately transfers the message to the car's display audio via the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so that the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed, and if the user's stress level is high, a comforting message such as "Please drive safely. Don't rush."

[0200] User

[0201] Before starting to drive, users must connect their smartphone to the display audio and activate Apple CarPlay or Android Auto. While driving, the user's device detects the sound of a siren and generates a warning message to display on the display device. By incorporating an emotion engine into this process, additional messages are also displayed according to the user's emotional state. This allows even hearing-impaired drivers to recognize the approach of an emergency vehicle and take appropriate action.

[0202] In this way, the present invention is a system that provides safe driving assistance to hearing-impaired people by identifying siren sounds, locating the sound source, generating and displaying warning messages based on that information, and also taking into account the user's emotional state and providing additional messages as needed.

[0203] The processing flow will be explained below.

[0204] server

[0205] Step 1: Receiving audio data

[0206] The server receives real-time voice data from user devices, which are uploaded via a specific API, which is then passed through a constantly connected real-time communication channel.

[0207] Step 2: Audio analysis

[0208] The server then inputs the received audio data into a voice analysis engine for prioritized analysis, which uses machine learning models to analyze the audio data and identify specific siren sounds.

[0209] Step 3: Calculate the direction and distance of the sound source

[0210] If the server identifies a siren, it runs an algorithm that calculates the direction and distance of the sound source, based on data such as the time difference between the arrival of sound waves.

[0211] Step 4: Emotion Engine Analysis

[0212] The server's emotion engine receives real-time biometric data from the user's device and analyzes the user's emotional state, including heart rate, galvanic skin response, and facial expressions.

[0213] Step 5: Generate a warning message

[0214] The server generates a warning message based on the calculation results. The warning message is generated as text in the form of, for example, "An ambulance is approaching 30 meters behind you on the right" or "There is room to evacuate about 1 meter to the left." If the server determines that the user is in a high stress state, it also generates an additional comforting message, such as, "Please drive safely. Don't rush."

[0215] Step 6: Sending a warning message

[0216] The server then sends the generated warning messages to the user's device in real time, using a specific API.

[0217] User terminal

[0218] Step 1: Receiving the warning message

[0219] The user terminal receives the alert message sent from the server via a real-time communication channel.

[0220] Step 2: Transfer to Display Audio

[0221] The user device will then transfer the received warning and comfort messages to the car's display audio via Apple CarPlay or Android Auto.

[0222] Display Audio

[0223] Step 1: Message display

[0224] The Display Audio receives warning and comfort messages sent from the terminal and instantly displays them on the screen in large letters that are easy for the driver to see.

[0225] User

[0226] Step 1: System Setup

[0227] When users get into their car, they need to connect their smartphone to the display audio and launch Apple CarPlay or Android Auto.

[0228] Step 2: Check for warnings

[0229] While driving, users can check warning and comforting messages displayed on the display audio, including information on the direction of approaching emergency vehicles, recommended evacuation actions, and messages based on the user's emotional state.

[0230] Step 3: Safe Operation

[0231] The user is expected to take appropriate safety measures based on the displayed warning and comforting messages. For example, if the message "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" is displayed, along with a comforting message such as "Please drive safely. Don't rush," the user is expected to take appropriate measures such as pulling over to the left.

[0232] Example 2

[0233] 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."

[0234] It is difficult for hearing-impaired drivers to properly recognize approaching emergency vehicles while driving, and the impact of the driver's emotional state on safe driving cannot be ignored. In such situations, a system is needed that allows drivers to recognize warnings in real time and receive appropriate comfort messages according to their emotional state. This is also expected to improve driver safety.

[0235] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving voice data from a user terminal, means for analyzing the voice data to identify a specific warning sound, means for calculating the direction and distance of the warning sound, means for generating a warning message based on information about the warning sound, means for acquiring biometric information from the user, means for analyzing the biometric information to identify an emotional state, means for generating a comforting message based on the emotional state, means for transmitting the warning message and the comforting message to the user terminal, means for transferring the message from the user terminal to a display device, and means for displaying the warning message and the comforting message on the display device. This allows drivers, including those who are hearing impaired, to recognize the approach of an emergency vehicle in real time while driving and to receive appropriate messages according to their emotional state.

[0236] A "user terminal" is a device for capturing and transmitting audio data to a server.

[0237] "Audio data" refers to data that includes ambient audio information captured by a user terminal.

[0238] "Specific warning sounds" refer to the sirens of emergency vehicles such as ambulances and police cars.

[0239] The "direction and distance of the warning sound" is information indicating the position where a particular warning sound is being generated and the distance to that position.

[0240] A "warning message" is warning information for a user that is generated based on the direction and distance of a specific warning sound.

[0241] "Biometric information" refers to physiological data such as the user's heart rate, galvanic skin response, and facial expressions.

[0242] The "emotional state" is the user's emotional state (for example, stress level) obtained by analyzing biometric information.

[0243] A "comforting message" is a message generated according to the user's emotional state to reassure the user.

[0244] A "display device" is a device that is installed in a user terminal or in a vehicle and that displays warning messages and comforting messages.

[0245] "Server" refers to a computer system that receives voice data and biometric information sent from a user terminal and generates and transmits warning messages and comfort messages based on the received data.

[0246] The present invention provides a system that receives voice data from a user terminal, analyzes the voice data, and identifies specific warning sounds based on the received voice data. The system then uploads the voice data to a server via a real-time communication channel using a specific API, analyzes the voice data using a machine learning model, and generates a warning message. The system also collects biometric information from the user, evaluates their emotional state based on the biometric information, and generates a comforting message as needed. Specific embodiments are described in detail below.

[0247] server

[0248] The server has the function of receiving real-time voice data sent from the user terminal. A specific WebSocket API is used for this reception, and the voice data is sent to the server through a real-time communication channel. The received voice data is temporarily stored in a buffer in memory.

[0249] The server then inputs the received audio data into a speech analysis engine, which uses a generative AI model such as TensorFlow to analyze the audio data and identify specific warning sounds (such as ambulance or police sirens). The features of the identified warning sounds are extracted by converting the waveform data into a spectrogram.

[0250] The direction and distance calculation function calculates the direction and distance of the identified warning sound, and based on this, the server generates a warning message such as "Ambulance approaching 30 meters behind to the right" and saves it in text format.

[0251] Furthermore, the emotion engine receives data such as heart rate, galvanic skin response, and facial expressions from the user's sensors using biometric information reception. This data is used by machine learning models using Emotion API and Azure® Cognitive Services to evaluate the user's emotional state. If the emotional state is stressful, an additional comforting message such as "Drive safely. Don't rush" is generated.

[0252] User terminal

[0253] The user device receives warning and comfort messages from the server and transmits them to the car's display device via Apple CarPlay or Android Auto. The received messages are displayed in large letters on the display audio so that the driver can see them at a glance.

[0254] User

[0255] Before starting to drive, users need to connect their smartphone to the car's display audio and activate Apple CarPlay or Android Auto. While driving, the user's device captures surrounding sounds and transmits them to the server in real time.

[0256] Example

[0257] For example, if a user connects their smartphone to Apple CarPlay before starting to drive and the device detects a warning sound from the rear right while driving, the server will generate a warning message saying, "An ambulance is approaching 30 meters behind the right." If the emotion engine detects a high stress level in the user, it will generate an additional comforting message saying, "Please drive safely. Don't rush." ​​These messages are displayed on the display audio via the user's device.

[0258] An example prompt for a generative AI model might look something like this:

[0259] "An ambulance is approaching 30 meters behind and to the right. The user's stress level is high, so please drive safely. Do not rush."

[0260] In this way, this system supports safe driving by recognizing approaching emergency vehicles in real time while hearing-impaired drivers are driving and providing appropriate messages according to their emotional state.

[0261] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0262] Step 1:

[0263] The server receives real-time audio data from the user device. The device captures surrounding audio data using a microphone and sends the data to the server using the WebSocket API. This audio data is stored as a buffer in the server's memory. The input is the audio data from the user device, and the output is the audio data stored in the buffer.

[0264] Step 2:

[0265] The server inputs the audio data stored in the buffer into a speech analysis engine. This analysis engine uses generative AI models such as TensorFlow to analyze the audio data. Specifically, it converts the waveform data into a spectrogram and extracts characteristic patterns of specific warning sounds (e.g., ambulance or police sirens). The input is the stored audio data, and the output is the result of the warning sound identification.

[0266] Step 3:

[0267] The server calculates the direction and distance of the identified warning sound based on the result of the warning sound identification. To do this, an algorithm is used to determine the direction of the sound source and calculate the distance based on the result of the voice analysis engine. The input is the identification result of the voice analysis engine, and the output is information on the direction and distance.

[0268] Step 4:

[0269] The server generates a warning message based on the direction and distance information. For example, it generates a text message in the format "An ambulance is approaching 30 meters behind and to the right." This message is temporarily stored in memory. The input is the direction and distance information, and the output is the text data of the warning message.

[0270] Step 5:

[0271] The emotion engine receives biometric data (heart rate, galvanic skin response, facial expressions, etc.) acquired from sensors on the user device. These data are captured in real time by the user device and sent to the server. The input is the biometric data from the user device, and the output is the received biometric data.

[0272] Step 6:

[0273] The emotion engine analyzes the user's emotional state based on biometric data. For analysis, it uses machine learning models such as the Emotion API and Azure Cognitive Services. For example, if a high stress level is detected, this information is stored in memory. The input is biometric data, and the output is a judgment of the user's emotional state.

[0274] Step 7:

[0275] The server generates the necessary comforting message based on the result of the emotional state judgment. For example, it generates a message in the format of "Please drive safely. Don't rush." ​​This message is also stored in memory in text format. The input is the result of the emotional state judgment, and the output is the text data of the comforting message.

[0276] Step 8:

[0277] The server transmits the generated warning and comfort messages to the user terminal using a real-time communication channel to a notification system or dedicated application of the user terminal, with the input being the text data of the warning and comfort messages and the output being the transmitted messages.

[0278] Step 9:

[0279] The user terminal transfers the received message to the car's display device via Apple CarPlay or Android Auto. The terminal formats the received message for display on the display audio and displays it in large letters on the screen. The input is the message from the server, and the output is the message displayed on the display device.

[0280] (Application example 2)

[0281] 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."

[0282] Conventional driving assistance systems detect siren sounds and generate warning messages, but they lack measures that take into account the driver's emotional state, making it difficult for drivers to respond appropriately when under high stress. Hearing-impaired drivers also have limited means of properly recognizing approaching emergency vehicles.

[0283] The identification processing 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 receiving voice data from a user terminal, means for analyzing the voice data to identify a siren sound, means for calculating the direction and distance of the siren sound, means for generating a warning message based on information about the siren sound, means for transmitting the warning message to the user terminal, means for acquiring biometric information data of the user and recognizing an emotional state, means for generating an additional message according to the emotional state, means for transferring the warning message and the additional message from the user terminal to a display device, and means for displaying the warning message and the additional message on the display device. This enables appropriate responses even when the driver is in a high-stress state, and further enables safe driving assistance for hearing-impaired people.

[0284] "Audio data" refers to sound wave information acquired from a user terminal, including conversations, environmental sounds, siren sounds, and the like.

[0285] A "siren sound" is a special sound made by emergency vehicles such as ambulances, fire engines, and police cars, and has a specific sound wave pattern.

[0286] "Biometric data" refers to various data obtained from the user's body, including heart rate, galvanic skin response, facial expressions, and the like.

[0287] "Emotional state" indicates the user's current emotional state, and includes stress level, pleasure, discomfort, and the like.

[0288] A "warning message" is a notification message generated based on the detection result of a siren sound, and is used to notify the driver of an approaching emergency situation.

[0289] The "additional message" is a message of encouragement or comfort that is generated according to the user's emotional state, and is intended to reduce the mental burden on the driver.

[0290] "Display device" refers to a display device connected to a vehicle or a user terminal for displaying warning messages and additional messages.

[0291] A "machine learning model" is an algorithm or framework that learns from past data and analyzes voice data and identifies patterns.

[0292] The present invention combines a system that receives voice data from a user terminal, identifies siren sounds, generates a warning message based on that information, and displays it on a display device with an emotion engine that recognizes the user's emotions. The system program and its processing are described in detail below.

[0293] The server has the function of receiving real-time voice data transmitted from the user terminal. The voice data is uploaded to the server using a specific API, which is transmitted through a constantly connected real-time communication channel. The server inputs the received voice data into a voice analysis engine. The engine uses a machine learning model to analyze the voice data and identify the specific siren sound.

[0294] For example, the server analyzes the waveforms in the audio data and determines whether a specific sound wave pattern matches the siren of an ambulance or police vehicle. If there is a match, it calculates the direction and distance of the sound source and generates a warning message based on this information. This warning message is generated as text in a format such as "An ambulance is approaching 30 meters behind you on the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is immediately sent to the user's device.

[0295] The emotion engine recognizes the user's emotional state and performs analysis using biometric data acquired through sensors on the user's device. The biometric data includes heart rate, galvanic skin response, and facial expressions, and uses this data to assess the user's stress level and other emotional states. For example, if a high stress level is detected, the server can generate an additional comforting message for the user and send it along with the warning message. This comforting message could be displayed in a format such as "Drive safely. Don't rush."

[0296] The user device receives the warning and comfort messages sent from the server. The device is a typical smartphone or tablet, and has the function of transmitting messages to the vehicle's display device (display audio) via Apple CarPlay or Android Auto. The warning messages displayed on the device are designed to be easily recognizable by the driver.

[0297] Specifically, when the user device receives a warning message, it immediately transfers the message to the car's display audio using the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed, and if the user's stress level is high, a comforting message such as "Please drive safely. Don't rush."

[0298] A concrete example of a prompt sentence could be input to a generative AI model in the following form:

[0299] The user is driving an autonomous vehicle. This application uses the in-car microphone to collect audio data and analyzes it on a server to determine whether a siren is present. If a siren is detected, a warning message is generated and displayed on the in-car display. At the same time, biometric sensors collect the user's heart rate and skin potential, and an emotion engine evaluates the user's stress level. If the stress level is high, an additional comforting message is displayed. This system supports the user in driving safely.

[0300] In this way, the present invention provides a system that combines real-time voice data analysis and an emotion engine to support safe driving by the driver.

[0301] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0302] Step 1:

[0303] The user starts the car engine and connects the smartphone to the display audio. The device launches the Apple CarPlay or Android Auto application, which prepares the device for collecting voice data and displaying messages. The input is the connection status between the smartphone and the in-car display. The output is a notification that the system is ready to operate.

[0304] Step 2:

[0305] The device begins collecting real-time audio data using the in-car microphone. Specifically, the audio data is buffered for a short period of time and then transmitted to the server at regular intervals. The input includes ambient sounds and conversations inside the car. The output is a real-time audio data stream.

[0306] Step 3:

[0307] The server receives the audio data sent from the device. The received audio data is analyzed using a machine learning model to identify the specific siren sound. The input is the audio data. The output is the presence or absence of a siren sound, and if identified, the direction and distance information.

[0308] Step 4:

[0309] The server generates a warning message based on the direction and distance information of the siren sound. Specifically, it analyzes the direction and distance of the sound source and creates a warning message such as "An ambulance is approaching 30 meters behind you to the right." The input is the direction and distance information of the siren sound. The output is the generated warning message.

[0310] Step 5:

[0311] The device acquires the user's biometric data. Specifically, it uses a heart rate sensor, a skin electrodermal response sensor, a facial recognition camera, etc. to collect the user's heart rate, skin electrodermal response, and facial expression data. The input is the user's biometric information. The output is real-time biometric data.

[0312] Step 6:

[0313] The server uses an emotion engine to analyze the biometric data and evaluate the user's emotional state. Specifically, it uses the collected biometric data to determine the user's emotional state, such as stress level and pleasantness / unpleasantness. The input is the biometric data, and the output is the evaluated emotional state.

[0314] Step 7:

[0315] The server generates an additional comforting message according to the user's emotional state. Specifically, if the user's stress level is high, the server generates a comforting message such as "Please drive safely. Don't rush." ​​The input is the user's emotional state. The output is the additional message.

