System

Abstract

A system is provided.SOLUTION: A system, comprising: means for collecting speech data; means for converting the speech data into text data; means for converting the text data into sign language steps; means for generating sign language videos based on the sign language steps; and means for transmitting the sign language videos to a display device for display.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the past, providing appropriate sign language interpretation to hearing-impaired people in real time at events such as lectures and group work required the presence of a sign language interpreter, making it difficult to achieve in situations with a large number of people. Furthermore, in situations where a sign language interpreter is not available, hearing-impaired people have limited means of obtaining information, impairing the smoothness of communication. The present invention aims to generate a sign language interpretation video in real time without the need for a sign language interpreter, enabling information to be conveyed to hearing-impaired people quickly and easily. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system including a means for collecting audio data, a means for converting the audio data into text data, a means for converting the text data into sign language steps, a means for generating a sign language video based on the sign language steps, and a means for transmitting and displaying the sign language video on a display device. This enables hearing-impaired people to obtain information in real time during lectures, group work, and other situations, eliminating the need for a sign language interpreter and enabling sign language interpretation to be provided in a wider range of situations. Furthermore, because this system processes audio data and sign language video in real time, users can receive sign language information without delay. Furthermore, by including a function for projecting sign language video onto a display device such as smart glasses, sign language interpretation can be implemented in everyday life.

[0006] "Audio Data" means sound in digital form collected through a microphone or other audio input device.

[0007] "Means for collecting" refers to the hardware and software functions for capturing audio data and transmitting it to a processing device.

[0008] "Text data" refers to text information converted from voice data using voice recognition technology.

[0009] "Means for converting" refers to the speech recognition engine or algorithm used to convert voice data into text data.

[0010] "Sign language steps" are individual actions and gestures in sign language based on text data.

[0011] "Means for converting into sign language steps" refers to an algorithm or program that converts text data into sign language steps.

[0012] "Sign language video" refers to video data showing visual sign language gestures generated based on sign language steps.

[0013] "Means for generating sign language images" refers to functions and technologies for creating sign language images based on sign language steps using 3D models or other visual representations.

[0014] A "display device" refers to a device such as a display or smart glasses that shows the generated sign language image to the user.

[0015] "Displaying means" refers to the hardware and software functions required to transmit the generated sign language video to a display device and play it appropriately. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention is a system that collects voice data in real time, converts the voice data into text data, further converts the text data into sign language steps to generate sign language video, and finally transmits the sign language video to a display device for display.

[0038] Overview of program processing

[0039] Collecting voice input

[0040] The user speaks into a microphone and the device collects the audio. Specifically, the audio data is stored digitally through the microphone and then sent to a server in real time. Appropriate noise-canceling technology is used to collect the audio, improving analysis accuracy.

[0041] Audio analysis

[0042] The server analyzes the received voice data using a speech recognition engine and converts it into text data. In this process, speech is converted to text using a speech recognition engine (for example, a general cloud-based speech recognition service). The converted text data is temporarily stored in memory.

[0043] Sign language translation of text data

[0044] The server sends this text data to a sign language translation algorithm, which converts it into sign language steps. The sign language translation algorithm analyzes the grammar and context of the text data and converts it into appropriate sign language expressions. When doing so, it takes into account the context and selects the most appropriate sign language gesture for any ambiguous parts.

[0045] Sign language video generation

[0046] The server generates sign language videos based on the sign language steps. Specifically, it uses 3D modeling technology and realistic animation to visually represent each sign gesture. In the process of generating the sign language videos, it seamlessly connects consecutive movements to naturally express the flow and rhythm of sign language.

[0047] Video transmission and display

[0048] The server transmits the generated sign language video to the device in real time. The device decodes the received sign language video and displays it on a display device such as a monitor or smart glasses at an appropriate frame rate and resolution. The user can see the sign language interpreter in real time through the display.

[0049] Specific examples

[0050] Use in conference rooms

[0051] When User A gives a presentation in a conference room, the terminal (the conference room's microphone and computer system) collects User A's speech and sends it to the server. The server converts the speech data into text data, and then converts the text data into sign language steps. The server then generates a sign language video and displays it on a monitor in the conference room. User B, who is hearing impaired, can understand what User A is saying in real time by watching the sign language video displayed on the monitor.

[0052] Personal use

[0053] When User X starts a conversation with a friend at a cafe, the device (smartphone) collects User X's voice and sends it to the server. The server converts the voice data into text data and then converts it into sign language steps. The server then generates a sign language video that is displayed on the smartphone or projected onto smart glasses. User Y, a friend with a hearing impairment, can understand the content of the conversation in real time by viewing the sign language video through the smart glasses.

[0054] In this way, the system of the present invention provides real-time sign language interpretation in a variety of situations, enabling the hearing impaired to obtain information quickly and accurately.

[0055] The processing flow will be explained below.

[0056] Specific processing steps of the program

[0057] Step 1: Collecting voice input

[0058] The user speaks into the microphone. The device collects the user's voice through a built-in or external microphone. Appropriate noise cancellation technology is used to collect the voice, ensuring clear voice data. The collected voice data is stored in digital format.

[0059] Step 2: Sending audio data

[0060] The collected voice data is sent to a server via the Internet in streaming format to maintain real-time performance.

[0061] Step 3: Receiving audio data

[0062] The server receives the transmitted audio data in real time. The received audio data is stored in a buffer. Buffering allows for temporary storage and sequential processing of audio data.

[0063] Step 4: Audio analysis

[0064] The server calls a speech recognition engine (e.g., a cloud-based speech recognition service) and converts the voice data stored in the buffer into text data, which is then temporarily stored in memory.

[0065] Step 5: Translating text data into sign language

[0066] The server sends the stored text data to a sign language translation algorithm, which analyzes the grammar and context of the text data and converts it into appropriate sign language steps. A context analysis algorithm is also used to deepen context understanding.

[0067] Step 6: Generate sign language video

[0068] The server generates sign language video based on the sign language steps. It uses image generation AI and a 3D modeling engine to generate 3D models and animations to visually represent each sign gesture. The generated sign language video is then seamlessly displayed as a series of movements.

[0069] Step 7: Compress the video data

[0070] The server compresses the generated sign language video data to ensure efficient use of bandwidth, using an appropriate codec to maintain data quality.

[0071] Step 8: Sending sign language video

[0072] The server sends the compressed sign language video data to the device via the Internet. The data is sent using an appropriate protocol (e.g., WebRTC) to maintain real-time transmission.

[0073] Step 9: Receiving sign language video

[0074] The terminal receives the transmitted sign language video data in real time, decodes the received video data, and prepares it for playback.

[0075] Step 10: Displaying the sign language video

[0076] The device projects the sign language image onto a display device such as a monitor or smart glasses. The frame rate and resolution of the image are adjusted to ensure smooth playback. The user can view the sign language image on the display device and understand the information.

[0077] The above steps will enable hearing-impaired people to obtain information in real time in a variety of situations, such as lectures and small group work.

[0078] Example 1

[0079] 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."

[0080] The present invention relates to a system that collects voice data in real time, converts the voice data into text data, then converts the text data into sign language steps to generate a sign language video, and finally transmits and displays the sign language video on a display device. Conventional voice recognition and sign language translation technologies have issues with real-time performance and accuracy, making it difficult for hearing-impaired users to obtain information in real time.

[0081] 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.

[0082] In this invention, the server includes means for applying noise canceling technology to process voice data in real time, means for analyzing text data with a sign language translation algorithm, and means for generating realistic sign language images using 3D modeling technology, thereby enabling highly accurate collection and processing of voice data and real-time generation and display of sign language images.

[0083] "Voice data" is a digital representation of voice, and is data collected from a user's speech via a microphone or the like.

[0084] "Text data" is character string data converted from voice data using voice recognition technology, and is data in a format that can be read by humans.

[0085] "Sign language steps" are a series of instructions and commands that express each step of sign language movement based on text data, and are intermediate data for generating sign language images.

[0086] "Sign language video" is a visual sign language expression generated based on sign language steps, and is video data expressed as animation using 3D modeling technology, etc.

[0087] A "display device" is a device that visually presents sign language images to users, and examples include monitors and smart glasses.

[0088] "Real-time processing" refers to a process in which the entire process, from data collection to display, is carried out in an extremely short time with almost no delay.

[0089] "Noise canceling technology" is a technology that suppresses background noise when collecting voice data, with the aim of clearly capturing only the user's speech.

[0090] A "sign language translation algorithm" is a formula or procedure for converting text data into sign language steps, and applies natural language processing technology.

[0091] "3D modeling technology" is a technique for creating visual objects using three-dimensional computer graphics, and is used to realistically represent each movement in sign language videos.

[0092] The present invention is a system that collects voice data in real time, converts the voice data into text data, further converts the text data into sign language steps to generate sign language video, and finally transmits the sign language video to a display device for display.

[0093] Overall system overview

[0094] This system consists of a user, a terminal, and a server. The user speaks into a microphone, and the terminal collects the voice data. The terminal sends the collected voice data to the server in real time. The server analyzes the voice data and converts it into text data, which is then converted into sign language steps. The server then generates a sign language video based on the sign language steps and sends the generated sign language video to the terminal in real time. The terminal decodes the sign language video and displays it on a display device, allowing the user to view the sign language video.

[0095] Specific examples of hardware and software used

[0096] Microphone: Collects user utterances.

[0097] Terminal: A communication device such as a personal computer or smartphone.

[0098] Server: Responsible for data processing and storage. Uses a speech recognition engine (e.g., a cloud-based speech recognition service).

[0099] Display devices: monitors, smart glasses, etc.

[0100] Specific examples of software include:

[0101] Speech recognition engine: Google Cloud Speech-to-Text API, etc.

[0102] Noise cancelling technologies: NVIDIA RTX Voice, Dolby Voice, etc.

[0103] Sign language translation algorithms: BERT and GPT models using natural language processing (NLP) techniques.

[0104] 3D modeling software: Blender and Unity are used to generate sign language videos.

[0105] Example of system operation

[0106] Example of use in a conference room

[0107] When User A gives a presentation in a conference room, a terminal (a microphone and computer system located in the conference room) collects User A's speech and converts it into digital audio data. This data is sent to a server in real time. The server receives the speech data and converts it into text data using a speech recognition engine. The converted text data is then converted into sign language steps using a sign language translation algorithm, and a sign language video is generated using 3D modeling software. The generated sign language video is sent to the terminal and displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can view the sign language video on the monitor and understand what User A is saying in real time.

[0108] Examples of personal use

[0109] When User X is talking with a friend at a cafe, the device (smartphone) collects User X's speech and sends it to a server in real time. The server receives the voice data and converts it into text data using a cloud-based speech recognition engine. The converted text data is converted into sign language steps using a sign language translation algorithm, and a sign language video is generated using 3D modeling software based on the sign language steps. The generated sign language video is sent to the smartphone and projected onto the smartphone screen or smart glasses. User Y, the friend who is hearing impaired, can view the sign language video through the smart glasses and understand the content of the conversation in real time.

[0110] Example prompts for generative AI models

[0111] "Collect audio data in real time as the user speaks into a microphone, and convert that audio data into text data. Next, convert the text data into sign language steps, and generate a sign language video using 3D modeling software. Then, send the generated sign language video to a display device and display it."

[0112] This system provides real-time sign language interpretation in a variety of situations, enabling the hearing impaired to obtain information quickly and accurately.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1:

[0115] The user speaks into the microphone. Voice data (analog signal) is captured by the microphone. This data is input into the device's voice collection module. The voice collection module converts the voice signal into digital form and applies noise cancellation technology. Specifically, the user's speech is collected by the microphone, and the device's voice input device (e.g., built-in microphone or external microphone) converts the analog signal into digital data, which is then subjected to noise cancellation processing. The output is digitized, noise-removed voice data.

[0116] Step 2:

[0117] The terminal transmits digital audio data to the server in real time. The audio data is buffered by the terminal's communication module and divided into packets of a certain size before being transmitted. Specifically, the terminal's communication protocol (e.g., TCP / IP) divides the audio data into packets and transmits them to the server via the network. The output is the packets of digital audio data transmitted to the server.

[0118] Step 3:

[0119] The server passes the received voice data to a voice recognition engine, which converts it into text data. In this process, the voice recognition engine (for example, Google Cloud Speech-to-Text) analyzes the voice signal, breaks it down into phonemes and phrases, and generates text data as a string of characters. Specifically, the voice recognition engine analyzes the voice waveform data and converts it into corresponding text data. The input is digital voice data, and the output is converted text data.

[0120] Step 4:

[0121] The server sends the text data to a sign language translation algorithm, which converts it into sign language steps. The sign language translation algorithm uses natural language processing (NLP) to analyze the grammar and context of the text and convert it into appropriate sign language expressions. Specifically, an NLP model (e.g., BERT) analyzes the context and determines the sign language steps for each word or phrase by referring to a sign language dictionary. The input is text data, and the output is sign language step data.

[0122] Step 5:

[0123] The server generates sign language video based on the sign language steps. Each sign gesture is animated using 3D modeling software (e.g., Blender or Unity). Specifically, the sign language steps are converted into 3D character movements, and keyframe technology and smooth transitions are used to generate realistic movements. The input is sign language step data, and the output is a 3D animated video of the sign language.

[0124] Step 6:

[0125] The server transmits the generated sign language video to the terminal in real time. The video data is encoded in real time and transmitted to the terminal. Specifically, the server's video encoding module compresses the sign language video in real time and transmits it to the terminal via the Internet. The output is sign language video data transmitted in real time.

