System

Abstract

A system is provided.SOLUTION: A system comprising: means for capturing audio data from a device; means for transmitting the captured audio data to a server; means for performing speech recognition and generating text data at the server; means for translating the generated text data into a target language; means for converting the translated text data into audio data; means for transmitting the converted audio data to the device; and means for playing the audio data at the device and providing it to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With modern globalization, communication between different languages ​​is becoming increasingly important, but cultural nuances and differences in dialects can make smooth communication difficult. Conventional translation devices and software lack the ability to handle real-time translation and diverse media (audio, text, video), posing significant challenges, particularly for face-to-face communication between different languages. The purpose of this invention is to provide a device that solves these problems and enables smooth communication between different languages. [Means for solving the problem]

[0005] The "multimodal multilingual real-time translation glasses" of the present invention are a system including the following means: Capturing voice data from a device and sending the data to a server; Performing voice recognition on the server, generating text data, and translating the text data into a target language; Converting the translated text data into voice data and sending the voice data to the device; Providing the user with the voice data played back on the device; Capturing image data from the device and sending it to a server; Recognizing characters from the image on the server, translating the recognized text data into a target language, and sending the text data to the device; Displaying the text data on an augmented reality display, the device provides a visual translation result; Capturing video data from the device and streaming it to a server; Extracting audio and text from the video on the server, translating them into the target language, and sending them to the device; Playing back the translated voice data and displaying the text data on an augmented reality display; Providing real-time translation results in both visual and auditory terms, enabling smooth communication between multiple languages.

[0006] A "device" is a wearable device worn by a user, which has the function of capturing audio data, image data, and video data and transmitting them to a server.

[0007] A "server" is a computer system that receives data sent from a device, performs processing such as voice recognition, character recognition, translation, and voice synthesis, and sends the processing results to the device.

[0008] "Voice data" refers to a digital recording of a voice signal uttered by a user or other speaker.

[0009] "Text data" is digital character information in the form of a string of characters extracted from audio or images.

[0010] "Translation" is the process of converting text data in one language into another target language.

[0011] "Speech recognition" is a technology that analyzes voice data and generates corresponding text data.

[0012] "Speech synthesis" is a technology that converts text data into voice data.

[0013] "Image data" is a digital image containing visual information captured by a camera or sensor.

[0014] "Character recognition" is the process of extracting characters from image data and converting them into text data.

[0015] "Video data" is digital video that includes a sequence of multiple image frames and an audio track.

[0016] "Augmented reality display" is a display technology that displays digital information overlaid on real-world visual information. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention is a system that translates multilingual speech, text, and video in real time using a wearable device worn by the user. This system consists of a "device" used by the user and a "server" that processes data.

[0039] 1. Real-time voice translation

[0040] Program processing

[0041] User

[0042] The user inputs voice through the device's built-in microphone, for example, "Hello, what's your plan today?"

[0043] Terminal

[0044] The device captures the input voice data and sends it to the server.

[0045] server

[0046] The server receives the voice data and converts it into text data using a speech recognition engine. For example, the text generated from the voice is "Hello, what are your plans for today?"

[0047] The server translates the generated text data into the target language. When translating from Japanese to English, the translation result is "Hello, what are your plans for today?"

[0048] The server sends this translated text data to a speech synthesis engine and converts it into voice data.

[0049] The server transmits the converted voice data to the terminal.

[0050] Terminal

[0051] The device then plays the received translated voice data through built-in earphones and provides it to the user, who can listen to the translated voice in real time.

[0052] 2. Real-time text translation

[0053] Program processing

[0054] User

[0055] The user uses the device's built-in camera to capture text on a sign or document.

[0056] Terminal

[0057] The terminal transmits the captured image data to the server.

[0058] server

[0059] The server receives the image data and uses an image recognition engine (OCR engine) to extract text from the image. For example, the text "restaurant" is extracted.

[0060] The server translates the extracted text data into the target language. When translating from Japanese to English, the translation result is "Restaurant."

[0061] The server sends the translated text data to the terminal.

[0062] Terminal

[0063] The device then displays the received translated text data on the AR display, allowing the user to view the translated text through the lenses of their glasses.

[0064] 3. Real-time video translation

[0065] Program processing

[0066] User

[0067] The user captures the video using the device's built-in camera.

[0068] Terminal

[0069] The terminal transmits the captured video data to the server via streaming.

[0070] server

[0071] The server receives the video data and uses a video analysis engine to extract audio and text from the video, such as the audio of someone speaking or the text displayed in the video.

[0072] The server converts the extracted speech into text using a speech recognition engine and translates it into the target language, and also translates the extracted text into the target language.

[0073] The translated voice data is converted back into voice data by a voice synthesis engine.

[0074] The server sends the translated voice and text data to the terminal.

[0075] Terminal

[0076] The device then plays the received translated audio data through the built-in earphones and displays the translated text data on the AR display, allowing users to confirm the translated content both visually and audibly.

[0077] This allows users to communicate smoothly in real time, overcoming language barriers. This system can be used in a variety of situations, such as international conferences, intercultural exchanges, and tourism, and will enable smooth communication between people of different languages.

[0078] The processing flow will be explained below.

[0079] Real-time voice translation

[0080] Step 1: User Speech Input

[0081] The user speaks into the device's built-in microphone, for example, "Hello, what's on the agenda today?"

[0082] Step 2: Capture and send audio data with your device

[0083] The device captures the user's voice with a microphone and sends the voice data to the server.

[0084] Step 3: Speech recognition by the server

[0085] The server receives the voice data and uses a speech recognition engine to convert the voice data into text, e.g., "Hello, what are your plans for today?"

[0086] Step 4: Text translation by server

[0087] The server sends the generated text data to the translation engine and translates it into the target language. Example: When translating from Japanese to English, it becomes "Hello, what are your plans for today?"

[0088] Step 5: Server-based speech synthesis

[0089] The server sends the translated text data to a text-to-speech (TTS) engine, which converts the text into audio data.

[0090] Step 6: Server sends audio data

[0091] The server transmits the converted voice data to the terminal.

[0092] Step 7: Play the audio data on the device

[0093] The device receives the audio data from the server and plays it through built-in earphones, providing the user with translated audio in real time.

[0094] Real-time text translation

[0095] Step 1: User text input

[0096] The user uses the device's built-in camera to capture text on a sign or document.

[0097] Step 2: Capture and send image data by the device

[0098] The terminal transmits the captured image data to the server.

[0099] Step 3: Character Recognition (OCR) by the Server

[0100] The server receives the image data and uses an image recognition engine to extract text from the image, e.g., the text "restaurant" is extracted.

[0101] Step 4: Text translation by server

[0102] The server sends the extracted text data to a translation engine and translates it into the target language. Example: When translating from Japanese to English, the result is "Restaurant."

[0103] Step 5: Server sends text data

[0104] The server sends the translated text data to the terminal.

[0105] Step 6: Displaying text data using a terminal

[0106] The device receives the translated text data and displays it on the AR display, allowing the user to view the translated text through the glasses' display.

[0107] Real-time video translation

[0108] Step 1: User's video input

[0109] The user captures the video using the device's built-in camera.

[0110] Step 2: Capture and send video data from your device

[0111] The device streams the captured video data to the server in real time.

[0112] Step 3: Receiving and analyzing video data by the server

[0113] The server receives the video data and uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[0114] Step 4: Speech recognition and text conversion on the server

[0115] The audio in the video is converted into text using a speech recognition engine.

[0116] Step 5: Text translation by server

[0117] The server sends the converted text data to a translation engine for translation into the target language.

[0118] Step 6: Server-based speech synthesis

[0119] The server converts the translated text data into voice data using a voice synthesis engine.

[0120] Step 7: Server sends voice and text data

[0121] The server transmits the translated voice data and text data to the terminal.

[0122] Step 8: Play and display the translation data on your device

[0123] The device plays the received audio data through the built-in earphones and displays the text data on the AR display, allowing users to confirm the translated content visually and audibly.

[0124] Through the above processing steps, users can translate audio, text, and video in multiple languages ​​in real time, enabling smooth communication.

[0125] Example 1

[0126] 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."

[0127] There is a demand for systems that enable smooth communication between multiple languages. In particular, there is a need for systems that can translate voice, text, and video in real time and provide the translation to users. Conventional technologies require the use of individual devices and applications, which can be cumbersome for users. Therefore, there is a need for a system that solves these problems and enables users to communicate smoothly between different languages.

[0128] 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.

[0129] In this invention, the server includes means for converting voice data into text data, means for translating the converted text data into a target language, and means for converting the translated text data into voice data, thereby enabling real-time translation between multiple languages.

[0130] A "device" is a wearable device that a user uses to perform operations such as input and display.

[0131] "Audio data" refers to data obtained by converting an audio signal into digital format.

[0132] "Text data" is data that has been converted from character information into digital format.

[0133] "Image data" is visual information converted into digital form.

[0134] "Video data" is data in digital form that contains a sequence of images and sounds.

[0135] A "server" is a computer system for processing and managing data.

[0136] "Speech recognition technology" is a technology that analyzes voice signals and converts them into corresponding text information.

[0137] "Target language" is the language into which the translation is to be made.

[0138] "Image processing technology" is a technology that analyzes image data and extracts necessary information.

[0139] An "augmented reality display device" is a device that displays digital information superimposed on visual information from the real world.

[0140] "Video analysis technology" is a technology that analyzes video data and extracts necessary information.

[0141] "Speech synthesis technology" is a technology that converts text data into voice data.

[0142] This invention is a system for real-time translation of multilingual speech, text, and video, and is composed of a wearable device that is easy for users to use and a server that supports it. The system operates as follows.

[0143] The user wears a wearable device, which incorporates a microphone, a camera, earphones, and an augmented reality display. When the user speaks, the microphone in the device captures the voice data, converts it into a digital format, and the device transmits the voice data to a server.

[0144] The server converts the received voice data into text data using voice recognition technology. For example, a general voice recognition API can be used for this voice recognition. The converted text data is translated into the target language. For example, a widely used translation API can be used for this translation. The translated text data is then converted into voice data using voice synthesis technology. The server then retransmits the voice data to the terminal, which then provides it to the user through earphones.

[0145] For example, if a user says, "Hello, what are your plans for today?", the system translates it into "Hello, what are your plans for today?" and provides it to the user as English speech.

[0146] Similarly, for real-time translation of text data, when a user captures text using a camera, the image data is sent to a server. The server then uses image processing technology to extract the text from the image and translate it into the target language. The translated text data is sent to the device and displayed to the user through an augmented reality display device. For example, if a user takes a photo of a sign that says "restaurant," it is translated and displayed as "Restaurant."

[0147] Real-time translation of video data is also possible. When a user captures video with a camera, the data is sent to a server, which uses video analysis technology to extract audio and text from the video. These are then translated into the target language, and the translated data is sent to the device as audio and text data. The device then plays the audio through earphones and displays the text through an augmented reality display.

[0148] This allows users to smoothly communicate between different languages ​​in real time. This system is expected to be used in a variety of situations, including international conferences, tourism, and intercultural exchange.

[0149] An example of a specific prompt is:

[0150] "Please translate what the user says in Japanese into English."

[0151] "Analyze the image, extract the Japanese text and translate it into English."

[0152] "Extract audio and text from video data and translate them into English."

[0153] This allows users to obtain fast and accurate translation results through generative AI models.

[0154] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0155] Real-time voice translation

[0156] Step 1:

[0157] The user inputs voice through the device's built-in microphone, for example, "Hello, what are your plans for today?" The input data is an analog voice signal.

[0158] Step 2:

[0159] The device converts the analog audio signal captured by the microphone into digital audio data. This conversion process is performed by an analog-to-digital converter (ADC). The converted digital audio data is then sent to the server.

[0160] Step 3:

[0161] The server receives the digital voice data and converts it into text data using voice recognition technology. For this process, a voice recognition API is used to generate text data from the input voice data, such as "Hello, what are your plans for today?"

[0162] Step 4:

[0163] The server translates the generated text data into the target language. In this step, a translation API is used to convert the text data from Japanese to English. For example, "Hello, what are your plans for today?" is translated into "Hello, what are your plans for today?"

[0164] Step 5:

[0165] The server converts the translated text data into audio data using speech synthesis technology. Based on the translated text data, "Hello, what are your plans for today?", the speech synthesis engine generates corresponding digital audio data.

[0166] Step 6:

[0167] The server transmits the generated audio data to the terminal, and the transmitted digital audio data includes appropriate metadata.

[0168] Step 7:

[0169] The device decodes the received audio data and plays it back to the user through the built-in earphones, allowing the user to listen to the translated English audio in real time.

[0170] Real-time text translation

[0171] Step 1:

[0172] A user uses the device's built-in camera to capture text on a sign or document, for example, taking a photo of a sign that says "Restaurant." The input data is image data.

[0173] Step 2:

[0174] The device sends the captured image data, including metadata such as resolution and timestamp, to the server.

[0175] Step 3:

[0176] The server receives the image data and uses image processing technology (OCR technology) to extract text from the image. For example, the text "restaurant" is extracted from an image of a sign.

[0177] Step 4:

[0178] The server translates the extracted text data into the target language using a translation API. For example, "restaurant" is translated to "Restaurant."

[0179] Step 5:

[0180] The server sends the translated text data to the device, including the translation results and associated metadata.

[0181] Step 6:

[0182] The device displays the received text data on the augmented reality display device, and the user can see the translated text "Restaurant" through the lenses of the glasses.

[0183] Real-time video translation

[0184] Step 1:

[0185] A user uses the device's built-in camera to capture video, for example, a foreign language news broadcast, and the input data is video data.

[0186] Step 2:

[0187] The device transmits the captured video data to the server in real time using streaming technology, and the data includes audio and video frame information.

[0188] Step 3:

[0189] The server receives the video data and uses video analysis technology to extract audio and text from the video, for example, audio and text from a news broadcast.

[0190] Step 4:

[0191] The server converts the extracted speech into text data and then translates it into the target language. Using speech recognition technology and a translation API, the text "What are you doing?" is translated into "What are you doing?"

[0192] Step 5:

[0193] The server uses speech synthesis technology to convert the translated text data into voice data, and also prepares the translated text data for transmission separately.

[0194] Step 6:

[0195] The server sends the translated voice data and text data to the terminal. The transmitted data includes the voice file and the text information.

[0196] Step 7:

[0197] The device then plays the received audio data through earphones and displays the text data on an augmented reality display device, allowing users to visually and aurally follow the conversations and subtitles in the video in real time.

[0198] This allows users to understand information and communicate in real time, regardless of language differences.

[0199] (Application example 1)

[0200] 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."

[0201] The present invention aims to provide a system that overcomes language barriers in autonomous vehicles and enables multilingual passengers to communicate smoothly with the vehicle. In current autonomous vehicles, communication between passengers and the vehicle is limited, making it difficult to convey information, especially when different languages ​​are spoken. This poses a problem of reduced passenger safety and satisfaction. To solve this problem, a system is needed that can achieve real-time translation between multiple languages ​​and provide passengers with appropriate information and responses.

[0202] 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.

[0203] In this invention, the server includes means for capturing voice data from the device, means for transmitting the captured voice data to the server, means for performing voice recognition in the server and generating text data, means for translating the generated text data into a target language, means for converting the translated text data into voice data, means for transmitting the converted voice data to the device, means for playing back the voice data in the device and providing it to a user, and means for performing multilingual communication between passengers and the vehicle in real time in an autonomous vehicle. This enables accurate and prompt information transmission between passengers and the vehicle, thereby improving passenger safety and satisfaction.

[0204] A "device" is a device for capturing audio data, image data, or video data and transmitting it to a server.

[0205] "Audio data" refers to data that has been converted from human speech or other sounds into digital signals.

[0206] A "server" is a central processing unit for processing, converting, and translating captured data.

[0207] "Speech recognition" is a technology that converts voice data into text data.

[0208] "Text data" is data that represents character information in digital form.

[0209] "Target language" is the language into which the text is to be translated.

[0210] "Converting into voice data" means converting text data back into voice format data using voice synthesis technology.

[0211] "Multilingual communication" is communication that takes place between two or more people who speak different languages.

[0212] An "autonomous vehicle" is a vehicle that operates autonomously without the need for a human driver.

[0213] An "augmented reality display" is a display technology that overlays digital information onto visual information from the real world.

[0214] "Video data" is data that converts moving images or videos into digital signals.

[0215] "Character recognition" is a technology for extracting character information from an image.

[0216] "Real-time" means that data is processed immediately, without delay.

[0217] The present invention embodies a multilingual communication system for an autonomous driving vehicle, and is configured as follows.

[0218] 1. Real-time voice translation

[0219] User

[0220] The user inputs instructions, questions, etc. in their native language through the device's built-in microphone. For example, they might say "Hello, please take me to the airport" in Japanese.

[0221] Terminal

[0222] The device captures the input voice data and sends it to a server, a process that uses a microphone and an internet connection.

[0223] server

[0224] The server receives the voice data and converts it into text data using a speech recognition engine (such as the Google Speech Recognition API). Next, it translates this text data into the target language (e.g., English) using a multilingual translation engine (such as the Google Translate API). After that, it converts the translated text data back into voice data using a speech synthesis engine (such as gTTS).

[0225] Terminal

[0226] The converted voice data is sent back to the device and played through the device's built-in speaker, allowing the user to hear the translated voice in real time.

[0227] 2. Real-time text translation

[0228] User

[0229] The user uses the device's built-in camera to capture text and sign information displayed on the in-car display.

[0230] Terminal

[0231] The terminal transmits the captured image data to the server.

[0232] server

[0233] The server receives the image data, extracts text from the image using an OCR (optical character recognition) engine (such as pytesseract), and then translates the extracted text data into the target language using a translation engine.

[0234] Terminal

[0235] The translated text data is displayed on an augmented reality display inside the vehicle, allowing the user to visually confirm the translated information.

[0236] 3. Real-time video translation

[0237] User

[0238] The user captures the video using the device's built-in camera.

[0239] Terminal

[0240] The terminal transmits the captured video data to the server in a streaming manner.

[0241] server

[0242] The server receives the video data and uses an analysis engine to extract the audio and text from the video. The extracted audio data is converted into text using a speech recognition engine and simultaneously translated into the target language. The text data is also translated into the target language.

[0243] Terminal

[0244] The device then plays the received translated audio data through built-in earphones and displays the translated text data on the augmented reality display, allowing users to confirm the translation both visually and audibly.

[0245] Specific examples

[0246] For example, if a foreign passenger gets into a self-driving vehicle and says in Japanese, "Hello, take me to the airport please," the system will process it as follows:

[0247] 1. Capture audio with a microphone.

[0248] 2. Send the captured audio data to the server.

[0249] 3. The server converts the voice data into text data and translates it into the target language.

[0250] 4. The translated text data is converted into audio data and sent back to the device.

[0251] 5. The terminal plays the translated audio and relays the information to the passenger.

[0252] Example prompts for generative AI models

[0253] When a foreign passenger boards an autonomous vehicle, he or she says in Japanese, "Hello, please take me to the airport." Write a program to translate this into English in real time and play it back. Use the Google Speech Recognition API for speech recognition and the googletrans library for translation. The final English audio will be generated and played back using the gtts library.

[0254] As described above, this system is designed to provide real-time multilingual translation of voice, text, and video to facilitate communication in self-driving vehicles.

[0255] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0256] Step 1:

[0257] The user inputs voice through the device's built-in microphone, and the voice data is captured in digital form. The input voice data is raw voice waveform data, which is temporarily stored on the device for further processing.

[0258] Step 2:

[0259] The device sends the captured audio data to the server via an internet connection, where it is transferred in real time using the appropriate protocol (e.g. HTTP, WebSocket) in its digital form.

[0260] Step 3:

[0261] The server inputs the received voice data into a voice recognition engine (e.g., Google Speech Recognition API) and converts the voice into text data. The voice recognition engine analyzes the voice waveform and generates a corresponding string of characters. At this stage, the input is voice data and the output is text data.

[0262] Step 4:

[0263] The generated text data is input to a multilingual translation engine (e.g., Google Translate API) and translated into the target language. The input here is the generated text data, and the output is the translated text data. The translation engine analyzes the input text and generates an appropriate translation result.

[0264] Step 5:

[0265] The translated text data is input to a speech synthesis engine (e.g., gTTS) and converted back into speech data. The speech synthesis engine analyzes the input text and generates a corresponding speech waveform. At this stage, the input is the translated text data and the output is speech data.

[0266] Step 6:

[0267] The server then sends the converted audio data back to the device, which receives it via its internet connection, where it is sent in its digital form.

[0268] Step 7:

[0269] The device plays the received voice data through the built-in speaker and provides it to the user, who can listen to the translated voice in real time. Here, the input is the voice data received from the server, and the output is the voice played from the speaker.

[0270] 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.

[0271] This system uses a wearable device worn by the user to translate multilingual audio, text, and video in real time, and also recognizes the user's emotions and reflects them in the translation results. This system consists of a "device" used by the user, a "server" that processes data, and an "emotion engine."

[0272] 1. Real-time voice translation

[0273] Program processing

[0274] User

[0275] The user speaks into the device's built-in microphone, for example, "Hello, what's on the agenda today?"

[0276] Terminal

[0277] The device captures the user's voice with a microphone and sends the voice data to the server.

[0278] server

[0279] The server receives the voice data and uses a speech recognition engine to convert the voice data into text, e.g., "Hello, what are your plans for today?"

[0280] The server uses an emotion engine to analyze the user's emotions from the voice data and generate emotion data. For example, if the user is recognized as happy, the emotion data will be "joy."

[0281] The server sends the generated text data along with the emotion data to the translation engine, which translates it into the target language. For example, when translating from Japanese to English, "Hello, what are your plans for today?" is given the emotion data of "joy."

[0282] The server converts the translated text data and emotion data into voice data that matches the user's target language.

[0283] The server transmits the converted voice data to the terminal.

[0284] Terminal

[0285] The device then plays the received translated voice data through built-in earphones, providing the user with real-time translated voice, which is played in a tone that reflects the user's emotions.