[0316] Step 8:

[0317] The server sends the warning message and the additional message to the user terminal. The input is the generated warning message and the additional message. The output is the completion of message transmission to the user terminal.

[0318] Step 9:

[0319] The terminal transfers the received warning messages and additional messages to the in-car display via the Apple CarPlay or Android Auto application. The input is the message from the server. The output is the completion of transfer to the in-car display.

[0320] Step 10:

[0321] The display device displays warning messages and additional messages on the screen. Specifically, the messages are displayed in large letters so that the driver can see them at a glance. The input is a message transferred from the terminal. The output is the message displayed on the screen.

[0322] By the above steps, the present invention allows drivers to respond appropriately even in high-stress situations, and enables hearing-impaired people to drive safely.

[0323] 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.

[0324] 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.

[0325] 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.

[0326] [Second embodiment]

[0327] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0328] 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.

[0329] 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).

[0330] 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.

[0331] 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.

[0332] 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).

[0333] 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.

[0334] 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.

[0335] 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.

[0336] 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.

[0337] 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.

[0338] 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."

[0339] The present invention relates to a system that receives audio data from a user terminal, analyzes the audio data to identify siren sounds, and generates a warning message based on that information and displays it on a display device.

[0340] server

[0341] The server has the function of receiving real-time voice data sent from the user terminal. The voice data is uploaded to the server using a specific API. The server inputs the received voice data into a voice analysis engine. This engine uses a machine learning model to identify siren sounds and further calculate the direction and distance of the sound source.

[0342] Specifically, the server analyzes the waveforms in the audio data and determines whether a specific sound wave pattern matches the siren of an ambulance or police vehicle. If there is a match, it calculates the direction and distance of the sound source and generates a warning message based on this information. This warning message takes the form of something like "An ambulance is approaching 30 meters behind you to the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is immediately sent to the user's device.

[0343] User terminal

[0344] The user terminal receives the warning message sent from the server. The terminal has the function of transferring the warning message to the vehicle's display device (display audio) via Apple CarPlay or Android Auto. The warning message displayed on the terminal is designed to be easily recognizable by the driver.

[0345] For example, when the user device receives a warning message, it immediately transfers the message to the car's display audio via the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so that the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed.

[0346] User

[0347] Before starting to drive, users need to connect their smartphone to the display audio and start Apple CarPlay or Android Auto. While driving, the user's device detects the sound of a siren and generates a warning message to display on the display device. This allows even hearing-impaired drivers to recognize the approach of an emergency vehicle and take appropriate action.

[0348] As a specific example of a driving scenario, if a user is driving and an ambulance approaches 30 meters behind them to the right, a warning message stating "Ambulance approaching 30 meters behind them to the right" will be displayed on the user's display audio. If a message is also displayed stating "There is room to move out of the way about 1 meter to the left," the user can move the car to the left and safely give way to the ambulance.

[0349] In this way, the present invention is a system that realizes safe driving assistance for hearing-impaired people by identifying siren sounds, locating the sound source, and generating and displaying warning messages based on the identified sounds.

[0350] The processing flow will be explained below.

[0351] server

[0352] Step 1: Receiving audio data

[0353] The server receives real-time voice data from the user terminal, which is uploaded via a specific API, which is transmitted through a constantly connected real-time communication channel.

[0354] Step 2: Audio analysis

[0355] The server then inputs the received audio data into a voice analysis engine for prioritized analysis, which uses machine learning models to analyze the audio data and identify the specific siren sound.

[0356] Step 3: Calculate the direction and distance of the sound source

[0357] If a siren sound is identified, the server runs an algorithm to calculate the direction and distance of the sound source, based on data such as the time difference between the arrival of the sound waves.

[0358] Step 4: Generate a warning message

[0359] The server generates a warning message based on the calculation results. The warning message is generated as text in the form of, for example, "An ambulance is approaching 30 meters behind you on the right" or "There is room to evacuate about 1 meter to the left."

[0360] Step 5: Sending a warning message

[0361] The server sends the generated warning message to the user terminal in real time, also using a specific API.

[0362] User terminal

[0363] Step 1: Receiving the warning message

[0364] The user terminal receives the alert message sent from the server via a real-time communication channel.

[0365] Step 2: Transfer to Display Audio

[0366] The user terminal transfers the received warning message to the car's display audio via Apple CarPlay or Android Auto.

[0367] Display Audio

[0368] Step 1: Message display

[0369] Display Audio receives warning messages sent from the terminal and immediately displays them on the screen in large letters that are easy for the driver to see.

[0370] User

[0371] Step 1: System Setup

[0372] When the user gets into the car, they connect their smartphone to the display audio and start Apple CarPlay or Android Auto.

[0373] Step 2: Check for warnings

[0374] While driving, the user checks the warning message displayed on the display audio, which describes the direction of the approaching emergency vehicle and the recommended evacuation action.

[0375] Step 3: Safe Operation

[0376] The user takes appropriate safety measures based on the displayed warning message. For example, if the message "An ambulance is approaching 30 meters behind and to the right" or "There is room to evacuate about 1 meter to the left" is displayed, the user will take measures such as moving the car to the left.

[0377] Example 1

[0378] 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."

[0379] Conventional driver assistance systems have made it difficult for hearing-impaired people to recognize approaching emergency vehicles, resulting in impaired driving safety. In particular, if they cannot hear the siren, it is difficult for them to take appropriate action, which can prevent them from quickly changing course or evacuating, increasing the risk of an accident.

[0380] 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.

[0381] In this invention, the server includes means for receiving voice data from a user terminal, means for analyzing the voice data to identify a siren sound, means for calculating the direction and distance of the siren sound, means for generating a warning message based on information about the siren sound, means for transmitting the warning message to the user terminal, means for transferring the warning message from the user terminal to a display device, means for displaying the warning message on the display device, and means for visually displaying the warning message so that the driver can easily recognize it, thereby enabling hearing-impaired people to reliably recognize an approaching emergency vehicle and safely evacuate.

[0382] A "user terminal" is an electronic device that has the function of collecting voice data and transmitting it to a server.

[0383] "Audio data" refers to data that records surrounding sounds as digital signals.

[0384] The "server" refers to a computer system that analyzes voice data received from a user terminal, generates a warning message, and transmits the message to the user terminal.

[0385] A "voice analysis engine" is software or algorithms that analyze received voice data and identify siren sounds.

[0386] A "machine learning model" is an algorithm that is optimized to perform a specific task (in this case, identifying siren sounds) using large amounts of training data.

[0387] A "warning message" is an information message that is generated based on the detection of a siren sound and its direction and distance, and that notifies the user of danger.

[0388] A "display device" refers to a device for visually displaying a warning message, such as a user terminal or a car display audio.

[0389] A "visual alert" is a visual signal or message that is displayed on a display device to convey information or a warning to a user.

[0390] The present invention relates to a system that receives audio data from a user terminal, analyzes the audio data to identify siren sounds, and generates a warning message based on that information and displays it on a display device.

[0391] server

[0392] The server has the function of receiving real-time voice data sent from the user device. The voice data is uploaded to the server using a specific API. Specifically, a WebSocket API or RESTful API can be used. The server is equipped with a voice analysis engine for analyzing the received voice data. This voice analysis engine is a model built using a machine learning framework such as TensorFlow or PyTorch.

[0393] The server analyzes the waveforms in the audio data and determines whether a particular sound wave pattern matches the siren of an ambulance or police vehicle. Once a siren is identified, it then uses a Python algorithm to calculate the direction and distance of the sound source, using techniques such as Time Difference of Arrival (TDOA). Based on the results of this calculation, the server generates a warning message. This warning message is generated in the form of, for example, "An ambulance is approaching 30 meters behind you to the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is sent to the user's device in real time.

[0394] User terminal

[0395] The user terminal has the function of receiving the warning message sent from the server, and then forwards this message to the vehicle's display device (display audio) via Apple CarPlay or Android Auto. The warning message is visually displayed in large letters so that the driver can easily recognize it.

[0396] For example, when a user device receives a warning message, it immediately transmits the message to the car's display audio via the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so that the driver can see it at a glance. For example, if the message "Ambulance approaching 30 meters behind to the right" is displayed, the driver can immediately recognize the information and take action.

[0397] User

[0398] Before starting to drive, users must connect their smartphone to the Display Audio and activate Apple CarPlay or Android Auto. While driving, the system identifies siren sounds and generates and displays a warning message, allowing even hearing-impaired drivers to recognize approaching emergency vehicles and respond appropriately.

[0399] As a specific example of a driving scenario, if a user is driving and an ambulance approaches 30 meters behind them to the right, the user's display audio will display "Ambulance approaching 30 meters behind them to the right." If it also displays "Room for evacuation approximately 1 meter to the left," the user can move the car to the left and safely give way to the ambulance.

[0400] Prompt Sentence Examples

[0401] Imagine a situation where a user is driving and hears the sound of an ambulance siren 30 meters behind them to the right. In this case, the server inputs the received audio data into the TensorFlow audio analysis engine, identifies the siren sound, calculates the direction and distance of the sound source, generates a warning message and sends it to the user's device. The user's device then transmits the warning message to the car's display audio via Apple CarPlay, and the display device displays something like "Ambulance approaching 30 meters behind the right." Please explain the specific processing steps.

[0402] In this way, the present invention is a system that helps hearing-impaired people drive safely by identifying the siren sound, locating the sound source, and generating and displaying a warning message based on that information.

[0403] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0404] Step 1:

[0405] The user gets into the car and connects the smartphone to the display audio.

[0406] Input: Smartphone, Display Audio

[0407] How it works: Connect your smartphone to the Display Audio using a USB cable and launch Apple CarPlay or Android Auto.

[0408] Output: Smartphone and Display Audio are connected

[0409] Step 2:

[0410] The user terminal captures the surrounding sounds in real time and transmits the sound data to the server.

[0411] Input: Ambient audio

[0412] How it works: It uses a smartphone's microphone to capture audio in real time, and then streams the captured audio data to a server via a WebSocket API.

[0413] Output: Audio data sent to the server

[0414] Step 3:

[0415] The server analyzes the received voice data using a machine learning model.

[0416] Input: Audio data sent to the server

[0417] How it works: The received audio data is input into the audio analysis engine, and the presence or absence of a siren is analyzed using a machine learning model using TensorFlow or PyTorch.

[0418] Output: Analysis result regarding the presence or absence of siren sound

[0419] Step 4:

[0420] The server identifies the siren sound and calculates the direction and distance of the sound source.

[0421] Input: Analysis result regarding the presence or absence of siren sound

[0422] How it works: If a siren sound is identified, a Python algorithm is used to calculate the direction and distance of the sound source using the TDOA (Time Difference of Arrival) method.

[0423] Output: Information about the direction and distance of the sound source

[0424] Step 5:

[0425] The server generates a warning message.

[0426] Input: Information about the direction and distance of the sound source

[0427] What it does: Based on the calculated direction and distance information, it creates a warning message such as "Ambulance approaching 30 meters behind and to the right." It also adds information about room to retreat if necessary.

[0428] Output: Generated warning message

[0429] Step 6:

[0430] The server sends a warning message to the user terminal.

[0431] Input: The generated warning message

[0432] Operation: Generated warning messages are sent in real time to the user's terminal via HTTP or WebSocket.

[0433] Output: The warning message delivered to the user's terminal.

[0434] Step 7:

[0435] The user terminal receives the alert message and forwards it to the display audio.

[0436] Input: Warning message delivered to the user's device

[0437] What it does: Transmits warning messages to the car's display audio via Apple CarPlay or Android Auto.

[0438] Output: Warning messages forwarded to Display Audio

[0439] Step 8:

[0440] The Display Audio will visually display a warning message.

[0441] Input: Warning message forwarded to Display Audio

[0442] What it does: A warning message is displayed on the screen in large letters so that the driver can see it at a glance. For example, it may say, "Ambulance approaching 30 meters behind and to the right."

[0443] Output: Warning message presented to the driver

[0444] (Application example 1)

[0445] 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."

[0446] In autonomous vehicles, there is a lack of support for drivers to quickly and reliably recognize approaching emergency vehicles and take appropriate driving action. Therefore, there is a need for a system that can accurately recognize approaching emergency vehicles and safely give way to them, especially for people with hearing impairments or who have difficulty distinguishing environmental sounds.

[0447] 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.

[0448] In this invention, the server includes means for receiving voice data from the user terminal, means for analyzing the voice data to identify a siren sound, means for calculating the direction and distance of the siren sound, means for generating a warning message based on information about the siren sound, means for transmitting the warning message to the user terminal, means for transferring the warning message from the user terminal to a display device, means for displaying the warning message on the display device, means for transmitting the voice data from the user terminal to a control system installed in the vehicle and notifying the vehicle control system of an approaching emergency vehicle, and means for the control system to perform an appropriate driving operation based on the warning message. This enables the autonomously driven vehicle to accurately recognize an approaching emergency vehicle and safely give way to it.

[0449] A "user terminal" is a device that collects voice data and transmits it to a server.

[0450] "Audio data" is a recording of sound collected from a user terminal.

[0451] A "siren" is a specific sound made by emergency vehicles.

[0452] The "warning message" is the content of a warning generated based on the direction and distance of the siren sound.

[0453] A "display device" is a device that visually or audibly conveys a warning message to a user.

[0454] A "control system" is a computer system installed in a vehicle that controls automatic driving.

[0455] A "machine learning model" is an algorithm trained to identify siren sounds based on large amounts of data.

[0456] An "autonomous vehicle" is a vehicle that navigates autonomously to a destination without the intervention of a human driver.

[0457] A system for implementing the present invention is configured as a driver assistance application for an autonomous vehicle, and includes a user terminal, a server, a control system, and a display device.

[0458] server

[0459] The server has the function of receiving real-time voice data sent from the user terminal. The voice data is uploaded to the server using a specific API. The server inputs the received voice data into a voice analysis engine. This engine uses a machine learning model to identify siren sounds and further calculate the direction and distance of the sound source. This machine learning model is an algorithm trained on a large amount of data.

[0460] Specifically, the server analyzes the waveforms in the audio data to determine whether a particular sound wave pattern matches the sound of an ambulance or police siren. If there is a match, it calculates the direction and distance of the sound source and uses this information to generate a warning message. This warning message is then transmitted to the car's control system, alerting the driver of the approaching emergency vehicle.

[0461] User terminal

[0462] The user terminal receives the warning message sent from the server. The terminal has a communication function for transmitting voice data and warning messages to the vehicle's control system. Furthermore, the terminal can also transfer warning messages to the vehicle's display device via Apple CarPlay or Android Auto.

[0463] For example, when the user device receives a warning message, it immediately transfers the message to the vehicle's display device via the Apple CarPlay or Android Auto application. The display device then displays the received message in large letters on the screen so that the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed.

[0464] Control Systems and Displays

[0465] The control system is a computer system installed in a vehicle that controls autonomous driving. The control system executes appropriate driving actions based on warning messages transmitted from a user terminal. For example, if the control system detects an approaching emergency vehicle, it can reduce the vehicle's speed and execute appropriate evacuation actions.

[0466] The display device is a display installed inside the autonomous vehicle that displays the warning message transferred from the user terminal, allowing the driver to visually confirm the approach of an emergency vehicle.

[0467] Adding specific examples

[0468] As a concrete example, when an autonomous vehicle is driving on a highway, this system monitors the surrounding environmental sounds. For example, if an ambulance is approaching from 150 meters behind the vehicle, the user device will identify the siren and immediately send the audio data to the server. The server will analyze the audio data, generate a warning message, and send it to the user device and the vehicle's control system. The control system will then take appropriate driving action based on the warning message, "An ambulance is approaching 150 meters behind. Please give way." The same message will also be displayed on the display device, allowing the driver to check the situation.

[0469] Example prompts for generative AI models

[0470] Examples of prompts that use generative AI models include the following format:

[0471] Analyze surrounding audio data and identify siren sounds. Calculate the direction and distance of the siren sound and generate a warning message in the following format:

[0472] "Emergency vehicle approaching Y meters in X direction"

[0473] Example: Audio data: "WAV format audio data"

[0474] Using this prompt sentence, the siren sound can be accurately distinguished from the ambient sound and a warning message can be generated based on the appropriate distance and direction.