[0126] Step 7:

[0127] The device decodes the received sign language video and displays it on a display device at an appropriate frame rate and resolution. The display device can be a monitor or smart glasses, through which the user can view the sign language interpreter in real time. Specifically, the device's decoding library (e.g., FFmpeg) decodes the sign language video and outputs it to the display device. The input is the received sign language video data, and the output is the sign language video displayed on the display device.

[0128] (Application example 1)

[0129] 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."

[0130] Conventional conversation and translation systems have had the problem of preventing hearing-impaired people from communicating smoothly with other people. In particular, in public places such as brick-and-mortar stores, there is a lack of appropriate sign language interpretation services when hearing-impaired people communicate with store staff and other customers. This has made it difficult for hearing-impaired people to understand conversations in real time, resulting in reduced communication efficiency.

[0131] 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.

[0132] In this invention, the server includes means for collecting voice data, means for converting the voice data into text data, means for converting the text data into sign language steps, means for generating a sign language video based on the sign language steps, means for transmitting the sign language video to a display device and displaying it, means for applying noise canceling technology to the means for collecting voice data, and means for displaying the sign language video on a display of smart glasses, thereby enabling a hearing-impaired person to understand the content of a conversation as a sign language video in real time.

[0133] "Audio data" refers to sound signals collected by an audio input device.

[0134] "Text data" refers to data that is generated by analyzing voice data and expressing it as text information.

[0135] "Sign language steps" refer to the steps for determining sign language actions based on text data.

[0136] "Sign language video" refers to a moving image display of information generated based on sign language steps.

[0137] "Display device" means a device for visually displaying sign language images. Examples include monitors and smart glasses.

[0138] "Noise canceling technology" refers to technology that reduces ambient noise when collecting audio.

[0139] "Smart glasses" refers to a device in the form of glasses that includes a display and has the ability to visually display information.

[0140] A "server" is a computer system for processing data, transforming data, and providing necessary services.

[0141] "Real-time" is a term that refers to near-simultaneous processing and display.

[0142] System Overview

[0143] This invention is composed of a comprehensive system including various voice input devices, a server, and a display device. Voice data is collected in real time, converted into text data, and then converted into sign language steps to generate sign language images, which are then sent to a display device for display.

[0144] Specific configuration

[0145] 1. Collecting voice input

[0146] The user speaks, and the audio is collected by a microphone in the device. The device then uses appropriate noise-canceling technology to reduce ambient noise and obtain clear audio data. For example, the audio of a staff member in a physical store where a conversation takes place is collected by a microphone built into smart glasses.

[0147] 2. Audio analysis

[0148] The collected voice data is sent to a server in real time and converted into text data using a voice recognition engine (e.g., Google Cloud Speech-to-Text API).

[0149] 3. Sign Language Translation of Text Data

[0150] The server converts this text data into sign language steps using a sign language translation algorithm (e.g., a sign language translation system using a deep learning model). The text data is analyzed for grammar and context, and converted into appropriate sign language gestures.

[0151] 4. Sign Language Video Generation

[0152] The server generates sign language videos based on the sign language steps, using 3D character modeling software (e.g., Blender or Unity) to visually represent realistic sign language gestures.

[0153] 5. Video transmission and display

[0154] The generated sign language image is sent in real time to a display device, specifically a smart glasses display. For example, a hearing-impaired person wearing the smart glasses can understand what a store clerk is saying through the sign language image.

[0155] Specific examples

[0156] Use in physical stores

[0157] When a hearing-impaired person visits a store, the smart glasses collect what the staff say and send it to the server. The server converts the voice data into text data and then converts it into sign language steps. A sign language video is then generated based on the sign language steps and sent to the smart glasses to be displayed in real time.

[0158] Prompt Sentence Examples

[0159] Developed a system that enables hearing-impaired people to understand what store clerks are saying through smart glasses. Explain the application that combines speech recognition and sign language translation.

[0160] ---

[0161] In this way, by specifically explaining the mode for carrying out the invention, a system is provided that enables hearing-impaired people to communicate smoothly in social situations.

[0162] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0163] Step 1: Collecting voice input

[0164] When a user speaks, the audio is picked up by the device's microphone. The device then uses noise-canceling technology to reduce ambient noise and capture clear audio data. To achieve this, the microphone captures the audio data in digital form and temporarily stores it in the device's memory.

[0165] Step 2: Sending audio data

[0166] The device transmits the collected voice data to a server in real time using data streaming technology over the Internet, where the server receives the voice data and temporarily stores it in memory.

[0167] Step 3: Audio analysis

[0168] The server analyzes the received voice data using a speech recognition engine (for example, Google Cloud Speech-to-Text API) and converts it into text data. To analyze the voice data, the server inputs the voice data into the speech recognition engine and obtains text data as output. The converted text data is temporarily stored in the server's memory.

[0169] Step 4: Sign Language Translation

[0170] The server sends the text data to a sign language translation algorithm (e.g., a sign language translation system using a deep learning model) and converts it into sign language steps. For this process, the server inputs the text data and obtains sign language step data. The server temporarily stores these sign language steps in memory.

[0171] Step 5: Generate sign language video

[0172] The server generates sign language video using 3D modeling technology (e.g., Blender or Unity) based on the sign language steps. It uses the sign language steps as input and creates sign language video as output. The generated sign language video is stored in the server's memory.

[0173] Step 6: Sending sign language video

[0174] The server transmits the generated sign language video in real time to the device via the Internet, and the device receives the sign language video and temporarily stores it in its memory.

[0175] Step 7: Displaying the sign language video

[0176] The device receives the sign language video and displays it on the smart glasses' display. It decodes the sign language video and adjusts the frame rate and resolution for display. Specifically, the display software processes the video data so that the user can see the video through the glasses.

[0177] 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.

[0178] This invention combines an emotion recognition engine with a system that collects voice data in real time, converts the voice data into text data, converts the text data into sign language steps to generate sign language video, and finally transmits and displays the sign language video on a display device. This allows the user's emotions to be reflected in the sign language video, enabling more expressive sign language interpretation.

[0179] Overview of program processing

[0180] Collecting voice input

[0181] When a user speaks into the microphone, the device collects the voice, using appropriate noise-canceling technology to obtain clear voice data and store it in digital form. The collected voice data is then sent to a server for real-time processing.

[0182] Analysis of audio data

[0183] The server receives the transmitted voice data in real time and converts it into text data using a voice recognition engine. The converted text data is temporarily stored in memory.

[0184] emotion recognition

[0185] The server uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the voice data. The emotion recognition engine determines the user's emotional state from the user's facial expressions, tone of voice, speech patterns, etc., and reflects this emotional data in the sign language translation process.

[0186] Sign language translation of text data

[0187] The server sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The algorithm uses emotional data as input to adjust the emphasis, rhythm, and expression of sign language gestures and movements.

[0188] Sign language video generation

[0189] The server generates a sign language video based on the sign language steps. During this process, image generation AI and a 3D modeling engine are used to visually represent the sign language gestures. The sign language video is dynamically adjusted based on the emotion data, creating a video that reflects the user's emotions.

[0190] Video data compression and transmission

[0191] The server compresses the generated sign language video data and transmits it to the terminal. The compression uses an appropriate codec to enable efficient transmission.

[0192] Receiving and displaying sign language video

[0193] The device receives and decodes the transmitted sign language video data in real time. The decoded sign language video is then projected onto a display device such as a monitor or smart glasses, allowing the user to view the sign language video in real time, reflecting their emotions.

[0194] Specific examples

[0195] Use in conference rooms

[0196] When User A gives a presentation in a conference room, the device collects User A's audio and video data and sends it to the server. The server converts the audio data into text data and analyzes User A's emotions using an emotion recognition engine. It then converts the text into sign language steps and generates a sign language video. This sign language video is displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can watch the sign language video and understand User A's presentation content and emotions in real time.

[0197] Personal use

[0198] When User X is talking with a friend at a cafe, the device collects User X's voice and video data and sends it to a server. The server converts the voice data into text and uses an emotion recognition engine to analyze the video data and voice intonation. It then converts the text into sign language steps and generates a sign language video. This sign language video is projected onto the smart glasses, allowing User Y to understand the content of the conversation, including the emotions of his friend User X, in real time through the sign language video.

[0199] In this way, the system of the present invention provides real-time sign language interpretation that reflects the user's emotions, enabling hearing-impaired people to obtain information with a richer range of expression.

[0200] The processing flow will be explained below.

[0201] Specific processing steps of the program

[0202] Step 1: Collecting voice input

[0203] The user speaks into the microphone. The device collects the user's voice through a built-in or external microphone. Appropriate noise cancellation technology is used to collect the voice, ensuring clear voice data. The collected voice data is stored in digital format.

[0204] Step 2: Sending audio data

[0205] The collected voice data is sent to a server via the Internet in streaming format to maintain real-time performance.

[0206] Step 3: Receiving audio data

[0207] The server receives the transmitted audio data in real time. The received audio data is stored in a buffer. Buffering allows for temporary storage and sequential processing of audio data.

[0208] Step 4: Audio analysis

[0209] The server invokes the speech recognition engine and converts the voice data stored in the buffer into text data, which is then temporarily stored in memory.

[0210] Step 5: Emotion Recognition

[0211] The server uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the voice data. The emotion recognition engine determines the user's emotional state from the user's facial expressions, tone of voice, speech patterns, etc., and reflects this emotional data in the sign language translation process.

[0212] Step 6: Translating text data into sign language

[0213] The server sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The algorithm uses emotional data as input to adjust the emphasis, rhythm, and expression of sign language gestures and movements.

[0214] Step 7: Generate sign language video

[0215] The server generates sign language video based on the sign language steps. It uses image generation AI and a 3D modeling engine to generate 3D models and animations to visually represent each sign gesture. The sign language video is dynamically adjusted based on emotion data, creating a video that reflects the user's emotions.

[0216] Step 8: Compress the video data

[0217] The server compresses the generated sign language video data to ensure efficient use of bandwidth, using an appropriate codec to maintain data quality.

[0218] Step 9: Sending sign language video

[0219] The server sends the compressed sign language video data to the terminal via the Internet. The data is sent in real time using an appropriate protocol.

[0220] Step 10: Receiving sign language video

[0221] The terminal receives the transmitted sign language video data in real time, decodes the received video data, and prepares it for playback.

[0222] Step 11: Displaying the sign language video

[0223] The device projects the sign language image onto a display device such as a monitor or smart glasses. The frame rate and resolution of the image are adjusted to ensure smooth playback. The user can view the sign language image on the display device and understand the information.

[0224] Specific examples

[0225] Example 1: Use in a conference room

[0226] When User A gives a presentation in a conference room, the device collects User A's audio and video data and sends it to the server. The server converts the audio data into text data and analyzes User A's emotions using an emotion recognition engine. It then converts the text into sign language steps and generates a sign language video. This sign language video is displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can watch the sign language video and understand User A's presentation content and emotions in real time.

[0227] Example 2: Personal use

[0228] When User X is having a conversation with a friend at a cafe, the device collects User X's voice and video data and sends it to the server. The server converts the voice data into text and uses an emotion recognition engine to analyze the video data and voice intonation. It then converts the text into sign language steps and generates a sign language video. This sign language video is projected onto the smart glasses, allowing User Y to understand the content of the conversation, including the emotions of his friend User X, through the sign language video in real time.

[0229] As described above, the system of the present invention provides real-time sign language interpretation that reflects the user's emotions, enabling hearing-impaired people to obtain information with a richer range of expressions.

[0230] Example 2

[0231] 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."

[0232] In today's communication environment, it is extremely difficult for the hearing impaired to understand audio information in real time. In particular, information containing emotional nuances in audio cannot be fully conveyed by conventional sign language interpretation systems. For this reason, there is a demand for a system that can convert audio information, including emotions, into sign language images that can be understood by the hearing impaired in real time.

[0233] 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.

[0234] In this invention, the server includes means for collecting voice data, means for converting the voice data into text data, means for analyzing the text data to generate emotion data, means for converting the text data and emotion data into sign language steps, means for generating a sign language video based on the sign language steps and emotion data, and means for transmitting the sign language video to a display device and displaying it. This converts the voice information, including emotions, into a sign language video, enabling hearing-impaired people to understand the voice information and its emotional nuances in real time.

[0235] "Audio data" refers to data that is a digital recording of a user's speech or other sounds.

[0236] A "means" is a device, method, or technique used to achieve a particular purpose.

[0237] "Text data" is digital data that has been converted from voice data into character information.

[0238] "Emotion data" is digital data that indicates the emotional state of the user analyzed from the user's voice and video.

[0239] A "sign language step" is a group of instructions or commands of sign language actions generated based on text data and emotion data.

[0240] "Sign language video" is video data that is visually expressed based on sign language steps.

[0241] "Display device" refers to a device for displaying sign language images, including monitors and smart glasses.

[0242] "Real time" refers to a state in which the processing from collecting audio data to generating and displaying sign language images is carried out without delay.

[0243] This invention is a system that collects voice data in real time, converts the voice data into text data, converts the text data into sign language steps to generate sign language video, and finally transmits the sign language video to a display device for display. This system incorporates an emotion recognition engine, which allows the user's emotions to be reflected in the sign language video, thereby achieving more expressive sign language interpretation.