[0286] 2. Real-time text translation

[0287] Program processing

[0288] User

[0289] The user uses the device's built-in camera to capture text on a sign or document.

[0290] Terminal

[0291] The terminal transmits the captured image data to the server.

[0292] server

[0293] The server receives the image data and uses an image recognition engine (OCR engine) to extract text from the image. For example, the text "restaurant" is extracted.

[0294] The server uses an emotion engine to analyze the emotion data from the user's facial expression at the time of capture. For example, if the user is determined to be surprised, the emotion data will be "surprise."

[0295] The server sends the extracted text data and emotion data to the translation engine, which translates it into the target language. For example, "restaurant" is translated as "Restaurant" and the emotion data "surprise" is added.

[0296] The server sends the translated text data and emotion data to the terminal.

[0297] Terminal

[0298] The device receives the translated text data and displays it on the AR display, which reflects the user's emotions with icons and colors, making it easier to understand intuitively.

[0299] 3. Real-time video translation

[0300] Program processing

[0301] User

[0302] The user captures the video using the device's built-in camera.

[0303] Terminal

[0304] The device streams the captured video data to the server in real time.

[0305] server

[0306] The server receives the video data and uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[0307] The server uses an emotion engine to analyze the user's facial expressions and tone of voice in real time to generate emotion data. For example, if the user is determined to be interested, the emotion data will be "interested."

[0308] The server converts the analyzed speech into text using a speech recognition engine, then sends the text and emotional data to a translation engine for translation into the target language.

[0309] The translated text data and emotion data are then converted into voice data by a voice synthesis engine.

[0310] The server sends the translated voice and text data to the terminal.

[0311] Terminal

[0312] The device plays the received audio data through the built-in earphones and displays the text data on the AR display, allowing users to see and hear the translated content while also visually understanding the emotions expressed.

[0313] This system enables users to translate multilingual audio, text, and video in real time, and even takes emotions into account for natural and intuitive communication. This system is expected to be used in a variety of situations, including international conferences, intercultural exchange, and tourism.

[0314] The processing flow will be explained below.

[0315] Real-time voice translation

[0316] Step 1: User Speech Input

[0317] The user speaks into the device's built-in microphone, for example, "Hello, what's on the agenda today?"

[0318] Step 2: Capture and send audio data with your device

[0319] The device captures the user's voice with a microphone and sends the voice data to the server.

[0320] Step 3: Speech recognition by the server

[0321] The server receives the voice data and uses a speech recognition engine to convert the voice data into text, e.g., "Hello, what are your plans for today?"

[0322] Step 4: Emotion recognition by the server

[0323] The server uses an emotion engine to analyze the user's emotion from the voice data. For example, if the user is recognized as happy from the voice tone and speaking style, the emotion data will be "joy."

[0324] Step 5: Text translation by server

[0325] The server sends the generated text data and emotion data to the translation engine, which translates it into the target language. For example, when translating from Japanese to English, "Hello, what are your plans for today?" is given with the emotion data "joy."

[0326] Step 6: Server-based speech synthesis

[0327] The server sends the translated text data and emotion data to a speech synthesis engine, which converts it into voice data. This voice is generated in a tone that reflects the user's emotion.

[0328] Step 7: Server sends audio data

[0329] The server transmits the converted voice data to the terminal.

[0330] Step 8: Play the audio data on the device

[0331] The device receives the voice data from the server and plays it through built-in earphones, providing the user with real-time translated speech that reflects the user's emotions.

[0332] Real-time text translation

[0333] Step 1: User text input

[0334] The user uses the device's built-in camera to capture text on a sign or document.

[0335] Step 2: Capture and send image data by the device

[0336] The terminal transmits the captured image data to the server.

[0337] Step 3: Character Recognition (OCR) by the Server

[0338] The server receives the image data and uses an image recognition engine to extract text from the image, e.g., the text "restaurant" is extracted.

[0339] Step 4: Emotion recognition by the server

[0340] The server uses the emotion engine to analyze the emotion from the user's facial expression at the time of capture. For example, if the user is determined to be surprised, the emotion data will be "surprise."

[0341] Step 5: Text translation by server

[0342] The server sends the extracted text data and emotion data to the translation engine, which translates it into the target language. For example, "restaurant" is translated as "Restaurant" and the emotion data "surprise" is added.

[0343] Step 6: Server sends text data

[0344] The server sends the translated text data and emotion data to the terminal.

[0345] Step 7: Displaying Text Data in a Terminal

[0346] The device receives the translated text data and displays it on the AR display, which reflects the user's emotions with icons and colors, making it easier to understand intuitively.

[0347] Real-time video translation

[0348] Step 1: User's video input

[0349] The user captures the video using the device's built-in camera.

[0350] Step 2: Capture and send video data from your device

[0351] The device streams the captured video data to the server in real time.

[0352] Step 3: Receiving and analyzing video data by the server

[0353] The server receives the video data and uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[0354] Step 4: Speech recognition and text conversion on the server

[0355] The audio in the video is converted into text using a speech recognition engine.

[0356] Step 5: Emotion recognition by the server

[0357] The server uses an emotion engine to analyze the user's facial expressions and tone of voice in real time to generate emotion data. For example, if the user is determined to be interested, the emotion data will be "interested."

[0358] Step 6: Text translation by server

[0359] The server sends the converted text data and emotion data to a translation engine for translation into the target language.

[0360] Step 7: Server-based speech synthesis

[0361] The server converts the translated text data and emotional data into voice data using a voice synthesis engine.

[0362] Step 8: Server sends voice and text data

[0363] The server transmits the translated voice data and text data to the terminal.

[0364] Step 9: Play and display the translation data on your device

[0365] The device plays the received audio data through the built-in earphones and displays the text data on the AR display, allowing users to see and hear the translated content while also visually understanding the emotions expressed.

[0366] This system enables users to translate multilingual audio, text, and video in real time, and even takes emotions into account for natural and intuitive communication. This system is expected to be used in a variety of situations, including international conferences, intercultural exchange, and tourism.

[0367] Example 2

[0368] 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."

[0369] In modern society, multilingual communication is becoming increasingly important, but current translation technology is not yet able to fully realize natural and intuitive communication. In particular, there are issues with providing translation results that reflect emotions and with real-time multilingual support, which are inconvenient for users in cross-cultural exchange, international conferences, tourism, and other situations.

[0370] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0371] In this invention, the server includes means for generating emotion data from voice data, means for translating the generated text data and emotion data into a target language, and means for converting the translated text data and emotion data into voice data, thereby making it possible to provide intuitive and natural translation results in real time that reflect the emotions contained in the user's voice.

[0372] "Device" refers to electronic equipment worn or used by a user, including hardware such as a microphone, camera, earphones, and augmented reality display.

[0373] "Server" refers to a computer system that receives audio data, image data, and video data and performs data processing such as analysis, translation, and conversion.

[0374] "Voice data" refers to data in which the voice uttered by the user is recorded in digital format.

[0375] "Text data" is text data generated by voice recognition or OCR (optical character recognition).

[0376] "Emotion data" is data that represents emotions analyzed from the user's voice and facial expressions.

[0377] "Translation engine" refers to software or algorithms that convert data from one language into data from another language.

[0378] A "voice recognition engine" refers to software or algorithms that analyze voice data and convert it into corresponding text data.

[0379] An "emotion engine" refers to software or algorithms that analyze voice data and facial expression data to estimate and digitize the user's emotions.

[0380] An "OCR engine" refers to software or algorithms that extract characters from image data.

[0381] A "video analysis engine" refers to software or algorithms that analyze the content of video data and extract information such as audio and text.

[0382] "Target language" refers to the language into which the translation result is to be produced.

[0383] A "speech synthesis engine" refers to software or algorithms that analyze text data and generate synthetic speech.

[0384] An "augmented reality display" refers to a display device that displays digital information overlaid on the real world.

[0385] "Translated data" refers to text data and related information that has been converted into a target language by a translation engine.

[0386] "Video data" refers to data recorded in the form of user-captured video.

[0387] This invention is a system that uses a wearable device worn by the user to translate multilingual voice, text, and video in real time, and also recognizes the user's emotions and reflects them in the translation results. This system consists of a "device" used by the user, a "server" that processes data, and an "emotion engine."

[0388] 1. Real-time voice translation

[0389] The user speaks into the device's built-in microphone, for example, saying, "Hello, what are your plans for today?" The device captures this voice with the microphone and sends it to the server as voice data.

[0390] The server receives the voice data and converts the voice into text data using a voice recognition engine (e.g., voice recognition software). The generated text is "Hello, what are your plans for today?". The server then uses an emotion engine (e.g., emotion analysis software) to generate emotion data from the voice. For example, the result of the data analysis is "joy."

[0391] The generated text data and emotion data are sent to a translation engine (e.g., a translation API) and translated into the target language. As an example of translation from Japanese to English, the text "Hello, what are your plans for today?" and emotion data "joy" are generated.

[0392] The server converts the translated text data and emotion data into voice data using a voice synthesis engine (e.g., text-to-speech software). The device then plays the received voice data through built-in earphones and provides it to the user in real time. This voice data reflects the user's emotions, enabling natural and intuitive communication.

[0393] 2. Real-time text translation

[0394] The user uses the built-in camera of the device to capture text on signs, documents, etc. The device then sends the captured image data to the server.

[0395] The server receives the image data and uses an OCR engine (e.g., optical character recognition software) to extract text from the image. An example of extracted text might be "restaurant." The server then uses an emotion engine to analyze the user's facial expression at the time of capture and generate emotion data, such as "surprise."

[0396] The server sends the extracted text data and emotion data to a translation engine, which translates it into the target language. As translation examples, emotion data such as "Restaurant" and "surprise" are generated. The device receives the translated text data and displays it on the AR display. Emotions are reflected in the display using icons and colors, allowing the user to understand intuitively.

[0397] 3. Real-time video translation

[0398] The user captures video using the device's built-in camera, and the device transmits the captured video data to the server in real time.

[0399] The server receives the video data and analyzes the video content using a video analysis engine (e.g., video analysis software). For example, it may extract audio and text from the video. Next, the server uses an emotion engine to analyze the user's facial expressions and tone of voice and generate emotion data, such as "interest."

[0400] The server converts the extracted speech into text data using a speech recognition engine, sends the text data and emotion data to a translation engine for translation into the target language, and then converts the translated text data and emotion data into speech data using a speech synthesis engine.

[0401] The device plays the translated voice data through the built-in earphones and displays the translated text data on the AR display, allowing users to see and hear the translated voice and text information in real time, and also visually understand emotions.

[0402] With the above configuration, users can translate multilingual voice, text, and video in real time, enabling natural and intuitive communication that takes emotions into account. This system is expected to be used in a variety of situations, including international conferences, intercultural exchange, and tourism.

[0403] Prompt Sentence Examples

[0404] "Please explain the process of a system that translates Japanese speech spoken by a user into English and plays it back in a voice that reflects the emotion of the translation."

[0405] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0406] Real-time voice translation

[0407] Step 1:

[0408] The user speaks into the device's built-in microphone, for example, "Hello, what's your plan today?"

[0409] Input: User's speech

[0410] Output: Analog audio signal captured by microphone

[0411] Step 2:

[0412] The device converts analog audio signals captured by the built-in microphone into digital audio data. Example: Converted WAV format audio data

[0413] Input: Analog audio signal

[0414] Output: Digital audio data (WAV format)

[0415] Step 3:

[0416] The terminal transmits the converted digital audio data to the server.

[0417] Input: Digital audio data

[0418] Output: Digital audio data sent to the server

[0419] Step 4:

[0420] The server uses a speech recognition engine to convert the received voice data into text data. For example, "Hello, what are your plans for today?"

[0421] Input: Digital audio data

[0422] Output: Text data

[0423] Step 5:

[0424] The server uses an emotion engine to analyze the user's emotions from the voice data. Example: The analysis results generate emotion data for "joy."

[0425] Input: Digital audio data

[0426] Output: Emotion data (e.g., "joy")

[0427] Step 6:

[0428] The server sends the generated text data and emotion data to the translation engine, which translates it into the target language. Example: Translating from Japanese to English, generating the text data "Hello, what are your plans for today?" and the emotion data "joy."

[0429] Input: Text data, emotion data

[0430] Output: Translated text data and emotion data

[0431] Step 7:

[0432] The server converts the translated text data and emotion data into voice data using a speech synthesis engine, generating English voice data that reflects the emotion of "joy."

[0433] Input: translated text data, emotion data

[0434] Output: Audio data (e.g., synthesized English voice)

[0435] Step 8:

[0436] The server transmits the generated voice data to the terminal.

[0437] Input: Audio data

[0438] Output: Audio data sent to the device

[0439] Step 9:

[0440] The device then plays the received audio data through the built-in earphones and provides it to the user, producing audio that reflects "joy."

[0441] Input: Audio data

[0442] Output: Audio played through earphones

[0443] Real-time text translation

[0444] Step 1:

[0445] The user captures text on a sign or document using the device's built-in camera. Example: capturing an image of a restaurant sign.

[0446] Input: Image of a sign or document

[0447] Output: Captured image data

[0448] Step 2:

[0449] The terminal transmits the captured image data to the server.

[0450] Input: Image data

[0451] Output: Image data sent to the server

[0452] Step 3:

[0453] The server uses an OCR engine to extract text from the image data, e.g., extracting the text "restaurant."

[0454] Input: Image data

[0455] Output: Extracted text data (e.g., "restaurant")

[0456] Step 4:

[0457] The server uses an emotion engine to generate emotion data from the user's facial expression at the time of capture. For example, the server generates emotion data for "surprise" through facial expression analysis.

[0458] Input: Image data

[0459] Output: Emotion data (e.g., "surprise")

[0460] Step 5:

[0461] The server sends the extracted text data and emotion data to the translation engine, which translates it into the target language. For example, "restaurant" is translated to "Restaurant" and the emotion data "surprise" is added.

[0462] Input: Text data, emotion data

[0463] Output: Translated text data and emotion data

[0464] Step 6:

[0465] The server sends the translated text data and emotion data to the terminal.

[0466] Input: translated text data, emotion data

[0467] Output: Data sent to the terminal

[0468] Step 7:

[0469] The device displays the translated text data on the AR display, with emotions reflected in icons and colors.

[0470] Input: translated text data, emotion data

[0471] Output: Text to be displayed on the AR display

[0472] Real-time video translation

[0473] Step 1:

[0474] The user captures video using the device's built-in camera. Example: capturing video of an intercultural exchange event.

[0475] Input: Video

[0476] Output: Captured video data

[0477] Step 2:

[0478] The device transmits the captured video data to the server in real time.

[0479] Input: Video data

[0480] Output: Video data sent to the server

[0481] Step 3:

[0482] The server uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[0483] Input: Video data

[0484] Output: Extracted audio data and text data

[0485] Step 4:

[0486] The server uses an emotion engine to analyze emotion data from the user's facial expressions and tone of voice. Example: Generate emotion data for "interest."

[0487] Input: Audio data, video data

[0488] Output: Emotion data (e.g., "interest")

[0489] Step 5:

[0490] The server converts the extracted speech into text data using a speech recognition engine, and then sends the text and emotion data to a translation engine for translation into the target language. For example, Japanese speech is translated into English text, and the emotion data "interest" is added.

[0491] Input: Voice data, emotion data

[0492] Output: Translated text data, emotion data

[0493] Step 6:

[0494] The server converts the translated text data and emotion data into voice data using a voice synthesis engine. Example: Generates English synthesized voice data.

[0495] Input: translated text data, emotion data

[0496] Output: Audio data

[0497] Step 7:

[0498] The server transmits the generated voice data and text data to the terminal.

[0499] Input: Audio data, text data

[0500] Output: Data sent to the terminal

[0501] Step 8:

[0502] The device plays the audio data through the built-in earphones and displays the text data on the AR display, allowing users to check the translation results visually and audibly and to visually understand the emotions.

[0503] Input: Audio data, text data

[0504] Output: Audio played through earphones, text displayed on the AR display

[0505] (Application example 2)

[0506] 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."

[0507] There is a need for a system that can not only translate multilingual speech, text, and video in real time, but also recognize the user's emotions and reflect them in the translation results. Conventional systems have difficulty in achieving natural and intuitive communication that takes the user's emotions into account, and there is a need for technology that supports smooth communication, especially in situations such as intercultural exchange and food delivery.

[0508] The specific processing by the specific 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 capturing voice data from the device, means for transmitting the captured voice data to the server, means for performing voice recognition in the server and generating text data, means for translating the generated text data into a target language, means for converting the translated text data into voice data, means for transmitting the converted voice data to the device, means for playing back the voice data in the device and providing it to the user, means for analyzing emotional data from the captured voice data, and means for reflecting the emotional data in the translation result. This enables communication that takes into account not only voice recognition and translation but also the user's emotions.

[0509] The device also includes means for capturing image data from the device, means for transmitting the captured image data to a server, means for recognizing characters from the image in the server, means for translating the recognized character data into a target language, means for transmitting the translated character data to the device, means for displaying the character data on an augmented reality display in the device, means for analyzing emotions from facial expression data at the time of capture, and means for reflecting the emotion data in the translation result, thereby realizing real-time translation of text and reflecting emotions.

[0510] The device further includes means for capturing video data from the device, means for transmitting the captured video data to a server, means for extracting audio and text from the video on the server, means for translating the extracted audio and text into a target language, means for transmitting the translated data to the device, means for playing the translated audio data on the device and displaying the text data on an augmented reality display, means for analyzing emotions from facial expressions and tone of voice at the time of video capture, and means for reflecting the emotional data in the translation result, thereby enabling real-time translation of multilingual video data and reflecting emotions.

[0511] "Device" refers to a terminal, including a wearable device or smartphone worn by a user, and is hardware that captures and plays audio, images, and video.

[0512] "Voice data" refers to a data format for digitally recording the voice uttered by the user and transmitting it to a server.

[0513] "Capture" refers to capturing audio, images, or video using input devices such as sensors, cameras, and microphones, and collecting them as digital data.

[0514] A "server" is a central computer system that processes data using various algorithms and engines such as speech recognition, translation, and sentiment analysis.

[0515] "Speech recognition" is a technology that analyzes voice data and converts its contents into text data.

[0516] "Text data" refers to digital data expressed as a string of characters, and is generated by speech recognition.

[0517] "Target language" refers to the final language desired in the translation process, and refers to a language that can be understood by the user.

[0518] "Translation" is the process of converting text data written in one language into another target language.

[0519] "Emotion data" refers to digital data that indicates the emotional state of a user extracted from their voice, facial expression, and behavior.

[0520] "Conversion to voice data" refers to the process of converting text data and accompanying emotion data back into voice format data using voice synthesis technology.

[0521] "Image data" refers to still images captured by a camera or other imaging device.

[0522] "Character recognition" is a technique for extracting text information from captured image data.

[0523] "Augmented reality display" refers to a display technology that overlays computer-generated information on the physical world.

[0524] "Video data" refers to digital data recorded in moving image format, and includes audio and video.

[0525] "Facial expression data" refers to digital data that indicates an emotional state analyzed from a user's facial expressions captured by a camera.

[0526] "Incorporating emotional data into translation results" refers to the process of incorporating analyzed emotional data into translated text or speech to improve the naturalness of communication.

[0527] This system uses a wearable device worn by the user to translate multilingual speech, text, and video in real time, and also recognizes the user's emotions and reflects them in the translation results. The system consists of the following components:

[0528] System Configuration

[0529] 1. Device: A wearable device worn by a user (e.g., smart glasses, smartphone) that captures and plays back audio, images, and video.

[0530] 2. Server: A central computer system that processes data and uses various algorithms such as speech recognition, translation, and sentiment analysis.

[0531] 3. Emotion engine: Equipped with algorithms that recognize the emotional state extracted from the user's voice, facial expressions, and behavior.

[0532] Hardware and software used

[0533] Smart glasses and smartphones: Used to capture the user's voice and video and display the results.

[0534] speech_recognition: A speech recognition library that converts user speech into text data.

[0535] googletrans: Provides a translation engine to translate speech-recognized text into the target language.

[0536] EmotionEngine: A custom engine that analyzes emotions from the user's voice data and facial expressions.

[0537] cv2 (OpenCV): Used to capture and process camera images.

[0538] ARDisplay: A custom class for displaying text on the AR display of smart glasses.

[0539] pyttsx3: A text-to-speech synthesis library for playing translated text as audio.

[0540] Processing flow

[0541] 1. Voice capture: The user speaks into the device's built-in microphone to capture voice data.

[0542] 2. Speech Recognition: The captured voice data is sent to the server, which uses a speech recognition engine to convert the voice data into text.

[0543] 3. Emotion analysis: Analyze the user's emotions from the voice data and generate emotion data.

[0544] 4. Translation: Use the recognized text and emotion data to translate into the target language.

[0545] 5. Display and voice playback: The translated text is combined with emotion data and displayed on the AR display of the smart glasses, along with voice feedback.

[0546] Specific examples

[0547] For example, if a food delivery person says in Japanese, "Hello, can I leave it at the front door?", the system will translate it as "Hello, can I leave it at the front door?" (joy) and provide audio feedback to the delivery person. If the customer responds with a smile, that emotion data will also be reflected.

[0548] Prompt Sentence Examples

[0549] When a user asks "How would you like to pay?" in Japanese, what are the results of the translation and sentiment analysis?

[0550] As described above, the present invention enables natural and smooth communication that takes into account the user's feelings while providing multilingual support in situations such as cross-cultural exchange and food delivery.

[0551] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0552] Step 1:

[0553] The user speaks into the device's microphone. The input is the user's live voice, which is captured by the device's built-in microphone and recorded as digital audio data. The output is digital audio data.

[0554] Step 2:

[0555] The device sends the captured digital audio data to the server. The input is the digital audio data sent to the server over a network connection. The output is the digital audio data transferred to the server.

[0556] Step 3:

[0557] The server uses a speech recognition engine to analyze the received digital voice data. The input is the digital voice data, and the speech recognition engine (speech_recognition library) converts this data into text data. The output is the generated text data.