[0475] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0476] Step 1:

[0477] The user terminal uses a built-in microphone to collect surrounding audio data in real time, which is recorded at a fixed sampling rate and continuously transmitted to the server.

[0478] Step 2:

[0479] The server receives the voice data sent from the user terminal. The voice data received by the server is uploaded through a predefined API. The input data is saved in a buffer.

[0480] Step 3:

[0481] The server inputs the received audio data into a voice analysis engine, which uses a generative AI model to analyze the audio data. Specifically, it analyzes the waveform of the input data and determines whether a particular sound wave pattern matches the sound of a siren.

[0482] Step 4:

[0483] The server calculates the direction and distance of the siren sound based on the output of the sound analysis engine. This calculation is performed using the difference in sound intensity and arrival time. The input data is waveform data containing the characteristics of the siren sound, and the output data is information on direction and distance.

[0484] Step 5:

[0485] The server generates a warning message based on the siren sound identification result and the direction and distance information. The warning message format is "Emergency vehicle approaching in X direction, Y meters." The input data is the direction and distance information, and the output data is the generated warning message.

[0486] Step 6:

[0487] The server sends the generated warning message to the user terminal, and the sent warning message is received through the communication module of the user terminal. The input data is the warning message, and the output data is a notification to the user terminal.

[0488] Step 7:

[0489] The user terminal transmits the received warning message to the vehicle's control system, which analyzes the message and takes appropriate driving action, such as reducing speed and giving way.

[0490] Step 8:

[0491] At the same time, the user device transmits a warning message to the vehicle's display device via Apple CarPlay or Android Auto. The display device then displays the warning message on the screen so that the driver can visually confirm it. Specifically, the message is displayed in large letters to alert the driver.

[0492] 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.

[0493] The present invention combines a system that receives voice data from a user terminal, analyzes the voice data to identify siren sounds, generates a warning message based on that information, and displays it on a display device with an emotion engine that recognizes the user's emotions.

[0494] server

[0495] The server has the function of receiving real-time voice data transmitted from the user terminal. The voice data is uploaded to the server using a specific API, which is transmitted through a constantly connected real-time communication channel. The server inputs the received voice data into a voice analysis engine. The engine analyzes the voice data using a machine learning model and identifies the specific siren sound.

[0496] Specifically, the server analyzes the waveforms in the audio data and determines whether a specific sound wave pattern matches the siren of an ambulance or police vehicle. If there is a match, it calculates the direction and distance of the sound source and generates a warning message based on this information. This warning message is generated as text in a format such as "An ambulance is approaching 30 meters behind you to the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is immediately sent to the user's device.

[0497] Emotion Engine

[0498] The emotion engine recognizes the user's emotional state and performs analysis using biometric data acquired through sensors on the user's device, including heart rate, galvanic skin response, and facial expressions, to assess the user's stress level and other emotional states.

[0499] Specifically, the emotion engine analyzes biometric information acquired in real time to determine whether the user is in a specific emotional state. For example, if a high stress level is detected, the server can generate an additional comforting message for the user and send it along with the warning message. This comforting message can be displayed in a format such as "Please drive safely. Don't rush."

[0500] User terminal

[0501] The user terminal receives the warning message and the comfort message sent from the server. The terminal has the function of transmitting the warning message and the comfort message to the display device (display audio) of the vehicle via Apple CarPlay or Android Auto. The warning message displayed on the terminal is designed to be easily recognizable by the driver.

[0502] For example, when the user device receives a warning message, it immediately transfers the message to the car's display audio via the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so that the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed, and if the user's stress level is high, a comforting message such as "Please drive safely. Don't rush."

[0503] User

[0504] Before starting to drive, users must connect their smartphone to the display audio and activate Apple CarPlay or Android Auto. While driving, the user's device detects the sound of a siren and generates a warning message to display on the display device. By incorporating an emotion engine into this process, additional messages are also displayed according to the user's emotional state. This allows even hearing-impaired drivers to recognize the approach of an emergency vehicle and take appropriate action.

[0505] In this way, the present invention is a system that provides safe driving assistance to hearing-impaired people by identifying siren sounds, locating the sound source, generating and displaying warning messages based on that information, and also taking into account the user's emotional state and providing additional messages as needed.

[0506] The processing flow will be explained below.

[0507] server

[0508] Step 1: Receiving audio data

[0509] The server receives real-time voice data from user devices, which are uploaded via a specific API, which is then passed through a constantly connected real-time communication channel.

[0510] Step 2: Audio analysis

[0511] The server then inputs the received audio data into a voice analysis engine for prioritized analysis, which uses machine learning models to analyze the audio data and identify specific siren sounds.

[0512] Step 3: Calculate the direction and distance of the sound source

[0513] If the server identifies a siren, it runs an algorithm that calculates the direction and distance of the sound source, based on data such as the time difference between the arrival of sound waves.

[0514] Step 4: Emotion Engine Analysis

[0515] The server's emotion engine receives real-time biometric data from the user's device and analyzes the user's emotional state, including heart rate, galvanic skin response, and facial expressions.

[0516] Step 5: Generate a warning message

[0517] The server generates a warning message based on the calculation results. The warning message is generated as text in the form of, for example, "An ambulance is approaching 30 meters behind you on the right" or "There is room to evacuate about 1 meter to the left." If the server determines that the user is in a high stress state, it also generates an additional comforting message, such as, "Please drive safely. Don't rush."

[0518] Step 6: Sending a warning message

[0519] The server then sends the generated warning messages to the user's device in real time, using a specific API.

[0520] User terminal

[0521] Step 1: Receiving the warning message

[0522] The user terminal receives the alert message sent from the server via a real-time communication channel.

[0523] Step 2: Transfer to Display Audio

[0524] The user device will then transfer the received warning and comfort messages to the car's display audio via Apple CarPlay or Android Auto.

[0525] Display Audio

[0526] Step 1: Message display

[0527] The Display Audio receives warning and comfort messages sent from the terminal and instantly displays them on the screen in large letters that are easy for the driver to see.

[0528] User

[0529] Step 1: System Setup

[0530] When users get into their car, they need to connect their smartphone to the display audio and launch Apple CarPlay or Android Auto.

[0531] Step 2: Check for warnings

[0532] While driving, users can check warning and comforting messages displayed on the display audio, including information on the direction of approaching emergency vehicles, recommended evacuation actions, and messages based on the user's emotional state.

[0533] Step 3: Safe Operation

[0534] The user is expected to take appropriate safety measures based on the displayed warning and comforting messages. For example, if the message "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" is displayed, along with a comforting message such as "Please drive safely. Don't rush," the user is expected to take appropriate measures such as pulling over to the left.

[0535] Example 2

[0536] 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."

[0537] It is difficult for hearing-impaired drivers to properly recognize approaching emergency vehicles while driving, and the impact of the driver's emotional state on safe driving cannot be ignored. In such situations, a system is needed that allows drivers to recognize warnings in real time and receive appropriate comfort messages according to their emotional state. This is also expected to improve driver safety.

[0538] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving voice data from a user terminal, means for analyzing the voice data to identify a specific warning sound, means for calculating the direction and distance of the warning sound, means for generating a warning message based on information about the warning sound, means for acquiring biometric information from the user, means for analyzing the biometric information to identify an emotional state, means for generating a comforting message based on the emotional state, means for transmitting the warning message and the comforting message to the user terminal, means for transferring the message from the user terminal to a display device, and means for displaying the warning message and the comforting message on the display device. This allows drivers, including those who are hearing impaired, to recognize the approach of an emergency vehicle in real time while driving and to receive appropriate messages according to their emotional state.

[0539] A "user terminal" is a device for capturing and transmitting audio data to a server.

[0540] "Audio data" refers to data that includes ambient audio information captured by a user terminal.

[0541] "Specific warning sounds" refer to the sirens of emergency vehicles such as ambulances and police cars.

[0542] The "direction and distance of the warning sound" is information indicating the position where a particular warning sound is being generated and the distance to that position.

[0543] A "warning message" is warning information for a user that is generated based on the direction and distance of a specific warning sound.

[0544] "Biometric information" refers to physiological data such as the user's heart rate, galvanic skin response, and facial expressions.

[0545] The "emotional state" is the user's emotional state (for example, stress level) obtained by analyzing biometric information.

[0546] A "comforting message" is a message generated according to the user's emotional state to reassure the user.

[0547] A "display device" is a device that is installed in a user terminal or in a vehicle and that displays warning messages and comforting messages.

[0548] "Server" refers to a computer system that receives voice data and biometric information sent from a user terminal and generates and transmits warning messages and comfort messages based on the received data.

[0549] The present invention provides a system that receives voice data from a user terminal, analyzes the voice data, and identifies specific warning sounds based on the received voice data. The system then uploads the voice data to a server via a real-time communication channel using a specific API, analyzes the voice data using a machine learning model, and generates a warning message. The system also collects biometric information from the user, evaluates their emotional state based on the biometric information, and generates a comforting message as needed. Specific embodiments are described in detail below.

[0550] server

[0551] The server has the function of receiving real-time voice data sent from the user terminal. A specific WebSocket API is used for this reception, and the voice data is sent to the server through a real-time communication channel. The received voice data is temporarily stored in a buffer in memory.

[0552] The server then inputs the received audio data into a speech analysis engine, which uses a generative AI model such as TensorFlow to analyze the audio data and identify specific warning sounds (such as ambulance or police sirens). The features of the identified warning sounds are extracted by converting the waveform data into a spectrogram.

[0553] The direction and distance calculation function calculates the direction and distance of the identified warning sound, and based on this, the server generates a warning message such as "Ambulance approaching 30 meters behind to the right" and saves it in text format.

[0554] Furthermore, the emotion engine uses biometric information reception to receive data such as heart rate, galvanic skin response, and facial expressions obtained from the user's sensors. This data is used by machine learning models using Emotion API and Azure Cognitive Services to evaluate the user's emotional state. If the emotional state is high stress, an additional comforting message such as "Please drive safely and don't rush" is generated.

[0555] User terminal

[0556] The user device receives warning and comfort messages from the server and transmits them to the car's display device via Apple CarPlay or Android Auto. The received messages are displayed in large letters on the display audio so that the driver can see them at a glance.

[0557] User

[0558] Before starting to drive, users need to connect their smartphone to the car's display audio and activate Apple CarPlay or Android Auto. While driving, the user's device captures surrounding sounds and transmits them to the server in real time.

[0559] Example

[0560] For example, if a user connects their smartphone to Apple CarPlay before starting to drive and the device detects a warning sound from the rear right while driving, the server will generate a warning message saying, "An ambulance is approaching 30 meters behind the right." If the emotion engine detects a high stress level in the user, it will generate an additional comforting message saying, "Please drive safely. Don't rush." ​​These messages are displayed on the display audio via the user's device.

[0561] An example prompt for a generative AI model might look something like this:

[0562] "An ambulance is approaching 30 meters behind and to the right. The user's stress level is high, so please drive safely. Do not rush."

[0563] In this way, this system supports safe driving by recognizing approaching emergency vehicles in real time while hearing-impaired drivers are driving and providing appropriate messages according to their emotional state.

[0564] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0565] Step 1:

[0566] The server receives real-time audio data from the user device. The device captures surrounding audio data using a microphone and sends the data to the server using the WebSocket API. This audio data is stored as a buffer in the server's memory. The input is the audio data from the user device, and the output is the audio data stored in the buffer.

[0567] Step 2:

[0568] The server inputs the audio data stored in the buffer into a speech analysis engine. This analysis engine uses generative AI models such as TensorFlow to analyze the audio data. Specifically, it converts the waveform data into a spectrogram and extracts characteristic patterns of specific warning sounds (e.g., ambulance or police sirens). The input is the stored audio data, and the output is the result of the warning sound identification.

[0569] Step 3:

[0570] The server calculates the direction and distance of the identified warning sound based on the result of the warning sound identification. To do this, an algorithm is used to determine the direction of the sound source and calculate the distance based on the result of the voice analysis engine. The input is the identification result of the voice analysis engine, and the output is information on the direction and distance.

[0571] Step 4:

[0572] The server generates a warning message based on the direction and distance information. For example, it generates a text message in the format "An ambulance is approaching 30 meters behind and to the right." This message is temporarily stored in memory. The input is the direction and distance information, and the output is the text data of the warning message.

[0573] Step 5:

[0574] The emotion engine receives biometric data (heart rate, galvanic skin response, facial expressions, etc.) acquired from sensors on the user device. These data are captured in real time by the user device and sent to the server. The input is the biometric data from the user device, and the output is the received biometric data.

[0575] Step 6:

[0576] The emotion engine analyzes the user's emotional state based on biometric data. For analysis, it uses machine learning models such as the Emotion API and Azure Cognitive Services. For example, if a high stress level is detected, this information is stored in memory. The input is biometric data, and the output is a judgment of the user's emotional state.

[0577] Step 7:

[0578] The server generates the necessary comforting message based on the result of the emotional state judgment. For example, it generates a message in the format of "Please drive safely. Don't rush." ​​This message is also stored in memory in text format. The input is the result of the emotional state judgment, and the output is the text data of the comforting message.

[0579] Step 8:

[0580] The server transmits the generated warning and comfort messages to the user terminal using a real-time communication channel to a notification system or dedicated application of the user terminal, with the input being the text data of the warning and comfort messages and the output being the transmitted messages.

[0581] Step 9:

[0582] The user terminal transfers the received message to the car's display device via Apple CarPlay or Android Auto. The terminal formats the received message for display on the display audio and displays it in large letters on the screen. The input is the message from the server, and the output is the message displayed on the display device.

[0583] (Application example 2)

[0584] 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."

[0585] Conventional driving assistance systems detect siren sounds and generate warning messages, but they lack measures that take into account the driver's emotional state, making it difficult for drivers to respond appropriately when under high stress. Hearing-impaired drivers also have limited means of properly recognizing approaching emergency vehicles.

[0586] The identification processing 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 receiving voice data from a user terminal, means for analyzing the voice data to identify a siren sound, means for calculating the direction and distance of the siren sound, means for generating a warning message based on information about the siren sound, means for transmitting the warning message to the user terminal, means for acquiring biometric information data of the user and recognizing an emotional state, means for generating an additional message according to the emotional state, means for transferring the warning message and the additional message from the user terminal to a display device, and means for displaying the warning message and the additional message on the display device. This enables appropriate responses even when the driver is in a high-stress state, and further enables safe driving assistance for hearing-impaired people.

[0587] "Audio data" refers to sound wave information acquired from a user terminal, including conversations, environmental sounds, siren sounds, and the like.

[0588] A "siren sound" is a special sound made by emergency vehicles such as ambulances, fire engines, and police cars, and has a specific sound wave pattern.

[0589] "Biometric data" refers to various data obtained from the user's body, including heart rate, galvanic skin response, facial expressions, and the like.

[0590] "Emotional state" indicates the user's current emotional state, and includes stress level, pleasure, discomfort, and the like.

[0591] A "warning message" is a notification message generated based on the detection result of a siren sound, and is used to notify the driver of an approaching emergency situation.

[0592] The "additional message" is a message of encouragement or comfort that is generated according to the user's emotional state, and is intended to reduce the mental burden on the driver.

[0593] "Display device" refers to a display device connected to a vehicle or a user terminal for displaying warning messages and additional messages.

[0594] A "machine learning model" is an algorithm or framework that learns from past data and analyzes voice data and identifies patterns.

[0595] The present invention combines a system that receives voice data from a user terminal, identifies siren sounds, generates a warning message based on that information, and displays it on a display device with an emotion engine that recognizes the user's emotions. The system program and its processing are described in detail below.

[0596] The server has the function of receiving real-time voice data transmitted from the user terminal. The voice data is uploaded to the server using a specific API, which is transmitted through a constantly connected real-time communication channel. The server inputs the received voice data into a voice analysis engine. The engine uses a machine learning model to analyze the voice data and identify the specific siren sound.

[0597] For example, the server analyzes the waveforms in the audio data and determines whether a specific sound wave pattern matches the siren of an ambulance or police vehicle. If there is a match, it calculates the direction and distance of the sound source and generates a warning message based on this information. This warning message is generated as text in a format such as "An ambulance is approaching 30 meters behind you on the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is immediately sent to the user's device.