[0244] System Configuration

[0245] This system mainly consists of the following components:

[0246] 1. Terminal: Collects audio and video data and sends it to the server.

[0247] 2. Server: Converts voice data into text data, analyzes emotion data, and generates sign language steps and sign language images.

[0248] 3. Display device: Displays the generated sign language image.

[0249] Hardware and Software

[0250] Devices: High-performance microphones (e.g., Bose QuietComfort or Apple AirPods Pro) are used to collect audio data, and high-resolution cameras (e.g., Logitech HD Pro Webcam) are used to collect video data. Noise-cancelling technology reduces background noise.

[0251] Server: We use high-performance cloud servers (e.g., Amazon Web Services and Microsoft Azure) for data processing, Google Cloud Speech-to-Text for speech recognition, and Microsoft Azure Emotion API for emotion recognition.

[0252] Display devices: Use monitors or smart glasses (e.g., Microsoft HoloLens or Google Glass) to display sign language images.

[0253] Specific examples

[0254] Use in conference rooms

[0255] When User A gives a presentation in a conference room, the device collects User A's audio and video data and sends it to the server in real time. The server converts the audio data into text data and analyzes User A's emotions using an emotion recognition engine. The server then converts the text data into sign language steps and generates a sign language video. This sign language video is displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can watch the sign language video and understand User A's presentation content and emotions in real time.

[0256] Personal use

[0257] When User X is talking with a friend at a cafe, the device collects User X's voice and video data and sends it to the server in real time. The server converts the voice data into text data and uses an emotion recognition engine to analyze the video data and voice intonation. The server then converts the text data into sign language steps and generates a sign language video. This sign language video is projected onto the smart glasses, allowing User Y to understand the content of the conversation, including the emotions of his friend User X, through the sign language video in real time.

[0258] Prompt Sentence Examples

[0259] "Please explain the process steps of your system to convert speech during a presentation into text and then convert it into emotionally relevant sign language video."

[0260] The above configuration enables real-time sign language interpretation that reflects the user's emotions, allowing hearing-impaired people to obtain information with a richer range of expressions.

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

[0262] Step 1:

[0263] When a user speaks into the microphone, the device collects voice data. The input is the user's voice, and the device uses the microphone to record this voice in digital format (e.g., WAV format). The device uses noise-canceling technology to reduce background noise and save clear voice data. The output is the collected voice data, which is sent to the server in real time.

[0264] Step 2:

[0265] The server runs the received voice data through a speech recognition engine. The input is voice data, and the server converts the voice data into text data using speech recognition software such as Google Cloud Speech-to-Text or IBM Watson Speech to Text. The speech recognition engine converts the voice into text using an acoustic model and a language model. The output is text data, which is temporarily stored in the server's memory.

[0266] Step 3:

[0267] The server analyzes the text data, as well as the user's video data and voice intonation. The inputs are the text data, the user's video data, and the voice intonation. The server uses an emotion recognition engine (for example, Microsoft Azure Emotion API) to determine the user's emotion. The analysis includes factors such as facial expression recognition, voice tone, and speech pattern. The output is emotion data that indicates the user's emotional state.

[0268] Step 4:

[0269] The server converts text data, including emotion data, into sign language steps. The input is text data and emotion data. The server uses a sign language translation algorithm such as SignAll to generate sign language steps from the text data and emotion data. The strength and speed of the gestures are adjusted based on the emotion data. The output is sign language steps. These sign language steps are expressed as action instructions or commands.

[0270] Step 5:

[0271] The server generates a sign language video based on the sign language steps. The input is the sign language steps and emotion data. The server uses image generation AI or a 3D modeling engine such as Unity 3D or Blender to create a sign language video that visually expresses the sign language steps. The character's facial expressions and movements are dynamically adjusted based on the emotion data. The output is the generated sign language video data.

[0272] Step 6:

[0273] The server compresses the generated sign language video data. The input is sign language video data, and the server efficiently compresses the video using the H.264 codec. The frame rate and bit rate are adjusted to reduce the amount of data while maintaining the video quality. The output is compressed sign language video data. After compression, it is sent to the terminal.

[0274] Step 7:

[0275] The device receives the transmitted sign language video data and decompresses it in real time. The input is compressed sign language video data, and the device decompresses the data using a decoding engine such as VLC Media Player or FFmpeg. The decoded sign language video is projected onto a display device such as a monitor or smart glasses. The output is a visually expressed sign language video, allowing the user to check the sign language video reflecting emotions in real time.

[0276] (Application example 2)

[0277] 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."

[0278] Conventional sign language translation systems are limited to converting audio data into text data and generating sign language images, which makes it difficult to reflect the user's emotions and nuances in the sign language. Furthermore, in noisy environments such as factories, audio communication is difficult, making it difficult to convey appropriate instructions to hearing-impaired employees in real time.

[0279] The specification process by the specification 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 collecting voice data, means for converting the voice data into text data, means for converting the text data into sign language steps, means for generating a sign language gesture video, means for determining the user's emotions using an emotion recognition engine, and means for reflecting the emotion data in the sign language translation process. This makes it possible to generate expressive sign language videos that incorporate the user's emotions and nuances in real time, and to convey appropriate and clear instructions to hearing-impaired employees, especially in noisy environments such as factories.

[0280] "Voice data" is data that represents human voice in digital form.

[0281] "Text data" is character information obtained by analyzing voice data.

[0282] A "sign language step" is a series of instructions for creating sign language gestures based on text data.

[0283] "Sign language video" is video data showing visual sign language expressions generated based on sign language steps.

[0284] "Display device" refers to hardware for visually displaying sign language images, including monitors and smart glasses.

[0285] A "server" is a computer system that analyzes audio data, generates sign language steps, and generates sign language images.

[0286] An "emotion recognition engine" is software that analyzes and determines a user's emotions from audio and video data.

[0287] "Emotion data" is user emotion information obtained as a result of analysis by the emotion recognition engine.

[0288] The "sign language translation process" is a series of processes that generate sign language steps and sign language images based on text data and emotion data.

[0289] A "visual projection device" is a device for displaying sign language images in real time, and includes smart glasses and in-factory displays.

[0290] This invention combines an emotion recognition engine with a system that collects voice data, converts the voice data into text data, converts the text data into sign language steps to generate sign language images, and finally transmits and displays the sign language images on a display device. This allows the user's emotions to be reflected in the sign language images, enabling real-time communication, particularly in environments such as factories.

[0291] The system operates in the following manner:

[0292] First, the device collects the user's voice data. It uses noise-canceling technology to capture clear voice data, stores it digitally, and transmits it to a server in real time.

[0293] The server then receives the voice data and converts it into text data using a speech recognition engine, which is then temporarily stored in memory.

[0294] The server then uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the audio data. The emotion recognition engine determines the user's emotional state from the user's facial expressions, tone of voice, speech patterns, etc., and reflects this emotional data in the sign language translation process.

[0295] The server then sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The algorithm uses the emotional data as input to adjust the emphasis, rhythm, and expression of the sign language gestures and movements.

[0296] The server then generates a sign language video based on the sign language steps. This process uses image generation AI and a 3D modeling engine to visually represent the sign language gestures. The sign language video is dynamically adjusted based on the emotion data, creating a video that reflects the user's emotions.

[0297] The generated sign language video data is compressed by the server and sent to the terminal using an appropriate codec (e.g., H.264) to enable efficient transmission.

[0298] Finally, the device receives and decodes the transmitted sign language video data in real time. The decoded sign language video is then projected onto a display device such as a monitor or smart glasses, allowing the user to view the sign language video in real time, reflecting their emotions.

[0299] As a concrete example, the system can be used in factories where verbal communication is difficult, allowing hearing-impaired employees to receive visual instructions in real time, significantly improving work efficiency and reducing errors.

[0300] Example prompt sentence:

[0301] "You will be asked to create a program that will collect the audio and video of the user speaking into a microphone and convert it into sign language video in real time. The sign language video will need to reflect the user's emotions using an emotion recognition engine. This sign language video will then be sent to a display device in the factory in real time and displayed."

[0302] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0303] Step 1:

[0304] Audio data collection

[0305] The device collects the user's voice data. Specifically, when the user speaks into the microphone, the device uses noise-canceling technology to capture clear voice data. The input is the user's voice, and the output is digital voice data. This voice data is sent to the server in real time.

[0306] Step 2:

[0307] Converting audio data to text

[0308] The server receives the voice data and converts it into text data using a speech recognition engine. The input is digital voice data, and the output is text data as character information. The server temporarily stores this text data in memory.

[0309] Step 3:

[0310] emotion recognition

[0311] The server uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the voice data. The input is the user's facial expression and tone of voice, and the output is emotion data that indicates the user's emotional state. The server acquires this emotion data and reflects it in the sign language translation process.

[0312] Step 4:

[0313] Step-by-step conversion of text data into sign language

[0314] The server sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The input is text data and emotional data, and the output is sign language steps, which are a series of sign language gesture instructions. The sign language translation algorithm adjusts the emphasis, rhythm, and expression of the sign language gestures based on the emotional data.

[0315] Step 5:

[0316] Sign language video generation

[0317] The server generates a sign language video based on the sign language steps. This process uses image generation AI and a 3D modeling engine. The input is the sign language steps, and the output is a visual sign language video. The generated sign language video reflects the user's emotions.

[0318] Step 6:

[0319] Video data compression

[0320] The server compresses the generated sign language video data. The input is the sign language video and the output is the compressed video data. The compression uses an appropriate codec (e.g., H.264) to enable efficient data transmission.

[0321] Step 7:

[0322] Sign language video transmission and display

[0323] The device receives and decodes the transmitted sign language video data in real time. The input is compressed video data, and the output is decoded sign language video. The device then projects this sign language video onto a display device such as a monitor or smart glasses. The user can view the sign language video that reflects emotions in real time.

[0324] 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.

[0325] 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.

[0326] 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.

[0327] [Second embodiment]

[0328] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0329] 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.

[0330] 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).

[0331] 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.

[0332] 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.

[0333] 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).

[0334] 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.

[0335] 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.

[0336] 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.

[0337] 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.

[0338] 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.

[0339] 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."

[0340] The present invention is a system that collects voice data in real time, converts the voice data into text data, further converts the text data into sign language steps to generate sign language video, and finally transmits the sign language video to a display device for display.

[0341] Overview of program processing

[0342] Collecting voice input

[0343] The user speaks into a microphone and the device collects the audio. Specifically, the audio data is stored digitally through the microphone and then sent to a server in real time. Appropriate noise-canceling technology is used to collect the audio, improving analysis accuracy.

[0344] Audio analysis

[0345] The server analyzes the received voice data using a speech recognition engine and converts it into text data. In this process, speech is converted to text using a speech recognition engine (for example, a general cloud-based speech recognition service). The converted text data is temporarily stored in memory.

[0346] Sign language translation of text data

[0347] The server sends this text data to a sign language translation algorithm, which converts it into sign language steps. The sign language translation algorithm analyzes the grammar and context of the text data and converts it into appropriate sign language expressions. When doing so, it takes into account the context and selects the most appropriate sign language gesture for any ambiguous parts.

[0348] Sign language video generation

[0349] The server generates sign language videos based on the sign language steps. Specifically, it uses 3D modeling technology and realistic animation to visually represent each sign gesture. In the process of generating the sign language videos, it seamlessly connects consecutive movements to naturally express the flow and rhythm of sign language.

[0350] Video transmission and display

[0351] The server transmits the generated sign language video to the device in real time. The device decodes the received sign language video and displays it on a display device such as a monitor or smart glasses at an appropriate frame rate and resolution. The user can see the sign language interpreter in real time through the display.

[0352] Specific examples

[0353] Use in conference rooms

[0354] When User A gives a presentation in a conference room, the terminal (the conference room's microphone and computer system) collects User A's speech and sends it to the server. The server converts the speech data into text data, and then converts the text data into sign language steps. The server then generates a sign language video and displays it on a monitor in the conference room. User B, who is hearing impaired, can understand what User A is saying in real time by watching the sign language video displayed on the monitor.

[0355] Personal use

[0356] When User X starts a conversation with a friend at a cafe, the device (smartphone) collects User X's voice and sends it to the server. The server converts the voice data into text data and then converts it into sign language steps. The server then generates a sign language video that is displayed on the smartphone or projected onto smart glasses. User Y, a friend with a hearing impairment, can understand the content of the conversation in real time by viewing the sign language video through the smart glasses.

[0357] In this way, the system of the present invention provides real-time sign language interpretation in a variety of situations, enabling the hearing impaired to obtain information quickly and accurately.

[0358] The processing flow will be explained below.

[0359] Specific processing steps of the program

[0360] Step 1: Collecting voice input

[0361] The user speaks into the microphone. The device collects the user's voice through a built-in or external microphone. Appropriate noise cancellation technology is used to collect the voice, ensuring clear voice data. The collected voice data is stored in digital format.

[0362] Step 2: Sending audio data

[0363] The collected voice data is sent to a server via the Internet in streaming format to maintain real-time performance.

[0364] Step 3: Receiving audio data

[0365] The server receives the transmitted audio data in real time. The received audio data is stored in a buffer. Buffering allows for temporary storage and sequential processing of audio data.

[0366] Step 4: Audio analysis

[0367] The server calls a speech recognition engine (e.g., a cloud-based speech recognition service) and converts the voice data stored in the buffer into text data, which is then temporarily stored in memory.