[0558] Step 4:

[0559] The server uses an emotion engine to analyze the user's emotions from the voice data. The input is digital voice data, which the emotion engine analyzes and generates emotion data. The output is emotion data.

[0560] Step 5:

[0561] The server sends the generated text data and emotion data to the translation engine to translate it into the target language. The input is text data and emotion data, which the translation engine (googletrans) translates into the target language. The output is the translated text data and emotion data.

[0562] Step 6:

[0563] The server converts the translated text data and emotion data into voice data using a voice synthesis engine. The input is the translated text data and emotion data, which the voice synthesis engine (pyttsx3) converts into voice data. The output is the converted voice data.

[0564] Step 7:

[0565] The server sends the converted audio data to the device. The input is the converted audio data sent to the device over a network connection. The output is the audio data transferred to the device.

[0566] Step 8:

[0567] The device plays the received audio data and provides it to the user. The input is the converted audio data, which is played through the built-in speaker or earphones. The output is the played audio.

[0568] Through the above steps, the user's voice is translated in real time, realizing natural communication that reflects emotions.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] [Second embodiment]

[0573] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0574] 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.

[0575] 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).

[0576] 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.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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."

[0585] This invention is a system that translates multilingual speech, text, and video in real time using a wearable device worn by the user. This system consists of a "device" used by the user and a "server" that processes data.

[0586] 1. Real-time voice translation

[0587] Program processing

[0588] User

[0589] The user inputs voice through the device's built-in microphone, for example, "Hello, what's your plan today?"

[0590] Terminal

[0591] The device captures the input voice data and sends it to the server.

[0592] server

[0593] The server receives the voice data and converts it into text data using a speech recognition engine. For example, the text generated from the voice is "Hello, what are your plans for today?"

[0594] The server translates the generated text data into the target language. When translating from Japanese to English, the translation result is "Hello, what are your plans for today?"

[0595] The server sends this translated text data to a speech synthesis engine and converts it into voice data.

[0596] The server transmits the converted voice data to the terminal.

[0597] Terminal

[0598] The device then plays the received translated voice data through built-in earphones and provides it to the user, who can listen to the translated voice in real time.

[0599] 2. Real-time text translation

[0600] Program processing

[0601] User

[0602] The user uses the device's built-in camera to capture text on a sign or document.

[0603] Terminal

[0604] The terminal transmits the captured image data to the server.

[0605] server

[0606] The server receives the image data and uses an image recognition engine (OCR engine) to extract text from the image. For example, the text "restaurant" is extracted.

[0607] The server translates the extracted text data into the target language. When translating from Japanese to English, the translation result is "Restaurant."

[0608] The server sends the translated text data to the terminal.

[0609] Terminal

[0610] The device then displays the received translated text data on the AR display, allowing the user to view the translated text through the lenses of their glasses.

[0611] 3. Real-time video translation

[0612] Program processing

[0613] User

[0614] The user captures the video using the device's built-in camera.

[0615] Terminal

[0616] The terminal transmits the captured video data to the server via streaming.

[0617] server

[0618] The server receives the video data and uses a video analysis engine to extract audio and text from the video, such as the audio of someone speaking or the text displayed in the video.

[0619] The server converts the extracted speech into text using a speech recognition engine and translates it into the target language, and also translates the extracted text into the target language.

[0620] The translated voice data is converted back into voice data by a voice synthesis engine.

[0621] The server sends the translated voice and text data to the terminal.

[0622] Terminal

[0623] The device then plays the received translated audio data through the built-in earphones and displays the translated text data on the AR display, allowing users to confirm the translated content both visually and audibly.

[0624] This allows users to communicate smoothly in real time, overcoming language barriers. This system can be used in a variety of situations, such as international conferences, intercultural exchanges, and tourism, and will enable smooth communication between people of different languages.

[0625] The processing flow will be explained below.

[0626] Real-time voice translation

[0627] Step 1: User Speech Input

[0628] The user speaks into the device's built-in microphone, for example, "Hello, what's on the agenda today?"

[0629] Step 2: Capture and send audio data with your device

[0630] The device captures the user's voice with a microphone and sends the voice data to the server.

[0631] Step 3: Speech recognition by the server

[0632] The server receives the voice data and uses a speech recognition engine to convert the voice data into text, e.g., "Hello, what are your plans for today?"

[0633] Step 4: Text translation by server

[0634] The server sends the generated text data to the translation engine and translates it into the target language. Example: When translating from Japanese to English, it becomes "Hello, what are your plans for today?"

[0635] Step 5: Server-based speech synthesis

[0636] The server sends the translated text data to a text-to-speech (TTS) engine, which converts the text into audio data.

[0637] Step 6: Server sends audio data

[0638] The server transmits the converted voice data to the terminal.

[0639] Step 7: Play the audio data on the device

[0640] The device receives the audio data from the server and plays it through built-in earphones, providing the user with translated audio in real time.

[0641] Real-time text translation

[0642] Step 1: User text input

[0643] The user uses the device's built-in camera to capture text on a sign or document.

[0644] Step 2: Capture and send image data by the device

[0645] The terminal transmits the captured image data to the server.

[0646] Step 3: Character Recognition (OCR) by the Server

[0647] The server receives the image data and uses an image recognition engine to extract text from the image, e.g., the text "restaurant" is extracted.

[0648] Step 4: Text translation by server

[0649] The server sends the extracted text data to a translation engine and translates it into the target language. Example: When translating from Japanese to English, the result is "Restaurant."

[0650] Step 5: Server sends text data

[0651] The server sends the translated text data to the terminal.

[0652] Step 6: Displaying text data using a terminal

[0653] The device receives the translated text data and displays it on the AR display, allowing the user to view the translated text through the glasses' display.

[0654] Real-time video translation

[0655] Step 1: User's video input

[0656] The user captures the video using the device's built-in camera.

[0657] Step 2: Capture and send video data from your device

[0658] The device streams the captured video data to the server in real time.

[0659] Step 3: Receiving and analyzing video data by the server

[0660] The server receives the video data and uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[0661] Step 4: Speech recognition and text conversion on the server

[0662] The audio in the video is converted into text using a speech recognition engine.

[0663] Step 5: Text translation by server

[0664] The server sends the converted text data to a translation engine for translation into the target language.

[0665] Step 6: Server-based speech synthesis

[0666] The server converts the translated text data into voice data using a voice synthesis engine.

[0667] Step 7: Server sends voice and text data

[0668] The server transmits the translated voice data and text data to the terminal.

[0669] Step 8: Play and display the translation data on your device

[0670] The device plays the received audio data through the built-in earphones and displays the text data on the AR display, allowing users to confirm the translated content visually and audibly.

[0671] Through the above processing steps, users can translate audio, text, and video in multiple languages ​​in real time, enabling smooth communication.

[0672] Example 1

[0673] 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."

[0674] There is a demand for systems that enable smooth communication between multiple languages. In particular, there is a need for systems that can translate voice, text, and video in real time and provide the translation to users. Conventional technologies require the use of individual devices and applications, which can be cumbersome for users. Therefore, there is a need for a system that solves these problems and enables users to communicate smoothly between different languages.

[0675] 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.

[0676] In this invention, the server includes means for converting voice data into text data, means for translating the converted text data into a target language, and means for converting the translated text data into voice data, thereby enabling real-time translation between multiple languages.

[0677] A "device" is a wearable device that a user uses to perform operations such as input and display.

[0678] "Audio data" refers to data obtained by converting an audio signal into digital format.

[0679] "Text data" is data that has been converted from character information into digital format.

[0680] "Image data" is visual information converted into digital form.

[0681] "Video data" is data in digital form that contains a sequence of images and sounds.

[0682] A "server" is a computer system for processing and managing data.

[0683] "Speech recognition technology" is a technology that analyzes voice signals and converts them into corresponding text information.

[0684] "Target language" is the language into which the translation is to be made.

[0685] "Image processing technology" is a technology that analyzes image data and extracts necessary information.

[0686] An "augmented reality display device" is a device that displays digital information superimposed on visual information from the real world.

[0687] "Video analysis technology" is a technology that analyzes video data and extracts necessary information.

[0688] "Speech synthesis technology" is a technology that converts text data into voice data.

[0689] This invention is a system for real-time translation of multilingual speech, text, and video, and is composed of a wearable device that is easy for users to use and a server that supports it. The system operates as follows.

[0690] The user wears a wearable device, which incorporates a microphone, a camera, earphones, and an augmented reality display. When the user speaks, the microphone in the device captures the voice data, converts it into a digital format, and the device transmits the voice data to a server.

[0691] The server converts the received voice data into text data using voice recognition technology. For example, a general voice recognition API can be used for this voice recognition. The converted text data is translated into the target language. For example, a widely used translation API can be used for this translation. The translated text data is then converted into voice data using voice synthesis technology. The server then retransmits the voice data to the terminal, which then provides it to the user through earphones.

[0692] For example, if a user says, "Hello, what are your plans for today?", the system translates it into "Hello, what are your plans for today?" and provides it to the user as English speech.

[0693] Similarly, for real-time translation of text data, when a user captures text using a camera, the image data is sent to a server. The server then uses image processing technology to extract the text from the image and translate it into the target language. The translated text data is sent to the device and displayed to the user through an augmented reality display device. For example, if a user takes a photo of a sign that says "restaurant," it is translated and displayed as "Restaurant."

[0694] Real-time translation of video data is also possible. When a user captures video with a camera, the data is sent to a server, which uses video analysis technology to extract audio and text from the video. These are then translated into the target language, and the translated data is sent to the device as audio and text data. The device then plays the audio through earphones and displays the text through an augmented reality display.

[0695] This allows users to smoothly communicate between different languages ​​in real time. This system is expected to be used in a variety of situations, including international conferences, tourism, and intercultural exchange.

[0696] An example of a specific prompt is:

[0697] "Please translate what the user says in Japanese into English."

[0698] "Analyze the image, extract the Japanese text and translate it into English."

[0699] "Extract audio and text from video data and translate them into English."

[0700] This allows users to obtain fast and accurate translation results through generative AI models.

[0701] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0702] Real-time voice translation

[0703] Step 1:

[0704] The user inputs voice through the device's built-in microphone, for example, "Hello, what are your plans for today?" The input data is an analog voice signal.

[0705] Step 2:

[0706] The device converts the analog audio signal captured by the microphone into digital audio data. This conversion process is performed by an analog-to-digital converter (ADC). The converted digital audio data is then sent to the server.

[0707] Step 3:

[0708] The server receives the digital voice data and converts it into text data using voice recognition technology. For this process, a voice recognition API is used to generate text data from the input voice data, such as "Hello, what are your plans for today?"

[0709] Step 4:

[0710] The server translates the generated text data into the target language. In this step, a translation API is used to convert the text data from Japanese to English. For example, "Hello, what are your plans for today?" is translated into "Hello, what are your plans for today?"

[0711] Step 5:

[0712] The server converts the translated text data into audio data using speech synthesis technology. Based on the translated text data, "Hello, what are your plans for today?", the speech synthesis engine generates corresponding digital audio data.

[0713] Step 6:

[0714] The server transmits the generated audio data to the terminal, and the transmitted digital audio data includes appropriate metadata.

[0715] Step 7:

[0716] The device decodes the received audio data and plays it back to the user through the built-in earphones, allowing the user to listen to the translated English audio in real time.

[0717] Real-time text translation

[0718] Step 1:

[0719] A user uses the device's built-in camera to capture text on a sign or document, for example, taking a photo of a sign that says "Restaurant." The input data is image data.

[0720] Step 2:

[0721] The device sends the captured image data, including metadata such as resolution and timestamp, to the server.

[0722] Step 3:

[0723] The server receives the image data and uses image processing technology (OCR technology) to extract text from the image. For example, the text "restaurant" is extracted from an image of a sign.

[0724] Step 4:

[0725] The server translates the extracted text data into the target language using a translation API. For example, "restaurant" is translated to "Restaurant."

[0726] Step 5:

[0727] The server sends the translated text data to the device, including the translation results and associated metadata.

[0728] Step 6:

[0729] The device displays the received text data on the augmented reality display device, and the user can see the translated text "Restaurant" through the lenses of the glasses.

[0730] Real-time video translation

[0731] Step 1:

[0732] A user uses the device's built-in camera to capture video, for example, a foreign language news broadcast, and the input data is video data.

[0733] Step 2:

[0734] The device transmits the captured video data to the server in real time using streaming technology, and the data includes audio and video frame information.

[0735] Step 3:

[0736] The server receives the video data and uses video analysis technology to extract audio and text from the video, for example, audio and text from a news broadcast.

[0737] Step 4:

[0738] The server converts the extracted speech into text data and then translates it into the target language. Using speech recognition technology and a translation API, the text "What are you doing?" is translated into "What are you doing?"

[0739] Step 5:

[0740] The server uses speech synthesis technology to convert the translated text data into voice data, and also prepares the translated text data for transmission separately.

[0741] Step 6:

[0742] The server sends the translated voice data and text data to the terminal. The transmitted data includes the voice file and the text information.

[0743] Step 7:

[0744] The device then plays the received audio data through earphones and displays the text data on an augmented reality display device, allowing users to visually and aurally follow the conversations and subtitles in the video in real time.

[0745] This allows users to understand information and communicate in real time, regardless of language differences.

[0746] (Application example 1)

[0747] 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."

[0748] The present invention aims to provide a system that overcomes language barriers in autonomous vehicles and enables multilingual passengers to communicate smoothly with the vehicle. In current autonomous vehicles, communication between passengers and the vehicle is limited, making it difficult to convey information, especially when different languages ​​are spoken. This poses a problem of reduced passenger safety and satisfaction. To solve this problem, a system is needed that can achieve real-time translation between multiple languages ​​and provide passengers with appropriate information and responses.

[0749] 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.

[0750] In this invention, the server includes means for capturing voice data from the device, means for transmitting the captured voice data to the server, means for performing voice recognition in the server and generating text data, means for translating the generated text data into a target language, means for converting the translated text data into voice data, means for transmitting the converted voice data to the device, means for playing back the voice data in the device and providing it to a user, and means for performing multilingual communication between passengers and the vehicle in real time in an autonomous vehicle. This enables accurate and prompt information transmission between passengers and the vehicle, thereby improving passenger safety and satisfaction.

[0751] A "device" is a device for capturing audio data, image data, or video data and transmitting it to a server.

[0752] "Audio data" refers to data that has been converted from human speech or other sounds into digital signals.

[0753] A "server" is a central processing unit for processing, converting, and translating captured data.

[0754] "Speech recognition" is a technology that converts voice data into text data.

[0755] "Text data" is data that represents character information in digital form.

[0756] "Target language" is the language into which the text is to be translated.

[0757] "Converting into voice data" means converting text data back into voice format data using voice synthesis technology.

[0758] "Multilingual communication" is communication that takes place between two or more people who speak different languages.

[0759] An "autonomous vehicle" is a vehicle that operates autonomously without the need for a human driver.

[0760] An "augmented reality display" is a display technology that overlays digital information onto visual information from the real world.

[0761] "Video data" is data that converts moving images or videos into digital signals.

[0762] "Character recognition" is a technology for extracting character information from an image.

[0763] "Real-time" means that data is processed immediately, without delay.

[0764] The present invention embodies a multilingual communication system for an autonomous driving vehicle, and is configured as follows.

[0765] 1. Real-time voice translation

[0766] User

[0767] The user inputs instructions, questions, etc. in their native language through the device's built-in microphone. For example, they might say "Hello, please take me to the airport" in Japanese.

[0768] Terminal

[0769] The device captures the input voice data and sends it to a server, a process that uses a microphone and an internet connection.

[0770] server

[0771] The server receives the voice data and converts it into text data using a speech recognition engine (such as the Google Speech Recognition API). Next, it translates this text data into the target language (e.g., English) using a multilingual translation engine (such as the Google Translate API). After that, it converts the translated text data back into voice data using a speech synthesis engine (such as gTTS).

[0772] Terminal

[0773] The converted voice data is sent back to the device and played through the device's built-in speaker, allowing the user to hear the translated voice in real time.

[0774] 2. Real-time text translation

[0775] User

[0776] The user uses the device's built-in camera to capture text and sign information displayed on the in-car display.

[0777] Terminal

[0778] The terminal transmits the captured image data to the server.

[0779] server

[0780] The server receives the image data, extracts text from the image using an OCR (optical character recognition) engine (such as pytesseract), and then translates the extracted text data into the target language using a translation engine.

[0781] Terminal

[0782] The translated text data is displayed on an augmented reality display inside the vehicle, allowing the user to visually confirm the translated information.

[0783] 3. Real-time video translation

[0784] User

[0785] The user captures the video using the device's built-in camera.

[0786] Terminal

[0787] The terminal transmits the captured video data to the server in a streaming manner.

[0788] server

[0789] The server receives the video data and uses an analysis engine to extract the audio and text from the video. The extracted audio data is converted into text using a speech recognition engine and simultaneously translated into the target language. The text data is also translated into the target language.

[0790] Terminal

[0791] The device then plays the received translated audio data through built-in earphones and displays the translated text data on the augmented reality display, allowing users to confirm the translation both visually and audibly.

[0792] Specific examples

[0793] For example, if a foreign passenger gets into a self-driving vehicle and says in Japanese, "Hello, take me to the airport please," the system will process it as follows:

[0794] 1. Capture audio with a microphone.

[0795] 2. Send the captured audio data to the server.

[0796] 3. The server converts the voice data into text data and translates it into the target language.

[0797] 4. The translated text data is converted into audio data and sent back to the device.

[0798] 5. The terminal plays the translated audio and relays the information to the passenger.

[0799] Example prompts for generative AI models

[0800] When a foreign passenger boards an autonomous vehicle, he or she says in Japanese, "Hello, please take me to the airport." Write a program to translate this into English in real time and play it back. Use the Google Speech Recognition API for speech recognition and the googletrans library for translation. The final English audio will be generated and played back using the gtts library.

[0801] As described above, this system is designed to provide real-time multilingual translation of voice, text, and video to facilitate communication in self-driving vehicles.

[0802] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0803] Step 1:

[0804] The user inputs voice through the device's built-in microphone, and the voice data is captured in digital form. The input voice data is raw voice waveform data, which is temporarily stored on the device for further processing.

[0805] Step 2:

[0806] The device sends the captured audio data to the server via an internet connection, where it is transferred in real time using the appropriate protocol (e.g. HTTP, WebSocket) in its digital form.

[0807] Step 3:

[0808] The server inputs the received voice data into a voice recognition engine (e.g., Google Speech Recognition API) and converts the voice into text data. The voice recognition engine analyzes the voice waveform and generates a corresponding string of characters. At this stage, the input is voice data and the output is text data.

[0809] Step 4:

[0810] The generated text data is input to a multilingual translation engine (e.g., Google Translate API) and translated into the target language. The input here is the generated text data, and the output is the translated text data. The translation engine analyzes the input text and generates an appropriate translation result.

[0811] Step 5:

[0812] The translated text data is input to a speech synthesis engine (e.g., gTTS) and converted back into speech data. The speech synthesis engine analyzes the input text and generates a corresponding speech waveform. At this stage, the input is the translated text data and the output is speech data.

[0813] Step 6:

[0814] The server then sends the converted audio data back to the device, which receives it via its internet connection, where it is sent in its digital form.

[0815] Step 7:

[0816] The device plays the received voice data through the built-in speaker and provides it to the user, who can listen to the translated voice in real time. Here, the input is the voice data received from the server, and the output is the voice played from the speaker.

[0817] 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.

[0818] This system uses a wearable device worn by the user to translate multilingual audio, text, and video in real time, and also recognizes the user's emotions and reflects them in the translation results. This system consists of a "device" used by the user, a "server" that processes data, and an "emotion engine."

[0819] 1. Real-time voice translation

[0820] Program processing

[0821] User

[0822] The user speaks into the device's built-in microphone, for example, "Hello, what's on the agenda today?"

[0823] Terminal

[0824] The device captures the user's voice with a microphone and sends the voice data to the server.

[0825] server

[0826] The server receives the voice data and uses a speech recognition engine to convert the voice data into text, e.g., "Hello, what are your plans for today?"

[0827] The server uses an emotion engine to analyze the user's emotions from the voice data and generate emotion data. For example, if the user is recognized as happy, the emotion data will be "joy."

[0828] The server sends the generated text data along with the emotion data to the translation engine, which translates it into the target language. For example, when translating from Japanese to English, "Hello, what are your plans for today?" is given the emotion data of "joy."

[0829] The server converts the translated text data and emotion data into voice data that matches the user's target language.

[0830] The server transmits the converted voice data to the terminal.

[0831] Terminal

[0832] The device then plays the received translated voice data through built-in earphones, providing the user with real-time translated voice, which is played in a tone that reflects the user's emotions.

[0833] 2. Real-time text translation

[0834] Program processing

[0835] User

[0836] The user uses the device's built-in camera to capture text on a sign or document.

[0837] Terminal

[0838] The terminal transmits the captured image data to the server.

[0839] server

[0840] The server receives the image data and uses an image recognition engine (OCR engine) to extract text from the image. For example, the text "restaurant" is extracted.

[0841] The server uses an emotion engine to analyze the emotion data from the user's facial expression at the time of capture. For example, if the user is determined to be surprised, the emotion data will be "surprise."

[0842] The server sends the extracted text data and emotion data to the translation engine, which translates it into the target language. For example, "restaurant" is translated as "Restaurant" and the emotion data "surprise" is added.

[0843] The server sends the translated text data and emotion data to the terminal.

[0844] Terminal

[0845] The device receives the translated text data and displays it on the AR display, which reflects the user's emotions with icons and colors, making it easier to understand intuitively.

[0846] 3. Real-time video translation

[0847] Program processing

[0848] User

[0849] The user captures the video using the device's built-in camera.

[0850] Terminal

[0851] The device streams the captured video data to the server in real time.