[0598] The emotion engine recognizes the user's emotional state and performs analysis using biometric data acquired through sensors on the user's device. The biometric data includes heart rate, galvanic skin response, and facial expressions, and uses this data to assess the user's stress level and other emotional states. For example, if a high stress level is detected, the server can generate an additional comforting message for the user and send it along with the warning message. This comforting message could be displayed in a format such as "Drive safely. Don't rush."

[0599] The user device receives the warning and comfort messages sent from the server. The device is a typical smartphone or tablet, and has the function of transmitting messages to the vehicle's display device (display audio) via Apple CarPlay or Android Auto. The warning messages displayed on the device are designed to be easily recognizable by the driver.

[0600] Specifically, when the user device receives a warning message, it immediately transfers the message to the car's display audio using the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed, and if the user's stress level is high, a comforting message such as "Please drive safely. Don't rush."

[0601] A concrete example of a prompt sentence could be input to a generative AI model in the following form:

[0602] The user is driving an autonomous vehicle. This application uses the in-car microphone to collect audio data and analyzes it on a server to determine whether a siren is present. If a siren is detected, a warning message is generated and displayed on the in-car display. At the same time, biometric sensors collect the user's heart rate and skin potential, and an emotion engine evaluates the user's stress level. If the stress level is high, an additional comforting message is displayed. This system supports the user in driving safely.

[0603] In this way, the present invention provides a system that combines real-time voice data analysis and an emotion engine to support safe driving by the driver.

[0604] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0605] Step 1:

[0606] The user starts the car engine and connects the smartphone to the display audio. The device launches the Apple CarPlay or Android Auto application, which prepares the device for collecting voice data and displaying messages. The input is the connection status between the smartphone and the in-car display. The output is a notification that the system is ready to operate.

[0607] Step 2:

[0608] The device begins collecting real-time audio data using the in-car microphone. Specifically, the audio data is buffered for a short period of time and then transmitted to the server at regular intervals. The input includes ambient sounds and conversations inside the car. The output is a real-time audio data stream.

[0609] Step 3:

[0610] The server receives the audio data sent from the device. The received audio data is analyzed using a machine learning model to identify the specific siren sound. The input is the audio data. The output is the presence or absence of a siren sound, and if identified, the direction and distance information.

[0611] Step 4:

[0612] The server generates a warning message based on the direction and distance information of the siren sound. Specifically, it analyzes the direction and distance of the sound source and creates a warning message such as "An ambulance is approaching 30 meters behind you to the right." The input is the direction and distance information of the siren sound. The output is the generated warning message.

[0613] Step 5:

[0614] The device acquires the user's biometric data. Specifically, it uses a heart rate sensor, a skin electrodermal response sensor, a facial recognition camera, etc. to collect the user's heart rate, skin electrodermal response, and facial expression data. The input is the user's biometric information. The output is real-time biometric data.

[0615] Step 6:

[0616] The server uses an emotion engine to analyze the biometric data and evaluate the user's emotional state. Specifically, it uses the collected biometric data to determine the user's emotional state, such as stress level and pleasantness / unpleasantness. The input is the biometric data, and the output is the evaluated emotional state.

[0617] Step 7:

[0618] The server generates an additional comforting message according to the user's emotional state. Specifically, if the user's stress level is high, the server generates a comforting message such as "Please drive safely. Don't rush." ​​The input is the user's emotional state. The output is the additional message.

[0619] Step 8:

[0620] The server sends the warning message and the additional message to the user terminal. The input is the generated warning message and the additional message. The output is the completion of message transmission to the user terminal.

[0621] Step 9:

[0622] The terminal transfers the received warning messages and additional messages to the in-car display via the Apple CarPlay or Android Auto application. The input is the message from the server. The output is the completion of transfer to the in-car display.

[0623] Step 10:

[0624] The display device displays warning messages and additional messages on the screen. Specifically, the messages are displayed in large letters so that the driver can see them at a glance. The input is a message transferred from the terminal. The output is the message displayed on the screen.

[0625] By the above steps, the present invention allows drivers to respond appropriately even in high-stress situations, and enables hearing-impaired people to drive safely.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] [Third embodiment]

[0630] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0631] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0632] 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).

[0633] 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.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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."

[0642] The present invention relates to a system that receives audio data from a user terminal, analyzes the audio data to identify siren sounds, and generates a warning message based on that information and displays it on a display device.

[0643] server

[0644] The server has the function of receiving real-time voice data sent from the user terminal. The voice data is uploaded to the server using a specific API. The server inputs the received voice data into a voice analysis engine. This engine uses a machine learning model to identify siren sounds and further calculate the direction and distance of the sound source.

[0645] Specifically, the server analyzes the waveforms in the audio data and determines whether a specific sound wave pattern matches the siren of an ambulance or police vehicle. If there is a match, it calculates the direction and distance of the sound source and generates a warning message based on this information. This warning message takes the form of something like "An ambulance is approaching 30 meters behind you to the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is immediately sent to the user's device.

[0646] User terminal

[0647] The user terminal receives the warning message sent from the server. The terminal has the function of transferring the warning message to the vehicle's display device (display audio) via Apple CarPlay or Android Auto. The warning message displayed on the terminal is designed to be easily recognizable by the driver.

[0648] For example, when the user device receives a warning message, it immediately transfers the message to the car's display audio via the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so that the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed.

[0649] User

[0650] Before starting to drive, users need to connect their smartphone to the display audio and start Apple CarPlay or Android Auto. While driving, the user's device detects the sound of a siren and generates a warning message to display on the display device. This allows even hearing-impaired drivers to recognize the approach of an emergency vehicle and take appropriate action.

[0651] As a specific example of a driving scenario, if a user is driving and an ambulance approaches 30 meters behind them to the right, a warning message stating "Ambulance approaching 30 meters behind them to the right" will be displayed on the user's display audio. If a message is also displayed stating "There is room to move out of the way about 1 meter to the left," the user can move the car to the left and safely give way to the ambulance.

[0652] In this way, the present invention is a system that realizes safe driving assistance for hearing-impaired people by identifying siren sounds, locating the sound source, and generating and displaying warning messages based on the identified sounds.

[0653] The processing flow will be explained below.

[0654] server

[0655] Step 1: Receiving audio data

[0656] The server receives real-time voice data from the user terminal, which is uploaded via a specific API, which is transmitted through a constantly connected real-time communication channel.

[0657] Step 2: Audio analysis

[0658] The server then inputs the received audio data into a voice analysis engine for prioritized analysis, which uses machine learning models to analyze the audio data and identify the specific siren sound.

[0659] Step 3: Calculate the direction and distance of the sound source

[0660] If a siren sound is identified, the server runs an algorithm to calculate the direction and distance of the sound source, based on data such as the time difference between the arrival of the sound waves.

[0661] Step 4: Generate a warning message

[0662] The server generates a warning message based on the calculation results. The warning message is generated as text in the form of, for example, "An ambulance is approaching 30 meters behind you on the right" or "There is room to evacuate about 1 meter to the left."

[0663] Step 5: Sending a warning message

[0664] The server sends the generated warning message to the user terminal in real time, also using a specific API.

[0665] User terminal

[0666] Step 1: Receiving the warning message

[0667] The user terminal receives the alert message sent from the server via a real-time communication channel.

[0668] Step 2: Transfer to Display Audio

[0669] The user terminal transfers the received warning message to the car's display audio via Apple CarPlay or Android Auto.

[0670] Display Audio

[0671] Step 1: Message display

[0672] Display Audio receives warning messages sent from the terminal and immediately displays them on the screen in large letters that are easy for the driver to see.

[0673] User

[0674] Step 1: System Setup

[0675] When the user gets into the car, they connect their smartphone to the display audio and start Apple CarPlay or Android Auto.

[0676] Step 2: Check for warnings

[0677] While driving, the user checks the warning message displayed on the display audio, which describes the direction of the approaching emergency vehicle and the recommended evacuation action.

[0678] Step 3: Safe Operation

[0679] The user takes appropriate safety measures based on the displayed warning message. For example, if the message "An ambulance is approaching 30 meters behind and to the right" or "There is room to evacuate about 1 meter to the left" is displayed, the user will take measures such as moving the car to the left.

[0680] Example 1

[0681] 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."

[0682] Conventional driver assistance systems have made it difficult for hearing-impaired people to recognize approaching emergency vehicles, resulting in impaired driving safety. In particular, if they cannot hear the siren, it is difficult for them to take appropriate action, which can prevent them from quickly changing course or evacuating, increasing the risk of an accident.

[0683] 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.

[0684] In this invention, the server includes means for receiving voice data from a user terminal, means for analyzing the voice data to identify a siren sound, means for calculating the direction and distance of the siren sound, means for generating a warning message based on information about the siren sound, means for transmitting the warning message to the user terminal, means for transferring the warning message from the user terminal to a display device, means for displaying the warning message on the display device, and means for visually displaying the warning message so that the driver can easily recognize it, thereby enabling hearing-impaired people to reliably recognize an approaching emergency vehicle and safely evacuate.

[0685] A "user terminal" is an electronic device that has the function of collecting voice data and transmitting it to a server.

[0686] "Audio data" refers to data that records surrounding sounds as digital signals.

[0687] The "server" refers to a computer system that analyzes voice data received from a user terminal, generates a warning message, and transmits the message to the user terminal.

[0688] A "voice analysis engine" is software or algorithms that analyze received voice data and identify siren sounds.

[0689] A "machine learning model" is an algorithm that is optimized to perform a specific task (in this case, identifying siren sounds) using large amounts of training data.

[0690] A "warning message" is an information message that is generated based on the detection of a siren sound and its direction and distance, and that notifies the user of danger.

[0691] A "display device" refers to a device for visually displaying a warning message, such as a user terminal or a car display audio.

[0692] A "visual alert" is a visual signal or message that is displayed on a display device to convey information or a warning to a user.

[0693] The present invention relates to a system that receives audio data from a user terminal, analyzes the audio data to identify siren sounds, and generates a warning message based on that information and displays it on a display device.

[0694] server

[0695] The server has the function of receiving real-time voice data sent from the user device. The voice data is uploaded to the server using a specific API. Specifically, a WebSocket API or RESTful API can be used. The server is equipped with a voice analysis engine for analyzing the received voice data. This voice analysis engine is a model built using a machine learning framework such as TensorFlow or PyTorch.

[0696] The server analyzes the waveforms in the audio data and determines whether a particular sound wave pattern matches the siren of an ambulance or police vehicle. Once a siren is identified, it then uses a Python algorithm to calculate the direction and distance of the sound source, using techniques such as Time Difference of Arrival (TDOA). Based on the results of this calculation, the server generates a warning message. This warning message is generated in the form of, for example, "An ambulance is approaching 30 meters behind you to the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is sent to the user's device in real time.

[0697] User terminal

[0698] The user terminal has the function of receiving the warning message sent from the server, and then forwards this message to the vehicle's display device (display audio) via Apple CarPlay or Android Auto. The warning message is visually displayed in large letters so that the driver can easily recognize it.

[0699] For example, when a user device receives a warning message, it immediately transmits the message to the car's display audio via the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so that the driver can see it at a glance. For example, if the message "Ambulance approaching 30 meters behind to the right" is displayed, the driver can immediately recognize the information and take action.

[0700] User

[0701] Before starting to drive, users must connect their smartphone to the Display Audio and activate Apple CarPlay or Android Auto. While driving, the system identifies siren sounds and generates and displays a warning message, allowing even hearing-impaired drivers to recognize approaching emergency vehicles and respond appropriately.

[0702] As a specific example of a driving scenario, if a user is driving and an ambulance approaches 30 meters behind them to the right, the user's display audio will display "Ambulance approaching 30 meters behind them to the right." If it also displays "Room for evacuation approximately 1 meter to the left," the user can move the car to the left and safely give way to the ambulance.

[0703] Prompt Sentence Examples

[0704] Imagine a situation where a user is driving and hears the sound of an ambulance siren 30 meters behind them to the right. In this case, the server inputs the received audio data into the TensorFlow audio analysis engine, identifies the siren sound, calculates the direction and distance of the sound source, generates a warning message and sends it to the user's device. The user's device then transmits the warning message to the car's display audio via Apple CarPlay, and the display device displays something like "Ambulance approaching 30 meters behind the right." Please explain the specific processing steps.

[0705] In this way, the present invention is a system that helps hearing-impaired people drive safely by identifying the siren sound, locating the sound source, and generating and displaying a warning message based on that information.

[0706] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0707] Step 1:

[0708] The user gets into the car and connects the smartphone to the display audio.

[0709] Input: Smartphone, Display Audio

[0710] How it works: Connect your smartphone to the Display Audio using a USB cable and launch Apple CarPlay or Android Auto.

[0711] Output: Smartphone and Display Audio are connected

[0712] Step 2:

[0713] The user terminal captures the surrounding sounds in real time and transmits the sound data to the server.

[0714] Input: Ambient audio

[0715] How it works: It uses a smartphone's microphone to capture audio in real time, and then streams the captured audio data to a server via a WebSocket API.

[0716] Output: Audio data sent to the server

[0717] Step 3:

[0718] The server analyzes the received voice data using a machine learning model.

[0719] Input: Audio data sent to the server

[0720] How it works: The received audio data is input into the audio analysis engine, and the presence or absence of a siren is analyzed using a machine learning model using TensorFlow or PyTorch.

[0721] Output: Analysis result regarding the presence or absence of siren sound

[0722] Step 4:

[0723] The server identifies the siren sound and calculates the direction and distance of the sound source.

[0724] Input: Analysis result regarding the presence or absence of siren sound

[0725] How it works: If a siren sound is identified, a Python algorithm is used to calculate the direction and distance of the sound source using the TDOA (Time Difference of Arrival) method.

[0726] Output: Information about the direction and distance of the sound source

[0727] Step 5:

[0728] The server generates a warning message.

[0729] Input: Information about the direction and distance of the sound source

[0730] What it does: Based on the calculated direction and distance information, it creates a warning message such as "Ambulance approaching 30 meters behind and to the right." It also adds information about room to retreat if necessary.

[0731] Output: Generated warning message

[0732] Step 6:

[0733] The server sends a warning message to the user terminal.

[0734] Input: The generated warning message

[0735] Operation: Generated warning messages are sent in real time to the user's terminal via HTTP or WebSocket.

[0736] Output: The warning message delivered to the user's terminal.

[0737] Step 7:

[0738] The user terminal receives the alert message and forwards it to the display audio.

[0739] Input: Warning message delivered to the user's device

[0740] What it does: Transmits warning messages to the car's display audio via Apple CarPlay or Android Auto.

[0741] Output: Warning messages forwarded to Display Audio

[0742] Step 8:

[0743] The Display Audio will visually display a warning message.

[0744] Input: Warning message forwarded to Display Audio

[0745] What it does: A warning message is displayed on the screen in large letters so that the driver can see it at a glance. For example, it may say, "Ambulance approaching 30 meters behind and to the right."

[0746] Output: Warning message presented to the driver

[0747] (Application example 1)

[0748] 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."

[0749] In autonomous vehicles, there is a lack of support for drivers to quickly and reliably recognize approaching emergency vehicles and take appropriate driving action. Therefore, there is a need for a system that can accurately recognize approaching emergency vehicles and safely give way to them, especially for people with hearing impairments or who have difficulty distinguishing environmental sounds.

[0750] 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.

[0751] In this invention, the server includes means for receiving voice data from the user terminal, means for analyzing the voice data to identify a siren sound, means for calculating the direction and distance of the siren sound, means for generating a warning message based on information about the siren sound, means for transmitting the warning message to the user terminal, means for transferring the warning message from the user terminal to a display device, means for displaying the warning message on the display device, means for transmitting the voice data from the user terminal to a control system installed in the vehicle and notifying the vehicle control system of an approaching emergency vehicle, and means for the control system to perform an appropriate driving operation based on the warning message. This enables the autonomously driven vehicle to accurately recognize an approaching emergency vehicle and safely give way to it.

[0752] A "user terminal" is a device that collects voice data and transmits it to a server.

[0753] "Audio data" is a recording of sound collected from a user terminal.