[0368] Step 5: Translating text data into sign language

[0369] The server sends the stored text data to a sign language translation algorithm, which analyzes the grammar and context of the text data and converts it into appropriate sign language steps. A context analysis algorithm is also used to deepen context understanding.

[0370] Step 6: Generate sign language video

[0371] The server generates sign language video based on the sign language steps. It uses image generation AI and a 3D modeling engine to generate 3D models and animations to visually represent each sign gesture. The generated sign language video is then seamlessly displayed as a series of movements.

[0372] Step 7: Compress the video data

[0373] The server compresses the generated sign language video data to ensure efficient use of bandwidth, using an appropriate codec to maintain data quality.

[0374] Step 8: Sending sign language video

[0375] The server sends the compressed sign language video data to the device via the Internet. The data is sent using an appropriate protocol (e.g., WebRTC) to maintain real-time transmission.

[0376] Step 9: Receiving sign language video

[0377] The terminal receives the transmitted sign language video data in real time, decodes the received video data, and prepares it for playback.

[0378] Step 10: Displaying the sign language video

[0379] The device projects the sign language image onto a display device such as a monitor or smart glasses. The frame rate and resolution of the image are adjusted to ensure smooth playback. The user can view the sign language image on the display device and understand the information.

[0380] The above steps will enable hearing-impaired people to obtain information in real time in a variety of situations, such as lectures and small group work.

[0381] Example 1

[0382] 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."

[0383] The present invention relates to a system that collects voice data in real time, converts the voice data into text data, then converts the text data into sign language steps to generate a sign language video, and finally transmits and displays the sign language video on a display device. Conventional voice recognition and sign language translation technologies have issues with real-time performance and accuracy, making it difficult for hearing-impaired users to obtain information in real time.

[0384] 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.

[0385] In this invention, the server includes means for applying noise canceling technology to process voice data in real time, means for analyzing text data with a sign language translation algorithm, and means for generating realistic sign language images using 3D modeling technology, thereby enabling highly accurate collection and processing of voice data and real-time generation and display of sign language images.

[0386] "Voice data" is a digital representation of voice, and is data collected from a user's speech via a microphone or the like.

[0387] "Text data" is character string data converted from voice data using voice recognition technology, and is data in a format that can be read by humans.

[0388] "Sign language steps" are a series of instructions and commands that express each step of sign language movement based on text data, and are intermediate data for generating sign language images.

[0389] "Sign language video" is a visual sign language expression generated based on sign language steps, and is video data expressed as animation using 3D modeling technology, etc.

[0390] A "display device" is a device that visually presents sign language images to users, and examples include monitors and smart glasses.

[0391] "Real-time processing" refers to a process in which the entire process, from data collection to display, is carried out in an extremely short time with almost no delay.

[0392] "Noise canceling technology" is a technology that suppresses background noise when collecting voice data, with the aim of clearly capturing only the user's speech.

[0393] A "sign language translation algorithm" is a formula or procedure for converting text data into sign language steps, and applies natural language processing technology.

[0394] "3D modeling technology" is a technique for creating visual objects using three-dimensional computer graphics, and is used to realistically represent each movement in sign language videos.

[0395] The present invention is a system that collects voice data in real time, converts the voice data into text data, further converts the text data into sign language steps to generate sign language video, and finally transmits the sign language video to a display device for display.

[0396] Overall system overview

[0397] This system consists of a user, a terminal, and a server. The user speaks into a microphone, and the terminal collects the voice data. The terminal sends the collected voice data to the server in real time. The server analyzes the voice data and converts it into text data, which is then converted into sign language steps. The server then generates a sign language video based on the sign language steps and sends the generated sign language video to the terminal in real time. The terminal decodes the sign language video and displays it on a display device, allowing the user to view the sign language video.

[0398] Specific examples of hardware and software used

[0399] Microphone: Collects user utterances.

[0400] Terminal: A communication device such as a personal computer or smartphone.

[0401] Server: Responsible for data processing and storage. Uses a speech recognition engine (e.g., a cloud-based speech recognition service).

[0402] Display devices: monitors, smart glasses, etc.

[0403] Specific examples of software include:

[0404] Speech recognition engine: Google Cloud Speech-to-Text API, etc.

[0405] Noise cancelling technologies: NVIDIA RTX Voice, Dolby Voice, etc.

[0406] Sign language translation algorithms: BERT and GPT models using natural language processing (NLP) techniques.

[0407] 3D modeling software: Blender and Unity are used to generate sign language videos.

[0408] Example of system operation

[0409] Example of use in a conference room

[0410] When User A gives a presentation in a conference room, a terminal (a microphone and computer system located in the conference room) collects User A's speech and converts it into digital audio data. This data is sent to a server in real time. The server receives the speech data and converts it into text data using a speech recognition engine. The converted text data is then converted into sign language steps using a sign language translation algorithm, and a sign language video is generated using 3D modeling software. The generated sign language video is sent to the terminal and displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can view the sign language video on the monitor and understand what User A is saying in real time.

[0411] Examples of personal use

[0412] When User X is talking with a friend at a cafe, the device (smartphone) collects User X's speech and sends it to a server in real time. The server receives the voice data and converts it into text data using a cloud-based speech recognition engine. The converted text data is converted into sign language steps using a sign language translation algorithm, and a sign language video is generated using 3D modeling software based on the sign language steps. The generated sign language video is sent to the smartphone and projected onto the smartphone screen or smart glasses. User Y, the friend who is hearing impaired, can view the sign language video through the smart glasses and understand the content of the conversation in real time.

[0413] Example prompts for generative AI models

[0414] "Collect audio data in real time as the user speaks into a microphone, and convert that audio data into text data. Next, convert the text data into sign language steps, and generate a sign language video using 3D modeling software. Then, send the generated sign language video to a display device and display it."

[0415] This system provides real-time sign language interpretation in a variety of situations, enabling the hearing impaired to obtain information quickly and accurately.

[0416] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0417] Step 1:

[0418] The user speaks into the microphone. Voice data (analog signal) is captured by the microphone. This data is input into the device's voice collection module. The voice collection module converts the voice signal into digital form and applies noise cancellation technology. Specifically, the user's speech is collected by the microphone, and the device's voice input device (e.g., built-in microphone or external microphone) converts the analog signal into digital data, which is then subjected to noise cancellation processing. The output is digitized, noise-removed voice data.

[0419] Step 2:

[0420] The terminal transmits digital audio data to the server in real time. The audio data is buffered by the terminal's communication module and divided into packets of a certain size before being transmitted. Specifically, the terminal's communication protocol (e.g., TCP / IP) divides the audio data into packets and transmits them to the server via the network. The output is the packets of digital audio data transmitted to the server.

[0421] Step 3:

[0422] The server passes the received voice data to a voice recognition engine, which converts it into text data. In this process, the voice recognition engine (for example, Google Cloud Speech-to-Text) analyzes the voice signal, breaks it down into phonemes and phrases, and generates text data as a string of characters. Specifically, the voice recognition engine analyzes the voice waveform data and converts it into corresponding text data. The input is digital voice data, and the output is converted text data.

[0423] Step 4:

[0424] The server sends the text data to a sign language translation algorithm, which converts it into sign language steps. The sign language translation algorithm uses natural language processing (NLP) to analyze the grammar and context of the text and convert it into appropriate sign language expressions. Specifically, an NLP model (e.g., BERT) analyzes the context and determines the sign language steps for each word or phrase by referring to a sign language dictionary. The input is text data, and the output is sign language step data.

[0425] Step 5:

[0426] The server generates sign language video based on the sign language steps. Each sign gesture is animated using 3D modeling software (e.g., Blender or Unity). Specifically, the sign language steps are converted into 3D character movements, and keyframe technology and smooth transitions are used to generate realistic movements. The input is sign language step data, and the output is a 3D animated video of the sign language.

[0427] Step 6:

[0428] The server transmits the generated sign language video to the terminal in real time. The video data is encoded in real time and transmitted to the terminal. Specifically, the server's video encoding module compresses the sign language video in real time and transmits it to the terminal via the Internet. The output is sign language video data transmitted in real time.

[0429] Step 7:

[0430] The device decodes the received sign language video and displays it on a display device at an appropriate frame rate and resolution. The display device can be a monitor or smart glasses, through which the user can view the sign language interpreter in real time. Specifically, the device's decoding library (e.g., FFmpeg) decodes the sign language video and outputs it to the display device. The input is the received sign language video data, and the output is the sign language video displayed on the display device.

[0431] (Application example 1)

[0432] 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."

[0433] Conventional conversation and translation systems have had the problem of preventing hearing-impaired people from communicating smoothly with other people. In particular, in public places such as brick-and-mortar stores, there is a lack of appropriate sign language interpretation services when hearing-impaired people communicate with store staff and other customers. This has made it difficult for hearing-impaired people to understand conversations in real time, resulting in reduced communication efficiency.

[0434] 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.

[0435] In this invention, the server includes means for collecting voice data, means for converting the voice data into text data, means for converting the text data into sign language steps, means for generating a sign language video based on the sign language steps, means for transmitting the sign language video to a display device and displaying it, means for applying noise canceling technology to the means for collecting voice data, and means for displaying the sign language video on a display of smart glasses, thereby enabling a hearing-impaired person to understand the content of a conversation as a sign language video in real time.

[0436] "Audio data" refers to sound signals collected by an audio input device.

[0437] "Text data" refers to data that is generated by analyzing voice data and expressing it as text information.

[0438] "Sign language steps" refer to the steps for determining sign language actions based on text data.

[0439] "Sign language video" refers to a moving image display of information generated based on sign language steps.

[0440] "Display device" means a device for visually displaying sign language images. Examples include monitors and smart glasses.

[0441] "Noise canceling technology" refers to technology that reduces ambient noise when collecting audio.

[0442] "Smart glasses" refers to a device in the form of glasses that includes a display and has the ability to visually display information.

[0443] A "server" is a computer system for processing data, transforming data, and providing necessary services.

[0444] "Real-time" is a term that refers to near-simultaneous processing and display.

[0445] System Overview

[0446] This invention is composed of a comprehensive system including various voice input devices, a server, and a display device. Voice data is collected in real time, converted into text data, and then converted into sign language steps to generate sign language images, which are then sent to a display device for display.

[0447] Specific configuration

[0448] 1. Collecting voice input

[0449] The user speaks, and the audio is collected by a microphone in the device. The device then uses appropriate noise-canceling technology to reduce ambient noise and obtain clear audio data. For example, the audio of a staff member in a physical store where a conversation takes place is collected by a microphone built into smart glasses.

[0450] 2. Audio analysis

[0451] The collected voice data is sent to a server in real time and converted into text data using a voice recognition engine (e.g., Google Cloud Speech-to-Text API).

[0452] 3. Sign Language Translation of Text Data

[0453] The server converts this text data into sign language steps using a sign language translation algorithm (e.g., a sign language translation system using a deep learning model). The text data is analyzed for grammar and context, and converted into appropriate sign language gestures.

[0454] 4. Sign Language Video Generation

[0455] The server generates sign language videos based on the sign language steps, using 3D character modeling software (e.g., Blender or Unity) to visually represent realistic sign language gestures.

[0456] 5. Video transmission and display

[0457] The generated sign language image is sent in real time to a display device, specifically a smart glasses display. For example, a hearing-impaired person wearing the smart glasses can understand what a store clerk is saying through the sign language image.

[0458] Specific examples

[0459] Use in physical stores

[0460] When a hearing-impaired person visits a store, the smart glasses collect what the staff say and send it to the server. The server converts the voice data into text data and then converts it into sign language steps. A sign language video is then generated based on the sign language steps and sent to the smart glasses to be displayed in real time.

[0461] Prompt Sentence Examples

[0462] Developed a system that enables hearing-impaired people to understand what store clerks are saying through smart glasses. Explain the application that combines speech recognition and sign language translation.

[0463] ---

[0464] In this way, by specifically explaining the mode for carrying out the invention, a system is provided that enables hearing-impaired people to communicate smoothly in social situations.

[0465] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0466] Step 1: Collecting voice input

[0467] When a user speaks, the audio is picked up by the device's microphone. The device then uses noise-canceling technology to reduce ambient noise and capture clear audio data. To achieve this, the microphone captures the audio data in digital form and temporarily stores it in the device's memory.

[0468] Step 2: Sending audio data

[0469] The device transmits the collected voice data to a server in real time using data streaming technology over the Internet, where the server receives the voice data and temporarily stores it in memory.

[0470] Step 3: Audio analysis

[0471] The server analyzes the received voice data using a speech recognition engine (for example, Google Cloud Speech-to-Text API) and converts it into text data. To analyze the voice data, the server inputs the voice data into the speech recognition engine and obtains text data as output. The converted text data is temporarily stored in the server's memory.

[0472] Step 4: Sign Language Translation

[0473] The server sends the text data to a sign language translation algorithm (e.g., a sign language translation system using a deep learning model) and converts it into sign language steps. For this process, the server inputs the text data and obtains sign language step data. The server temporarily stores these sign language steps in memory.

[0474] Step 5: Generate sign language video

[0475] The server generates sign language video using 3D modeling technology (e.g., Blender or Unity) based on the sign language steps. It uses the sign language steps as input and creates sign language video as output. The generated sign language video is stored in the server's memory.

[0476] Step 6: Sending sign language video

[0477] The server transmits the generated sign language video in real time to the device via the Internet, and the device receives the sign language video and temporarily stores it in its memory.