[0852] server

[0853] The server receives the video data and uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[0854] The server uses an emotion engine to analyze the user's facial expressions and tone of voice in real time to generate emotion data. For example, if the user is determined to be interested, the emotion data will be "interested."

[0855] The server converts the analyzed speech into text using a speech recognition engine, then sends the text and emotional data to a translation engine for translation into the target language.

[0856] The translated text data and emotion data are then converted into voice data by a voice synthesis engine.

[0857] The server sends the translated voice and text data to the terminal.

[0858] Terminal

[0859] The device plays the received audio data through the built-in earphones and displays the text data on the AR display, allowing users to see and hear the translated content while also visually understanding the emotions expressed.

[0860] This system enables users to translate multilingual audio, text, and video in real time, and even takes emotions into account for natural and intuitive communication. This system is expected to be used in a variety of situations, including international conferences, intercultural exchange, and tourism.

[0861] The processing flow will be explained below.

[0862] Real-time voice translation

[0863] Step 1: User Speech Input

[0864] The user speaks into the device's built-in microphone, for example, "Hello, what's on the agenda today?"

[0865] Step 2: Capture and send audio data with your device

[0866] The device captures the user's voice with a microphone and sends the voice data to the server.

[0867] Step 3: Speech recognition by the server

[0868] The server receives the voice data and uses a speech recognition engine to convert the voice data into text, e.g., "Hello, what are your plans for today?"

[0869] Step 4: Emotion recognition by the server

[0870] The server uses an emotion engine to analyze the user's emotion from the voice data. For example, if the user is recognized as happy from the voice tone and speaking style, the emotion data will be "joy."

[0871] Step 5: Text translation by server

[0872] The server sends the generated text data and emotion data to the translation engine, which translates it into the target language. For example, when translating from Japanese to English, "Hello, what are your plans for today?" is given with the emotion data "joy."

[0873] Step 6: Server-based speech synthesis

[0874] The server sends the translated text data and emotion data to a speech synthesis engine, which converts it into voice data. This voice is generated in a tone that reflects the user's emotion.

[0875] Step 7: Server sends audio data

[0876] The server transmits the converted voice data to the terminal.

[0877] Step 8: Play the audio data on the device

[0878] The device receives the voice data from the server and plays it through built-in earphones, providing the user with real-time translated speech that reflects the user's emotions.

[0879] Real-time text translation

[0880] Step 1: User text input

[0881] The user uses the device's built-in camera to capture text on a sign or document.

[0882] Step 2: Capture and send image data by the device

[0883] The terminal transmits the captured image data to the server.

[0884] Step 3: Character Recognition (OCR) by the Server

[0885] The server receives the image data and uses an image recognition engine to extract text from the image, e.g., the text "restaurant" is extracted.

[0886] Step 4: Emotion recognition by the server

[0887] The server uses the emotion engine to analyze the emotion from the user's facial expression at the time of capture. For example, if the user is determined to be surprised, the emotion data will be "surprise."

[0888] Step 5: Text translation by server

[0889] The server sends the extracted text data and emotion data to the translation engine, which translates it into the target language. For example, "restaurant" is translated as "Restaurant" and the emotion data "surprise" is added.

[0890] Step 6: Server sends text data

[0891] The server sends the translated text data and emotion data to the terminal.

[0892] Step 7: Displaying Text Data in a Terminal

[0893] The device receives the translated text data and displays it on the AR display, which reflects the user's emotions with icons and colors, making it easier to understand intuitively.

[0894] Real-time video translation

[0895] Step 1: User's video input

[0896] The user captures the video using the device's built-in camera.

[0897] Step 2: Capture and send video data from your device

[0898] The device streams the captured video data to the server in real time.

[0899] Step 3: Receiving and analyzing video data by the server

[0900] The server receives the video data and uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[0901] Step 4: Speech recognition and text conversion on the server

[0902] The audio in the video is converted into text using a speech recognition engine.

[0903] Step 5: Emotion recognition by the server

[0904] The server uses an emotion engine to analyze the user's facial expressions and tone of voice in real time to generate emotion data. For example, if the user is determined to be interested, the emotion data will be "interested."

[0905] Step 6: Text translation by server

[0906] The server sends the converted text data and emotion data to a translation engine for translation into the target language.

[0907] Step 7: Server-based speech synthesis

[0908] The server converts the translated text data and emotional data into voice data using a voice synthesis engine.

[0909] Step 8: Server sends voice and text data

[0910] The server transmits the translated voice data and text data to the terminal.

[0911] Step 9: Play and display the translation data on your device

[0912] The device plays the received audio data through the built-in earphones and displays the text data on the AR display, allowing users to see and hear the translated content while also visually understanding the emotions expressed.

[0913] This system enables users to translate multilingual audio, text, and video in real time, and even takes emotions into account for natural and intuitive communication. This system is expected to be used in a variety of situations, including international conferences, intercultural exchange, and tourism.

[0914] Example 2

[0915] 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."

[0916] In modern society, multilingual communication is becoming increasingly important, but current translation technology is not yet able to fully realize natural and intuitive communication. In particular, there are issues with providing translation results that reflect emotions and with real-time multilingual support, which are inconvenient for users in cross-cultural exchange, international conferences, tourism, and other situations.

[0917] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0918] In this invention, the server includes means for generating emotion data from voice data, means for translating the generated text data and emotion data into a target language, and means for converting the translated text data and emotion data into voice data, thereby making it possible to provide intuitive and natural translation results in real time that reflect the emotions contained in the user's voice.

[0919] "Device" refers to electronic equipment worn or used by a user, including hardware such as a microphone, camera, earphones, and augmented reality display.

[0920] "Server" refers to a computer system that receives audio data, image data, and video data and performs data processing such as analysis, translation, and conversion.

[0921] "Voice data" refers to data in which the voice uttered by the user is recorded in digital format.

[0922] "Text data" is text data generated by voice recognition or OCR (optical character recognition).

[0923] "Emotion data" is data that represents emotions analyzed from the user's voice and facial expressions.

[0924] "Translation engine" refers to software or algorithms that convert data from one language into data from another language.

[0925] A "voice recognition engine" refers to software or algorithms that analyze voice data and convert it into corresponding text data.

[0926] An "emotion engine" refers to software or algorithms that analyze voice data and facial expression data to estimate and digitize the user's emotions.

[0927] An "OCR engine" refers to software or algorithms that extract characters from image data.

[0928] A "video analysis engine" refers to software or algorithms that analyze the content of video data and extract information such as audio and text.

[0929] "Target language" refers to the language into which the translation result is to be produced.

[0930] A "speech synthesis engine" refers to software or algorithms that analyze text data and generate synthetic speech.

[0931] An "augmented reality display" refers to a display device that displays digital information overlaid on the real world.

[0932] "Translated data" refers to text data and related information that has been converted into a target language by a translation engine.

[0933] "Video data" refers to data recorded in the form of user-captured video.

[0934] This invention is a system that uses a wearable device worn by the user to translate multilingual voice, text, and video in real time, and also recognizes the user's emotions and reflects them in the translation results. This system consists of a "device" used by the user, a "server" that processes data, and an "emotion engine."

[0935] 1. Real-time voice translation

[0936] The user speaks into the device's built-in microphone, for example, saying, "Hello, what are your plans for today?" The device captures this voice with the microphone and sends it to the server as voice data.

[0937] The server receives the voice data and converts the voice into text data using a voice recognition engine (e.g., voice recognition software). The generated text is "Hello, what are your plans for today?". The server then uses an emotion engine (e.g., emotion analysis software) to generate emotion data from the voice. For example, the result of the data analysis is "joy."

[0938] The generated text data and emotion data are sent to a translation engine (e.g., a translation API) and translated into the target language. As an example of translation from Japanese to English, the text "Hello, what are your plans for today?" and emotion data "joy" are generated.

[0939] The server converts the translated text data and emotion data into voice data using a voice synthesis engine (e.g., text-to-speech software). The device then plays the received voice data through built-in earphones and provides it to the user in real time. This voice data reflects the user's emotions, enabling natural and intuitive communication.

[0940] 2. Real-time text translation

[0941] The user uses the built-in camera of the device to capture text on signs, documents, etc. The device then sends the captured image data to the server.

[0942] The server receives the image data and uses an OCR engine (e.g., optical character recognition software) to extract text from the image. An example of extracted text might be "restaurant." The server then uses an emotion engine to analyze the user's facial expression at the time of capture and generate emotion data, such as "surprise."

[0943] The server sends the extracted text data and emotion data to a translation engine, which translates it into the target language. As translation examples, emotion data such as "Restaurant" and "surprise" are generated. The device receives the translated text data and displays it on the AR display. Emotions are reflected in the display using icons and colors, allowing the user to understand intuitively.

[0944] 3. Real-time video translation

[0945] The user captures video using the device's built-in camera, and the device transmits the captured video data to the server in real time.

[0946] The server receives the video data and analyzes the video content using a video analysis engine (e.g., video analysis software). For example, it may extract audio and text from the video. Next, the server uses an emotion engine to analyze the user's facial expressions and tone of voice and generate emotion data, such as "interest."

[0947] The server converts the extracted speech into text data using a speech recognition engine, sends the text data and emotion data to a translation engine for translation into the target language, and then converts the translated text data and emotion data into speech data using a speech synthesis engine.

[0948] The device plays the translated voice data through the built-in earphones and displays the translated text data on the AR display, allowing users to see and hear the translated voice and text information in real time, and also visually understand emotions.

[0949] With the above configuration, users can translate multilingual voice, text, and video in real time, enabling natural and intuitive communication that takes emotions into account. This system is expected to be used in a variety of situations, including international conferences, intercultural exchange, and tourism.

[0950] Prompt Sentence Examples

[0951] "Please explain the process of a system that translates Japanese speech spoken by a user into English and plays it back in a voice that reflects the emotion of the translation."

[0952] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0953] Real-time voice translation

[0954] Step 1:

[0955] The user speaks into the device's built-in microphone, for example, "Hello, what's your plan today?"

[0956] Input: User's speech

[0957] Output: Analog audio signal captured by microphone

[0958] Step 2:

[0959] The device converts analog audio signals captured by the built-in microphone into digital audio data. Example: Converted WAV format audio data

[0960] Input: Analog audio signal

[0961] Output: Digital audio data (WAV format)

[0962] Step 3:

[0963] The terminal transmits the converted digital audio data to the server.

[0964] Input: Digital audio data

[0965] Output: Digital audio data sent to the server

[0966] Step 4:

[0967] The server uses a speech recognition engine to convert the received voice data into text data. For example, "Hello, what are your plans for today?"

[0968] Input: Digital audio data

[0969] Output: Text data

[0970] Step 5:

[0971] The server uses an emotion engine to analyze the user's emotions from the voice data. Example: The analysis results generate emotion data for "joy."

[0972] Input: Digital audio data

[0973] Output: Emotion data (e.g., "joy")

[0974] Step 6:

[0975] The server sends the generated text data and emotion data to the translation engine, which translates it into the target language. Example: Translating from Japanese to English, generating the text data "Hello, what are your plans for today?" and the emotion data "joy."

[0976] Input: Text data, emotion data

[0977] Output: Translated text data and emotion data

[0978] Step 7:

[0979] The server converts the translated text data and emotion data into voice data using a speech synthesis engine, generating English voice data that reflects the emotion of "joy."

[0980] Input: translated text data, emotion data

[0981] Output: Audio data (e.g., synthesized English voice)

[0982] Step 8:

[0983] The server transmits the generated voice data to the terminal.

[0984] Input: Audio data

[0985] Output: Audio data sent to the device

[0986] Step 9:

[0987] The device then plays the received audio data through the built-in earphones and provides it to the user, producing audio that reflects "joy."

[0988] Input: Audio data

[0989] Output: Audio played through earphones

[0990] Real-time text translation

[0991] Step 1:

[0992] The user captures text on a sign or document using the device's built-in camera. Example: capturing an image of a restaurant sign.

[0993] Input: Image of a sign or document

[0994] Output: Captured image data

[0995] Step 2:

[0996] The terminal transmits the captured image data to the server.

[0997] Input: Image data

[0998] Output: Image data sent to the server

[0999] Step 3:

[1000] The server uses an OCR engine to extract text from the image data, e.g., extracting the text "restaurant."

[1001] Input: Image data

[1002] Output: Extracted text data (e.g., "restaurant")

[1003] Step 4:

[1004] The server uses an emotion engine to generate emotion data from the user's facial expression at the time of capture. For example, the server generates emotion data for "surprise" through facial expression analysis.

[1005] Input: Image data

[1006] Output: Emotion data (e.g., "surprise")

[1007] Step 5:

[1008] The server sends the extracted text data and emotion data to the translation engine, which translates it into the target language. For example, "restaurant" is translated to "Restaurant" and the emotion data "surprise" is added.

[1009] Input: Text data, emotion data

[1010] Output: Translated text data and emotion data

[1011] Step 6:

[1012] The server sends the translated text data and emotion data to the terminal.

[1013] Input: translated text data, emotion data

[1014] Output: Data sent to the terminal

[1015] Step 7:

[1016] The device displays the translated text data on the AR display, with emotions reflected in icons and colors.

[1017] Input: translated text data, emotion data

[1018] Output: Text to be displayed on the AR display

[1019] Real-time video translation

[1020] Step 1:

[1021] The user captures video using the device's built-in camera. Example: capturing video of an intercultural exchange event.

[1022] Input: Video

[1023] Output: Captured video data

[1024] Step 2:

[1025] The device transmits the captured video data to the server in real time.

[1026] Input: Video data

[1027] Output: Video data sent to the server

[1028] Step 3:

[1029] The server uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[1030] Input: Video data

[1031] Output: Extracted audio data and text data

[1032] Step 4:

[1033] The server uses an emotion engine to analyze emotion data from the user's facial expressions and tone of voice. Example: Generate emotion data for "interest."

[1034] Input: Audio data, video data

[1035] Output: Emotion data (e.g., "interest")

[1036] Step 5:

[1037] The server converts the extracted speech into text data using a speech recognition engine, and then sends the text and emotion data to a translation engine for translation into the target language. For example, Japanese speech is translated into English text, and the emotion data "interest" is added.

[1038] Input: Voice data, emotion data

[1039] Output: Translated text data, emotion data

[1040] Step 6:

[1041] The server converts the translated text data and emotion data into voice data using a voice synthesis engine. Example: Generates English synthesized voice data.

[1042] Input: translated text data, emotion data

[1043] Output: Audio data

[1044] Step 7:

[1045] The server transmits the generated voice data and text data to the terminal.

[1046] Input: Audio data, text data

[1047] Output: Data sent to the terminal

[1048] Step 8:

[1049] The device plays the audio data through the built-in earphones and displays the text data on the AR display, allowing users to check the translation results visually and audibly and to visually understand the emotions.

[1050] Input: Audio data, text data

[1051] Output: Audio played through earphones, text displayed on the AR display

[1052] (Application example 2)

[1053] 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."

[1054] There is a need for a system that can not only translate multilingual speech, text, and video in real time, but also recognize the user's emotions and reflect them in the translation results. Conventional systems have difficulty in achieving natural and intuitive communication that takes the user's emotions into account, and there is a need for technology that supports smooth communication, especially in situations such as intercultural exchange and food delivery.

[1055] The specific processing by the specific 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 capturing voice data from the device, means for transmitting the captured voice data to the server, means for performing voice recognition in the server and generating text data, means for translating the generated text data into a target language, means for converting the translated text data into voice data, means for transmitting the converted voice data to the device, means for playing back the voice data in the device and providing it to the user, means for analyzing emotional data from the captured voice data, and means for reflecting the emotional data in the translation result. This enables communication that takes into account not only voice recognition and translation but also the user's emotions.

[1056] The device also includes means for capturing image data from the device, means for transmitting the captured image data to a server, means for recognizing characters from the image in the server, means for translating the recognized character data into a target language, means for transmitting the translated character data to the device, means for displaying the character data on an augmented reality display in the device, means for analyzing emotions from facial expression data at the time of capture, and means for reflecting the emotion data in the translation result, thereby realizing real-time translation of text and reflecting emotions.

[1057] The device further includes means for capturing video data from the device, means for transmitting the captured video data to a server, means for extracting audio and text from the video on the server, means for translating the extracted audio and text into a target language, means for transmitting the translated data to the device, means for playing the translated audio data on the device and displaying the text data on an augmented reality display, means for analyzing emotions from facial expressions and tone of voice at the time of video capture, and means for reflecting the emotional data in the translation result, thereby enabling real-time translation of multilingual video data and reflecting emotions.

[1058] "Device" refers to a terminal, including a wearable device or smartphone worn by a user, and is hardware that captures and plays audio, images, and video.

[1059] "Voice data" refers to a data format for digitally recording the voice uttered by the user and transmitting it to a server.

[1060] "Capture" refers to capturing audio, images, or video using input devices such as sensors, cameras, and microphones, and collecting them as digital data.

[1061] A "server" is a central computer system that processes data using various algorithms and engines such as speech recognition, translation, and sentiment analysis.

[1062] "Speech recognition" is a technology that analyzes voice data and converts its contents into text data.

[1063] "Text data" refers to digital data expressed as a string of characters, and is generated by speech recognition.

[1064] "Target language" refers to the final language desired in the translation process, and refers to a language that can be understood by the user.

[1065] "Translation" is the process of converting text data written in one language into another target language.

[1066] "Emotion data" refers to digital data that indicates the emotional state of a user extracted from their voice, facial expression, and behavior.

[1067] "Conversion to voice data" refers to the process of converting text data and accompanying emotion data back into voice format data using voice synthesis technology.

[1068] "Image data" refers to still images captured by a camera or other imaging device.

[1069] "Character recognition" is a technique for extracting text information from captured image data.

[1070] "Augmented reality display" refers to a display technology that overlays computer-generated information on the physical world.

[1071] "Video data" refers to digital data recorded in moving image format, and includes audio and video.

[1072] "Facial expression data" refers to digital data that indicates an emotional state analyzed from a user's facial expressions captured by a camera.

[1073] "Incorporating emotional data into translation results" refers to the process of incorporating analyzed emotional data into translated text or speech to improve the naturalness of communication.

[1074] This system uses a wearable device worn by the user to translate multilingual speech, text, and video in real time, and also recognizes the user's emotions and reflects them in the translation results. The system consists of the following components:

[1075] System Configuration

[1076] 1. Device: A wearable device worn by a user (e.g., smart glasses, smartphone) that captures and plays back audio, images, and video.

[1077] 2. Server: A central computer system that processes data and uses various algorithms such as speech recognition, translation, and sentiment analysis.

[1078] 3. Emotion engine: Equipped with algorithms that recognize the emotional state extracted from the user's voice, facial expressions, and behavior.

[1079] Hardware and software used

[1080] Smart glasses and smartphones: Used to capture the user's voice and video and display the results.

[1081] speech_recognition: A speech recognition library that converts user speech into text data.

[1082] googletrans: Provides a translation engine to translate speech-recognized text into the target language.

[1083] EmotionEngine: A custom engine that analyzes emotions from the user's voice data and facial expressions.

[1084] cv2 (OpenCV): Used to capture and process camera images.

[1085] ARDisplay: A custom class for displaying text on the AR display of smart glasses.

[1086] pyttsx3: A text-to-speech synthesis library for playing translated text as audio.

[1087] Processing flow

[1088] 1. Voice capture: The user speaks into the device's built-in microphone to capture voice data.

[1089] 2. Speech Recognition: The captured voice data is sent to the server, which uses a speech recognition engine to convert the voice data into text.

[1090] 3. Emotion analysis: Analyze the user's emotions from the voice data and generate emotion data.

[1091] 4. Translation: Use the recognized text and emotion data to translate into the target language.

[1092] 5. Display and voice playback: The translated text is combined with emotion data and displayed on the AR display of the smart glasses, along with voice feedback.

[1093] Specific examples

[1094] For example, if a food delivery person says in Japanese, "Hello, can I leave it at the front door?", the system will translate it as "Hello, can I leave it at the front door?" (joy) and provide audio feedback to the delivery person. If the customer responds with a smile, that emotion data will also be reflected.

[1095] Prompt Sentence Examples

[1096] When a user asks "How would you like to pay?" in Japanese, what are the results of the translation and sentiment analysis?

[1097] As described above, the present invention enables natural and smooth communication that takes into account the user's feelings while providing multilingual support in situations such as cross-cultural exchange and food delivery.

[1098] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1099] Step 1:

[1100] The user speaks into the device's microphone. The input is the user's live voice, which is captured by the device's built-in microphone and recorded as digital audio data. The output is digital audio data.

[1101] Step 2:

[1102] The device sends the captured digital audio data to the server. The input is the digital audio data sent to the server over a network connection. The output is the digital audio data transferred to the server.

[1103] Step 3:

[1104] The server uses a speech recognition engine to analyze the received digital voice data. The input is the digital voice data, and the speech recognition engine (speech_recognition library) converts this data into text data. The output is the generated text data.

[1105] Step 4:

[1106] The server uses an emotion engine to analyze the user's emotions from the voice data. The input is digital voice data, which the emotion engine analyzes and generates emotion data. The output is emotion data.

[1107] Step 5:

[1108] The server sends the generated text data and emotion data to the translation engine to translate it into the target language. The input is text data and emotion data, which the translation engine (googletrans) translates into the target language. The output is the translated text data and emotion data.

[1109] Step 6:

[1110] The server converts the translated text data and emotion data into voice data using a voice synthesis engine. The input is the translated text data and emotion data, which the voice synthesis engine (pyttsx3) converts into voice data. The output is the converted voice data.

[1111] Step 7:

[1112] The server sends the converted audio data to the device. The input is the converted audio data sent to the device over a network connection. The output is the audio data transferred to the device.

[1113] Step 8:

[1114] The device plays the received audio data and provides it to the user. The input is the converted audio data, which is played through the built-in speaker or earphones. The output is the played audio.

[1115] Through the above steps, the user's voice is translated in real time, realizing natural communication that reflects emotions.

[1116] 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.

[1117] 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.

[1118] 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.

[1119] [Third embodiment]

[1120] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1121] 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.