[0754] A "siren" is a specific sound made by emergency vehicles.

[0755] The "warning message" is the content of a warning generated based on the direction and distance of the siren sound.

[0756] A "display device" is a device that visually or audibly conveys a warning message to a user.

[0757] A "control system" is a computer system installed in a vehicle that controls automatic driving.

[0758] A "machine learning model" is an algorithm trained to identify siren sounds based on large amounts of data.

[0759] An "autonomous vehicle" is a vehicle that navigates autonomously to a destination without the intervention of a human driver.

[0760] A system for implementing the present invention is configured as a driver assistance application for an autonomous vehicle, and includes a user terminal, a server, a control system, and a display device.

[0761] server

[0762] The server has the function of receiving real-time voice data sent from the user terminal. The voice data is uploaded to the server using a specific API. The server inputs the received voice data into a voice analysis engine. This engine uses a machine learning model to identify siren sounds and further calculate the direction and distance of the sound source. This machine learning model is an algorithm trained on a large amount of data.

[0763] Specifically, the server analyzes the waveforms in the audio data to determine whether a particular sound wave pattern matches the sound of an ambulance or police siren. If there is a match, it calculates the direction and distance of the sound source and uses this information to generate a warning message. This warning message is then transmitted to the car's control system, alerting the driver of the approaching emergency vehicle.

[0764] User terminal

[0765] The user terminal receives the warning message sent from the server. The terminal has a communication function for transmitting voice data and warning messages to the vehicle's control system. Furthermore, the terminal can also transfer warning messages to the vehicle's display device via Apple CarPlay or Android Auto.

[0766] For example, when the user device receives a warning message, it immediately transfers the message to the vehicle's display device via the Apple CarPlay or Android Auto application. The display device then displays the received message in large letters on the screen so that the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed.

[0767] Control Systems and Displays

[0768] The control system is a computer system installed in a vehicle that controls autonomous driving. The control system executes appropriate driving actions based on warning messages transmitted from a user terminal. For example, if the control system detects an approaching emergency vehicle, it can reduce the vehicle's speed and execute appropriate evacuation actions.

[0769] The display device is a display installed inside the autonomous vehicle that displays the warning message transferred from the user terminal, allowing the driver to visually confirm the approach of an emergency vehicle.

[0770] Adding specific examples

[0771] As a concrete example, when an autonomous vehicle is driving on a highway, this system monitors the surrounding environmental sounds. For example, if an ambulance is approaching from 150 meters behind the vehicle, the user device will identify the siren and immediately send the audio data to the server. The server will analyze the audio data, generate a warning message, and send it to the user device and the vehicle's control system. The control system will then take appropriate driving action based on the warning message, "An ambulance is approaching 150 meters behind. Please give way." The same message will also be displayed on the display device, allowing the driver to check the situation.

[0772] Example prompts for generative AI models

[0773] Examples of prompts that use generative AI models include the following format:

[0774] Analyze surrounding audio data and identify siren sounds. Calculate the direction and distance of the siren sound and generate a warning message in the following format:

[0775] "Emergency vehicle approaching Y meters in X direction"

[0776] Example: Audio data: "WAV format audio data"

[0777] Using this prompt sentence, the siren sound can be accurately distinguished from the ambient sound and a warning message can be generated based on the appropriate distance and direction.

[0778] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0779] Step 1:

[0780] The user terminal uses a built-in microphone to collect surrounding audio data in real time, which is recorded at a fixed sampling rate and continuously transmitted to the server.

[0781] Step 2:

[0782] The server receives the voice data sent from the user terminal. The voice data received by the server is uploaded through a predefined API. The input data is saved in a buffer.

[0783] Step 3:

[0784] The server inputs the received audio data into a voice analysis engine, which uses a generative AI model to analyze the audio data. Specifically, it analyzes the waveform of the input data and determines whether a particular sound wave pattern matches the sound of a siren.

[0785] Step 4:

[0786] The server calculates the direction and distance of the siren sound based on the output of the sound analysis engine. This calculation is performed using the difference in sound intensity and arrival time. The input data is waveform data containing the characteristics of the siren sound, and the output data is information on direction and distance.

[0787] Step 5:

[0788] The server generates a warning message based on the siren sound identification result and the direction and distance information. The warning message format is "Emergency vehicle approaching in X direction, Y meters." The input data is the direction and distance information, and the output data is the generated warning message.

[0789] Step 6:

[0790] The server sends the generated warning message to the user terminal, and the sent warning message is received through the communication module of the user terminal. The input data is the warning message, and the output data is a notification to the user terminal.

[0791] Step 7:

[0792] The user terminal transmits the received warning message to the vehicle's control system, which analyzes the message and takes appropriate driving action, such as reducing speed and giving way.

[0793] Step 8:

[0794] At the same time, the user device transmits a warning message to the vehicle's display device via Apple CarPlay or Android Auto. The display device then displays the warning message on the screen so that the driver can visually confirm it. Specifically, the message is displayed in large letters to alert the driver.

[0795] 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.

[0796] The present invention combines a system that receives voice data from a user terminal, analyzes the voice data to identify siren sounds, generates a warning message based on that information, and displays it on a display device with an emotion engine that recognizes the user's emotions.

[0797] server

[0798] The server has the function of receiving real-time voice data transmitted from the user terminal. The voice data is uploaded to the server using a specific API, which is transmitted through a constantly connected real-time communication channel. The server inputs the received voice data into a voice analysis engine. The engine analyzes the voice data using a machine learning model and identifies the specific siren sound.

[0799] Specifically, the server analyzes the waveforms in the audio data and determines whether a specific sound wave pattern matches the siren of an ambulance or police vehicle. If there is a match, it calculates the direction and distance of the sound source and generates a warning message based on this information. This warning message is generated as text in a format such as "An ambulance is approaching 30 meters behind you to the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is immediately sent to the user's device.

[0800] Emotion Engine

[0801] The emotion engine recognizes the user's emotional state and performs analysis using biometric data acquired through sensors on the user's device, including heart rate, galvanic skin response, and facial expressions, to assess the user's stress level and other emotional states.

[0802] Specifically, the emotion engine analyzes biometric information acquired in real time to determine whether the user is in a specific emotional state. For example, if a high stress level is detected, the server can generate an additional comforting message for the user and send it along with the warning message. This comforting message can be displayed in a format such as "Please drive safely. Don't rush."

[0803] User terminal

[0804] The user terminal receives the warning message and the comfort message sent from the server. The terminal has the function of transmitting the warning message and the comfort message to the display device (display audio) of the vehicle via Apple CarPlay or Android Auto. The warning message displayed on the terminal is designed to be easily recognizable by the driver.

[0805] For example, when the user device receives a warning message, it immediately transfers the message to the car's display audio via the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so that the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed, and if the user's stress level is high, a comforting message such as "Please drive safely. Don't rush."

[0806] User

[0807] Before starting to drive, users must connect their smartphone to the display audio and activate Apple CarPlay or Android Auto. While driving, the user's device detects the sound of a siren and generates a warning message to display on the display device. By incorporating an emotion engine into this process, additional messages are also displayed according to the user's emotional state. This allows even hearing-impaired drivers to recognize the approach of an emergency vehicle and take appropriate action.

[0808] In this way, the present invention is a system that provides safe driving assistance to hearing-impaired people by identifying siren sounds, locating the sound source, generating and displaying warning messages based on that information, and also taking into account the user's emotional state and providing additional messages as needed.

[0809] The processing flow will be explained below.

[0810] server

[0811] Step 1: Receiving audio data

[0812] The server receives real-time voice data from user devices, which are uploaded via a specific API, which is then passed through a constantly connected real-time communication channel.

[0813] Step 2: Audio analysis

[0814] The server then inputs the received audio data into a voice analysis engine for prioritized analysis, which uses machine learning models to analyze the audio data and identify specific siren sounds.

[0815] Step 3: Calculate the direction and distance of the sound source

[0816] If the server identifies a siren, it runs an algorithm that calculates the direction and distance of the sound source, based on data such as the time difference between the arrival of sound waves.

[0817] Step 4: Emotion Engine Analysis

[0818] The server's emotion engine receives real-time biometric data from the user's device and analyzes the user's emotional state, including heart rate, galvanic skin response, and facial expressions.

[0819] Step 5: Generate a warning message

[0820] The server generates a warning message based on the calculation results. The warning message is generated as text in the form of, for example, "An ambulance is approaching 30 meters behind you on the right" or "There is room to evacuate about 1 meter to the left." If the server determines that the user is in a high stress state, it also generates an additional comforting message, such as, "Please drive safely. Don't rush."

[0821] Step 6: Sending a warning message

[0822] The server then sends the generated warning messages to the user's device in real time, using a specific API.

[0823] User terminal

[0824] Step 1: Receiving the warning message

[0825] The user terminal receives the alert message sent from the server via a real-time communication channel.

[0826] Step 2: Transfer to Display Audio

[0827] The user device will then transfer the received warning and comfort messages to the car's display audio via Apple CarPlay or Android Auto.

[0828] Display Audio

[0829] Step 1: Message display

[0830] The Display Audio receives warning and comfort messages sent from the terminal and instantly displays them on the screen in large letters that are easy for the driver to see.

[0831] User

[0832] Step 1: System Setup

[0833] When users get into their car, they need to connect their smartphone to the display audio and launch Apple CarPlay or Android Auto.

[0834] Step 2: Check for warnings

[0835] While driving, users can check warning and comforting messages displayed on the display audio, including information on the direction of approaching emergency vehicles, recommended evacuation actions, and messages based on the user's emotional state.

[0836] Step 3: Safe Operation

[0837] The user is expected to take appropriate safety measures based on the displayed warning and comforting messages. For example, if the message "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" is displayed, along with a comforting message such as "Please drive safely. Don't rush," the user is expected to take appropriate measures such as pulling over to the left.

[0838] Example 2

[0839] 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."

[0840] It is difficult for hearing-impaired drivers to properly recognize approaching emergency vehicles while driving, and the impact of the driver's emotional state on safe driving cannot be ignored. In such situations, a system is needed that allows drivers to recognize warnings in real time and receive appropriate comfort messages according to their emotional state. This is also expected to improve driver safety.

[0841] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving voice data from a user terminal, means for analyzing the voice data to identify a specific warning sound, means for calculating the direction and distance of the warning sound, means for generating a warning message based on information about the warning sound, means for acquiring biometric information from the user, means for analyzing the biometric information to identify an emotional state, means for generating a comforting message based on the emotional state, means for transmitting the warning message and the comforting message to the user terminal, means for transferring the message from the user terminal to a display device, and means for displaying the warning message and the comforting message on the display device. This allows drivers, including those who are hearing impaired, to recognize the approach of an emergency vehicle in real time while driving and to receive appropriate messages according to their emotional state.

[0842] A "user terminal" is a device for capturing and transmitting audio data to a server.

[0843] "Audio data" refers to data that includes ambient audio information captured by a user terminal.

[0844] "Specific warning sounds" refer to the sirens of emergency vehicles such as ambulances and police cars.

[0845] The "direction and distance of the warning sound" is information indicating the position where a particular warning sound is being generated and the distance to that position.

[0846] A "warning message" is warning information for a user that is generated based on the direction and distance of a specific warning sound.

[0847] "Biometric information" refers to physiological data such as the user's heart rate, galvanic skin response, and facial expressions.

[0848] The "emotional state" is the user's emotional state (for example, stress level) obtained by analyzing biometric information.

[0849] A "comforting message" is a message generated according to the user's emotional state to reassure the user.

[0850] A "display device" is a device that is installed in a user terminal or in a vehicle and that displays warning messages and comforting messages.

[0851] "Server" refers to a computer system that receives voice data and biometric information sent from a user terminal and generates and transmits warning messages and comfort messages based on the received data.

[0852] The present invention provides a system that receives voice data from a user terminal, analyzes the voice data, and identifies specific warning sounds based on the received voice data. The system then uploads the voice data to a server via a real-time communication channel using a specific API, analyzes the voice data using a machine learning model, and generates a warning message. The system also collects biometric information from the user, evaluates their emotional state based on the biometric information, and generates a comforting message as needed. Specific embodiments are described in detail below.

[0853] server

[0854] The server has the function of receiving real-time voice data sent from the user terminal. A specific WebSocket API is used for this reception, and the voice data is sent to the server through a real-time communication channel. The received voice data is temporarily stored in a buffer in memory.

[0855] The server then inputs the received audio data into a speech analysis engine, which uses a generative AI model such as TensorFlow to analyze the audio data and identify specific warning sounds (such as ambulance or police sirens). The features of the identified warning sounds are extracted by converting the waveform data into a spectrogram.

[0856] The direction and distance calculation function calculates the direction and distance of the identified warning sound, and based on this, the server generates a warning message such as "Ambulance approaching 30 meters behind to the right" and saves it in text format.

[0857] Furthermore, the emotion engine uses biometric information reception to receive data such as heart rate, galvanic skin response, and facial expressions obtained from the user's sensors. This data is used by machine learning models using Emotion API and Azure Cognitive Services to evaluate the user's emotional state. If the emotional state is high stress, an additional comforting message such as "Please drive safely and don't rush" is generated.

[0858] User terminal

[0859] The user device receives warning and comfort messages from the server and transmits them to the car's display device via Apple CarPlay or Android Auto. The received messages are displayed in large letters on the display audio so that the driver can see them at a glance.

[0860] User

[0861] Before starting to drive, users need to connect their smartphone to the car's display audio and activate Apple CarPlay or Android Auto. While driving, the user's device captures surrounding sounds and transmits them to the server in real time.

[0862] Example

[0863] For example, if a user connects their smartphone to Apple CarPlay before starting to drive and the device detects a warning sound from the rear right while driving, the server will generate a warning message saying, "An ambulance is approaching 30 meters behind the right." If the emotion engine detects a high stress level in the user, it will generate an additional comforting message saying, "Please drive safely. Don't rush." ​​These messages are displayed on the display audio via the user's device.

[0864] An example prompt for a generative AI model might look something like this:

[0865] "An ambulance is approaching 30 meters behind and to the right. The user's stress level is high, so please drive safely. Do not rush."

[0866] In this way, this system supports safe driving by recognizing approaching emergency vehicles in real time while hearing-impaired drivers are driving and providing appropriate messages according to their emotional state.

[0867] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0868] Step 1:

[0869] The server receives real-time audio data from the user device. The device captures surrounding audio data using a microphone and sends the data to the server using the WebSocket API. This audio data is stored as a buffer in the server's memory. The input is the audio data from the user device, and the output is the audio data stored in the buffer.

[0870] Step 2:

[0871] The server inputs the audio data stored in the buffer into a speech analysis engine. This analysis engine uses generative AI models such as TensorFlow to analyze the audio data. Specifically, it converts the waveform data into a spectrogram and extracts characteristic patterns of specific warning sounds (e.g., ambulance or police sirens). The input is the stored audio data, and the output is the result of the warning sound identification.

[0872] Step 3:

[0873] The server calculates the direction and distance of the identified warning sound based on the result of the warning sound identification. To do this, an algorithm is used to determine the direction of the sound source and calculate the distance based on the result of the voice analysis engine. The input is the identification result of the voice analysis engine, and the output is information on the direction and distance.

[0874] Step 4:

[0875] The server generates a warning message based on the direction and distance information. For example, it generates a text message in the format "An ambulance is approaching 30 meters behind and to the right." This message is temporarily stored in memory. The input is the direction and distance information, and the output is the text data of the warning message.

[0876] Step 5:

[0877] The emotion engine receives biometric data (heart rate, galvanic skin response, facial expressions, etc.) acquired from sensors on the user device. These data are captured in real time by the user device and sent to the server. The input is the biometric data from the user device, and the output is the received biometric data.

[0878] Step 6:

[0879] The emotion engine analyzes the user's emotional state based on biometric data. For analysis, it uses machine learning models such as the Emotion API and Azure Cognitive Services. For example, if a high stress level is detected, this information is stored in memory. The input is biometric data, and the output is a judgment of the user's emotional state.

[0880] Step 7:

[0881] The server generates the necessary comforting message based on the result of the emotional state judgment. For example, it generates a message in the format of "Please drive safely. Don't rush." ​​This message is also stored in memory in text format. The input is the result of the emotional state judgment, and the output is the text data of the comforting message.