[0478] Step 7: Displaying the sign language video

[0479] The device receives the sign language video and displays it on the smart glasses' display. It decodes the sign language video and adjusts the frame rate and resolution for display. Specifically, the display software processes the video data so that the user can see the video through the glasses.

[0480] 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.

[0481] This invention combines an emotion recognition engine with a system that collects voice data in real time, converts the voice data into text data, converts the text data into sign language steps to generate sign language video, and finally transmits and displays the sign language video on a display device. This allows the user's emotions to be reflected in the sign language video, enabling more expressive sign language interpretation.

[0482] Overview of program processing

[0483] Collecting voice input

[0484] When a user speaks into the microphone, the device collects the voice, using appropriate noise-canceling technology to obtain clear voice data and store it in digital form. The collected voice data is then sent to a server for real-time processing.

[0485] Analysis of audio data

[0486] The server receives the transmitted voice data in real time and converts it into text data using a voice recognition engine. The converted text data is temporarily stored in memory.

[0487] emotion recognition

[0488] The server uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the voice data. The emotion recognition engine determines the user's emotional state from the user's facial expressions, tone of voice, speech patterns, etc., and reflects this emotional data in the sign language translation process.

[0489] Sign language translation of text data

[0490] The server sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The algorithm uses emotional data as input to adjust the emphasis, rhythm, and expression of sign language gestures and movements.

[0491] Sign language video generation

[0492] The server generates a sign language video based on the sign language steps. During this process, image generation AI and a 3D modeling engine are used to visually represent the sign language gestures. The sign language video is dynamically adjusted based on the emotion data, creating a video that reflects the user's emotions.

[0493] Video data compression and transmission

[0494] The server compresses the generated sign language video data and transmits it to the terminal. The compression uses an appropriate codec to enable efficient transmission.

[0495] Receiving and displaying sign language video

[0496] The device receives and decodes the transmitted sign language video data in real time. The decoded sign language video is then projected onto a display device such as a monitor or smart glasses, allowing the user to view the sign language video in real time, reflecting their emotions.

[0497] Specific examples

[0498] Use in conference rooms

[0499] When User A gives a presentation in a conference room, the device collects User A's audio and video data and sends it to the server. The server converts the audio data into text data and analyzes User A's emotions using an emotion recognition engine. It then converts the text into sign language steps and generates a sign language video. This sign language video is displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can watch the sign language video and understand User A's presentation content and emotions in real time.

[0500] Personal use

[0501] When User X is talking with a friend at a cafe, the device collects User X's voice and video data and sends it to a server. The server converts the voice data into text and uses an emotion recognition engine to analyze the video data and voice intonation. It then converts the text into sign language steps and generates a sign language video. This sign language video is projected onto the smart glasses, allowing User Y to understand the content of the conversation, including the emotions of his friend User X, in real time through the sign language video.

[0502] In this way, the system of the present invention provides real-time sign language interpretation that reflects the user's emotions, enabling hearing-impaired people to obtain information with a richer range of expression.

[0503] The processing flow will be explained below.

[0504] Specific processing steps of the program

[0505] Step 1: Collecting voice input

[0506] The user speaks into the microphone. The device collects the user's voice through a built-in or external microphone. Appropriate noise cancellation technology is used to collect the voice, ensuring clear voice data. The collected voice data is stored in digital format.

[0507] Step 2: Sending audio data

[0508] The collected voice data is sent to a server via the Internet in streaming format to maintain real-time performance.

[0509] Step 3: Receiving audio data

[0510] The server receives the transmitted audio data in real time. The received audio data is stored in a buffer. Buffering allows for temporary storage and sequential processing of audio data.

[0511] Step 4: Audio analysis

[0512] The server invokes the speech recognition engine and converts the voice data stored in the buffer into text data, which is then temporarily stored in memory.

[0513] Step 5: Emotion Recognition

[0514] The server uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the voice data. The emotion recognition engine determines the user's emotional state from the user's facial expressions, tone of voice, speech patterns, etc., and reflects this emotional data in the sign language translation process.

[0515] Step 6: Translating text data into sign language

[0516] The server sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The algorithm uses emotional data as input to adjust the emphasis, rhythm, and expression of sign language gestures and movements.

[0517] Step 7: Generate sign language video

[0518] The server generates sign language video based on the sign language steps. It uses image generation AI and a 3D modeling engine to generate 3D models and animations to visually represent each sign gesture. The sign language video is dynamically adjusted based on emotion data, creating a video that reflects the user's emotions.

[0519] Step 8: Compress the video data

[0520] The server compresses the generated sign language video data to ensure efficient use of bandwidth, using an appropriate codec to maintain data quality.

[0521] Step 9: Sending sign language video

[0522] The server sends the compressed sign language video data to the terminal via the Internet. The data is sent in real time using an appropriate protocol.

[0523] Step 10: Receiving sign language video

[0524] The terminal receives the transmitted sign language video data in real time, decodes the received video data, and prepares it for playback.

[0525] Step 11: Displaying the sign language video

[0526] The device projects the sign language image onto a display device such as a monitor or smart glasses. The frame rate and resolution of the image are adjusted to ensure smooth playback. The user can view the sign language image on the display device and understand the information.

[0527] Specific examples

[0528] Example 1: Use in a conference room

[0529] When User A gives a presentation in a conference room, the device collects User A's audio and video data and sends it to the server. The server converts the audio data into text data and analyzes User A's emotions using an emotion recognition engine. It then converts the text into sign language steps and generates a sign language video. This sign language video is displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can watch the sign language video and understand User A's presentation content and emotions in real time.

[0530] Example 2: Personal use

[0531] When User X is having a conversation with a friend at a cafe, the device collects User X's voice and video data and sends it to the server. The server converts the voice data into text and uses an emotion recognition engine to analyze the video data and voice intonation. It then converts the text into sign language steps and generates a sign language video. This sign language video is projected onto the smart glasses, allowing User Y to understand the content of the conversation, including the emotions of his friend User X, through the sign language video in real time.

[0532] As described above, the system of the present invention provides real-time sign language interpretation that reflects the user's emotions, enabling hearing-impaired people to obtain information with a richer range of expressions.

[0533] Example 2

[0534] 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."

[0535] In today's communication environment, it is extremely difficult for the hearing impaired to understand audio information in real time. In particular, information containing emotional nuances in audio cannot be fully conveyed by conventional sign language interpretation systems. For this reason, there is a demand for a system that can convert audio information, including emotions, into sign language images that can be understood by the hearing impaired in real time.

[0536] 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.

[0537] In this invention, the server includes means for collecting voice data, means for converting the voice data into text data, means for analyzing the text data to generate emotion data, means for converting the text data and emotion data into sign language steps, means for generating a sign language video based on the sign language steps and emotion data, and means for transmitting the sign language video to a display device and displaying it. This converts the voice information, including emotions, into a sign language video, enabling hearing-impaired people to understand the voice information and its emotional nuances in real time.

[0538] "Audio data" refers to data that is a digital recording of a user's speech or other sounds.

[0539] A "means" is a device, method, or technique used to achieve a particular purpose.

[0540] "Text data" is digital data that has been converted from voice data into character information.

[0541] "Emotion data" is digital data that indicates the emotional state of the user analyzed from the user's voice and video.

[0542] A "sign language step" is a group of instructions or commands of sign language actions generated based on text data and emotion data.

[0543] "Sign language video" is video data that is visually expressed based on sign language steps.

[0544] "Display device" refers to a device for displaying sign language images, including monitors and smart glasses.

[0545] "Real time" refers to a state in which the processing from collecting audio data to generating and displaying sign language images is carried out without delay.

[0546] This invention is a system that collects voice data in real time, converts the voice data into text data, converts the text data into sign language steps to generate sign language video, and finally transmits the sign language video to a display device for display. This system incorporates an emotion recognition engine, which allows the user's emotions to be reflected in the sign language video, thereby achieving more expressive sign language interpretation.

[0547] System Configuration

[0548] This system mainly consists of the following components:

[0549] 1. Terminal: Collects audio and video data and sends it to the server.

[0550] 2. Server: Converts voice data into text data, analyzes emotion data, and generates sign language steps and sign language images.

[0551] 3. Display device: Displays the generated sign language image.

[0552] Hardware and Software

[0553] Devices: High-performance microphones (e.g., Bose QuietComfort or Apple AirPods Pro) are used to collect audio data, and high-resolution cameras (e.g., Logitech HD Pro Webcam) are used to collect video data. Noise-cancelling technology reduces background noise.

[0554] Server: We use high-performance cloud servers (e.g., Amazon Web Services and Microsoft Azure) for data processing, Google Cloud Speech-to-Text for speech recognition, and Microsoft Azure Emotion API for emotion recognition.

[0555] Display devices: Use monitors or smart glasses (e.g., Microsoft HoloLens or Google Glass) to display sign language images.

[0556] Specific examples

[0557] Use in conference rooms

[0558] When User A gives a presentation in a conference room, the device collects User A's audio and video data and sends it to the server in real time. The server converts the audio data into text data and analyzes User A's emotions using an emotion recognition engine. The server then converts the text data into sign language steps and generates a sign language video. This sign language video is displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can watch the sign language video and understand User A's presentation content and emotions in real time.

[0559] Personal use

[0560] When User X is talking with a friend at a cafe, the device collects User X's voice and video data and sends it to the server in real time. The server converts the voice data into text data and uses an emotion recognition engine to analyze the video data and voice intonation. The server then converts the text data into sign language steps and generates a sign language video. This sign language video is projected onto the smart glasses, allowing User Y to understand the content of the conversation, including the emotions of his friend User X, through the sign language video in real time.

[0561] Prompt Sentence Examples

[0562] "Please explain the process steps of your system to convert speech during a presentation into text and then convert it into emotionally relevant sign language video."

[0563] The above configuration enables real-time sign language interpretation that reflects the user's emotions, allowing hearing-impaired people to obtain information with a richer range of expressions.

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

[0565] Step 1:

[0566] When a user speaks into the microphone, the device collects voice data. The input is the user's voice, and the device uses the microphone to record this voice in digital format (e.g., WAV format). The device uses noise-canceling technology to reduce background noise and save clear voice data. The output is the collected voice data, which is sent to the server in real time.

[0567] Step 2:

[0568] The server runs the received voice data through a speech recognition engine. The input is voice data, and the server converts the voice data into text data using speech recognition software such as Google Cloud Speech-to-Text or IBM Watson Speech to Text. The speech recognition engine converts the voice into text using an acoustic model and a language model. The output is text data, which is temporarily stored in the server's memory.

[0569] Step 3:

[0570] The server analyzes the text data, as well as the user's video data and voice intonation. The inputs are the text data, the user's video data, and the voice intonation. The server uses an emotion recognition engine (for example, Microsoft Azure Emotion API) to determine the user's emotion. The analysis includes factors such as facial expression recognition, voice tone, and speech pattern. The output is emotion data that indicates the user's emotional state.

[0571] Step 4:

[0572] The server converts text data, including emotion data, into sign language steps. The input is text data and emotion data. The server uses a sign language translation algorithm such as SignAll to generate sign language steps from the text data and emotion data. The strength and speed of the gestures are adjusted based on the emotion data. The output is sign language steps. These sign language steps are expressed as action instructions or commands.

[0573] Step 5:

[0574] The server generates a sign language video based on the sign language steps. The input is the sign language steps and emotion data. The server uses image generation AI or a 3D modeling engine such as Unity 3D or Blender to create a sign language video that visually expresses the sign language steps. The character's facial expressions and movements are dynamically adjusted based on the emotion data. The output is the generated sign language video data.

[0575] Step 6:

[0576] The server compresses the generated sign language video data. The input is sign language video data, and the server efficiently compresses the video using the H.264 codec. The frame rate and bit rate are adjusted to reduce the amount of data while maintaining the video quality. The output is compressed sign language video data. After compression, it is sent to the terminal.

[0577] Step 7:

[0578] The device receives the transmitted sign language video data and decompresses it in real time. The input is compressed sign language video data, and the device decompresses the data using a decoding engine such as VLC Media Player or FFmpeg. The decoded sign language video is projected onto a display device such as a monitor or smart glasses. The output is a visually expressed sign language video, allowing the user to check the sign language video reflecting emotions in real time.

[0579] (Application example 2)

[0580] 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."

[0581] Conventional sign language translation systems are limited to converting audio data into text data and generating sign language images, which makes it difficult to reflect the user's emotions and nuances in the sign language. Furthermore, in noisy environments such as factories, audio communication is difficult, making it difficult to convey appropriate instructions to hearing-impaired employees in real time.

[0582] The specification process by the specification 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 collecting voice data, means for converting the voice data into text data, means for converting the text data into sign language steps, means for generating a sign language gesture video, means for determining the user's emotions using an emotion recognition engine, and means for reflecting the emotion data in the sign language translation process. This makes it possible to generate expressive sign language videos that incorporate the user's emotions and nuances in real time, and to convey appropriate and clear instructions to hearing-impaired employees, especially in noisy environments such as factories.

[0583] "Voice data" is data that represents human voice in digital form.

[0584] "Text data" is character information obtained by analyzing voice data.

[0585] A "sign language step" is a series of instructions for creating sign language gestures based on text data.

[0586] "Sign language video" is video data showing visual sign language expressions generated based on sign language steps.

[0587] "Display device" refers to hardware for visually displaying sign language images, including monitors and smart glasses.

[0588] A "server" is a computer system that analyzes audio data, generates sign language steps, and generates sign language images.