[1122] 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).

[1123] 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.

[1124] 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.

[1125] 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).

[1126] 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.

[1127] 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.

[1128] 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.

[1129] 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.

[1130] 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.

[1131] 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."

[1132] This invention is a system that translates multilingual speech, text, and video in real time using a wearable device worn by the user. This system consists of a "device" used by the user and a "server" that processes data.

[1133] 1. Real-time voice translation

[1134] Program processing

[1135] User

[1136] The user inputs voice through the device's built-in microphone, for example, "Hello, what's your plan today?"

[1137] Terminal

[1138] The device captures the input voice data and sends it to the server.

[1139] server

[1140] The server receives the voice data and converts it into text data using a speech recognition engine. For example, the text generated from the voice is "Hello, what are your plans for today?"

[1141] The server translates the generated text data into the target language. When translating from Japanese to English, the translation result is "Hello, what are your plans for today?"

[1142] The server sends this translated text data to a speech synthesis engine and converts it into voice data.

[1143] The server transmits the converted voice data to the terminal.

[1144] Terminal

[1145] The device then plays the received translated voice data through built-in earphones and provides it to the user, who can listen to the translated voice in real time.

[1146] 2. Real-time text translation

[1147] Program processing

[1148] User

[1149] The user uses the device's built-in camera to capture text on a sign or document.

[1150] Terminal

[1151] The terminal transmits the captured image data to the server.

[1152] server

[1153] The server receives the image data and uses an image recognition engine (OCR engine) to extract text from the image. For example, the text "restaurant" is extracted.

[1154] The server translates the extracted text data into the target language. When translating from Japanese to English, the translation result is "Restaurant."

[1155] The server sends the translated text data to the terminal.

[1156] Terminal

[1157] The device then displays the received translated text data on the AR display, allowing the user to view the translated text through the lenses of their glasses.

[1158] 3. Real-time video translation

[1159] Program processing

[1160] User

[1161] The user captures the video using the device's built-in camera.

[1162] Terminal

[1163] The terminal transmits the captured video data to the server via streaming.

[1164] server

[1165] The server receives the video data and uses a video analysis engine to extract audio and text from the video, such as the audio of someone speaking or the text displayed in the video.

[1166] The server converts the extracted speech into text using a speech recognition engine and translates it into the target language, and also translates the extracted text into the target language.

[1167] The translated voice data is converted back into voice data by a voice synthesis engine.

[1168] The server sends the translated voice and text data to the terminal.

[1169] Terminal

[1170] The device then plays the received translated audio data through the built-in earphones and displays the translated text data on the AR display, allowing users to confirm the translated content both visually and audibly.

[1171] This allows users to communicate smoothly in real time, overcoming language barriers. This system can be used in a variety of situations, such as international conferences, intercultural exchanges, and tourism, and will enable smooth communication between people of different languages.

[1172] The processing flow will be explained below.

[1173] Real-time voice translation

[1174] Step 1: User Speech Input

[1175] The user speaks into the device's built-in microphone, for example, "Hello, what's on the agenda today?"

[1176] Step 2: Capture and send audio data with your device

[1177] The device captures the user's voice with a microphone and sends the voice data to the server.

[1178] Step 3: Speech recognition by the server

[1179] The server receives the voice data and uses a speech recognition engine to convert the voice data into text, e.g., "Hello, what are your plans for today?"

[1180] Step 4: Text translation by server

[1181] The server sends the generated text data to the translation engine and translates it into the target language. Example: When translating from Japanese to English, it becomes "Hello, what are your plans for today?"

[1182] Step 5: Server-based speech synthesis

[1183] The server sends the translated text data to a text-to-speech (TTS) engine, which converts the text into audio data.

[1184] Step 6: Server sends audio data

[1185] The server transmits the converted voice data to the terminal.

[1186] Step 7: Play the audio data on the device

[1187] The device receives the audio data from the server and plays it through built-in earphones, providing the user with translated audio in real time.

[1188] Real-time text translation

[1189] Step 1: User text input

[1190] The user uses the device's built-in camera to capture text on a sign or document.

[1191] Step 2: Capture and send image data by the device

[1192] The terminal transmits the captured image data to the server.

[1193] Step 3: Character Recognition (OCR) by the Server

[1194] The server receives the image data and uses an image recognition engine to extract text from the image, e.g., the text "restaurant" is extracted.

[1195] Step 4: Text translation by server

[1196] The server sends the extracted text data to a translation engine and translates it into the target language. Example: When translating from Japanese to English, the result is "Restaurant."

[1197] Step 5: Server sends text data

[1198] The server sends the translated text data to the terminal.

[1199] Step 6: Displaying text data using a terminal

[1200] The device receives the translated text data and displays it on the AR display, allowing the user to view the translated text through the glasses' display.

[1201] Real-time video translation

[1202] Step 1: User's video input

[1203] The user captures the video using the device's built-in camera.

[1204] Step 2: Capture and send video data from your device

[1205] The device streams the captured video data to the server in real time.

[1206] Step 3: Receiving and analyzing video data by the server

[1207] The server receives the video data and uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[1208] Step 4: Speech recognition and text conversion on the server

[1209] The audio in the video is converted into text using a speech recognition engine.

[1210] Step 5: Text translation by server

[1211] The server sends the converted text data to a translation engine for translation into the target language.

[1212] Step 6: Server-based speech synthesis

[1213] The server converts the translated text data into voice data using a voice synthesis engine.

[1214] Step 7: Server sends voice and text data

[1215] The server transmits the translated voice data and text data to the terminal.

[1216] Step 8: Play and display the translation data on your device

[1217] The device plays the received audio data through the built-in earphones and displays the text data on the AR display, allowing users to confirm the translated content visually and audibly.

[1218] Through the above processing steps, users can translate audio, text, and video in multiple languages ​​in real time, enabling smooth communication.

[1219] Example 1

[1220] 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."

[1221] There is a demand for systems that enable smooth communication between multiple languages. In particular, there is a need for systems that can translate voice, text, and video in real time and provide the translation to users. Conventional technologies require the use of individual devices and applications, which can be cumbersome for users. Therefore, there is a need for a system that solves these problems and enables users to communicate smoothly between different languages.

[1222] 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.

[1223] In this invention, the server includes means for converting voice data into text data, means for translating the converted text data into a target language, and means for converting the translated text data into voice data, thereby enabling real-time translation between multiple languages.

[1224] A "device" is a wearable device that a user uses to perform operations such as input and display.

[1225] "Audio data" refers to data obtained by converting an audio signal into digital format.

[1226] "Text data" is data that has been converted from character information into digital format.

[1227] "Image data" is visual information converted into digital form.

[1228] "Video data" is data in digital form that contains a sequence of images and sounds.

[1229] A "server" is a computer system for processing and managing data.

[1230] "Speech recognition technology" is a technology that analyzes voice signals and converts them into corresponding text information.

[1231] "Target language" is the language into which the translation is to be made.

[1232] "Image processing technology" is a technology that analyzes image data and extracts necessary information.

[1233] An "augmented reality display device" is a device that displays digital information superimposed on visual information from the real world.

[1234] "Video analysis technology" is a technology that analyzes video data and extracts necessary information.

[1235] "Speech synthesis technology" is a technology that converts text data into voice data.

[1236] This invention is a system for real-time translation of multilingual speech, text, and video, and is composed of a wearable device that is easy for users to use and a server that supports it. The system operates as follows.

[1237] The user wears a wearable device, which incorporates a microphone, a camera, earphones, and an augmented reality display. When the user speaks, the microphone in the device captures the voice data, converts it into a digital format, and the device transmits the voice data to a server.

[1238] The server converts the received voice data into text data using voice recognition technology. For example, a general voice recognition API can be used for this voice recognition. The converted text data is translated into the target language. For example, a widely used translation API can be used for this translation. The translated text data is then converted into voice data using voice synthesis technology. The server then retransmits the voice data to the terminal, which then provides it to the user through earphones.

[1239] For example, if a user says, "Hello, what are your plans for today?", the system translates it into "Hello, what are your plans for today?" and provides it to the user as English speech.

[1240] Similarly, for real-time translation of text data, when a user captures text using a camera, the image data is sent to a server. The server then uses image processing technology to extract the text from the image and translate it into the target language. The translated text data is sent to the device and displayed to the user through an augmented reality display device. For example, if a user takes a photo of a sign that says "restaurant," it is translated and displayed as "Restaurant."

[1241] Real-time translation of video data is also possible. When a user captures video with a camera, the data is sent to a server, which uses video analysis technology to extract audio and text from the video. These are then translated into the target language, and the translated data is sent to the device as audio and text data. The device then plays the audio through earphones and displays the text through an augmented reality display.

[1242] This allows users to smoothly communicate between different languages ​​in real time. This system is expected to be used in a variety of situations, including international conferences, tourism, and intercultural exchange.

[1243] An example of a specific prompt is:

[1244] "Please translate what the user says in Japanese into English."

[1245] "Analyze the image, extract the Japanese text and translate it into English."

[1246] "Extract audio and text from video data and translate them into English."

[1247] This allows users to obtain fast and accurate translation results through generative AI models.

[1248] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1249] Real-time voice translation

[1250] Step 1:

[1251] The user inputs voice through the device's built-in microphone, for example, "Hello, what are your plans for today?" The input data is an analog voice signal.

[1252] Step 2:

[1253] The device converts the analog audio signal captured by the microphone into digital audio data. This conversion process is performed by an analog-to-digital converter (ADC). The converted digital audio data is then sent to the server.

[1254] Step 3:

[1255] The server receives the digital voice data and converts it into text data using voice recognition technology. For this process, a voice recognition API is used to generate text data from the input voice data, such as "Hello, what are your plans for today?"

[1256] Step 4:

[1257] The server translates the generated text data into the target language. In this step, a translation API is used to convert the text data from Japanese to English. For example, "Hello, what are your plans for today?" is translated into "Hello, what are your plans for today?"

[1258] Step 5:

[1259] The server converts the translated text data into audio data using speech synthesis technology. Based on the translated text data, "Hello, what are your plans for today?", the speech synthesis engine generates corresponding digital audio data.

[1260] Step 6:

[1261] The server transmits the generated audio data to the terminal, and the transmitted digital audio data includes appropriate metadata.

[1262] Step 7:

[1263] The device decodes the received audio data and plays it back to the user through the built-in earphones, allowing the user to listen to the translated English audio in real time.

[1264] Real-time text translation

[1265] Step 1:

[1266] A user uses the device's built-in camera to capture text on a sign or document, for example, taking a photo of a sign that says "Restaurant." The input data is image data.

[1267] Step 2:

[1268] The device sends the captured image data, including metadata such as resolution and timestamp, to the server.

[1269] Step 3:

[1270] The server receives the image data and uses image processing technology (OCR technology) to extract text from the image. For example, the text "restaurant" is extracted from an image of a sign.

[1271] Step 4:

[1272] The server translates the extracted text data into the target language using a translation API. For example, "restaurant" is translated to "Restaurant."

[1273] Step 5:

[1274] The server sends the translated text data to the device, including the translation results and associated metadata.

[1275] Step 6:

[1276] The device displays the received text data on the augmented reality display device, and the user can see the translated text "Restaurant" through the lenses of the glasses.

[1277] Real-time video translation

[1278] Step 1:

[1279] A user uses the device's built-in camera to capture video, for example, a foreign language news broadcast, and the input data is video data.

[1280] Step 2:

[1281] The device transmits the captured video data to the server in real time using streaming technology, and the data includes audio and video frame information.

[1282] Step 3:

[1283] The server receives the video data and uses video analysis technology to extract audio and text from the video, for example, audio and text from a news broadcast.

[1284] Step 4:

[1285] The server converts the extracted speech into text data and then translates it into the target language. Using speech recognition technology and a translation API, the text "What are you doing?" is translated into "What are you doing?"

[1286] Step 5:

[1287] The server uses speech synthesis technology to convert the translated text data into voice data, and also prepares the translated text data for transmission separately.

[1288] Step 6:

[1289] The server sends the translated voice data and text data to the terminal. The transmitted data includes the voice file and the text information.

[1290] Step 7:

[1291] The device then plays the received audio data through earphones and displays the text data on an augmented reality display device, allowing users to visually and aurally follow the conversations and subtitles in the video in real time.

[1292] This allows users to understand information and communicate in real time, regardless of language differences.

[1293] (Application example 1)

[1294] 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."

[1295] The present invention aims to provide a system that overcomes language barriers in autonomous vehicles and enables multilingual passengers to communicate smoothly with the vehicle. In current autonomous vehicles, communication between passengers and the vehicle is limited, making it difficult to convey information, especially when different languages ​​are spoken. This poses a problem of reduced passenger safety and satisfaction. To solve this problem, a system is needed that can achieve real-time translation between multiple languages ​​and provide passengers with appropriate information and responses.

[1296] 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.

[1297] In this invention, the server includes means for capturing voice data from the device, means for transmitting the captured voice data to the server, means for performing voice recognition in the server and generating text data, means for translating the generated text data into a target language, means for converting the translated text data into voice data, means for transmitting the converted voice data to the device, means for playing back the voice data in the device and providing it to a user, and means for performing multilingual communication between passengers and the vehicle in real time in an autonomous vehicle. This enables accurate and prompt information transmission between passengers and the vehicle, thereby improving passenger safety and satisfaction.

[1298] A "device" is a device for capturing audio data, image data, or video data and transmitting it to a server.

[1299] "Audio data" refers to data that has been converted from human speech or other sounds into digital signals.

[1300] A "server" is a central processing unit for processing, converting, and translating captured data.

[1301] "Speech recognition" is a technology that converts voice data into text data.

[1302] "Text data" is data that represents character information in digital form.

[1303] "Target language" is the language into which the text is to be translated.

[1304] "Converting into voice data" means converting text data back into voice format data using voice synthesis technology.

[1305] "Multilingual communication" is communication that takes place between two or more people who speak different languages.

[1306] An "autonomous vehicle" is a vehicle that operates autonomously without the need for a human driver.

[1307] An "augmented reality display" is a display technology that overlays digital information onto visual information from the real world.

[1308] "Video data" is data that converts moving images or videos into digital signals.

[1309] "Character recognition" is a technology for extracting character information from an image.

[1310] "Real-time" means that data is processed immediately, without delay.

[1311] The present invention embodies a multilingual communication system for an autonomous driving vehicle, and is configured as follows.

[1312] 1. Real-time voice translation

[1313] User

[1314] The user inputs instructions, questions, etc. in their native language through the device's built-in microphone. For example, they might say "Hello, please take me to the airport" in Japanese.

[1315] Terminal

[1316] The device captures the input voice data and sends it to a server, a process that uses a microphone and an internet connection.

[1317] server

[1318] The server receives the voice data and converts it into text data using a speech recognition engine (such as the Google Speech Recognition API). Next, it translates this text data into the target language (e.g., English) using a multilingual translation engine (such as the Google Translate API). After that, it converts the translated text data back into voice data using a speech synthesis engine (such as gTTS).

[1319] Terminal

[1320] The converted voice data is sent back to the device and played through the device's built-in speaker, allowing the user to hear the translated voice in real time.

[1321] 2. Real-time text translation

[1322] User

[1323] The user uses the device's built-in camera to capture text and sign information displayed on the in-car display.

[1324] Terminal

[1325] The terminal transmits the captured image data to the server.

[1326] server

[1327] The server receives the image data, extracts text from the image using an OCR (optical character recognition) engine (such as pytesseract), and then translates the extracted text data into the target language using a translation engine.

[1328] Terminal

[1329] The translated text data is displayed on an augmented reality display inside the vehicle, allowing the user to visually confirm the translated information.

[1330] 3. Real-time video translation

[1331] User

[1332] The user captures the video using the device's built-in camera.

[1333] Terminal

[1334] The terminal transmits the captured video data to the server in a streaming manner.

[1335] server

[1336] The server receives the video data and uses an analysis engine to extract the audio and text from the video. The extracted audio data is converted into text using a speech recognition engine and simultaneously translated into the target language. The text data is also translated into the target language.

[1337] Terminal

[1338] The device then plays the received translated audio data through built-in earphones and displays the translated text data on the augmented reality display, allowing users to confirm the translation both visually and audibly.

[1339] Specific examples

[1340] For example, if a foreign passenger gets into a self-driving vehicle and says in Japanese, "Hello, take me to the airport please," the system will process it as follows:

[1341] 1. Capture audio with a microphone.

[1342] 2. Send the captured audio data to the server.

[1343] 3. The server converts the voice data into text data and translates it into the target language.

[1344] 4. The translated text data is converted into audio data and sent back to the device.

[1345] 5. The terminal plays the translated audio and relays the information to the passenger.

[1346] Example prompts for generative AI models

[1347] When a foreign passenger boards an autonomous vehicle, he or she says in Japanese, "Hello, please take me to the airport." Write a program to translate this into English in real time and play it back. Use the Google Speech Recognition API for speech recognition and the googletrans library for translation. The final English audio will be generated and played back using the gtts library.

[1348] As described above, this system is designed to provide real-time multilingual translation of voice, text, and video to facilitate communication in self-driving vehicles.

[1349] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1350] Step 1:

[1351] The user inputs voice through the device's built-in microphone, and the voice data is captured in digital form. The input voice data is raw voice waveform data, which is temporarily stored on the device for further processing.

[1352] Step 2:

[1353] The device sends the captured audio data to the server via an internet connection, where it is transferred in real time using the appropriate protocol (e.g. HTTP, WebSocket) in its digital form.

[1354] Step 3:

[1355] The server inputs the received voice data into a voice recognition engine (e.g., Google Speech Recognition API) and converts the voice into text data. The voice recognition engine analyzes the voice waveform and generates a corresponding string of characters. At this stage, the input is voice data and the output is text data.

[1356] Step 4:

[1357] The generated text data is input to a multilingual translation engine (e.g., Google Translate API) and translated into the target language. The input here is the generated text data, and the output is the translated text data. The translation engine analyzes the input text and generates an appropriate translation result.

[1358] Step 5:

[1359] The translated text data is input to a speech synthesis engine (e.g., gTTS) and converted back into speech data. The speech synthesis engine analyzes the input text and generates a corresponding speech waveform. At this stage, the input is the translated text data and the output is speech data.

[1360] Step 6:

[1361] The server then sends the converted audio data back to the device, which receives it via its internet connection, where it is sent in its digital form.

[1362] Step 7:

[1363] The device plays the received voice data through the built-in speaker and provides it to the user, who can listen to the translated voice in real time. Here, the input is the voice data received from the server, and the output is the voice played from the speaker.

[1364] 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.

[1365] This system uses a wearable device worn by the user to translate multilingual audio, text, and video in real time, and also recognizes the user's emotions and reflects them in the translation results. This system consists of a "device" used by the user, a "server" that processes data, and an "emotion engine."

[1366] 1. Real-time voice translation

[1367] Program processing

[1368] User

[1369] The user speaks into the device's built-in microphone, for example, "Hello, what's on the agenda today?"

[1370] Terminal

[1371] The device captures the user's voice with a microphone and sends the voice data to the server.

[1372] server

[1373] The server receives the voice data and uses a speech recognition engine to convert the voice data into text, e.g., "Hello, what are your plans for today?"

[1374] The server uses an emotion engine to analyze the user's emotions from the voice data and generate emotion data. For example, if the user is recognized as happy, the emotion data will be "joy."

[1375] The server sends the generated text data along with the emotion data to the translation engine, which translates it into the target language. For example, when translating from Japanese to English, "Hello, what are your plans for today?" is given the emotion data of "joy."

[1376] The server converts the translated text data and emotion data into voice data that matches the user's target language.

[1377] The server transmits the converted voice data to the terminal.

[1378] Terminal

[1379] The device then plays the received translated voice data through built-in earphones, providing the user with real-time translated voice, which is played in a tone that reflects the user's emotions.

[1380] 2. Real-time text translation

[1381] Program processing

[1382] User

[1383] The user uses the device's built-in camera to capture text on a sign or document.

[1384] Terminal

[1385] The terminal transmits the captured image data to the server.

[1386] server

[1387] The server receives the image data and uses an image recognition engine (OCR engine) to extract text from the image. For example, the text "restaurant" is extracted.

[1388] The server uses an emotion engine to analyze the emotion data from the user's facial expression at the time of capture. For example, if the user is determined to be surprised, the emotion data will be "surprise."

[1389] The server sends the extracted text data and emotion data to the translation engine, which translates it into the target language. For example, "restaurant" is translated as "Restaurant" and the emotion data "surprise" is added.

[1390] The server sends the translated text data and emotion data to the terminal.

[1391] Terminal

[1392] The device receives the translated text data and displays it on the AR display, which reflects the user's emotions with icons and colors, making it easier to understand intuitively.

[1393] 3. Real-time video translation

[1394] Program processing

[1395] User

[1396] The user captures the video using the device's built-in camera.

[1397] Terminal

[1398] The device streams the captured video data to the server in real time.

[1399] server

[1400] The server receives the video data and uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[1401] The server uses an emotion engine to analyze the user's facial expressions and tone of voice in real time to generate emotion data. For example, if the user is determined to be interested, the emotion data will be "interested."

[1402] The server converts the analyzed speech into text using a speech recognition engine, then sends the text and emotional data to a translation engine for translation into the target language.

[1403] The translated text data and emotion data are then converted into voice data by a voice synthesis engine.

[1404] The server sends the translated voice and text data to the terminal.

[1405] Terminal

[1406] The device plays the received audio data through the built-in earphones and displays the text data on the AR display, allowing users to see and hear the translated content while also visually understanding the emotions expressed.

[1407] This system enables users to translate multilingual audio, text, and video in real time, and even takes emotions into account for natural and intuitive communication. This system is expected to be used in a variety of situations, including international conferences, intercultural exchange, and tourism.

[1408] The processing flow will be explained below.

[1409] Real-time voice translation

[1410] Step 1: User Speech Input

[1411] The user speaks into the device's built-in microphone, for example, "Hello, what's on the agenda today?"