[0882] Step 8:

[0883] The server transmits the generated warning and comfort messages to the user terminal using a real-time communication channel to a notification system or dedicated application of the user terminal, with the input being the text data of the warning and comfort messages and the output being the transmitted messages.

[0884] Step 9:

[0885] The user terminal transfers the received message to the car's display device via Apple CarPlay or Android Auto. The terminal formats the received message for display on the display audio and displays it in large letters on the screen. The input is the message from the server, and the output is the message displayed on the display device.

[0886] (Application example 2)

[0887] 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."

[0888] Conventional driving assistance systems detect siren sounds and generate warning messages, but they lack measures that take into account the driver's emotional state, making it difficult for drivers to respond appropriately when under high stress. Hearing-impaired drivers also have limited means of properly recognizing approaching emergency vehicles.

[0889] The identification processing 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 receiving voice data from a user terminal, means for analyzing the voice data to identify a siren sound, means for calculating the direction and distance of the siren sound, means for generating a warning message based on information about the siren sound, means for transmitting the warning message to the user terminal, means for acquiring biometric information data of the user and recognizing an emotional state, means for generating an additional message according to the emotional state, means for transferring the warning message and the additional message from the user terminal to a display device, and means for displaying the warning message and the additional message on the display device. This enables appropriate responses even when the driver is in a high-stress state, and further enables safe driving assistance for hearing-impaired people.

[0890] "Audio data" refers to sound wave information acquired from a user terminal, including conversations, environmental sounds, siren sounds, and the like.

[0891] A "siren sound" is a special sound made by emergency vehicles such as ambulances, fire engines, and police cars, and has a specific sound wave pattern.

[0892] "Biometric data" refers to various data obtained from the user's body, including heart rate, galvanic skin response, facial expressions, and the like.

[0893] "Emotional state" indicates the user's current emotional state, and includes stress level, pleasure, discomfort, and the like.

[0894] A "warning message" is a notification message generated based on the detection result of a siren sound, and is used to notify the driver of an approaching emergency situation.

[0895] The "additional message" is a message of encouragement or comfort that is generated according to the user's emotional state, and is intended to reduce the mental burden on the driver.

[0896] "Display device" refers to a display device connected to a vehicle or a user terminal for displaying warning messages and additional messages.

[0897] A "machine learning model" is an algorithm or framework that learns from past data and analyzes voice data and identifies patterns.

[0898] The present invention combines a system that receives voice data from a user terminal, identifies siren sounds, generates a warning message based on that information, and displays it on a display device with an emotion engine that recognizes the user's emotions. The system program and its processing are described in detail below.

[0899] The server has the function of receiving real-time voice data transmitted from the user terminal. The voice data is uploaded to the server using a specific API, which is transmitted through a constantly connected real-time communication channel. The server inputs the received voice data into a voice analysis engine. The engine uses a machine learning model to analyze the voice data and identify the specific siren sound.

[0900] For example, the server analyzes the waveforms in the audio data and determines whether a specific sound wave pattern matches the siren of an ambulance or police vehicle. If there is a match, it calculates the direction and distance of the sound source and generates a warning message based on this information. This warning message is generated as text in a format such as "An ambulance is approaching 30 meters behind you on the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is immediately sent to the user's device.

[0901] The emotion engine recognizes the user's emotional state and performs analysis using biometric data acquired through sensors on the user's device. The biometric data includes heart rate, galvanic skin response, and facial expressions, and uses this data to assess the user's stress level and other emotional states. For example, if a high stress level is detected, the server can generate an additional comforting message for the user and send it along with the warning message. This comforting message could be displayed in a format such as "Drive safely. Don't rush."

[0902] The user device receives the warning and comfort messages sent from the server. The device is a typical smartphone or tablet, and has the function of transmitting messages to the vehicle's display device (display audio) via Apple CarPlay or Android Auto. The warning messages displayed on the device are designed to be easily recognizable by the driver.

[0903] Specifically, when the user device receives a warning message, it immediately transfers the message to the car's display audio using the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed, and if the user's stress level is high, a comforting message such as "Please drive safely. Don't rush."

[0904] A concrete example of a prompt sentence could be input to a generative AI model in the following form:

[0905] The user is driving an autonomous vehicle. This application uses the in-car microphone to collect audio data and analyzes it on a server to determine whether a siren is present. If a siren is detected, a warning message is generated and displayed on the in-car display. At the same time, biometric sensors collect the user's heart rate and skin potential, and an emotion engine evaluates the user's stress level. If the stress level is high, an additional comforting message is displayed. This system supports the user in driving safely.

[0906] In this way, the present invention provides a system that combines real-time voice data analysis and an emotion engine to support safe driving by the driver.

[0907] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0908] Step 1:

[0909] The user starts the car engine and connects the smartphone to the display audio. The device launches the Apple CarPlay or Android Auto application, which prepares the device for collecting voice data and displaying messages. The input is the connection status between the smartphone and the in-car display. The output is a notification that the system is ready to operate.

[0910] Step 2:

[0911] The device begins collecting real-time audio data using the in-car microphone. Specifically, the audio data is buffered for a short period of time and then transmitted to the server at regular intervals. The input includes ambient sounds and conversations inside the car. The output is a real-time audio data stream.

[0912] Step 3:

[0913] The server receives the audio data sent from the device. The received audio data is analyzed using a machine learning model to identify the specific siren sound. The input is the audio data. The output is the presence or absence of a siren sound, and if identified, the direction and distance information.

[0914] Step 4:

[0915] The server generates a warning message based on the direction and distance information of the siren sound. Specifically, it analyzes the direction and distance of the sound source and creates a warning message such as "An ambulance is approaching 30 meters behind you to the right." The input is the direction and distance information of the siren sound. The output is the generated warning message.

[0916] Step 5:

[0917] The device acquires the user's biometric data. Specifically, it uses a heart rate sensor, a skin electrodermal response sensor, a facial recognition camera, etc. to collect the user's heart rate, skin electrodermal response, and facial expression data. The input is the user's biometric information. The output is real-time biometric data.

[0918] Step 6:

[0919] The server uses an emotion engine to analyze the biometric data and evaluate the user's emotional state. Specifically, it uses the collected biometric data to determine the user's emotional state, such as stress level and pleasantness / unpleasantness. The input is the biometric data, and the output is the evaluated emotional state.

[0920] Step 7:

[0921] The server generates an additional comforting message according to the user's emotional state. Specifically, if the user's stress level is high, the server generates a comforting message such as "Please drive safely. Don't rush." ​​The input is the user's emotional state. The output is the additional message.

[0922] Step 8:

[0923] The server sends the warning message and the additional message to the user terminal. The input is the generated warning message and the additional message. The output is the completion of message transmission to the user terminal.

[0924] Step 9:

[0925] The terminal transfers the received warning messages and additional messages to the in-car display via the Apple CarPlay or Android Auto application. The input is the message from the server. The output is the completion of transfer to the in-car display.

[0926] Step 10:

[0927] The display device displays warning messages and additional messages on the screen. Specifically, the messages are displayed in large letters so that the driver can see them at a glance. The input is a message transferred from the terminal. The output is the message displayed on the screen.

[0928] By the above steps, the present invention allows drivers to respond appropriately even in high-stress situations, and enables hearing-impaired people to drive safely.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] [Fourth embodiment]

[0933] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0934] 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.

[0935] 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).

[0936] 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.

[0937] 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.

[0938] 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).

[0939] 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.

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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.

[0945] 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."

[0946] The present invention relates to a system that receives audio data from a user terminal, analyzes the audio data to identify siren sounds, and generates a warning message based on that information and displays it on a display device.

[0947] server

[0948] The server has the function of receiving real-time voice data sent from the user terminal. The voice data is uploaded to the server using a specific API. The server inputs the received voice data into a voice analysis engine. This engine uses a machine learning model to identify siren sounds and further calculate the direction and distance of the sound source.

[0949] Specifically, the server analyzes the waveforms in the audio data and determines whether a specific sound wave pattern matches the siren of an ambulance or police vehicle. If there is a match, it calculates the direction and distance of the sound source and generates a warning message based on this information. This warning message takes the form of something like "An ambulance is approaching 30 meters behind you to the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is immediately sent to the user's device.

[0950] User terminal

[0951] The user terminal receives the warning message sent from the server. The terminal has the function of transferring the warning message to the vehicle's display device (display audio) via Apple CarPlay or Android Auto. The warning message displayed on the terminal is designed to be easily recognizable by the driver.

[0952] For example, when the user device receives a warning message, it immediately transfers the message to the car's display audio via the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so that the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed.

[0953] User

[0954] Before starting to drive, users need to connect their smartphone to the display audio and start Apple CarPlay or Android Auto. While driving, the user's device detects the sound of a siren and generates a warning message to display on the display device. This allows even hearing-impaired drivers to recognize the approach of an emergency vehicle and take appropriate action.

[0955] As a specific example of a driving scenario, if a user is driving and an ambulance approaches 30 meters behind them to the right, a warning message stating "Ambulance approaching 30 meters behind them to the right" will be displayed on the user's display audio. If a message is also displayed stating "There is room to move out of the way about 1 meter to the left," the user can move the car to the left and safely give way to the ambulance.

[0956] In this way, the present invention is a system that realizes safe driving assistance for hearing-impaired people by identifying siren sounds, locating the sound source, and generating and displaying warning messages based on the identified sounds.

[0957] The processing flow will be explained below.

[0958] server

[0959] Step 1: Receiving audio data

[0960] The server receives real-time voice data from the user terminal, which is uploaded via a specific API, which is transmitted through a constantly connected real-time communication channel.

[0961] Step 2: Audio analysis

[0962] The server then inputs the received audio data into a voice analysis engine for prioritized analysis, which uses machine learning models to analyze the audio data and identify the specific siren sound.

[0963] Step 3: Calculate the direction and distance of the sound source

[0964] If a siren sound is identified, the server runs an algorithm to calculate the direction and distance of the sound source, based on data such as the time difference between the arrival of the sound waves.

[0965] Step 4: Generate a warning message

[0966] The server generates a warning message based on the calculation results. The warning message is generated as text in the form of, for example, "An ambulance is approaching 30 meters behind you on the right" or "There is room to evacuate about 1 meter to the left."

[0967] Step 5: Sending a warning message

[0968] The server sends the generated warning message to the user terminal in real time, also using a specific API.

[0969] User terminal

[0970] Step 1: Receiving the warning message

[0971] The user terminal receives the alert message sent from the server via a real-time communication channel.

[0972] Step 2: Transfer to Display Audio

[0973] The user terminal transfers the received warning message to the car's display audio via Apple CarPlay or Android Auto.

[0974] Display Audio

[0975] Step 1: Message display

[0976] Display Audio receives warning messages sent from the terminal and immediately displays them on the screen in large letters that are easy for the driver to see.

[0977] User

[0978] Step 1: System Setup

[0979] When the user gets into the car, they connect their smartphone to the display audio and start Apple CarPlay or Android Auto.

[0980] Step 2: Check for warnings

[0981] While driving, the user checks the warning message displayed on the display audio, which describes the direction of the approaching emergency vehicle and the recommended evacuation action.

[0982] Step 3: Safe Operation

[0983] The user takes appropriate safety measures based on the displayed warning message. For example, if the message "An ambulance is approaching 30 meters behind and to the right" or "There is room to evacuate about 1 meter to the left" is displayed, the user will take measures such as moving the car to the left.

[0984] Example 1

[0985] 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."

[0986] Conventional driver assistance systems have made it difficult for hearing-impaired people to recognize approaching emergency vehicles, resulting in impaired driving safety. In particular, if they cannot hear the siren, it is difficult for them to take appropriate action, which can prevent them from quickly changing course or evacuating, increasing the risk of an accident.

[0987] 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.

[0988] In this invention, the server includes means for receiving voice data from a user terminal, means for analyzing the voice data to identify a siren sound, means for calculating the direction and distance of the siren sound, means for generating a warning message based on information about the siren sound, means for transmitting the warning message to the user terminal, means for transferring the warning message from the user terminal to a display device, means for displaying the warning message on the display device, and means for visually displaying the warning message so that the driver can easily recognize it, thereby enabling hearing-impaired people to reliably recognize an approaching emergency vehicle and safely evacuate.

[0989] A "user terminal" is an electronic device that has the function of collecting voice data and transmitting it to a server.

[0990] "Audio data" refers to data that records surrounding sounds as digital signals.

[0991] The "server" refers to a computer system that analyzes voice data received from a user terminal, generates a warning message, and transmits the message to the user terminal.

[0992] A "voice analysis engine" is software or algorithms that analyze received voice data and identify siren sounds.

[0993] A "machine learning model" is an algorithm that is optimized to perform a specific task (in this case, identifying siren sounds) using large amounts of training data.

[0994] A "warning message" is an information message that is generated based on the detection of a siren sound and its direction and distance, and that notifies the user of danger.

[0995] A "display device" refers to a device for visually displaying a warning message, such as a user terminal or a car display audio.

[0996] A "visual alert" is a visual signal or message that is displayed on a display device to convey information or a warning to a user.

[0997] The present invention relates to a system that receives audio data from a user terminal, analyzes the audio data to identify siren sounds, and generates a warning message based on that information and displays it on a display device.

[0998] server

[0999] The server has the function of receiving real-time voice data sent from the user device. The voice data is uploaded to the server using a specific API. Specifically, a WebSocket API or RESTful API can be used. The server is equipped with a voice analysis engine for analyzing the received voice data. This voice analysis engine is a model built using a machine learning framework such as TensorFlow or PyTorch.

[1000] The server analyzes the waveforms in the audio data and determines whether a particular sound wave pattern matches the siren of an ambulance or police vehicle. Once a siren is identified, it then uses a Python algorithm to calculate the direction and distance of the sound source, using techniques such as Time Difference of Arrival (TDOA). Based on the results of this calculation, the server generates a warning message. This warning message is generated in the form of, for example, "An ambulance is approaching 30 meters behind you to the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is sent to the user's device in real time.

[1001] User terminal

[1002] The user terminal has the function of receiving the warning message sent from the server, and then forwards this message to the vehicle's display device (display audio) via Apple CarPlay or Android Auto. The warning message is visually displayed in large letters so that the driver can easily recognize it.

[1003] For example, when a user device receives a warning message, it immediately transmits the message to the car's display audio via the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so that the driver can see it at a glance. For example, if the message "Ambulance approaching 30 meters behind to the right" is displayed, the driver can immediately recognize the information and take action.

[1004] User

[1005] Before starting to drive, users must connect their smartphone to the Display Audio and activate Apple CarPlay or Android Auto. While driving, the system identifies siren sounds and generates and displays a warning message, allowing even hearing-impaired drivers to recognize approaching emergency vehicles and respond appropriately.

[1006] As a specific example of a driving scenario, if a user is driving and an ambulance approaches 30 meters behind them to the right, the user's display audio will display "Ambulance approaching 30 meters behind them to the right." If it also displays "Room for evacuation approximately 1 meter to the left," the user can move the car to the left and safely give way to the ambulance.

[1007] Prompt Sentence Examples

[1008] Imagine a situation where a user is driving and hears the sound of an ambulance siren 30 meters behind them to the right. In this case, the server inputs the received audio data into the TensorFlow audio analysis engine, identifies the siren sound, calculates the direction and distance of the sound source, generates a warning message and sends it to the user's device. The user's device then transmits the warning message to the car's display audio via Apple CarPlay, and the display device displays something like "Ambulance approaching 30 meters behind the right." Please explain the specific processing steps.

[1009] In this way, the present invention is a system that helps hearing-impaired people drive safely by identifying the siren sound, locating the sound source, and generating and displaying a warning message based on that information.

[1010] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1011] Step 1:

[1012] The user gets into the car and connects the smartphone to the display audio.

[1013] Input: Smartphone, Display Audio

[1014] How it works: Connect your smartphone to the Display Audio using a USB cable and launch Apple CarPlay or Android Auto.

[1015] Output: Smartphone and Display Audio are connected

[1016] Step 2:

[1017] The user terminal captures the surrounding sounds in real time and transmits the sound data to the server.