[0589] An "emotion recognition engine" is software that analyzes and determines a user's emotions from audio and video data.

[0590] "Emotion data" is user emotion information obtained as a result of analysis by the emotion recognition engine.

[0591] The "sign language translation process" is a series of processes that generate sign language steps and sign language images based on text data and emotion data.

[0592] A "visual projection device" is a device for displaying sign language images in real time, and includes smart glasses and in-factory displays.

[0593] This invention combines an emotion recognition engine with a system that collects voice data, converts the voice data into text data, converts the text data into sign language steps to generate sign language images, and finally transmits and displays the sign language images on a display device. This allows the user's emotions to be reflected in the sign language images, enabling real-time communication, particularly in environments such as factories.

[0594] The system operates in the following manner:

[0595] First, the device collects the user's voice data. It uses noise-canceling technology to capture clear voice data, stores it digitally, and transmits it to a server in real time.

[0596] The server then receives the voice data and converts it into text data using a speech recognition engine, which is then temporarily stored in memory.

[0597] The server then uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the audio data. The emotion recognition engine determines the user's emotional state from the user's facial expressions, tone of voice, speech patterns, etc., and reflects this emotional data in the sign language translation process.

[0598] The server then sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The algorithm uses the emotional data as input to adjust the emphasis, rhythm, and expression of the sign language gestures and movements.

[0599] The server then generates a sign language video based on the sign language steps. This process uses image generation AI and a 3D modeling engine to visually represent the sign language gestures. The sign language video is dynamically adjusted based on the emotion data, creating a video that reflects the user's emotions.

[0600] The generated sign language video data is compressed by the server and sent to the terminal using an appropriate codec (e.g., H.264) to enable efficient transmission.

[0601] Finally, the device receives and decodes the transmitted sign language video data in real time. The decoded sign language video is then projected onto a display device such as a monitor or smart glasses, allowing the user to view the sign language video in real time, reflecting their emotions.

[0602] As a concrete example, the system can be used in factories where verbal communication is difficult, allowing hearing-impaired employees to receive visual instructions in real time, significantly improving work efficiency and reducing errors.

[0603] Example prompt sentence:

[0604] "You will be asked to create a program that will collect the audio and video of the user speaking into a microphone and convert it into sign language video in real time. The sign language video will need to reflect the user's emotions using an emotion recognition engine. This sign language video will then be sent to a display device in the factory in real time and displayed."

[0605] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0606] Step 1:

[0607] Audio data collection

[0608] The device collects the user's voice data. Specifically, when the user speaks into the microphone, the device uses noise-canceling technology to capture clear voice data. The input is the user's voice, and the output is digital voice data. This voice data is sent to the server in real time.

[0609] Step 2:

[0610] Converting audio data to text

[0611] The server receives the voice data and converts it into text data using a speech recognition engine. The input is digital voice data, and the output is text data as character information. The server temporarily stores this text data in memory.

[0612] Step 3:

[0613] emotion recognition

[0614] The server uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the voice data. The input is the user's facial expression and tone of voice, and the output is emotion data that indicates the user's emotional state. The server acquires this emotion data and reflects it in the sign language translation process.

[0615] Step 4:

[0616] Step-by-step conversion of text data into sign language

[0617] The server sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The input is text data and emotional data, and the output is sign language steps, which are a series of sign language gesture instructions. The sign language translation algorithm adjusts the emphasis, rhythm, and expression of the sign language gestures based on the emotional data.

[0618] Step 5:

[0619] Sign language video generation

[0620] The server generates a sign language video based on the sign language steps. This process uses image generation AI and a 3D modeling engine. The input is the sign language steps, and the output is a visual sign language video. The generated sign language video reflects the user's emotions.

[0621] Step 6:

[0622] Video data compression

[0623] The server compresses the generated sign language video data. The input is the sign language video and the output is the compressed video data. The compression uses an appropriate codec (e.g., H.264) to enable efficient data transmission.

[0624] Step 7:

[0625] Sign language video transmission and display

[0626] The device receives and decodes the transmitted sign language video data in real time. The input is compressed video data, and the output is decoded sign language video. The device then projects this sign language video onto a display device such as a monitor or smart glasses. The user can view the sign language video that reflects emotions in real time.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] [Third embodiment]

[0631] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0632] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0633] 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).

[0634] 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.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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."

[0643] The present invention is a system that collects voice data in real time, converts the voice data into text data, further converts the text data into sign language steps to generate sign language video, and finally transmits the sign language video to a display device for display.

[0644] Overview of program processing

[0645] Collecting voice input

[0646] The user speaks into a microphone and the device collects the audio. Specifically, the audio data is stored digitally through the microphone and then sent to a server in real time. Appropriate noise-canceling technology is used to collect the audio, improving analysis accuracy.

[0647] Audio analysis

[0648] The server analyzes the received voice data using a speech recognition engine and converts it into text data. In this process, speech is converted to text using a speech recognition engine (for example, a general cloud-based speech recognition service). The converted text data is temporarily stored in memory.

[0649] Sign language translation of text data

[0650] The server sends this text data to a sign language translation algorithm, which converts it into sign language steps. The sign language translation algorithm analyzes the grammar and context of the text data and converts it into appropriate sign language expressions. When doing so, it takes into account the context and selects the most appropriate sign language gesture for any ambiguous parts.

[0651] Sign language video generation

[0652] The server generates sign language videos based on the sign language steps. Specifically, it uses 3D modeling technology and realistic animation to visually represent each sign gesture. In the process of generating the sign language videos, it seamlessly connects consecutive movements to naturally express the flow and rhythm of sign language.

[0653] Video transmission and display

[0654] The server transmits the generated sign language video to the device in real time. The device decodes the received sign language video and displays it on a display device such as a monitor or smart glasses at an appropriate frame rate and resolution. The user can see the sign language interpreter in real time through the display.

[0655] Specific examples

[0656] Use in conference rooms

[0657] When User A gives a presentation in a conference room, the terminal (the conference room's microphone and computer system) collects User A's speech and sends it to the server. The server converts the speech data into text data, and then converts the text data into sign language steps. The server then generates a sign language video and displays it on a monitor in the conference room. User B, who is hearing impaired, can understand what User A is saying in real time by watching the sign language video displayed on the monitor.

[0658] Personal use

[0659] When User X starts a conversation with a friend at a cafe, the device (smartphone) collects User X's voice and sends it to the server. The server converts the voice data into text data and then converts it into sign language steps. The server then generates a sign language video that is displayed on the smartphone or projected onto smart glasses. User Y, a friend with a hearing impairment, can understand the content of the conversation in real time by viewing the sign language video through the smart glasses.

[0660] In this way, the system of the present invention provides real-time sign language interpretation in a variety of situations, enabling the hearing impaired to obtain information quickly and accurately.

[0661] The processing flow will be explained below.

[0662] Specific processing steps of the program

[0663] Step 1: Collecting voice input

[0664] The user speaks into the microphone. The device collects the user's voice through a built-in or external microphone. Appropriate noise cancellation technology is used to collect the voice, ensuring clear voice data. The collected voice data is stored in digital format.

[0665] Step 2: Sending audio data

[0666] The collected voice data is sent to a server via the Internet in streaming format to maintain real-time performance.

[0667] Step 3: Receiving audio data

[0668] The server receives the transmitted audio data in real time. The received audio data is stored in a buffer. Buffering allows for temporary storage and sequential processing of audio data.

[0669] Step 4: Audio analysis

[0670] The server calls a speech recognition engine (e.g., a cloud-based speech recognition service) and converts the voice data stored in the buffer into text data, which is then temporarily stored in memory.

[0671] Step 5: Translating text data into sign language

[0672] The server sends the stored text data to a sign language translation algorithm, which analyzes the grammar and context of the text data and converts it into appropriate sign language steps. A context analysis algorithm is also used to deepen context understanding.

[0673] Step 6: Generate sign language video

[0674] The server generates sign language video based on the sign language steps. It uses image generation AI and a 3D modeling engine to generate 3D models and animations to visually represent each sign gesture. The generated sign language video is then seamlessly displayed as a series of movements.

[0675] Step 7: Compress the video data

[0676] The server compresses the generated sign language video data to ensure efficient use of bandwidth, using an appropriate codec to maintain data quality.

[0677] Step 8: Sending sign language video

[0678] The server sends the compressed sign language video data to the device via the Internet. The data is sent using an appropriate protocol (e.g., WebRTC) to maintain real-time transmission.

[0679] Step 9: Receiving sign language video

[0680] The terminal receives the transmitted sign language video data in real time, decodes the received video data, and prepares it for playback.

[0681] Step 10: Displaying the sign language video

[0682] The device projects the sign language image onto a display device such as a monitor or smart glasses. The frame rate and resolution of the image are adjusted to ensure smooth playback. The user can view the sign language image on the display device and understand the information.

[0683] The above steps will enable hearing-impaired people to obtain information in real time in a variety of situations, such as lectures and small group work.

[0684] Example 1

[0685] 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."

[0686] The present invention relates to a system that collects voice data in real time, converts the voice data into text data, then converts the text data into sign language steps to generate a sign language video, and finally transmits and displays the sign language video on a display device. Conventional voice recognition and sign language translation technologies have issues with real-time performance and accuracy, making it difficult for hearing-impaired users to obtain information in real time.

[0687] 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.

[0688] In this invention, the server includes means for applying noise canceling technology to process voice data in real time, means for analyzing text data with a sign language translation algorithm, and means for generating realistic sign language images using 3D modeling technology, thereby enabling highly accurate collection and processing of voice data and real-time generation and display of sign language images.

[0689] "Voice data" is a digital representation of voice, and is data collected from a user's speech via a microphone or the like.

[0690] "Text data" is character string data converted from voice data using voice recognition technology, and is data in a format that can be read by humans.

[0691] "Sign language steps" are a series of instructions and commands that express each step of sign language movement based on text data, and are intermediate data for generating sign language images.

[0692] "Sign language video" is a visual sign language expression generated based on sign language steps, and is video data expressed as animation using 3D modeling technology, etc.

[0693] A "display device" is a device that visually presents sign language images to users, and examples include monitors and smart glasses.

[0694] "Real-time processing" refers to a process in which the entire process, from data collection to display, is carried out in an extremely short time with almost no delay.

[0695] "Noise canceling technology" is a technology that suppresses background noise when collecting voice data, with the aim of clearly capturing only the user's speech.

[0696] A "sign language translation algorithm" is a formula or procedure for converting text data into sign language steps, and applies natural language processing technology.

[0697] "3D modeling technology" is a technique for creating visual objects using three-dimensional computer graphics, and is used to realistically represent each movement in sign language videos.

[0698] The present invention is a system that collects voice data in real time, converts the voice data into text data, further converts the text data into sign language steps to generate sign language video, and finally transmits the sign language video to a display device for display.

[0699] Overall system overview

[0700] This system consists of a user, a terminal, and a server. The user speaks into a microphone, and the terminal collects the voice data. The terminal sends the collected voice data to the server in real time. The server analyzes the voice data and converts it into text data, which is then converted into sign language steps. The server then generates a sign language video based on the sign language steps and sends the generated sign language video to the terminal in real time. The terminal decodes the sign language video and displays it on a display device, allowing the user to view the sign language video.

[0701] Specific examples of hardware and software used

[0702] Microphone: Collects user utterances.

[0703] Terminal: A communication device such as a personal computer or smartphone.

[0704] Server: Responsible for data processing and storage. Uses a speech recognition engine (e.g., a cloud-based speech recognition service).

[0705] Display devices: monitors, smart glasses, etc.

[0706] Specific examples of software include:

[0707] Speech recognition engine: Google Cloud Speech-to-Text API, etc.

[0708] Noise cancelling technologies: NVIDIA RTX Voice, Dolby Voice, etc.

[0709] Sign language translation algorithms: BERT and GPT models using natural language processing (NLP) techniques.

[0710] 3D modeling software: Blender and Unity are used to generate sign language videos.

[0711] Example of system operation

[0712] Example of use in a conference room

[0713] When User A gives a presentation in a conference room, a terminal (a microphone and computer system located in the conference room) collects User A's speech and converts it into digital audio data. This data is sent to a server in real time. The server receives the speech data and converts it into text data using a speech recognition engine. The converted text data is then converted into sign language steps using a sign language translation algorithm, and a sign language video is generated using 3D modeling software. The generated sign language video is sent to the terminal and displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can view the sign language video on the monitor and understand what User A is saying in real time.

[0714] Examples of personal use

[0715] When User X is talking with a friend at a cafe, the device (smartphone) collects User X's speech and sends it to a server in real time. The server receives the voice data and converts it into text data using a cloud-based speech recognition engine. The converted text data is converted into sign language steps using a sign language translation algorithm, and a sign language video is generated using 3D modeling software based on the sign language steps. The generated sign language video is sent to the smartphone and projected onto the smartphone screen or smart glasses. User Y, the friend who is hearing impaired, can view the sign language video through the smart glasses and understand the content of the conversation in real time.

[0716] Example prompts for generative AI models

[0717] "Collect audio data in real time as the user speaks into a microphone, and convert that audio data into text data. Next, convert the text data into sign language steps, and generate a sign language video using 3D modeling software. Then, send the generated sign language video to a display device and display it."

[0718] This system provides real-time sign language interpretation in a variety of situations, enabling the hearing impaired to obtain information quickly and accurately.