[1412] Step 2: Capture and send audio data with your device

[1413] The device captures the user's voice with a microphone and sends the voice data to the server.

[1414] Step 3: Speech recognition by the server

[1415] The server receives the voice data and uses a speech recognition engine to convert the voice data into text, e.g., "Hello, what are your plans for today?"

[1416] Step 4: Emotion recognition by the server

[1417] The server uses an emotion engine to analyze the user's emotion from the voice data. For example, if the user is recognized as happy from the voice tone and speaking style, the emotion data will be "joy."

[1418] Step 5: Text translation by server

[1419] The server sends the generated text data and emotion data to the translation engine, which translates it into the target language. For example, when translating from Japanese to English, "Hello, what are your plans for today?" is given with the emotion data "joy."

[1420] Step 6: Server-based speech synthesis

[1421] The server sends the translated text data and emotion data to a speech synthesis engine, which converts it into voice data. This voice is generated in a tone that reflects the user's emotion.

[1422] Step 7: Server sends audio data

[1423] The server transmits the converted voice data to the terminal.

[1424] Step 8: Play the audio data on the device

[1425] The device receives the voice data from the server and plays it through built-in earphones, providing the user with real-time translated speech that reflects the user's emotions.

[1426] Real-time text translation

[1427] Step 1: User text input

[1428] The user uses the device's built-in camera to capture text on a sign or document.

[1429] Step 2: Capture and send image data by the device

[1430] The terminal transmits the captured image data to the server.

[1431] Step 3: Character Recognition (OCR) by the Server

[1432] The server receives the image data and uses an image recognition engine to extract text from the image, e.g., the text "restaurant" is extracted.

[1433] Step 4: Emotion recognition by the server

[1434] The server uses the emotion engine to analyze the emotion from the user's facial expression at the time of capture. For example, if the user is determined to be surprised, the emotion data will be "surprise."

[1435] Step 5: Text translation by server

[1436] The server sends the extracted text data and emotion data to the translation engine, which translates it into the target language. For example, "restaurant" is translated as "Restaurant" and the emotion data "surprise" is added.

[1437] Step 6: Server sends text data

[1438] The server sends the translated text data and emotion data to the terminal.

[1439] Step 7: Displaying Text Data in a Terminal

[1440] The device receives the translated text data and displays it on the AR display, which reflects the user's emotions with icons and colors, making it easier to understand intuitively.

[1441] Real-time video translation

[1442] Step 1: User's video input

[1443] The user captures the video using the device's built-in camera.

[1444] Step 2: Capture and send video data from your device

[1445] The device streams the captured video data to the server in real time.

[1446] Step 3: Receiving and analyzing video data by the server

[1447] The server receives the video data and uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[1448] Step 4: Speech recognition and text conversion on the server

[1449] The audio in the video is converted into text using a speech recognition engine.

[1450] Step 5: Emotion recognition by the server

[1451] The server uses an emotion engine to analyze the user's facial expressions and tone of voice in real time to generate emotion data. For example, if the user is determined to be interested, the emotion data will be "interested."

[1452] Step 6: Text translation by server

[1453] The server sends the converted text data and emotion data to a translation engine for translation into the target language.

[1454] Step 7: Server-based speech synthesis

[1455] The server converts the translated text data and emotional data into voice data using a voice synthesis engine.

[1456] Step 8: Server sends voice and text data

[1457] The server transmits the translated voice data and text data to the terminal.

[1458] Step 9: Play and display the translation data on your device

[1459] The device plays the received audio data through the built-in earphones and displays the text data on the AR display, allowing users to see and hear the translated content while also visually understanding the emotions expressed.

[1460] This system enables users to translate multilingual audio, text, and video in real time, and even takes emotions into account for natural and intuitive communication. This system is expected to be used in a variety of situations, including international conferences, intercultural exchange, and tourism.

[1461] Example 2

[1462] 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."

[1463] In modern society, multilingual communication is becoming increasingly important, but current translation technology is not yet able to fully realize natural and intuitive communication. In particular, there are issues with providing translation results that reflect emotions and with real-time multilingual support, which are inconvenient for users in cross-cultural exchange, international conferences, tourism, and other situations.

[1464] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1465] In this invention, the server includes means for generating emotion data from voice data, means for translating the generated text data and emotion data into a target language, and means for converting the translated text data and emotion data into voice data, thereby making it possible to provide intuitive and natural translation results in real time that reflect the emotions contained in the user's voice.

[1466] "Device" refers to electronic equipment worn or used by a user, including hardware such as a microphone, camera, earphones, and augmented reality display.

[1467] "Server" refers to a computer system that receives audio data, image data, and video data and performs data processing such as analysis, translation, and conversion.

[1468] "Voice data" refers to data in which the voice uttered by the user is recorded in digital format.

[1469] "Text data" is text data generated by voice recognition or OCR (optical character recognition).

[1470] "Emotion data" is data that represents emotions analyzed from the user's voice and facial expressions.

[1471] "Translation engine" refers to software or algorithms that convert data from one language into data from another language.

[1472] A "voice recognition engine" refers to software or algorithms that analyze voice data and convert it into corresponding text data.

[1473] An "emotion engine" refers to software or algorithms that analyze voice data and facial expression data to estimate and digitize the user's emotions.

[1474] An "OCR engine" refers to software or algorithms that extract characters from image data.

[1475] A "video analysis engine" refers to software or algorithms that analyze the content of video data and extract information such as audio and text.

[1476] "Target language" refers to the language into which the translation result is to be produced.

[1477] A "speech synthesis engine" refers to software or algorithms that analyze text data and generate synthetic speech.

[1478] An "augmented reality display" refers to a display device that displays digital information overlaid on the real world.

[1479] "Translated data" refers to text data and related information that has been converted into a target language by a translation engine.

[1480] "Video data" refers to data recorded in the form of user-captured video.

[1481] This invention is a system that uses a wearable device worn by the user to translate multilingual voice, text, and video in real time, and also recognizes the user's emotions and reflects them in the translation results. This system consists of a "device" used by the user, a "server" that processes data, and an "emotion engine."

[1482] 1. Real-time voice translation

[1483] The user speaks into the device's built-in microphone, for example, saying, "Hello, what are your plans for today?" The device captures this voice with the microphone and sends it to the server as voice data.

[1484] The server receives the voice data and converts the voice into text data using a voice recognition engine (e.g., voice recognition software). The generated text is "Hello, what are your plans for today?". The server then uses an emotion engine (e.g., emotion analysis software) to generate emotion data from the voice. For example, the result of the data analysis is "joy."

[1485] The generated text data and emotion data are sent to a translation engine (e.g., a translation API) and translated into the target language. As an example of translation from Japanese to English, the text "Hello, what are your plans for today?" and emotion data "joy" are generated.

[1486] The server converts the translated text data and emotion data into voice data using a voice synthesis engine (e.g., text-to-speech software). The device then plays the received voice data through built-in earphones and provides it to the user in real time. This voice data reflects the user's emotions, enabling natural and intuitive communication.

[1487] 2. Real-time text translation

[1488] The user uses the built-in camera of the device to capture text on signs, documents, etc. The device then sends the captured image data to the server.

[1489] The server receives the image data and uses an OCR engine (e.g., optical character recognition software) to extract text from the image. An example of extracted text might be "restaurant." The server then uses an emotion engine to analyze the user's facial expression at the time of capture and generate emotion data, such as "surprise."

[1490] The server sends the extracted text data and emotion data to a translation engine, which translates it into the target language. As translation examples, emotion data such as "Restaurant" and "surprise" are generated. The device receives the translated text data and displays it on the AR display. Emotions are reflected in the display using icons and colors, allowing the user to understand intuitively.

[1491] 3. Real-time video translation

[1492] The user captures video using the device's built-in camera, and the device transmits the captured video data to the server in real time.

[1493] The server receives the video data and analyzes the video content using a video analysis engine (e.g., video analysis software). For example, it may extract audio and text from the video. Next, the server uses an emotion engine to analyze the user's facial expressions and tone of voice and generate emotion data, such as "interest."

[1494] The server converts the extracted speech into text data using a speech recognition engine, sends the text data and emotion data to a translation engine for translation into the target language, and then converts the translated text data and emotion data into speech data using a speech synthesis engine.

[1495] The device plays the translated voice data through the built-in earphones and displays the translated text data on the AR display, allowing users to see and hear the translated voice and text information in real time, and also visually understand emotions.

[1496] With the above configuration, users can translate multilingual voice, text, and video in real time, enabling natural and intuitive communication that takes emotions into account. This system is expected to be used in a variety of situations, including international conferences, intercultural exchange, and tourism.

[1497] Prompt Sentence Examples

[1498] "Please explain the process of a system that translates Japanese speech spoken by a user into English and plays it back in a voice that reflects the emotion of the translation."

[1499] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1500] Real-time voice translation

[1501] Step 1:

[1502] The user speaks into the device's built-in microphone, for example, "Hello, what's your plan today?"

[1503] Input: User's speech

[1504] Output: Analog audio signal captured by microphone

[1505] Step 2:

[1506] The device converts analog audio signals captured by the built-in microphone into digital audio data. Example: Converted WAV format audio data

[1507] Input: Analog audio signal

[1508] Output: Digital audio data (WAV format)

[1509] Step 3:

[1510] The terminal transmits the converted digital audio data to the server.

[1511] Input: Digital audio data

[1512] Output: Digital audio data sent to the server

[1513] Step 4:

[1514] The server uses a speech recognition engine to convert the received voice data into text data. For example, "Hello, what are your plans for today?"

[1515] Input: Digital audio data

[1516] Output: Text data

[1517] Step 5:

[1518] The server uses an emotion engine to analyze the user's emotions from the voice data. Example: The analysis results generate emotion data for "joy."

[1519] Input: Digital audio data

[1520] Output: Emotion data (e.g., "joy")

[1521] Step 6:

[1522] The server sends the generated text data and emotion data to the translation engine, which translates it into the target language. Example: Translating from Japanese to English, generating the text data "Hello, what are your plans for today?" and the emotion data "joy."

[1523] Input: Text data, emotion data

[1524] Output: Translated text data and emotion data

[1525] Step 7:

[1526] The server converts the translated text data and emotion data into voice data using a speech synthesis engine, generating English voice data that reflects the emotion of "joy."

[1527] Input: translated text data, emotion data

[1528] Output: Audio data (e.g., synthesized English voice)

[1529] Step 8:

[1530] The server transmits the generated voice data to the terminal.

[1531] Input: Audio data

[1532] Output: Audio data sent to the device

[1533] Step 9:

[1534] The device then plays the received audio data through the built-in earphones and provides it to the user, producing audio that reflects "joy."

[1535] Input: Audio data

[1536] Output: Audio played through earphones

[1537] Real-time text translation

[1538] Step 1:

[1539] The user captures text on a sign or document using the device's built-in camera. Example: capturing an image of a restaurant sign.

[1540] Input: Image of a sign or document

[1541] Output: Captured image data

[1542] Step 2:

[1543] The terminal transmits the captured image data to the server.

[1544] Input: Image data

[1545] Output: Image data sent to the server

[1546] Step 3:

[1547] The server uses an OCR engine to extract text from the image data, e.g., extracting the text "restaurant."

[1548] Input: Image data

[1549] Output: Extracted text data (e.g., "restaurant")

[1550] Step 4:

[1551] The server uses an emotion engine to generate emotion data from the user's facial expression at the time of capture. For example, the server generates emotion data for "surprise" through facial expression analysis.

[1552] Input: Image data

[1553] Output: Emotion data (e.g., "surprise")

[1554] Step 5:

[1555] The server sends the extracted text data and emotion data to the translation engine, which translates it into the target language. For example, "restaurant" is translated to "Restaurant" and the emotion data "surprise" is added.

[1556] Input: Text data, emotion data

[1557] Output: Translated text data and emotion data

[1558] Step 6:

[1559] The server sends the translated text data and emotion data to the terminal.

[1560] Input: translated text data, emotion data

[1561] Output: Data sent to the terminal

[1562] Step 7:

[1563] The device displays the translated text data on the AR display, with emotions reflected in icons and colors.

[1564] Input: translated text data, emotion data

[1565] Output: Text to be displayed on the AR display

[1566] Real-time video translation

[1567] Step 1:

[1568] The user captures video using the device's built-in camera. Example: capturing video of an intercultural exchange event.

[1569] Input: Video

[1570] Output: Captured video data

[1571] Step 2:

[1572] The device transmits the captured video data to the server in real time.

[1573] Input: Video data

[1574] Output: Video data sent to the server

[1575] Step 3:

[1576] The server uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[1577] Input: Video data

[1578] Output: Extracted audio data and text data

[1579] Step 4:

[1580] The server uses an emotion engine to analyze emotion data from the user's facial expressions and tone of voice. Example: Generate emotion data for "interest."

[1581] Input: Audio data, video data

[1582] Output: Emotion data (e.g., "interest")

[1583] Step 5:

[1584] The server converts the extracted speech into text data using a speech recognition engine, and then sends the text and emotion data to a translation engine for translation into the target language. For example, Japanese speech is translated into English text, and the emotion data "interest" is added.

[1585] Input: Voice data, emotion data

[1586] Output: Translated text data, emotion data

[1587] Step 6:

[1588] The server converts the translated text data and emotion data into voice data using a voice synthesis engine. Example: Generates English synthesized voice data.

[1589] Input: translated text data, emotion data

[1590] Output: Audio data

[1591] Step 7:

[1592] The server transmits the generated voice data and text data to the terminal.

[1593] Input: Audio data, text data

[1594] Output: Data sent to the terminal

[1595] Step 8:

[1596] The device plays the audio data through the built-in earphones and displays the text data on the AR display, allowing users to check the translation results visually and audibly and to visually understand the emotions.

[1597] Input: Audio data, text data

[1598] Output: Audio played through earphones, text displayed on the AR display

[1599] (Application example 2)

[1600] 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."

[1601] There is a need for a system that can not only translate multilingual speech, text, and video in real time, but also recognize the user's emotions and reflect them in the translation results. Conventional systems have difficulty in achieving natural and intuitive communication that takes the user's emotions into account, and there is a need for technology that supports smooth communication, especially in situations such as intercultural exchange and food delivery.

[1602] The specific processing by the specific 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 capturing voice data from the device, means for transmitting the captured voice data to the server, means for performing voice recognition in the server and generating text data, means for translating the generated text data into a target language, means for converting the translated text data into voice data, means for transmitting the converted voice data to the device, means for playing back the voice data in the device and providing it to the user, means for analyzing emotional data from the captured voice data, and means for reflecting the emotional data in the translation result. This enables communication that takes into account not only voice recognition and translation but also the user's emotions.

[1603] The device also includes means for capturing image data from the device, means for transmitting the captured image data to a server, means for recognizing characters from the image in the server, means for translating the recognized character data into a target language, means for transmitting the translated character data to the device, means for displaying the character data on an augmented reality display in the device, means for analyzing emotions from facial expression data at the time of capture, and means for reflecting the emotion data in the translation result, thereby realizing real-time translation of text and reflecting emotions.

[1604] The device further includes means for capturing video data from the device, means for transmitting the captured video data to a server, means for extracting audio and text from the video on the server, means for translating the extracted audio and text into a target language, means for transmitting the translated data to the device, means for playing the translated audio data on the device and displaying the text data on an augmented reality display, means for analyzing emotions from facial expressions and tone of voice at the time of video capture, and means for reflecting the emotional data in the translation result, thereby enabling real-time translation of multilingual video data and reflecting emotions.

[1605] "Device" refers to a terminal, including a wearable device or smartphone worn by a user, and is hardware that captures and plays audio, images, and video.

[1606] "Voice data" refers to a data format for digitally recording the voice uttered by the user and transmitting it to a server.

[1607] "Capture" refers to capturing audio, images, or video using input devices such as sensors, cameras, and microphones, and collecting them as digital data.

[1608] A "server" is a central computer system that processes data using various algorithms and engines such as speech recognition, translation, and sentiment analysis.

[1609] "Speech recognition" is a technology that analyzes voice data and converts its contents into text data.

[1610] "Text data" refers to digital data expressed as a string of characters, and is generated by speech recognition.

[1611] "Target language" refers to the final language desired in the translation process, and refers to a language that can be understood by the user.

[1612] "Translation" is the process of converting text data written in one language into another target language.

[1613] "Emotion data" refers to digital data that indicates the emotional state of a user extracted from their voice, facial expression, and behavior.

[1614] "Conversion to voice data" refers to the process of converting text data and accompanying emotion data back into voice format data using voice synthesis technology.

[1615] "Image data" refers to still images captured by a camera or other imaging device.

[1616] "Character recognition" is a technique for extracting text information from captured image data.

[1617] "Augmented reality display" refers to a display technology that overlays computer-generated information on the physical world.

[1618] "Video data" refers to digital data recorded in moving image format, and includes audio and video.

[1619] "Facial expression data" refers to digital data that indicates an emotional state analyzed from a user's facial expressions captured by a camera.

[1620] "Incorporating emotional data into translation results" refers to the process of incorporating analyzed emotional data into translated text or speech to improve the naturalness of communication.

[1621] This system uses a wearable device worn by the user to translate multilingual speech, text, and video in real time, and also recognizes the user's emotions and reflects them in the translation results. The system consists of the following components:

[1622] System Configuration

[1623] 1. Device: A wearable device worn by a user (e.g., smart glasses, smartphone) that captures and plays back audio, images, and video.

[1624] 2. Server: A central computer system that processes data and uses various algorithms such as speech recognition, translation, and sentiment analysis.

[1625] 3. Emotion engine: Equipped with algorithms that recognize the emotional state extracted from the user's voice, facial expressions, and behavior.

[1626] Hardware and software used

[1627] Smart glasses and smartphones: Used to capture the user's voice and video and display the results.

[1628] speech_recognition: A speech recognition library that converts user speech into text data.

[1629] googletrans: Provides a translation engine to translate speech-recognized text into the target language.

[1630] EmotionEngine: A custom engine that analyzes emotions from the user's voice data and facial expressions.

[1631] cv2 (OpenCV): Used to capture and process camera images.

[1632] ARDisplay: A custom class for displaying text on the AR display of smart glasses.

[1633] pyttsx3: A text-to-speech synthesis library for playing translated text as audio.

[1634] Processing flow

[1635] 1. Voice capture: The user speaks into the device's built-in microphone to capture voice data.

[1636] 2. Speech Recognition: The captured voice data is sent to the server, which uses a speech recognition engine to convert the voice data into text.

[1637] 3. Emotion analysis: Analyze the user's emotions from the voice data and generate emotion data.

[1638] 4. Translation: Use the recognized text and emotion data to translate into the target language.

[1639] 5. Display and voice playback: The translated text is combined with emotion data and displayed on the AR display of the smart glasses, along with voice feedback.

[1640] Specific examples

[1641] For example, if a food delivery person says in Japanese, "Hello, can I leave it at the front door?", the system will translate it as "Hello, can I leave it at the front door?" (joy) and provide audio feedback to the delivery person. If the customer responds with a smile, that emotion data will also be reflected.

[1642] Prompt Sentence Examples

[1643] When a user asks "How would you like to pay?" in Japanese, what are the results of the translation and sentiment analysis?

[1644] As described above, the present invention enables natural and smooth communication that takes into account the user's feelings while providing multilingual support in situations such as cross-cultural exchange and food delivery.

[1645] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1646] Step 1:

[1647] The user speaks into the device's microphone. The input is the user's live voice, which is captured by the device's built-in microphone and recorded as digital audio data. The output is digital audio data.

[1648] Step 2:

[1649] The device sends the captured digital audio data to the server. The input is the digital audio data sent to the server over a network connection. The output is the digital audio data transferred to the server.

[1650] Step 3:

[1651] The server uses a speech recognition engine to analyze the received digital voice data. The input is the digital voice data, and the speech recognition engine (speech_recognition library) converts this data into text data. The output is the generated text data.

[1652] Step 4:

[1653] The server uses an emotion engine to analyze the user's emotions from the voice data. The input is digital voice data, which the emotion engine analyzes and generates emotion data. The output is emotion data.

[1654] Step 5:

[1655] The server sends the generated text data and emotion data to the translation engine to translate it into the target language. The input is text data and emotion data, which the translation engine (googletrans) translates into the target language. The output is the translated text data and emotion data.

[1656] Step 6:

[1657] The server converts the translated text data and emotion data into voice data using a voice synthesis engine. The input is the translated text data and emotion data, which the voice synthesis engine (pyttsx3) converts into voice data. The output is the converted voice data.

[1658] Step 7:

[1659] The server sends the converted audio data to the device. The input is the converted audio data sent to the device over a network connection. The output is the audio data transferred to the device.

[1660] Step 8:

[1661] The device plays the received audio data and provides it to the user. The input is the converted audio data, which is played through the built-in speaker or earphones. The output is the played audio.

[1662] Through the above steps, the user's voice is translated in real time, realizing natural communication that reflects emotions.

[1663] 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.

[1664] 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.

[1665] 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.

[1666] [Fourth embodiment]

[1667] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1668] 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.

[1669] 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).

[1670] 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.

[1671] 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.

[1672] 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).

[1673] 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.

[1674] 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.

[1675] 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.

[1676] 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.

[1677] 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.

[1678] 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.

[1679] 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."

[1680] This invention is a system that translates multilingual speech, text, and video in real time using a wearable device worn by the user. This system consists of a "device" used by the user and a "server" that processes data.

[1681] 1. Real-time voice translation

[1682] Program processing

[1683] User

[1684] The user inputs voice through the device's built-in microphone, for example, "Hello, what's your plan today?"

[1685] Terminal

[1686] The device captures the input voice data and sends it to the server.

[1687] server

[1688] The server receives the voice data and converts it into text data using a speech recognition engine. For example, the text generated from the voice is "Hello, what are your plans for today?"

[1689] The server translates the generated text data into the target language. When translating from Japanese to English, the translation result is "Hello, what are your plans for today?"

[1690] The server sends this translated text data to a speech synthesis engine and converts it into voice data.