[1018] Input: Ambient audio

[1019] How it works: It uses a smartphone's microphone to capture audio in real time, and then streams the captured audio data to a server via a WebSocket API.

[1020] Output: Audio data sent to the server

[1021] Step 3:

[1022] The server analyzes the received voice data using a machine learning model.

[1023] Input: Audio data sent to the server

[1024] How it works: The received audio data is input into the audio analysis engine, and the presence or absence of a siren is analyzed using a machine learning model using TensorFlow or PyTorch.

[1025] Output: Analysis result regarding the presence or absence of siren sound

[1026] Step 4:

[1027] The server identifies the siren sound and calculates the direction and distance of the sound source.

[1028] Input: Analysis result regarding the presence or absence of siren sound

[1029] How it works: If a siren sound is identified, a Python algorithm is used to calculate the direction and distance of the sound source using the TDOA (Time Difference of Arrival) method.

[1030] Output: Information about the direction and distance of the sound source

[1031] Step 5:

[1032] The server generates a warning message.

[1033] Input: Information about the direction and distance of the sound source

[1034] What it does: Based on the calculated direction and distance information, it creates a warning message such as "Ambulance approaching 30 meters behind and to the right." It also adds information about room to retreat if necessary.

[1035] Output: Generated warning message

[1036] Step 6:

[1037] The server sends a warning message to the user terminal.

[1038] Input: The generated warning message

[1039] Operation: Generated warning messages are sent in real time to the user's terminal via HTTP or WebSocket.

[1040] Output: The warning message delivered to the user's terminal.

[1041] Step 7:

[1042] The user terminal receives the alert message and forwards it to the display audio.

[1043] Input: Warning message delivered to the user's device

[1044] What it does: Transmits warning messages to the car's display audio via Apple CarPlay or Android Auto.

[1045] Output: Warning messages forwarded to Display Audio

[1046] Step 8:

[1047] The Display Audio will visually display a warning message.

[1048] Input: Warning message forwarded to Display Audio

[1049] What it does: A warning message is displayed on the screen in large letters so that the driver can see it at a glance. For example, it may say, "Ambulance approaching 30 meters behind and to the right."

[1050] Output: Warning message presented to the driver

[1051] (Application example 1)

[1052] 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."

[1053] In autonomous vehicles, there is a lack of support for drivers to quickly and reliably recognize approaching emergency vehicles and take appropriate driving action. Therefore, there is a need for a system that can accurately recognize approaching emergency vehicles and safely give way to them, especially for people with hearing impairments or who have difficulty distinguishing environmental sounds.

[1054] 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.

[1055] In this invention, the server includes means for receiving voice data from the user terminal, means for analyzing the voice data to identify a siren sound, means for calculating the direction and distance of the siren sound, means for generating a warning message based on information about the siren sound, means for transmitting the warning message to the user terminal, means for transferring the warning message from the user terminal to a display device, means for displaying the warning message on the display device, means for transmitting the voice data from the user terminal to a control system installed in the vehicle and notifying the vehicle control system of an approaching emergency vehicle, and means for the control system to perform an appropriate driving operation based on the warning message. This enables the autonomously driven vehicle to accurately recognize an approaching emergency vehicle and safely give way to it.

[1056] A "user terminal" is a device that collects voice data and transmits it to a server.

[1057] "Audio data" is a recording of sound collected from a user terminal.

[1058] A "siren" is a specific sound made by emergency vehicles.

[1059] The "warning message" is the content of a warning generated based on the direction and distance of the siren sound.

[1060] A "display device" is a device that visually or audibly conveys a warning message to a user.

[1061] A "control system" is a computer system installed in a vehicle that controls automatic driving.

[1062] A "machine learning model" is an algorithm trained to identify siren sounds based on large amounts of data.

[1063] An "autonomous vehicle" is a vehicle that navigates autonomously to a destination without the intervention of a human driver.

[1064] A system for implementing the present invention is configured as a driver assistance application for an autonomous vehicle, and includes a user terminal, a server, a control system, and a display device.

[1065] server

[1066] The server has the function of receiving real-time voice data sent from the user terminal. The voice data is uploaded to the server using a specific API. The server inputs the received voice data into a voice analysis engine. This engine uses a machine learning model to identify siren sounds and further calculate the direction and distance of the sound source. This machine learning model is an algorithm trained on a large amount of data.

[1067] Specifically, the server analyzes the waveforms in the audio data to determine whether a particular sound wave pattern matches the sound of an ambulance or police siren. If there is a match, it calculates the direction and distance of the sound source and uses this information to generate a warning message. This warning message is then transmitted to the car's control system, alerting the driver of the approaching emergency vehicle.

[1068] User terminal

[1069] The user terminal receives the warning message sent from the server. The terminal has a communication function for transmitting voice data and warning messages to the vehicle's control system. Furthermore, the terminal can also transfer warning messages to the vehicle's display device via Apple CarPlay or Android Auto.

[1070] For example, when the user device receives a warning message, it immediately transfers the message to the vehicle's display device via the Apple CarPlay or Android Auto application. The display device then displays the received message in large letters on the screen so that the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed.

[1071] Control Systems and Displays

[1072] The control system is a computer system installed in a vehicle that controls autonomous driving. The control system executes appropriate driving actions based on warning messages transmitted from a user terminal. For example, if the control system detects an approaching emergency vehicle, it can reduce the vehicle's speed and execute appropriate evacuation actions.

[1073] The display device is a display installed inside the autonomous vehicle that displays the warning message transferred from the user terminal, allowing the driver to visually confirm the approach of an emergency vehicle.

[1074] Adding specific examples

[1075] As a concrete example, when an autonomous vehicle is driving on a highway, this system monitors the surrounding environmental sounds. For example, if an ambulance is approaching from 150 meters behind the vehicle, the user device will identify the siren and immediately send the audio data to the server. The server will analyze the audio data, generate a warning message, and send it to the user device and the vehicle's control system. The control system will then take appropriate driving action based on the warning message, "An ambulance is approaching 150 meters behind. Please give way." The same message will also be displayed on the display device, allowing the driver to check the situation.

[1076] Example prompts for generative AI models

[1077] Examples of prompts that use generative AI models include the following format:

[1078] Analyze surrounding audio data and identify siren sounds. Calculate the direction and distance of the siren sound and generate a warning message in the following format:

[1079] "Emergency vehicle approaching Y meters in X direction"

[1080] Example: Audio data: "WAV format audio data"

[1081] Using this prompt sentence, the siren sound can be accurately distinguished from the ambient sound and a warning message can be generated based on the appropriate distance and direction.

[1082] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1083] Step 1:

[1084] The user terminal uses a built-in microphone to collect surrounding audio data in real time, which is recorded at a fixed sampling rate and continuously transmitted to the server.

[1085] Step 2:

[1086] The server receives the voice data sent from the user terminal. The voice data received by the server is uploaded through a predefined API. The input data is saved in a buffer.

[1087] Step 3:

[1088] The server inputs the received audio data into a voice analysis engine, which uses a generative AI model to analyze the audio data. Specifically, it analyzes the waveform of the input data and determines whether a particular sound wave pattern matches the sound of a siren.

[1089] Step 4:

[1090] The server calculates the direction and distance of the siren sound based on the output of the sound analysis engine. This calculation is performed using the difference in sound intensity and arrival time. The input data is waveform data containing the characteristics of the siren sound, and the output data is information on direction and distance.

[1091] Step 5:

[1092] The server generates a warning message based on the siren sound identification result and the direction and distance information. The warning message format is "Emergency vehicle approaching in X direction, Y meters." The input data is the direction and distance information, and the output data is the generated warning message.

[1093] Step 6:

[1094] The server sends the generated warning message to the user terminal, and the sent warning message is received through the communication module of the user terminal. The input data is the warning message, and the output data is a notification to the user terminal.

[1095] Step 7:

[1096] The user terminal transmits the received warning message to the vehicle's control system, which analyzes the message and takes appropriate driving action, such as reducing speed and giving way.

[1097] Step 8:

[1098] At the same time, the user device transmits a warning message to the vehicle's display device via Apple CarPlay or Android Auto. The display device then displays the warning message on the screen so that the driver can visually confirm it. Specifically, the message is displayed in large letters to alert the driver.

[1099] 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.

[1100] The present invention combines a system that receives voice data from a user terminal, analyzes the voice data to identify siren sounds, generates a warning message based on that information, and displays it on a display device with an emotion engine that recognizes the user's emotions.

[1101] server

[1102] The server has the function of receiving real-time voice data transmitted from the user terminal. The voice data is uploaded to the server using a specific API, which is transmitted through a constantly connected real-time communication channel. The server inputs the received voice data into a voice analysis engine. The engine analyzes the voice data using a machine learning model and identifies the specific siren sound.

[1103] Specifically, the server analyzes the waveforms in the audio data and determines whether a specific sound wave pattern matches the siren of an ambulance or police vehicle. If there is a match, it calculates the direction and distance of the sound source and generates a warning message based on this information. This warning message is generated as text in a format such as "An ambulance is approaching 30 meters behind you to the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is immediately sent to the user's device.

[1104] Emotion Engine

[1105] The emotion engine recognizes the user's emotional state and performs analysis using biometric data acquired through sensors on the user's device, including heart rate, galvanic skin response, and facial expressions, to assess the user's stress level and other emotional states.

[1106] Specifically, the emotion engine analyzes biometric information acquired in real time to determine whether the user is in a specific emotional state. For example, if a high stress level is detected, the server can generate an additional comforting message for the user and send it along with the warning message. This comforting message can be displayed in a format such as "Please drive safely. Don't rush."

[1107] User terminal

[1108] The user terminal receives the warning message and the comfort message sent from the server. The terminal has the function of transmitting the warning message and the comfort message to the display device (display audio) of the vehicle via Apple CarPlay or Android Auto. The warning message displayed on the terminal is designed to be easily recognizable by the driver.

[1109] For example, when the user device receives a warning message, it immediately transfers the message to the car's display audio via the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so that the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed, and if the user's stress level is high, a comforting message such as "Please drive safely. Don't rush."

[1110] User

[1111] Before starting to drive, users must connect their smartphone to the display audio and activate Apple CarPlay or Android Auto. While driving, the user's device detects the sound of a siren and generates a warning message to display on the display device. By incorporating an emotion engine into this process, additional messages are also displayed according to the user's emotional state. This allows even hearing-impaired drivers to recognize the approach of an emergency vehicle and take appropriate action.

[1112] In this way, the present invention is a system that provides safe driving assistance to hearing-impaired people by identifying siren sounds, locating the sound source, generating and displaying warning messages based on that information, and also taking into account the user's emotional state and providing additional messages as needed.

[1113] The processing flow will be explained below.

[1114] server

[1115] Step 1: Receiving audio data

[1116] The server receives real-time voice data from user devices, which are uploaded via a specific API, which is then passed through a constantly connected real-time communication channel.

[1117] Step 2: Audio analysis

[1118] The server then inputs the received audio data into a voice analysis engine for prioritized analysis, which uses machine learning models to analyze the audio data and identify specific siren sounds.

[1119] Step 3: Calculate the direction and distance of the sound source

[1120] If the server identifies a siren, it runs an algorithm that calculates the direction and distance of the sound source, based on data such as the time difference between the arrival of sound waves.

[1121] Step 4: Emotion Engine Analysis

[1122] The server's emotion engine receives real-time biometric data from the user's device and analyzes the user's emotional state, including heart rate, galvanic skin response, and facial expressions.

[1123] Step 5: Generate a warning message

[1124] The server generates a warning message based on the calculation results. The warning message is generated as text in the form of, for example, "An ambulance is approaching 30 meters behind you on the right" or "There is room to evacuate about 1 meter to the left." If the server determines that the user is in a high stress state, it also generates an additional comforting message, such as, "Please drive safely. Don't rush."

[1125] Step 6: Sending a warning message

[1126] The server then sends the generated warning messages to the user's device in real time, using a specific API.

[1127] User terminal

[1128] Step 1: Receiving the warning message

[1129] The user terminal receives the alert message sent from the server via a real-time communication channel.

[1130] Step 2: Transfer to Display Audio

[1131] The user device will then transfer the received warning and comfort messages to the car's display audio via Apple CarPlay or Android Auto.

[1132] Display Audio

[1133] Step 1: Message display

[1134] The Display Audio receives warning and comfort messages sent from the terminal and instantly displays them on the screen in large letters that are easy for the driver to see.

[1135] User

[1136] Step 1: System Setup

[1137] When users get into their car, they need to connect their smartphone to the display audio and launch Apple CarPlay or Android Auto.

[1138] Step 2: Check for warnings

[1139] While driving, users can check warning and comforting messages displayed on the display audio, including information on the direction of approaching emergency vehicles, recommended evacuation actions, and messages based on the user's emotional state.

[1140] Step 3: Safe Operation

[1141] The user is expected to take appropriate safety measures based on the displayed warning and comforting messages. For example, if the message "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" is displayed, along with a comforting message such as "Please drive safely. Don't rush," the user is expected to take appropriate measures such as pulling over to the left.

[1142] Example 2

[1143] 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."

[1144] It is difficult for hearing-impaired drivers to properly recognize approaching emergency vehicles while driving, and the impact of the driver's emotional state on safe driving cannot be ignored. In such situations, a system is needed that allows drivers to recognize warnings in real time and receive appropriate comfort messages according to their emotional state. This is also expected to improve driver safety.

[1145] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving voice data from a user terminal, means for analyzing the voice data to identify a specific warning sound, means for calculating the direction and distance of the warning sound, means for generating a warning message based on information about the warning sound, means for acquiring biometric information from the user, means for analyzing the biometric information to identify an emotional state, means for generating a comforting message based on the emotional state, means for transmitting the warning message and the comforting message to the user terminal, means for transferring the message from the user terminal to a display device, and means for displaying the warning message and the comforting message on the display device. This allows drivers, including those who are hearing impaired, to recognize the approach of an emergency vehicle in real time while driving and to receive appropriate messages according to their emotional state.

[1146] A "user terminal" is a device for capturing and transmitting audio data to a server.

[1147] "Audio data" refers to data that includes ambient audio information captured by a user terminal.

[1148] "Specific warning sounds" refer to the sirens of emergency vehicles such as ambulances and police cars.

[1149] The "direction and distance of the warning sound" is information indicating the position where a particular warning sound is being generated and the distance to that position.

[1150] A "warning message" is warning information for a user that is generated based on the direction and distance of a specific warning sound.

[1151] "Biometric information" refers to physiological data such as the user's heart rate, galvanic skin response, and facial expressions.

[1152] The "emotional state" is the user's emotional state (for example, stress level) obtained by analyzing biometric information.

[1153] A "comforting message" is a message generated according to the user's emotional state to reassure the user.

[1154] A "display device" is a device that is installed in a user terminal or in a vehicle and that displays warning messages and comforting messages.

[1155] "Server" refers to a computer system that receives voice data and biometric information sent from a user terminal and generates and transmits warning messages and comfort messages based on the received data.

[1156] The present invention provides a system that receives voice data from a user terminal, analyzes the voice data, and identifies specific warning sounds based on the received voice data. The system then uploads the voice data to a server via a real-time communication channel using a specific API, analyzes the voice data using a machine learning model, and generates a warning message. The system also collects biometric information from the user, evaluates their emotional state based on the biometric information, and generates a comforting message as needed. Specific embodiments are described in detail below.

[1157] server

[1158] The server has the function of receiving real-time voice data sent from the user terminal. A specific WebSocket API is used for this reception, and the voice data is sent to the server through a real-time communication channel. The received voice data is temporarily stored in a buffer in memory.

[1159] The server then inputs the received audio data into a speech analysis engine, which uses a generative AI model such as TensorFlow to analyze the audio data and identify specific warning sounds (such as ambulance or police sirens). The features of the identified warning sounds are extracted by converting the waveform data into a spectrogram.

[1160] The direction and distance calculation function calculates the direction and distance of the identified warning sound, and based on this, the server generates a warning message such as "Ambulance approaching 30 meters behind to the right" and saves it in text format.