[0719] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0720] Step 1:

[0721] The user speaks into the microphone. Voice data (analog signal) is captured by the microphone. This data is input into the device's voice collection module. The voice collection module converts the voice signal into digital form and applies noise cancellation technology. Specifically, the user's speech is collected by the microphone, and the device's voice input device (e.g., built-in microphone or external microphone) converts the analog signal into digital data, which is then subjected to noise cancellation processing. The output is digitized, noise-removed voice data.

[0722] Step 2:

[0723] The terminal transmits digital audio data to the server in real time. The audio data is buffered by the terminal's communication module and divided into packets of a certain size before being transmitted. Specifically, the terminal's communication protocol (e.g., TCP / IP) divides the audio data into packets and transmits them to the server via the network. The output is the packets of digital audio data transmitted to the server.

[0724] Step 3:

[0725] The server passes the received voice data to a voice recognition engine, which converts it into text data. In this process, the voice recognition engine (for example, Google Cloud Speech-to-Text) analyzes the voice signal, breaks it down into phonemes and phrases, and generates text data as a string of characters. Specifically, the voice recognition engine analyzes the voice waveform data and converts it into corresponding text data. The input is digital voice data, and the output is converted text data.

[0726] Step 4:

[0727] The server sends the text data to a sign language translation algorithm, which converts it into sign language steps. The sign language translation algorithm uses natural language processing (NLP) to analyze the grammar and context of the text and convert it into appropriate sign language expressions. Specifically, an NLP model (e.g., BERT) analyzes the context and determines the sign language steps for each word or phrase by referring to a sign language dictionary. The input is text data, and the output is sign language step data.

[0728] Step 5:

[0729] The server generates sign language video based on the sign language steps. Each sign gesture is animated using 3D modeling software (e.g., Blender or Unity). Specifically, the sign language steps are converted into 3D character movements, and keyframe technology and smooth transitions are used to generate realistic movements. The input is sign language step data, and the output is a 3D animated video of the sign language.

[0730] Step 6:

[0731] The server transmits the generated sign language video to the terminal in real time. The video data is encoded in real time and transmitted to the terminal. Specifically, the server's video encoding module compresses the sign language video in real time and transmits it to the terminal via the Internet. The output is sign language video data transmitted in real time.

[0732] Step 7:

[0733] The device decodes the received sign language video and displays it on a display device at an appropriate frame rate and resolution. The display device can be a monitor or smart glasses, through which the user can view the sign language interpreter in real time. Specifically, the device's decoding library (e.g., FFmpeg) decodes the sign language video and outputs it to the display device. The input is the received sign language video data, and the output is the sign language video displayed on the display device.

[0734] (Application example 1)

[0735] 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."

[0736] Conventional conversation and translation systems have had the problem of preventing hearing-impaired people from communicating smoothly with other people. In particular, in public places such as brick-and-mortar stores, there is a lack of appropriate sign language interpretation services when hearing-impaired people communicate with store staff and other customers. This has made it difficult for hearing-impaired people to understand conversations in real time, resulting in reduced communication efficiency.

[0737] 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.

[0738] In this invention, the server includes means for collecting voice data, means for converting the voice data into text data, means for converting the text data into sign language steps, means for generating a sign language video based on the sign language steps, means for transmitting the sign language video to a display device and displaying it, means for applying noise canceling technology to the means for collecting voice data, and means for displaying the sign language video on a display of smart glasses, thereby enabling a hearing-impaired person to understand the content of a conversation as a sign language video in real time.

[0739] "Audio data" refers to sound signals collected by an audio input device.

[0740] "Text data" refers to data that is generated by analyzing voice data and expressing it as text information.

[0741] "Sign language steps" refer to the steps for determining sign language actions based on text data.

[0742] "Sign language video" refers to a moving image display of information generated based on sign language steps.

[0743] "Display device" means a device for visually displaying sign language images. Examples include monitors and smart glasses.

[0744] "Noise canceling technology" refers to technology that reduces ambient noise when collecting audio.

[0745] "Smart glasses" refers to a device in the form of glasses that includes a display and has the ability to visually display information.

[0746] A "server" is a computer system for processing data, transforming data, and providing necessary services.

[0747] "Real-time" is a term that refers to near-simultaneous processing and display.

[0748] System Overview

[0749] This invention is composed of a comprehensive system including various voice input devices, a server, and a display device. Voice data is collected in real time, converted into text data, and then converted into sign language steps to generate sign language images, which are then sent to a display device for display.

[0750] Specific configuration

[0751] 1. Collecting voice input

[0752] The user speaks, and the audio is collected by a microphone in the device. The device then uses appropriate noise-canceling technology to reduce ambient noise and obtain clear audio data. For example, the audio of a staff member in a physical store where a conversation takes place is collected by a microphone built into smart glasses.

[0753] 2. Audio analysis

[0754] The collected voice data is sent to a server in real time and converted into text data using a voice recognition engine (e.g., Google Cloud Speech-to-Text API).

[0755] 3. Sign Language Translation of Text Data

[0756] The server converts this text data into sign language steps using a sign language translation algorithm (e.g., a sign language translation system using a deep learning model). The text data is analyzed for grammar and context, and converted into appropriate sign language gestures.

[0757] 4. Sign Language Video Generation

[0758] The server generates sign language videos based on the sign language steps, using 3D character modeling software (e.g., Blender or Unity) to visually represent realistic sign language gestures.

[0759] 5. Video transmission and display

[0760] The generated sign language image is sent in real time to a display device, specifically a smart glasses display. For example, a hearing-impaired person wearing the smart glasses can understand what a store clerk is saying through the sign language image.

[0761] Specific examples

[0762] Use in physical stores

[0763] When a hearing-impaired person visits a store, the smart glasses collect what the staff say and send it to the server. The server converts the voice data into text data and then converts it into sign language steps. A sign language video is then generated based on the sign language steps and sent to the smart glasses to be displayed in real time.

[0764] Prompt Sentence Examples

[0765] Developed a system that enables hearing-impaired people to understand what store clerks are saying through smart glasses. Explain the application that combines speech recognition and sign language translation.

[0766] ---

[0767] In this way, by specifically explaining the mode for carrying out the invention, a system is provided that enables hearing-impaired people to communicate smoothly in social situations.

[0768] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0769] Step 1: Collecting voice input

[0770] When a user speaks, the audio is picked up by the device's microphone. The device then uses noise-canceling technology to reduce ambient noise and capture clear audio data. To achieve this, the microphone captures the audio data in digital form and temporarily stores it in the device's memory.

[0771] Step 2: Sending audio data

[0772] The device transmits the collected voice data to a server in real time using data streaming technology over the Internet, where the server receives the voice data and temporarily stores it in memory.

[0773] Step 3: Audio analysis

[0774] The server analyzes the received voice data using a speech recognition engine (for example, Google Cloud Speech-to-Text API) and converts it into text data. To analyze the voice data, the server inputs the voice data into the speech recognition engine and obtains text data as output. The converted text data is temporarily stored in the server's memory.

[0775] Step 4: Sign Language Translation

[0776] The server sends the text data to a sign language translation algorithm (e.g., a sign language translation system using a deep learning model) and converts it into sign language steps. For this process, the server inputs the text data and obtains sign language step data. The server temporarily stores these sign language steps in memory.

[0777] Step 5: Generate sign language video

[0778] The server generates sign language video using 3D modeling technology (e.g., Blender or Unity) based on the sign language steps. It uses the sign language steps as input and creates sign language video as output. The generated sign language video is stored in the server's memory.

[0779] Step 6: Sending sign language video

[0780] The server transmits the generated sign language video in real time to the device via the Internet, and the device receives the sign language video and temporarily stores it in its memory.

[0781] Step 7: Displaying the sign language video

[0782] The device receives the sign language video and displays it on the smart glasses' display. It decodes the sign language video and adjusts the frame rate and resolution for display. Specifically, the display software processes the video data so that the user can see the video through the glasses.

[0783] 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.

[0784] This invention combines an emotion recognition engine with a system that collects voice data in real time, converts the voice data into text data, converts the text data into sign language steps to generate sign language video, and finally transmits and displays the sign language video on a display device. This allows the user's emotions to be reflected in the sign language video, enabling more expressive sign language interpretation.

[0785] Overview of program processing

[0786] Collecting voice input

[0787] When a user speaks into the microphone, the device collects the voice, using appropriate noise-canceling technology to obtain clear voice data and store it in digital form. The collected voice data is then sent to a server for real-time processing.

[0788] Analysis of audio data

[0789] The server receives the transmitted voice data in real time and converts it into text data using a voice recognition engine. The converted text data is temporarily stored in memory.

[0790] emotion recognition

[0791] The server uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the voice data. The emotion recognition engine determines the user's emotional state from the user's facial expressions, tone of voice, speech patterns, etc., and reflects this emotional data in the sign language translation process.

[0792] Sign language translation of text data

[0793] The server sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The algorithm uses emotional data as input to adjust the emphasis, rhythm, and expression of sign language gestures and movements.

[0794] Sign language video generation

[0795] The server generates a sign language video based on the sign language steps. During this process, image generation AI and a 3D modeling engine are used to visually represent the sign language gestures. The sign language video is dynamically adjusted based on the emotion data, creating a video that reflects the user's emotions.

[0796] Video data compression and transmission

[0797] The server compresses the generated sign language video data and transmits it to the terminal. The compression uses an appropriate codec to enable efficient transmission.

[0798] Receiving and displaying sign language video

[0799] The device receives and decodes the transmitted sign language video data in real time. The decoded sign language video is then projected onto a display device such as a monitor or smart glasses, allowing the user to view the sign language video in real time, reflecting their emotions.

[0800] Specific examples

[0801] Use in conference rooms

[0802] When User A gives a presentation in a conference room, the device collects User A's audio and video data and sends it to the server. The server converts the audio data into text data and analyzes User A's emotions using an emotion recognition engine. It then converts the text into sign language steps and generates a sign language video. This sign language video is displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can watch the sign language video and understand User A's presentation content and emotions in real time.

[0803] Personal use

[0804] When User X is talking with a friend at a cafe, the device collects User X's voice and video data and sends it to a server. The server converts the voice data into text and uses an emotion recognition engine to analyze the video data and voice intonation. It then converts the text into sign language steps and generates a sign language video. This sign language video is projected onto the smart glasses, allowing User Y to understand the content of the conversation, including the emotions of his friend User X, in real time through the sign language video.

[0805] In this way, the system of the present invention provides real-time sign language interpretation that reflects the user's emotions, enabling hearing-impaired people to obtain information with a richer range of expression.

[0806] The processing flow will be explained below.

[0807] Specific processing steps of the program

[0808] Step 1: Collecting voice input

[0809] The user speaks into the microphone. The device collects the user's voice through a built-in or external microphone. Appropriate noise cancellation technology is used to collect the voice, ensuring clear voice data. The collected voice data is stored in digital format.

[0810] Step 2: Sending audio data

[0811] The collected voice data is sent to a server via the Internet in streaming format to maintain real-time performance.

[0812] Step 3: Receiving audio data

[0813] The server receives the transmitted audio data in real time. The received audio data is stored in a buffer. Buffering allows for temporary storage and sequential processing of audio data.

[0814] Step 4: Audio analysis

[0815] The server invokes the speech recognition engine and converts the voice data stored in the buffer into text data, which is then temporarily stored in memory.

[0816] Step 5: Emotion Recognition

[0817] The server uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the voice data. The emotion recognition engine determines the user's emotional state from the user's facial expressions, tone of voice, speech patterns, etc., and reflects this emotional data in the sign language translation process.

[0818] Step 6: Translating text data into sign language

[0819] The server sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The algorithm uses emotional data as input to adjust the emphasis, rhythm, and expression of sign language gestures and movements.

[0820] Step 7: Generate sign language video

[0821] The server generates sign language video based on the sign language steps. It uses image generation AI and a 3D modeling engine to generate 3D models and animations to visually represent each sign gesture. The sign language video is dynamically adjusted based on emotion data, creating a video that reflects the user's emotions.

[0822] Step 8: Compress the video data

[0823] The server compresses the generated sign language video data to ensure efficient use of bandwidth, using an appropriate codec to maintain data quality.

[0824] Step 9: Sending sign language video

[0825] The server sends the compressed sign language video data to the terminal via the Internet. The data is sent in real time using an appropriate protocol.

[0826] Step 10: Receiving sign language video

[0827] The terminal receives the transmitted sign language video data in real time, decodes the received video data, and prepares it for playback.

[0828] Step 11: Displaying the sign language video

[0829] The device projects the sign language image onto a display device such as a monitor or smart glasses. The frame rate and resolution of the image are adjusted to ensure smooth playback. The user can view the sign language image on the display device and understand the information.

[0830] Specific examples

[0831] Example 1: Use in a conference room

[0832] When User A gives a presentation in a conference room, the device collects User A's audio and video data and sends it to the server. The server converts the audio data into text data and analyzes User A's emotions using an emotion recognition engine. It then converts the text into sign language steps and generates a sign language video. This sign language video is displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can watch the sign language video and understand User A's presentation content and emotions in real time.

[0833] Example 2: Personal use

[0834] When User X is having a conversation with a friend at a cafe, the device collects User X's voice and video data and sends it to the server. The server converts the voice data into text and uses an emotion recognition engine to analyze the video data and voice intonation. It then converts the text into sign language steps and generates a sign language video. This sign language video is projected onto the smart glasses, allowing User Y to understand the content of the conversation, including the emotions of his friend User X, through the sign language video in real time.