[1691] The server transmits the converted voice data to the terminal.

[1692] Terminal

[1693] The device then plays the received translated voice data through built-in earphones and provides it to the user, who can listen to the translated voice in real time.

[1694] 2. Real-time text translation

[1695] Program processing

[1696] User

[1697] The user uses the device's built-in camera to capture text on a sign or document.

[1698] Terminal

[1699] The terminal transmits the captured image data to the server.

[1700] server

[1701] The server receives the image data and uses an image recognition engine (OCR engine) to extract text from the image. For example, the text "restaurant" is extracted.

[1702] The server translates the extracted text data into the target language. When translating from Japanese to English, the translation result is "Restaurant."

[1703] The server sends the translated text data to the terminal.

[1704] Terminal

[1705] The device then displays the received translated text data on the AR display, allowing the user to view the translated text through the lenses of their glasses.

[1706] 3. Real-time video translation

[1707] Program processing

[1708] User

[1709] The user captures the video using the device's built-in camera.

[1710] Terminal

[1711] The terminal transmits the captured video data to the server via streaming.

[1712] server

[1713] The server receives the video data and uses a video analysis engine to extract audio and text from the video, such as the audio of someone speaking or the text displayed in the video.

[1714] The server converts the extracted speech into text using a speech recognition engine and translates it into the target language, and also translates the extracted text into the target language.

[1715] The translated voice data is converted back into voice data by a voice synthesis engine.

[1716] The server sends the translated voice and text data to the terminal.

[1717] Terminal

[1718] The device then plays the received translated audio data through the built-in earphones and displays the translated text data on the AR display, allowing users to confirm the translated content both visually and audibly.

[1719] This allows users to communicate smoothly in real time, overcoming language barriers. This system can be used in a variety of situations, such as international conferences, intercultural exchanges, and tourism, and will enable smooth communication between people of different languages.

[1720] The processing flow will be explained below.

[1721] Real-time voice translation

[1722] Step 1: User Speech Input

[1723] The user speaks into the device's built-in microphone, for example, "Hello, what's on the agenda today?"

[1724] Step 2: Capture and send audio data with your device

[1725] The device captures the user's voice with a microphone and sends the voice data to the server.

[1726] Step 3: Speech recognition by the server

[1727] The server receives the voice data and uses a speech recognition engine to convert the voice data into text, e.g., "Hello, what are your plans for today?"

[1728] Step 4: Text translation by server

[1729] The server sends the generated text data to the translation engine and translates it into the target language. Example: When translating from Japanese to English, it becomes "Hello, what are your plans for today?"

[1730] Step 5: Server-based speech synthesis

[1731] The server sends the translated text data to a text-to-speech (TTS) engine, which converts the text into audio data.

[1732] Step 6: Server sends audio data

[1733] The server transmits the converted voice data to the terminal.

[1734] Step 7: Play the audio data on the device

[1735] The device receives the audio data from the server and plays it through built-in earphones, providing the user with translated audio in real time.

[1736] Real-time text translation

[1737] Step 1: User text input

[1738] The user uses the device's built-in camera to capture text on a sign or document.

[1739] Step 2: Capture and send image data by the device

[1740] The terminal transmits the captured image data to the server.

[1741] Step 3: Character Recognition (OCR) by the Server

[1742] The server receives the image data and uses an image recognition engine to extract text from the image, e.g., the text "restaurant" is extracted.

[1743] Step 4: Text translation by server

[1744] The server sends the extracted text data to a translation engine and translates it into the target language. Example: When translating from Japanese to English, the result is "Restaurant."

[1745] Step 5: Server sends text data

[1746] The server sends the translated text data to the terminal.

[1747] Step 6: Displaying text data using a terminal

[1748] The device receives the translated text data and displays it on the AR display, allowing the user to view the translated text through the glasses' display.

[1749] Real-time video translation

[1750] Step 1: User's video input

[1751] The user captures the video using the device's built-in camera.

[1752] Step 2: Capture and send video data from your device

[1753] The device streams the captured video data to the server in real time.

[1754] Step 3: Receiving and analyzing video data by the server

[1755] The server receives the video data and uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[1756] Step 4: Speech recognition and text conversion on the server

[1757] The audio in the video is converted into text using a speech recognition engine.

[1758] Step 5: Text translation by server

[1759] The server sends the converted text data to a translation engine for translation into the target language.

[1760] Step 6: Server-based speech synthesis

[1761] The server converts the translated text data into voice data using a voice synthesis engine.

[1762] Step 7: Server sends voice and text data

[1763] The server transmits the translated voice data and text data to the terminal.

[1764] Step 8: Play and display the translation data on your device

[1765] The device plays the received audio data through the built-in earphones and displays the text data on the AR display, allowing users to confirm the translated content visually and audibly.

[1766] Through the above processing steps, users can translate audio, text, and video in multiple languages ​​in real time, enabling smooth communication.

[1767] Example 1

[1768] 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."

[1769] There is a demand for systems that enable smooth communication between multiple languages. In particular, there is a need for systems that can translate voice, text, and video in real time and provide the translation to users. Conventional technologies require the use of individual devices and applications, which can be cumbersome for users. Therefore, there is a need for a system that solves these problems and enables users to communicate smoothly between different languages.

[1770] 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.

[1771] In this invention, the server includes means for converting voice data into text data, means for translating the converted text data into a target language, and means for converting the translated text data into voice data, thereby enabling real-time translation between multiple languages.

[1772] A "device" is a wearable device that a user uses to perform operations such as input and display.

[1773] "Audio data" refers to data obtained by converting an audio signal into digital format.

[1774] "Text data" is data that has been converted from character information into digital format.

[1775] "Image data" is visual information converted into digital form.

[1776] "Video data" is data in digital form that contains a sequence of images and sounds.

[1777] A "server" is a computer system for processing and managing data.

[1778] "Speech recognition technology" is a technology that analyzes voice signals and converts them into corresponding text information.

[1779] "Target language" is the language into which the translation is to be made.

[1780] "Image processing technology" is a technology that analyzes image data and extracts necessary information.

[1781] An "augmented reality display device" is a device that displays digital information superimposed on visual information from the real world.

[1782] "Video analysis technology" is a technology that analyzes video data and extracts necessary information.

[1783] "Speech synthesis technology" is a technology that converts text data into voice data.

[1784] This invention is a system for real-time translation of multilingual speech, text, and video, and is composed of a wearable device that is easy for users to use and a server that supports it. The system operates as follows.

[1785] The user wears a wearable device, which incorporates a microphone, a camera, earphones, and an augmented reality display. When the user speaks, the microphone in the device captures the voice data, converts it into a digital format, and the device transmits the voice data to a server.

[1786] The server converts the received voice data into text data using voice recognition technology. For example, a general voice recognition API can be used for this voice recognition. The converted text data is translated into the target language. For example, a widely used translation API can be used for this translation. The translated text data is then converted into voice data using voice synthesis technology. The server then retransmits the voice data to the terminal, which then provides it to the user through earphones.

[1787] For example, if a user says, "Hello, what are your plans for today?", the system translates it into "Hello, what are your plans for today?" and provides it to the user as English speech.

[1788] Similarly, for real-time translation of text data, when a user captures text using a camera, the image data is sent to a server. The server then uses image processing technology to extract the text from the image and translate it into the target language. The translated text data is sent to the device and displayed to the user through an augmented reality display device. For example, if a user takes a photo of a sign that says "restaurant," it is translated and displayed as "Restaurant."

[1789] Real-time translation of video data is also possible. When a user captures video with a camera, the data is sent to a server, which uses video analysis technology to extract audio and text from the video. These are then translated into the target language, and the translated data is sent to the device as audio and text data. The device then plays the audio through earphones and displays the text through an augmented reality display.

[1790] This allows users to smoothly communicate between different languages ​​in real time. This system is expected to be used in a variety of situations, including international conferences, tourism, and intercultural exchange.

[1791] An example of a specific prompt is:

[1792] "Please translate what the user says in Japanese into English."

[1793] "Analyze the image, extract the Japanese text and translate it into English."

[1794] "Extract audio and text from video data and translate them into English."

[1795] This allows users to obtain fast and accurate translation results through generative AI models.

[1796] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1797] Real-time voice translation

[1798] Step 1:

[1799] The user inputs voice through the device's built-in microphone, for example, "Hello, what are your plans for today?" The input data is an analog voice signal.

[1800] Step 2:

[1801] The device converts the analog audio signal captured by the microphone into digital audio data. This conversion process is performed by an analog-to-digital converter (ADC). The converted digital audio data is then sent to the server.

[1802] Step 3:

[1803] The server receives the digital voice data and converts it into text data using voice recognition technology. For this process, a voice recognition API is used to generate text data from the input voice data, such as "Hello, what are your plans for today?"

[1804] Step 4:

[1805] The server translates the generated text data into the target language. In this step, a translation API is used to convert the text data from Japanese to English. For example, "Hello, what are your plans for today?" is translated into "Hello, what are your plans for today?"

[1806] Step 5:

[1807] The server converts the translated text data into audio data using speech synthesis technology. Based on the translated text data, "Hello, what are your plans for today?", the speech synthesis engine generates corresponding digital audio data.

[1808] Step 6:

[1809] The server transmits the generated audio data to the terminal, and the transmitted digital audio data includes appropriate metadata.

[1810] Step 7:

[1811] The device decodes the received audio data and plays it back to the user through the built-in earphones, allowing the user to listen to the translated English audio in real time.

[1812] Real-time text translation

[1813] Step 1:

[1814] A user uses the device's built-in camera to capture text on a sign or document, for example, taking a photo of a sign that says "Restaurant." The input data is image data.

[1815] Step 2:

[1816] The device sends the captured image data, including metadata such as resolution and timestamp, to the server.

[1817] Step 3:

[1818] The server receives the image data and uses image processing technology (OCR technology) to extract text from the image. For example, the text "restaurant" is extracted from an image of a sign.

[1819] Step 4:

[1820] The server translates the extracted text data into the target language using a translation API. For example, "restaurant" is translated to "Restaurant."

[1821] Step 5:

[1822] The server sends the translated text data to the device, including the translation results and associated metadata.

[1823] Step 6:

[1824] The device displays the received text data on the augmented reality display device, and the user can see the translated text "Restaurant" through the lenses of the glasses.

[1825] Real-time video translation

[1826] Step 1:

[1827] A user uses the device's built-in camera to capture video, for example, a foreign language news broadcast, and the input data is video data.

[1828] Step 2:

[1829] The device transmits the captured video data to the server in real time using streaming technology, and the data includes audio and video frame information.

[1830] Step 3:

[1831] The server receives the video data and uses video analysis technology to extract audio and text from the video, for example, audio and text from a news broadcast.

[1832] Step 4:

[1833] The server converts the extracted speech into text data and then translates it into the target language. Using speech recognition technology and a translation API, the text "What are you doing?" is translated into "What are you doing?"

[1834] Step 5:

[1835] The server uses speech synthesis technology to convert the translated text data into voice data, and also prepares the translated text data for transmission separately.

[1836] Step 6:

[1837] The server sends the translated voice data and text data to the terminal. The transmitted data includes the voice file and the text information.

[1838] Step 7:

[1839] The device then plays the received audio data through earphones and displays the text data on an augmented reality display device, allowing users to visually and aurally follow the conversations and subtitles in the video in real time.

[1840] This allows users to understand information and communicate in real time, regardless of language differences.

[1841] (Application example 1)

[1842] 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."

[1843] The present invention aims to provide a system that overcomes language barriers in autonomous vehicles and enables multilingual passengers to communicate smoothly with the vehicle. In current autonomous vehicles, communication between passengers and the vehicle is limited, making it difficult to convey information, especially when different languages ​​are spoken. This poses a problem of reduced passenger safety and satisfaction. To solve this problem, a system is needed that can achieve real-time translation between multiple languages ​​and provide passengers with appropriate information and responses.

[1844] 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.

[1845] In this invention, the server includes means for capturing voice data from the device, means for transmitting the captured voice data to the server, means for performing voice recognition in the server and generating text data, means for translating the generated text data into a target language, means for converting the translated text data into voice data, means for transmitting the converted voice data to the device, means for playing back the voice data in the device and providing it to a user, and means for performing multilingual communication between passengers and the vehicle in real time in an autonomous vehicle. This enables accurate and prompt information transmission between passengers and the vehicle, thereby improving passenger safety and satisfaction.

[1846] A "device" is a device for capturing audio data, image data, or video data and transmitting it to a server.

[1847] "Audio data" refers to data that has been converted from human speech or other sounds into digital signals.

[1848] A "server" is a central processing unit for processing, converting, and translating captured data.

[1849] "Speech recognition" is a technology that converts voice data into text data.

[1850] "Text data" is data that represents character information in digital form.

[1851] "Target language" is the language into which the text is to be translated.

[1852] "Converting into voice data" means converting text data back into voice format data using voice synthesis technology.

[1853] "Multilingual communication" is communication that takes place between two or more people who speak different languages.

[1854] An "autonomous vehicle" is a vehicle that operates autonomously without the need for a human driver.

[1855] An "augmented reality display" is a display technology that overlays digital information onto visual information from the real world.

[1856] "Video data" is data that converts moving images or videos into digital signals.

[1857] "Character recognition" is a technology for extracting character information from an image.

[1858] "Real-time" means that data is processed immediately, without delay.

[1859] The present invention embodies a multilingual communication system for an autonomous driving vehicle, and is configured as follows.

[1860] 1. Real-time voice translation

[1861] User

[1862] The user inputs instructions, questions, etc. in their native language through the device's built-in microphone. For example, they might say "Hello, please take me to the airport" in Japanese.

[1863] Terminal

[1864] The device captures the input voice data and sends it to a server, a process that uses a microphone and an internet connection.

[1865] server

[1866] The server receives the voice data and converts it into text data using a speech recognition engine (such as the Google Speech Recognition API). Next, it translates this text data into the target language (e.g., English) using a multilingual translation engine (such as the Google Translate API). After that, it converts the translated text data back into voice data using a speech synthesis engine (such as gTTS).

[1867] Terminal

[1868] The converted voice data is sent back to the device and played through the device's built-in speaker, allowing the user to hear the translated voice in real time.

[1869] 2. Real-time text translation

[1870] User

[1871] The user uses the device's built-in camera to capture text and sign information displayed on the in-car display.

[1872] Terminal

[1873] The terminal transmits the captured image data to the server.

[1874] server

[1875] The server receives the image data, extracts text from the image using an OCR (optical character recognition) engine (such as pytesseract), and then translates the extracted text data into the target language using a translation engine.

[1876] Terminal

[1877] The translated text data is displayed on an augmented reality display inside the vehicle, allowing the user to visually confirm the translated information.

[1878] 3. Real-time video translation

[1879] User

[1880] The user captures the video using the device's built-in camera.

[1881] Terminal

[1882] The terminal transmits the captured video data to the server in a streaming manner.

[1883] server

[1884] The server receives the video data and uses an analysis engine to extract the audio and text from the video. The extracted audio data is converted into text using a speech recognition engine and simultaneously translated into the target language. The text data is also translated into the target language.

[1885] Terminal

[1886] The device then plays the received translated audio data through built-in earphones and displays the translated text data on the augmented reality display, allowing users to confirm the translation both visually and audibly.

[1887] Specific examples

[1888] For example, if a foreign passenger gets into a self-driving vehicle and says in Japanese, "Hello, take me to the airport please," the system will process it as follows:

[1889] 1. Capture audio with a microphone.

[1890] 2. Send the captured audio data to the server.

[1891] 3. The server converts the voice data into text data and translates it into the target language.

[1892] 4. The translated text data is converted into audio data and sent back to the device.

[1893] 5. The terminal plays the translated audio and relays the information to the passenger.

[1894] Example prompts for generative AI models

[1895] When a foreign passenger boards an autonomous vehicle, he or she says in Japanese, "Hello, please take me to the airport." Write a program to translate this into English in real time and play it back. Use the Google Speech Recognition API for speech recognition and the googletrans library for translation. The final English audio will be generated and played back using the gtts library.

[1896] As described above, this system is designed to provide real-time multilingual translation of voice, text, and video to facilitate communication in self-driving vehicles.

[1897] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1898] Step 1:

[1899] The user inputs voice through the device's built-in microphone, and the voice data is captured in digital form. The input voice data is raw voice waveform data, which is temporarily stored on the device for further processing.

[1900] Step 2:

[1901] The device sends the captured audio data to the server via an internet connection, where it is transferred in real time using the appropriate protocol (e.g. HTTP, WebSocket) in its digital form.

[1902] Step 3:

[1903] The server inputs the received voice data into a voice recognition engine (e.g., Google Speech Recognition API) and converts the voice into text data. The voice recognition engine analyzes the voice waveform and generates a corresponding string of characters. At this stage, the input is voice data and the output is text data.

[1904] Step 4:

[1905] The generated text data is input to a multilingual translation engine (e.g., Google Translate API) and translated into the target language. The input here is the generated text data, and the output is the translated text data. The translation engine analyzes the input text and generates an appropriate translation result.

[1906] Step 5:

[1907] The translated text data is input to a speech synthesis engine (e.g., gTTS) and converted back into speech data. The speech synthesis engine analyzes the input text and generates a corresponding speech waveform. At this stage, the input is the translated text data and the output is speech data.

[1908] Step 6:

[1909] The server then sends the converted audio data back to the device, which receives it via its internet connection, where it is sent in its digital form.

[1910] Step 7:

[1911] The device plays the received voice data through the built-in speaker and provides it to the user, who can listen to the translated voice in real time. Here, the input is the voice data received from the server, and the output is the voice played from the speaker.

[1912] 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.

[1913] This system uses a wearable device worn by the user to translate multilingual audio, text, and video in real time, and also recognizes the user's emotions and reflects them in the translation results. This system consists of a "device" used by the user, a "server" that processes data, and an "emotion engine."

[1914] 1. Real-time voice translation

[1915] Program processing

[1916] User

[1917] The user speaks into the device's built-in microphone, for example, "Hello, what's on the agenda today?"

[1918] Terminal

[1919] The device captures the user's voice with a microphone and sends the voice data to the server.

[1920] server

[1921] The server receives the voice data and uses a speech recognition engine to convert the voice data into text, e.g., "Hello, what are your plans for today?"

[1922] The server uses an emotion engine to analyze the user's emotions from the voice data and generate emotion data. For example, if the user is recognized as happy, the emotion data will be "joy."

[1923] The server sends the generated text data along with the emotion data to the translation engine, which translates it into the target language. For example, when translating from Japanese to English, "Hello, what are your plans for today?" is given the emotion data of "joy."

[1924] The server converts the translated text data and emotion data into voice data that matches the user's target language.

[1925] The server transmits the converted voice data to the terminal.

[1926] Terminal

[1927] The device then plays the received translated voice data through built-in earphones, providing the user with real-time translated voice, which is played in a tone that reflects the user's emotions.

[1928] 2. Real-time text translation

[1929] Program processing

[1930] User

[1931] The user uses the device's built-in camera to capture text on a sign or document.

[1932] Terminal

[1933] The terminal transmits the captured image data to the server.

[1934] server

[1935] The server receives the image data and uses an image recognition engine (OCR engine) to extract text from the image. For example, the text "restaurant" is extracted.

[1936] The server uses an emotion engine to analyze the emotion data from the user's facial expression at the time of capture. For example, if the user is determined to be surprised, the emotion data will be "surprise."

[1937] The server sends the extracted text data and emotion data to the translation engine, which translates it into the target language. For example, "restaurant" is translated as "Restaurant" and the emotion data "surprise" is added.

[1938] The server sends the translated text data and emotion data to the terminal.

[1939] Terminal

[1940] The device receives the translated text data and displays it on the AR display, which reflects the user's emotions with icons and colors, making it easier to understand intuitively.

[1941] 3. Real-time video translation

[1942] Program processing

[1943] User

[1944] The user captures the video using the device's built-in camera.

[1945] Terminal

[1946] The device streams the captured video data to the server in real time.

[1947] server

[1948] The server receives the video data and uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[1949] The server uses an emotion engine to analyze the user's facial expressions and tone of voice in real time to generate emotion data. For example, if the user is determined to be interested, the emotion data will be "interested."

[1950] The server converts the analyzed speech into text using a speech recognition engine, then sends the text and emotional data to a translation engine for translation into the target language.

[1951] The translated text data and emotion data are then converted into voice data by a voice synthesis engine.

[1952] The server sends the translated voice and text data to the terminal.

[1953] Terminal

[1954] The device plays the received audio data through the built-in earphones and displays the text data on the AR display, allowing users to see and hear the translated content while also visually understanding the emotions expressed.

[1955] This system enables users to translate multilingual audio, text, and video in real time, and even takes emotions into account for natural and intuitive communication. This system is expected to be used in a variety of situations, including international conferences, intercultural exchange, and tourism.

[1956] The processing flow will be explained below.

[1957] Real-time voice translation

[1958] Step 1: User Speech Input

[1959] The user speaks into the device's built-in microphone, for example, "Hello, what's on the agenda today?"

[1960] Step 2: Capture and send audio data with your device

[1961] The device captures the user's voice with a microphone and sends the voice data to the server.

[1962] Step 3: Speech recognition by the server

[1963] The server receives the voice data and uses a speech recognition engine to convert the voice data into text, e.g., "Hello, what are your plans for today?"

[1964] Step 4: Emotion recognition by the server

[1965] The server uses an emotion engine to analyze the user's emotion from the voice data. For example, if the user is recognized as happy from the voice tone and speaking style, the emotion data will be "joy."

[1966] Step 5: Text translation by server

[1967] The server sends the generated text data and emotion data to the translation engine, which translates it into the target language. For example, when translating from Japanese to English, "Hello, what are your plans for today?" is given with the emotion data "joy."

[1968] Step 6: Server-based speech synthesis

[1969] The server sends the translated text data and emotion data to a speech synthesis engine, which converts it into voice data. This voice is generated in a tone that reflects the user's emotion.

[1970] Step 7: Server sends audio data

[1971] The server transmits the converted voice data to the terminal.