[1161] Furthermore, the emotion engine uses biometric information reception to receive data such as heart rate, galvanic skin response, and facial expressions obtained from the user's sensors. This data is used by machine learning models using Emotion API and Azure Cognitive Services to evaluate the user's emotional state. If the emotional state is high stress, an additional comforting message such as "Please drive safely and don't rush" is generated.

[1162] User terminal

[1163] The user device receives warning and comfort messages from the server and transmits them to the car's display device via Apple CarPlay or Android Auto. The received messages are displayed in large letters on the display audio so that the driver can see them at a glance.

[1164] User

[1165] Before starting to drive, users need to connect their smartphone to the car's display audio and activate Apple CarPlay or Android Auto. While driving, the user's device captures surrounding sounds and transmits them to the server in real time.

[1166] Example

[1167] For example, if a user connects their smartphone to Apple CarPlay before starting to drive and the device detects a warning sound from the rear right while driving, the server will generate a warning message saying, "An ambulance is approaching 30 meters behind the right." If the emotion engine detects a high stress level in the user, it will generate an additional comforting message saying, "Please drive safely. Don't rush." ​​These messages are displayed on the display audio via the user's device.

[1168] An example prompt for a generative AI model might look something like this:

[1169] "An ambulance is approaching 30 meters behind and to the right. The user's stress level is high, so please drive safely. Do not rush."

[1170] In this way, this system supports safe driving by recognizing approaching emergency vehicles in real time while hearing-impaired drivers are driving and providing appropriate messages according to their emotional state.

[1171] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1172] Step 1:

[1173] The server receives real-time audio data from the user device. The device captures surrounding audio data using a microphone and sends the data to the server using the WebSocket API. This audio data is stored as a buffer in the server's memory. The input is the audio data from the user device, and the output is the audio data stored in the buffer.

[1174] Step 2:

[1175] The server inputs the audio data stored in the buffer into a speech analysis engine. This analysis engine uses generative AI models such as TensorFlow to analyze the audio data. Specifically, it converts the waveform data into a spectrogram and extracts characteristic patterns of specific warning sounds (e.g., ambulance or police sirens). The input is the stored audio data, and the output is the result of the warning sound identification.

[1176] Step 3:

[1177] The server calculates the direction and distance of the identified warning sound based on the result of the warning sound identification. To do this, an algorithm is used to determine the direction of the sound source and calculate the distance based on the result of the voice analysis engine. The input is the identification result of the voice analysis engine, and the output is information on the direction and distance.

[1178] Step 4:

[1179] The server generates a warning message based on the direction and distance information. For example, it generates a text message in the format "An ambulance is approaching 30 meters behind and to the right." This message is temporarily stored in memory. The input is the direction and distance information, and the output is the text data of the warning message.

[1180] Step 5:

[1181] The emotion engine receives biometric data (heart rate, galvanic skin response, facial expressions, etc.) acquired from sensors on the user device. These data are captured in real time by the user device and sent to the server. The input is the biometric data from the user device, and the output is the received biometric data.

[1182] Step 6:

[1183] The emotion engine analyzes the user's emotional state based on biometric data. For analysis, it uses machine learning models such as the Emotion API and Azure Cognitive Services. For example, if a high stress level is detected, this information is stored in memory. The input is biometric data, and the output is a judgment of the user's emotional state.

[1184] Step 7:

[1185] The server generates the necessary comforting message based on the result of the emotional state judgment. For example, it generates a message in the format of "Please drive safely. Don't rush." ​​This message is also stored in memory in text format. The input is the result of the emotional state judgment, and the output is the text data of the comforting message.

[1186] Step 8:

[1187] The server transmits the generated warning and comfort messages to the user terminal using a real-time communication channel to a notification system or dedicated application of the user terminal, with the input being the text data of the warning and comfort messages and the output being the transmitted messages.

[1188] Step 9:

[1189] The user terminal transfers the received message to the car's display device via Apple CarPlay or Android Auto. The terminal formats the received message for display on the display audio and displays it in large letters on the screen. The input is the message from the server, and the output is the message displayed on the display device.

[1190] (Application example 2)

[1191] 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."

[1192] Conventional driving assistance systems detect siren sounds and generate warning messages, but they lack measures that take into account the driver's emotional state, making it difficult for drivers to respond appropriately when under high stress. Hearing-impaired drivers also have limited means of properly recognizing approaching emergency vehicles.

[1193] The identification processing 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 receiving voice data from a user terminal, means for analyzing the voice data to identify a siren sound, means for calculating the direction and distance of the siren sound, means for generating a warning message based on information about the siren sound, means for transmitting the warning message to the user terminal, means for acquiring biometric information data of the user and recognizing an emotional state, means for generating an additional message according to the emotional state, means for transferring the warning message and the additional message from the user terminal to a display device, and means for displaying the warning message and the additional message on the display device. This enables appropriate responses even when the driver is in a high-stress state, and further enables safe driving assistance for hearing-impaired people.

[1194] "Audio data" refers to sound wave information acquired from a user terminal, including conversations, environmental sounds, siren sounds, and the like.

[1195] A "siren sound" is a special sound made by emergency vehicles such as ambulances, fire engines, and police cars, and has a specific sound wave pattern.

[1196] "Biometric data" refers to various data obtained from the user's body, including heart rate, galvanic skin response, facial expressions, and the like.

[1197] "Emotional state" indicates the user's current emotional state, and includes stress level, pleasure, discomfort, and the like.

[1198] A "warning message" is a notification message generated based on the detection result of a siren sound, and is used to notify the driver of an approaching emergency situation.

[1199] The "additional message" is a message of encouragement or comfort that is generated according to the user's emotional state, and is intended to reduce the mental burden on the driver.

[1200] "Display device" refers to a display device connected to a vehicle or a user terminal for displaying warning messages and additional messages.

[1201] A "machine learning model" is an algorithm or framework that learns from past data and analyzes voice data and identifies patterns.

[1202] The present invention combines a system that receives voice data from a user terminal, identifies siren sounds, generates a warning message based on that information, and displays it on a display device with an emotion engine that recognizes the user's emotions. The system program and its processing are described in detail below.

[1203] The server has the function of receiving real-time voice data transmitted from the user terminal. The voice data is uploaded to the server using a specific API, which is transmitted through a constantly connected real-time communication channel. The server inputs the received voice data into a voice analysis engine. The engine uses a machine learning model to analyze the voice data and identify the specific siren sound.

[1204] For example, the server analyzes the waveforms in the audio data and determines whether a specific sound wave pattern matches the siren of an ambulance or police vehicle. If there is a match, it calculates the direction and distance of the sound source and generates a warning message based on this information. This warning message is generated as text in a format such as "An ambulance is approaching 30 meters behind you on the right" or "There is room to evacuate about 1 meter to the left." The generated warning message is immediately sent to the user's device.

[1205] The emotion engine recognizes the user's emotional state and performs analysis using biometric data acquired through sensors on the user's device. The biometric data includes heart rate, galvanic skin response, and facial expressions, and uses this data to assess the user's stress level and other emotional states. For example, if a high stress level is detected, the server can generate an additional comforting message for the user and send it along with the warning message. This comforting message could be displayed in a format such as "Drive safely. Don't rush."

[1206] The user device receives the warning and comfort messages sent from the server. The device is a typical smartphone or tablet, and has the function of transmitting messages to the vehicle's display device (display audio) via Apple CarPlay or Android Auto. The warning messages displayed on the device are designed to be easily recognizable by the driver.

[1207] Specifically, when the user device receives a warning message, it immediately transfers the message to the car's display audio using the Apple CarPlay or Android Auto application. The display audio displays the received message in large letters on the screen so the driver can see it at a glance. For example, messages such as "An ambulance is approaching 30 meters behind you on the right" and "There is room to evacuate about 1 meter to the left" may be displayed, and if the user's stress level is high, a comforting message such as "Please drive safely. Don't rush."

[1208] A concrete example of a prompt sentence could be input to a generative AI model in the following form:

[1209] The user is driving an autonomous vehicle. This application uses the in-car microphone to collect audio data and analyzes it on a server to determine whether a siren is present. If a siren is detected, a warning message is generated and displayed on the in-car display. At the same time, biometric sensors collect the user's heart rate and skin potential, and an emotion engine evaluates the user's stress level. If the stress level is high, an additional comforting message is displayed. This system supports the user in driving safely.

[1210] In this way, the present invention provides a system that combines real-time voice data analysis and an emotion engine to support safe driving by the driver.

[1211] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1212] Step 1:

[1213] The user starts the car engine and connects the smartphone to the display audio. The device launches the Apple CarPlay or Android Auto application, which prepares the device for collecting voice data and displaying messages. The input is the connection status between the smartphone and the in-car display. The output is a notification that the system is ready to operate.

[1214] Step 2:

[1215] The device begins collecting real-time audio data using the in-car microphone. Specifically, the audio data is buffered for a short period of time and then transmitted to the server at regular intervals. The input includes ambient sounds and conversations inside the car. The output is a real-time audio data stream.

[1216] Step 3:

[1217] The server receives the audio data sent from the device. The received audio data is analyzed using a machine learning model to identify the specific siren sound. The input is the audio data. The output is the presence or absence of a siren sound, and if identified, the direction and distance information.

[1218] Step 4:

[1219] The server generates a warning message based on the direction and distance information of the siren sound. Specifically, it analyzes the direction and distance of the sound source and creates a warning message such as "An ambulance is approaching 30 meters behind you to the right." The input is the direction and distance information of the siren sound. The output is the generated warning message.

[1220] Step 5:

[1221] The device acquires the user's biometric data. Specifically, it uses a heart rate sensor, a skin electrodermal response sensor, a facial recognition camera, etc. to collect the user's heart rate, skin electrodermal response, and facial expression data. The input is the user's biometric information. The output is real-time biometric data.

[1222] Step 6:

[1223] The server uses an emotion engine to analyze the biometric data and evaluate the user's emotional state. Specifically, it uses the collected biometric data to determine the user's emotional state, such as stress level and pleasantness / unpleasantness. The input is the biometric data, and the output is the evaluated emotional state.

[1224] Step 7:

[1225] The server generates an additional comforting message according to the user's emotional state. Specifically, if the user's stress level is high, the server generates a comforting message such as "Please drive safely. Don't rush." ​​The input is the user's emotional state. The output is the additional message.

[1226] Step 8:

[1227] The server sends the warning message and the additional message to the user terminal. The input is the generated warning message and the additional message. The output is the completion of message transmission to the user terminal.

[1228] Step 9:

[1229] The terminal transfers the received warning messages and additional messages to the in-car display via the Apple CarPlay or Android Auto application. The input is the message from the server. The output is the completion of transfer to the in-car display.

[1230] Step 10:

[1231] The display device displays warning messages and additional messages on the screen. Specifically, the messages are displayed in large letters so that the driver can see them at a glance. The input is a message transferred from the terminal. The output is the message displayed on the screen.

[1232] By the above steps, the present invention allows drivers to respond appropriately even in high-stress situations, and enables hearing-impaired people to drive safely.

[1233] 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.

[1234] 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.

[1235] 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.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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).

[1240] 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.

[1241] 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."

[1242] 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.

[1243] 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).

[1244] 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.

[1245] 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.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] 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.

[1253] 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.

[1254] The following is further disclosed regarding the above embodiment.

[1255] (Claim 1)

[1256] means for receiving voice data from a user terminal;

[1257] means for analyzing the audio data to identify a siren sound;

[1258] means for calculating the direction and distance of the siren sound;

[1259] means for generating a warning message based on the siren sound information;

[1260] means for transmitting the warning message to a user terminal;

[1261] means for transferring a warning message from the user terminal to a display device;

[1262] means for displaying the warning message on the display device;

[1263] A system including:

[1264] (Claim 2)

[1265] 10. The system of claim 1, wherein the user terminal also issues an audible or visual alert to display the warning message.

[1266] (Claim 3)

[1267] 10. The system of claim 1, wherein the means for analyzing the audio data to identify a siren sound analyzes the audio data using a machine learning model.

[1268] "Example 1"

[1269] (Claim 1)

[1270] means for receiving voice data from a user terminal;

[1271] means for analyzing the audio data to identify a siren sound;

[1272] means for calculating the direction and distance of the siren sound;

[1273] means for generating a warning message based on the siren sound information;

[1274] means for transmitting the warning message to a user terminal;

[1275] means for transferring a warning message from the user terminal to a display device;

[1276] means for displaying the warning message on the display device;

[1277] a means for visually displaying the warning message so that the driver can easily recognize it;

[1278] A system including:

[1279] (Claim 2)

[1280] 10. The system of claim 1, wherein the user terminal also issues an audible or visual alert to display the warning message.

[1281] (Claim 3)

[1282] 10. The system of claim 1, wherein the means for analyzing the audio data to identify a siren sound analyzes the audio data using a machine learning model.

[1283] "Application Example 1"

[1284] (Claim 1)

[1285] means for receiving voice data from a user terminal;

[1286] means for analyzing the audio data to identify a siren sound;

[1287] means for calculating the direction and distance of the siren sound;

[1288] means for generating a warning message based on the siren sound information;

[1289] means for transmitting the warning message to a user terminal;

[1290] means for transferring a warning message from the user terminal to a display device;

[1291] means for displaying the warning message on the display device;

[1292] means for transmitting voice data from the user terminal to a control system mounted on the vehicle and notifying the control system of the vehicle of the approach of an emergency vehicle;

[1293] means for the control system to take appropriate operational action based on the warning message;

[1294] A system including:

[1295] (Claim 2)

[1296] 10. The system of claim 1, wherein the user terminal also issues an audible or visual alert to display the warning message.

[1297] (Claim 3)

[1298] 10. The system of claim 1, wherein the means for analyzing the audio data to identify a siren sound analyzes the audio data using a machine learning model.

[1299] "Example 2: Combining Emotion Engines"

[1300] (Claim 1)

[1301] means for receiving voice data from a user terminal;

[1302] means for analyzing the audio data to identify a specific warning sound;

[1303] means for calculating the direction and distance of the warning sound;

[1304] means for generating a warning message based on information about the warning sound;

[1305] means for acquiring biometric information from a user;

[1306] means for analyzing the biological information to identify an emotional state;

[1307] means for generating a comforting message based on said emotional state;

[1308] means for transmitting the warning message and the comfort message to a user terminal;

[1309] means for transferring the message from the user terminal to a display device;

[1310] means for displaying the warning message and the comfort message on the display device;

[1311] A system including:

[1312] (Claim 2)

[1313] 10. The system of claim 1, wherein the user terminal issues an audible or visual alert to display the message.

[1314] (Claim 3)

[1315] 10. The system of claim 1, wherein the means for analyzing the audio data to identify a particular warning sound analyzes the audio data using a generative AI model.

[1316] "Application example 2 when combining emotion engines"

[1317] (Claim 1)

[1318] means for receiving voice data from a user terminal;

[1319] means for analyzing the audio data to identify a siren sound;

[1320] means for calculating the direction and distance of the siren sound;

[1321] means for generating a warning message based on the siren sound information;

[1322] means for transmitting the warning message to a user terminal;

[1323] means for acquiring biometric data of a user to recognize the emotional state of the user;

[1324] means for generating additional messages in response to the emotional state;

[1325] means for transferring warning messages and additional messages from the user terminal to a display device;

[1326] means for displaying the warning message and an additional message on the display device;

[1327] A system including:

[1328] (Claim 2)

[1329] 10. The system of claim 1, wherein the user terminal also issues an audible or visual alert to display the warning message and any additional messages.

[1330] (Claim 3)

[1331] 10. The system of claim 1, wherein the means for analyzing the audio data to identify a siren sound analyzes the audio data using a machine learning model. [Explanation of symbols]

[1332] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving voice data from a user terminal; means for analyzing the audio data to identify a siren sound; means for calculating the direction and distance of the siren sound; means for generating a warning message based on the siren sound information; means for transmitting the warning message to a user terminal; means for transferring a warning message from the user terminal to a display device; means for displaying the warning message on the display device; A system including:

2. 10. The system of claim 1, wherein the user terminal also issues an audible or visual alert to display the warning message.

3. 10. The system of claim 1, wherein the means for analyzing the audio data to identify siren sounds analyzes the audio data using a machine learning model.

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