[1972] Step 8: Play the audio data on the device

[1973] The device receives the voice data from the server and plays it through built-in earphones, providing the user with real-time translated speech that reflects the user's emotions.

[1974] Real-time text translation

[1975] Step 1: User text input

[1976] The user uses the device's built-in camera to capture text on a sign or document.

[1977] Step 2: Capture and send image data by the device

[1978] The terminal transmits the captured image data to the server.

[1979] Step 3: Character Recognition (OCR) by the Server

[1980] The server receives the image data and uses an image recognition engine to extract text from the image, e.g., the text "restaurant" is extracted.

[1981] Step 4: Emotion recognition by the server

[1982] The server uses the emotion engine to analyze the emotion from the user's facial expression at the time of capture. For example, if the user is determined to be surprised, the emotion data will be "surprise."

[1983] Step 5: Text translation by server

[1984] The server sends the extracted text data and emotion data to the translation engine, which translates it into the target language. For example, "restaurant" is translated as "Restaurant" and the emotion data "surprise" is added.

[1985] Step 6: Server sends text data

[1986] The server sends the translated text data and emotion data to the terminal.

[1987] Step 7: Displaying Text Data in a Terminal

[1988] The device receives the translated text data and displays it on the AR display, which reflects the user's emotions with icons and colors, making it easier to understand intuitively.

[1989] Real-time video translation

[1990] Step 1: User's video input

[1991] The user captures the video using the device's built-in camera.

[1992] Step 2: Capture and send video data from your device

[1993] The device streams the captured video data to the server in real time.

[1994] Step 3: Receiving and analyzing video data by the server

[1995] The server receives the video data and uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[1996] Step 4: Speech recognition and text conversion on the server

[1997] The audio in the video is converted into text using a speech recognition engine.

[1998] Step 5: Emotion recognition by the server

[1999] The server uses an emotion engine to analyze the user's facial expressions and tone of voice in real time to generate emotion data. For example, if the user is determined to be interested, the emotion data will be "interested."

[2000] Step 6: Text translation by server

[2001] The server sends the converted text data and emotion data to a translation engine for translation into the target language.

[2002] Step 7: Server-based speech synthesis

[2003] The server converts the translated text data and emotional data into voice data using a voice synthesis engine.

[2004] Step 8: Server sends voice and text data

[2005] The server transmits the translated voice data and text data to the terminal.

[2006] Step 9: Play and display the translation data on your device

[2007] The device plays the received audio data through the built-in earphones and displays the text data on the AR display, allowing users to see and hear the translated content while also visually understanding the emotions expressed.

[2008] This system enables users to translate multilingual audio, text, and video in real time, and even takes emotions into account for natural and intuitive communication. This system is expected to be used in a variety of situations, including international conferences, intercultural exchange, and tourism.

[2009] Example 2

[2010] 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."

[2011] In modern society, multilingual communication is becoming increasingly important, but current translation technology is not yet able to fully realize natural and intuitive communication. In particular, there are issues with providing translation results that reflect emotions and with real-time multilingual support, which are inconvenient for users in cross-cultural exchange, international conferences, tourism, and other situations.

[2012] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2013] In this invention, the server includes means for generating emotion data from voice data, means for translating the generated text data and emotion data into a target language, and means for converting the translated text data and emotion data into voice data, thereby making it possible to provide intuitive and natural translation results in real time that reflect the emotions contained in the user's voice.

[2014] "Device" refers to electronic equipment worn or used by a user, including hardware such as a microphone, camera, earphones, and augmented reality display.

[2015] "Server" refers to a computer system that receives audio data, image data, and video data and performs data processing such as analysis, translation, and conversion.

[2016] "Voice data" refers to data in which the voice uttered by the user is recorded in digital format.

[2017] "Text data" is text data generated by voice recognition or OCR (optical character recognition).

[2018] "Emotion data" is data that represents emotions analyzed from the user's voice and facial expressions.

[2019] "Translation engine" refers to software or algorithms that convert data from one language into data from another language.

[2020] A "voice recognition engine" refers to software or algorithms that analyze voice data and convert it into corresponding text data.

[2021] An "emotion engine" refers to software or algorithms that analyze voice data and facial expression data to estimate and digitize the user's emotions.

[2022] An "OCR engine" refers to software or algorithms that extract characters from image data.

[2023] A "video analysis engine" refers to software or algorithms that analyze the content of video data and extract information such as audio and text.

[2024] "Target language" refers to the language into which the translation result is to be produced.

[2025] A "speech synthesis engine" refers to software or algorithms that analyze text data and generate synthetic speech.

[2026] An "augmented reality display" refers to a display device that displays digital information overlaid on the real world.

[2027] "Translated data" refers to text data and related information that has been converted into a target language by a translation engine.

[2028] "Video data" refers to data recorded in the form of user-captured video.

[2029] This invention is a system that uses a wearable device worn by the user to translate multilingual voice, text, and video in real time, and also recognizes the user's emotions and reflects them in the translation results. This system consists of a "device" used by the user, a "server" that processes data, and an "emotion engine."

[2030] 1. Real-time voice translation

[2031] The user speaks into the device's built-in microphone, for example, saying, "Hello, what are your plans for today?" The device captures this voice with the microphone and sends it to the server as voice data.

[2032] The server receives the voice data and converts the voice into text data using a voice recognition engine (e.g., voice recognition software). The generated text is "Hello, what are your plans for today?". The server then uses an emotion engine (e.g., emotion analysis software) to generate emotion data from the voice. For example, the result of the data analysis is "joy."

[2033] The generated text data and emotion data are sent to a translation engine (e.g., a translation API) and translated into the target language. As an example of translation from Japanese to English, the text "Hello, what are your plans for today?" and emotion data "joy" are generated.

[2034] The server converts the translated text data and emotion data into voice data using a voice synthesis engine (e.g., text-to-speech software). The device then plays the received voice data through built-in earphones and provides it to the user in real time. This voice data reflects the user's emotions, enabling natural and intuitive communication.

[2035] 2. Real-time text translation

[2036] The user uses the built-in camera of the device to capture text on signs, documents, etc. The device then sends the captured image data to the server.

[2037] The server receives the image data and uses an OCR engine (e.g., optical character recognition software) to extract text from the image. An example of extracted text might be "restaurant." The server then uses an emotion engine to analyze the user's facial expression at the time of capture and generate emotion data, such as "surprise."

[2038] The server sends the extracted text data and emotion data to a translation engine, which translates it into the target language. As translation examples, emotion data such as "Restaurant" and "surprise" are generated. The device receives the translated text data and displays it on the AR display. Emotions are reflected in the display using icons and colors, allowing the user to understand intuitively.

[2039] 3. Real-time video translation

[2040] The user captures video using the device's built-in camera, and the device transmits the captured video data to the server in real time.

[2041] The server receives the video data and analyzes the video content using a video analysis engine (e.g., video analysis software). For example, it may extract audio and text from the video. Next, the server uses an emotion engine to analyze the user's facial expressions and tone of voice and generate emotion data, such as "interest."

[2042] The server converts the extracted speech into text data using a speech recognition engine, sends the text data and emotion data to a translation engine for translation into the target language, and then converts the translated text data and emotion data into speech data using a speech synthesis engine.

[2043] The device plays the translated voice data through the built-in earphones and displays the translated text data on the AR display, allowing users to see and hear the translated voice and text information in real time, and also visually understand emotions.

[2044] With the above configuration, users can translate multilingual voice, text, and video in real time, enabling natural and intuitive communication that takes emotions into account. This system is expected to be used in a variety of situations, including international conferences, intercultural exchange, and tourism.

[2045] Prompt Sentence Examples

[2046] "Please explain the process of a system that translates Japanese speech spoken by a user into English and plays it back in a voice that reflects the emotion of the translation."

[2047] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2048] Real-time voice translation

[2049] Step 1:

[2050] The user speaks into the device's built-in microphone, for example, "Hello, what's your plan today?"

[2051] Input: User's speech

[2052] Output: Analog audio signal captured by microphone

[2053] Step 2:

[2054] The device converts analog audio signals captured by the built-in microphone into digital audio data. Example: Converted WAV format audio data

[2055] Input: Analog audio signal

[2056] Output: Digital audio data (WAV format)

[2057] Step 3:

[2058] The terminal transmits the converted digital audio data to the server.

[2059] Input: Digital audio data

[2060] Output: Digital audio data sent to the server

[2061] Step 4:

[2062] The server uses a speech recognition engine to convert the received voice data into text data. For example, "Hello, what are your plans for today?"

[2063] Input: Digital audio data

[2064] Output: Text data

[2065] Step 5:

[2066] The server uses an emotion engine to analyze the user's emotions from the voice data. Example: The analysis results generate emotion data for "joy."

[2067] Input: Digital audio data

[2068] Output: Emotion data (e.g., "joy")

[2069] Step 6:

[2070] The server sends the generated text data and emotion data to the translation engine, which translates it into the target language. Example: Translating from Japanese to English, generating the text data "Hello, what are your plans for today?" and the emotion data "joy."

[2071] Input: Text data, emotion data

[2072] Output: Translated text data and emotion data

[2073] Step 7:

[2074] The server converts the translated text data and emotion data into voice data using a speech synthesis engine, generating English voice data that reflects the emotion of "joy."

[2075] Input: translated text data, emotion data

[2076] Output: Audio data (e.g., synthesized English voice)

[2077] Step 8:

[2078] The server transmits the generated voice data to the terminal.

[2079] Input: Audio data

[2080] Output: Audio data sent to the device

[2081] Step 9:

[2082] The device then plays the received audio data through the built-in earphones and provides it to the user, producing audio that reflects "joy."

[2083] Input: Audio data

[2084] Output: Audio played through earphones

[2085] Real-time text translation

[2086] Step 1:

[2087] The user captures text on a sign or document using the device's built-in camera. Example: capturing an image of a restaurant sign.

[2088] Input: Image of a sign or document

[2089] Output: Captured image data

[2090] Step 2:

[2091] The terminal transmits the captured image data to the server.

[2092] Input: Image data

[2093] Output: Image data sent to the server

[2094] Step 3:

[2095] The server uses an OCR engine to extract text from the image data, e.g., extracting the text "restaurant."

[2096] Input: Image data

[2097] Output: Extracted text data (e.g., "restaurant")

[2098] Step 4:

[2099] The server uses an emotion engine to generate emotion data from the user's facial expression at the time of capture. For example, the server generates emotion data for "surprise" through facial expression analysis.

[2100] Input: Image data

[2101] Output: Emotion data (e.g., "surprise")

[2102] Step 5:

[2103] The server sends the extracted text data and emotion data to the translation engine, which translates it into the target language. For example, "restaurant" is translated to "Restaurant" and the emotion data "surprise" is added.

[2104] Input: Text data, emotion data

[2105] Output: Translated text data and emotion data

[2106] Step 6:

[2107] The server sends the translated text data and emotion data to the terminal.

[2108] Input: translated text data, emotion data

[2109] Output: Data sent to the terminal

[2110] Step 7:

[2111] The device displays the translated text data on the AR display, with emotions reflected in icons and colors.

[2112] Input: translated text data, emotion data

[2113] Output: Text to be displayed on the AR display

[2114] Real-time video translation

[2115] Step 1:

[2116] The user captures video using the device's built-in camera. Example: capturing video of an intercultural exchange event.

[2117] Input: Video

[2118] Output: Captured video data

[2119] Step 2:

[2120] The device transmits the captured video data to the server in real time.

[2121] Input: Video data

[2122] Output: Video data sent to the server

[2123] Step 3:

[2124] The server uses a video analysis engine to analyze the video content, e.g., extracting audio and displayed text from the video.

[2125] Input: Video data

[2126] Output: Extracted audio data and text data

[2127] Step 4:

[2128] The server uses an emotion engine to analyze emotion data from the user's facial expressions and tone of voice. Example: Generate emotion data for "interest."

[2129] Input: Audio data, video data

[2130] Output: Emotion data (e.g., "interest")

[2131] Step 5:

[2132] The server converts the extracted speech into text data using a speech recognition engine, and then sends the text and emotion data to a translation engine for translation into the target language. For example, Japanese speech is translated into English text, and the emotion data "interest" is added.

[2133] Input: Voice data, emotion data

[2134] Output: Translated text data, emotion data

[2135] Step 6:

[2136] The server converts the translated text data and emotion data into voice data using a voice synthesis engine. Example: Generates English synthesized voice data.

[2137] Input: translated text data, emotion data

[2138] Output: Audio data

[2139] Step 7:

[2140] The server transmits the generated voice data and text data to the terminal.

[2141] Input: Audio data, text data

[2142] Output: Data sent to the terminal

[2143] Step 8:

[2144] The device plays the audio data through the built-in earphones and displays the text data on the AR display, allowing users to check the translation results visually and audibly and to visually understand the emotions.

[2145] Input: Audio data, text data

[2146] Output: Audio played through earphones, text displayed on the AR display

[2147] (Application example 2)

[2148] 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."

[2149] There is a need for a system that can not only translate multilingual speech, text, and video in real time, but also recognize the user's emotions and reflect them in the translation results. Conventional systems have difficulty in achieving natural and intuitive communication that takes the user's emotions into account, and there is a need for technology that supports smooth communication, especially in situations such as intercultural exchange and food delivery.

[2150] The specific processing by the specific 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 capturing voice data from the device, means for transmitting the captured voice data to the server, means for performing voice recognition in the server and generating text data, means for translating the generated text data into a target language, means for converting the translated text data into voice data, means for transmitting the converted voice data to the device, means for playing back the voice data in the device and providing it to the user, means for analyzing emotional data from the captured voice data, and means for reflecting the emotional data in the translation result. This enables communication that takes into account not only voice recognition and translation but also the user's emotions.

[2151] The device also includes means for capturing image data from the device, means for transmitting the captured image data to a server, means for recognizing characters from the image in the server, means for translating the recognized character data into a target language, means for transmitting the translated character data to the device, means for displaying the character data on an augmented reality display in the device, means for analyzing emotions from facial expression data at the time of capture, and means for reflecting the emotion data in the translation result, thereby realizing real-time translation of text and reflecting emotions.

[2152] The device further includes means for capturing video data from the device, means for transmitting the captured video data to a server, means for extracting audio and text from the video on the server, means for translating the extracted audio and text into a target language, means for transmitting the translated data to the device, means for playing the translated audio data on the device and displaying the text data on an augmented reality display, means for analyzing emotions from facial expressions and tone of voice at the time of video capture, and means for reflecting the emotional data in the translation result, thereby enabling real-time translation of multilingual video data and reflecting emotions.

[2153] "Device" refers to a terminal, including a wearable device or smartphone worn by a user, and is hardware that captures and plays audio, images, and video.

[2154] "Voice data" refers to a data format for digitally recording the voice uttered by the user and transmitting it to a server.

[2155] "Capture" refers to capturing audio, images, or video using input devices such as sensors, cameras, and microphones, and collecting them as digital data.

[2156] A "server" is a central computer system that processes data using various algorithms and engines such as speech recognition, translation, and sentiment analysis.

[2157] "Speech recognition" is a technology that analyzes voice data and converts its contents into text data.

[2158] "Text data" refers to digital data expressed as a string of characters, and is generated by speech recognition.

[2159] "Target language" refers to the final language desired in the translation process, and refers to a language that can be understood by the user.

[2160] "Translation" is the process of converting text data written in one language into another target language.

[2161] "Emotion data" refers to digital data that indicates the emotional state of a user extracted from their voice, facial expression, and behavior.

[2162] "Conversion to voice data" refers to the process of converting text data and accompanying emotion data back into voice format data using voice synthesis technology.

[2163] "Image data" refers to still images captured by a camera or other imaging device.

[2164] "Character recognition" is a technique for extracting text information from captured image data.

[2165] "Augmented reality display" refers to a display technology that overlays computer-generated information on the physical world.

[2166] "Video data" refers to digital data recorded in moving image format, and includes audio and video.

[2167] "Facial expression data" refers to digital data that indicates an emotional state analyzed from a user's facial expressions captured by a camera.

[2168] "Incorporating emotional data into translation results" refers to the process of incorporating analyzed emotional data into translated text or speech to improve the naturalness of communication.

[2169] This system uses a wearable device worn by the user to translate multilingual speech, text, and video in real time, and also recognizes the user's emotions and reflects them in the translation results. The system consists of the following components:

[2170] System Configuration

[2171] 1. Device: A wearable device worn by a user (e.g., smart glasses, smartphone) that captures and plays back audio, images, and video.

[2172] 2. Server: A central computer system that processes data and uses various algorithms such as speech recognition, translation, and sentiment analysis.

[2173] 3. Emotion engine: Equipped with algorithms that recognize the emotional state extracted from the user's voice, facial expressions, and behavior.

[2174] Hardware and software used

[2175] Smart glasses and smartphones: Used to capture the user's voice and video and display the results.

[2176] speech_recognition: A speech recognition library that converts user speech into text data.

[2177] googletrans: Provides a translation engine to translate speech-recognized text into the target language.

[2178] EmotionEngine: A custom engine that analyzes emotions from the user's voice data and facial expressions.

[2179] cv2 (OpenCV): Used to capture and process camera images.

[2180] ARDisplay: A custom class for displaying text on the AR display of smart glasses.

[2181] pyttsx3: A text-to-speech synthesis library for playing translated text as audio.

[2182] Processing flow

[2183] 1. Voice capture: The user speaks into the device's built-in microphone to capture voice data.

[2184] 2. Speech Recognition: The captured voice data is sent to the server, which uses a speech recognition engine to convert the voice data into text.

[2185] 3. Emotion analysis: Analyze the user's emotions from the voice data and generate emotion data.

[2186] 4. Translation: Use the recognized text and emotion data to translate into the target language.

[2187] 5. Display and voice playback: The translated text is combined with emotion data and displayed on the AR display of the smart glasses, along with voice feedback.

[2188] Specific examples

[2189] For example, if a food delivery person says in Japanese, "Hello, can I leave it at the front door?", the system will translate it as "Hello, can I leave it at the front door?" (joy) and provide audio feedback to the delivery person. If the customer responds with a smile, that emotion data will also be reflected.

[2190] Prompt Sentence Examples

[2191] When a user asks "How would you like to pay?" in Japanese, what are the results of the translation and sentiment analysis?

[2192] As described above, the present invention enables natural and smooth communication that takes into account the user's feelings while providing multilingual support in situations such as cross-cultural exchange and food delivery.

[2193] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2194] Step 1:

[2195] The user speaks into the device's microphone. The input is the user's live voice, which is captured by the device's built-in microphone and recorded as digital audio data. The output is digital audio data.

[2196] Step 2:

[2197] The device sends the captured digital audio data to the server. The input is the digital audio data sent to the server over a network connection. The output is the digital audio data transferred to the server.

[2198] Step 3:

[2199] The server uses a speech recognition engine to analyze the received digital voice data. The input is the digital voice data, and the speech recognition engine (speech_recognition library) converts this data into text data. The output is the generated text data.

[2200] Step 4:

[2201] The server uses an emotion engine to analyze the user's emotions from the voice data. The input is digital voice data, which the emotion engine analyzes and generates emotion data. The output is emotion data.

[2202] Step 5:

[2203] The server sends the generated text data and emotion data to the translation engine to translate it into the target language. The input is text data and emotion data, which the translation engine (googletrans) translates into the target language. The output is the translated text data and emotion data.

[2204] Step 6:

[2205] The server converts the translated text data and emotion data into voice data using a voice synthesis engine. The input is the translated text data and emotion data, which the voice synthesis engine (pyttsx3) converts into voice data. The output is the converted voice data.

[2206] Step 7:

[2207] The server sends the converted audio data to the device. The input is the converted audio data sent to the device over a network connection. The output is the audio data transferred to the device.

[2208] Step 8:

[2209] The device plays the received audio data and provides it to the user. The input is the converted audio data, which is played through the built-in speaker or earphones. The output is the played audio.

[2210] Through the above steps, the user's voice is translated in real time, realizing natural communication that reflects emotions.

[2211] 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.

[2212] 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.

[2213] 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.

[2214] 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.

[2215] 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.

[2216] 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.

[2217] 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).

[2218] 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.

[2219] 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."

[2220] 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.

[2221] 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).

[2222] 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.

[2223] 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.

[2224] 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.

[2225] 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.

[2226] 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.

[2227] 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.

[2228] 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.

[2229] 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.

[2230] 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.

[2231] 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.

[2232] The following is further disclosed regarding the above embodiment.

[2233] (Claim 1)

[2234] means for capturing audio data from the device;

[2235] means for transmitting the captured audio data to a server;

[2236] a means for performing speech recognition in a server and generating text data;

[2237] means for translating the generated text data into a target language;

[2238] means for converting the translated text data into audio data;

[2239] means for transmitting the converted audio data to the device;

[2240] means for playing audio data on the device and providing it to a user;

[2241] A system including:

[2242] (Claim 2)

[2243] means for capturing image data from the device;

[2244] means for transmitting the captured image data to a server;

[2245] a means for recognizing characters from an image in the server;

[2246] means for translating the recognized character data into a target language;

[2247] means for transmitting the translated character data to the device;

[2248] means for displaying the character...

Claims

1. means for capturing audio data from the device; means for transmitting the captured audio data to a server; a means for performing speech recognition in a server and generating text data; means for translating the generated text data into a target language; means for converting the translated text data into audio data; means for transmitting the converted audio data to the device; means for playing audio data on the device and providing it to a user; A system including:

2. means for capturing image data from the device; means for transmitting the captured image data to a server; a means for recognizing characters from an image in the server; means for translating the recognized character data into a target language; means for transmitting the translated character data to the device; means for displaying the character data on an augmented reality display in the device; The system of claim 1 , comprising:

3. means for capturing video data from the device; means for transmitting the captured video data to a server; means for extracting audio and text from the video in the server; means for translating the extracted speech and text into a target language; means for transmitting the translated data to the device; means for playing the translated audio data in the device and displaying the translated text data on an augmented reality display; The system of claim 1 , comprising:

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