[0835] As described above, the system of the present invention provides real-time sign language interpretation that reflects the user's emotions, enabling hearing-impaired people to obtain information with a richer range of expressions.

[0836] Example 2

[0837] 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."

[0838] In today's communication environment, it is extremely difficult for the hearing impaired to understand audio information in real time. In particular, information containing emotional nuances in audio cannot be fully conveyed by conventional sign language interpretation systems. For this reason, there is a demand for a system that can convert audio information, including emotions, into sign language images that can be understood by the hearing impaired in real time.

[0839] 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.

[0840] In this invention, the server includes means for collecting voice data, means for converting the voice data into text data, means for analyzing the text data to generate emotion data, means for converting the text data and emotion data into sign language steps, means for generating a sign language video based on the sign language steps and emotion data, and means for transmitting the sign language video to a display device and displaying it. This converts the voice information, including emotions, into a sign language video, enabling hearing-impaired people to understand the voice information and its emotional nuances in real time.

[0841] "Audio data" refers to data that is a digital recording of a user's speech or other sounds.

[0842] A "means" is a device, method, or technique used to achieve a particular purpose.

[0843] "Text data" is digital data that has been converted from voice data into character information.

[0844] "Emotion data" is digital data that indicates the emotional state of the user analyzed from the user's voice and video.

[0845] A "sign language step" is a group of instructions or commands of sign language actions generated based on text data and emotion data.

[0846] "Sign language video" is video data that is visually expressed based on sign language steps.

[0847] "Display device" refers to a device for displaying sign language images, including monitors and smart glasses.

[0848] "Real time" refers to a state in which the processing from collecting audio data to generating and displaying sign language images is carried out without delay.

[0849] This invention is a system that collects voice data in real time, converts the voice data into text data, converts the text data into sign language steps to generate sign language video, and finally transmits the sign language video to a display device for display. This system incorporates an emotion recognition engine, which allows the user's emotions to be reflected in the sign language video, thereby achieving more expressive sign language interpretation.

[0850] System Configuration

[0851] This system mainly consists of the following components:

[0852] 1. Terminal: Collects audio and video data and sends it to the server.

[0853] 2. Server: Converts voice data into text data, analyzes emotion data, and generates sign language steps and sign language images.

[0854] 3. Display device: Displays the generated sign language image.

[0855] Hardware and Software

[0856] Devices: High-performance microphones (e.g., Bose QuietComfort or Apple AirPods Pro) are used to collect audio data, and high-resolution cameras (e.g., Logitech HD Pro Webcam) are used to collect video data. Noise-cancelling technology reduces background noise.

[0857] Server: We use high-performance cloud servers (e.g., Amazon Web Services and Microsoft Azure) for data processing, Google Cloud Speech-to-Text for speech recognition, and Microsoft Azure Emotion API for emotion recognition.

[0858] Display devices: Use monitors or smart glasses (e.g., Microsoft HoloLens or Google Glass) to display sign language images.

[0859] Specific examples

[0860] Use in conference rooms

[0861] When User A gives a presentation in a conference room, the device collects User A's audio and video data and sends it to the server in real time. The server converts the audio data into text data and analyzes User A's emotions using an emotion recognition engine. The server then converts the text data into sign language steps and generates a sign language video. This sign language video is displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can watch the sign language video and understand User A's presentation content and emotions in real time.

[0862] Personal use

[0863] When User X is talking with a friend at a cafe, the device collects User X's voice and video data and sends it to the server in real time. The server converts the voice data into text data and uses an emotion recognition engine to analyze the video data and voice intonation. The server then converts the text data into sign language steps and generates a sign language video. This sign language video is projected onto the smart glasses, allowing User Y to understand the content of the conversation, including the emotions of his friend User X, through the sign language video in real time.

[0864] Prompt Sentence Examples

[0865] "Please explain the process steps of your system to convert speech during a presentation into text and then convert it into emotionally relevant sign language video."

[0866] The above configuration enables real-time sign language interpretation that reflects the user's emotions, allowing hearing-impaired people to obtain information with a richer range of expressions.

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

[0868] Step 1:

[0869] When a user speaks into the microphone, the device collects voice data. The input is the user's voice, and the device uses the microphone to record this voice in digital format (e.g., WAV format). The device uses noise-canceling technology to reduce background noise and save clear voice data. The output is the collected voice data, which is sent to the server in real time.

[0870] Step 2:

[0871] The server runs the received voice data through a speech recognition engine. The input is voice data, and the server converts the voice data into text data using speech recognition software such as Google Cloud Speech-to-Text or IBM Watson Speech to Text. The speech recognition engine converts the voice into text using an acoustic model and a language model. The output is text data, which is temporarily stored in the server's memory.

[0872] Step 3:

[0873] The server analyzes the text data, as well as the user's video data and voice intonation. The inputs are the text data, the user's video data, and the voice intonation. The server uses an emotion recognition engine (for example, Microsoft Azure Emotion API) to determine the user's emotion. The analysis includes factors such as facial expression recognition, voice tone, and speech pattern. The output is emotion data that indicates the user's emotional state.

[0874] Step 4:

[0875] The server converts text data, including emotion data, into sign language steps. The input is text data and emotion data. The server uses a sign language translation algorithm such as SignAll to generate sign language steps from the text data and emotion data. The strength and speed of the gestures are adjusted based on the emotion data. The output is sign language steps. These sign language steps are expressed as action instructions or commands.

[0876] Step 5:

[0877] The server generates a sign language video based on the sign language steps. The input is the sign language steps and emotion data. The server uses image generation AI or a 3D modeling engine such as Unity 3D or Blender to create a sign language video that visually expresses the sign language steps. The character's facial expressions and movements are dynamically adjusted based on the emotion data. The output is the generated sign language video data.

[0878] Step 6:

[0879] The server compresses the generated sign language video data. The input is sign language video data, and the server efficiently compresses the video using the H.264 codec. The frame rate and bit rate are adjusted to reduce the amount of data while maintaining the video quality. The output is compressed sign language video data. After compression, it is sent to the terminal.

[0880] Step 7:

[0881] The device receives the transmitted sign language video data and decompresses it in real time. The input is compressed sign language video data, and the device decompresses the data using a decoding engine such as VLC Media Player or FFmpeg. The decoded sign language video is projected onto a display device such as a monitor or smart glasses. The output is a visually expressed sign language video, allowing the user to check the sign language video reflecting emotions in real time.

[0882] (Application example 2)

[0883] 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."

[0884] Conventional sign language translation systems are limited to converting audio data into text data and generating sign language images, which makes it difficult to reflect the user's emotions and nuances in the sign language. Furthermore, in noisy environments such as factories, audio communication is difficult, making it difficult to convey appropriate instructions to hearing-impaired employees in real time.

[0885] The specification process by the specification 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 collecting voice data, means for converting the voice data into text data, means for converting the text data into sign language steps, means for generating a sign language gesture video, means for determining the user's emotions using an emotion recognition engine, and means for reflecting the emotion data in the sign language translation process. This makes it possible to generate expressive sign language videos that incorporate the user's emotions and nuances in real time, and to convey appropriate and clear instructions to hearing-impaired employees, especially in noisy environments such as factories.

[0886] "Voice data" is data that represents human voice in digital form.

[0887] "Text data" is character information obtained by analyzing voice data.

[0888] A "sign language step" is a series of instructions for creating sign language gestures based on text data.

[0889] "Sign language video" is video data showing visual sign language expressions generated based on sign language steps.

[0890] "Display device" refers to hardware for visually displaying sign language images, including monitors and smart glasses.

[0891] A "server" is a computer system that analyzes audio data, generates sign language steps, and generates sign language images.

[0892] An "emotion recognition engine" is software that analyzes and determines a user's emotions from audio and video data.

[0893] "Emotion data" is user emotion information obtained as a result of analysis by the emotion recognition engine.

[0894] The "sign language translation process" is a series of processes that generate sign language steps and sign language images based on text data and emotion data.

[0895] A "visual projection device" is a device for displaying sign language images in real time, and includes smart glasses and in-factory displays.

[0896] This invention combines an emotion recognition engine with a system that collects voice data, converts the voice data into text data, converts the text data into sign language steps to generate sign language images, and finally transmits and displays the sign language images on a display device. This allows the user's emotions to be reflected in the sign language images, enabling real-time communication, particularly in environments such as factories.

[0897] The system operates in the following manner:

[0898] First, the device collects the user's voice data. It uses noise-canceling technology to capture clear voice data, stores it digitally, and transmits it to a server in real time.

[0899] The server then receives the voice data and converts it into text data using a speech recognition engine, which is then temporarily stored in memory.

[0900] The server then uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the audio data. The emotion recognition engine determines the user's emotional state from the user's facial expressions, tone of voice, speech patterns, etc., and reflects this emotional data in the sign language translation process.

[0901] The server then sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The algorithm uses the emotional data as input to adjust the emphasis, rhythm, and expression of the sign language gestures and movements.

[0902] The server then generates a sign language video based on the sign language steps. This process uses image generation AI and a 3D modeling engine to visually represent the sign language gestures. The sign language video is dynamically adjusted based on the emotion data, creating a video that reflects the user's emotions.

[0903] The generated sign language video data is compressed by the server and sent to the terminal using an appropriate codec (e.g., H.264) to enable efficient transmission.

[0904] Finally, the device receives and decodes the transmitted sign language video data in real time. The decoded sign language video is then projected onto a display device such as a monitor or smart glasses, allowing the user to view the sign language video in real time, reflecting their emotions.

[0905] As a concrete example, the system can be used in factories where verbal communication is difficult, allowing hearing-impaired employees to receive visual instructions in real time, significantly improving work efficiency and reducing errors.

[0906] Example prompt sentence:

[0907] "You will be asked to create a program that will collect the audio and video of the user speaking into a microphone and convert it into sign language video in real time. The sign language video will need to reflect the user's emotions using an emotion recognition engine. This sign language video will then be sent to a display device in the factory in real time and displayed."

[0908] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0909] Step 1:

[0910] Audio data collection

[0911] The device collects the user's voice data. Specifically, when the user speaks into the microphone, the device uses noise-canceling technology to capture clear voice data. The input is the user's voice, and the output is digital voice data. This voice data is sent to the server in real time.

[0912] Step 2:

[0913] Converting audio data to text

[0914] The server receives the voice data and converts it into text data using a speech recognition engine. The input is digital voice data, and the output is text data as character information. The server temporarily stores this text data in memory.

[0915] Step 3:

[0916] emotion recognition

[0917] The server uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the voice data. The input is the user's facial expression and tone of voice, and the output is emotion data that indicates the user's emotional state. The server acquires this emotion data and reflects it in the sign language translation process.

[0918] Step 4:

[0919] Step-by-step conversion of text data into sign language

[0920] The server sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The input is text data and emotional data, and the output is sign language steps, which are a series of sign language gesture instructions. The sign language translation algorithm adjusts the emphasis, rhythm, and expression of the sign language gestures based on the emotional data.

[0921] Step 5:

[0922] Sign language video generation

[0923] The server generates a sign language video based on the sign language steps. This process uses image generation AI and a 3D modeling engine. The input is the sign language steps, and the output is a visual sign language video. The generated sign language video reflects the user's emotions.

[0924] Step 6:

[0925] Video data compression

[0926] The server compresses the generated sign language video data. The input is the sign language video and the output is the compressed video data. The compression uses an appropriate codec (e.g., H.264) to enable efficient data transmission.

[0927] Step 7:

[0928] Sign language video transmission and display

[0929] The device receives and decodes the transmitted sign language video data in real time. The input is compressed video data, and the output is decoded sign language video. The device then projects this sign language video onto a display device such as a monitor or smart glasses. The user can view the sign language video that reflects emotions in real time.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] [Fourth embodiment]

[0934] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0935] 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.

[0936] 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).

[0937] 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.

[0938] 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.

[0939] 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).

[0940] 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.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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.

[0945] 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.

[0946] 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."

[0947] The present invention is a system that collects voice data in real time, converts the voice data into text data, further converts the text data into sign language steps to generate sign language video, and finally transmits the sign language video to a display device for display.

[0948] Overview of program processing

[0949] Collecting voice input

[0950] The user speaks into a microphone and the device collects the audio. Specifically, the audio data is stored digitally through the microphone and then sent to a server in real time. Appropriate noise-canceling technology is used to collect the audio, improving analysis accuracy.

[0951] Audio analysis

[0952] The server analyzes the received voice data using a speech recognition engine and converts it into text data. In this process, speech is converted to text using a speech recognition engine (for example, a general cloud-based speech recognition service). The converted text data is temporarily stored in memory.

[0953] Sign language translation of text data

[0954] The server sends this text data to a sign language translation algorithm, which converts it into sign language steps. The sign language translation algorithm analyzes the grammar and context of the text data and converts it into appropriate sign language expressions. When doing so, it takes into account the context and selects the most appropriate sign language gesture for any ambiguous parts.

[0955] Sign language video generation

[0956] The server generates sign language videos based on the sign language steps. Specifically, it uses 3D modeling technology and realistic animation to visually represent each sign gesture. In the process of generating the sign language videos, it seamlessly connects consecutive movements to naturally express the flow and rhythm of sign language.

[0957] Video transmission and display

[0958] The server transmits the generated sign language video to the device in real time. The device decodes the received sign language video and displays it on a display device such as a monitor or smart glasses at an appropriate frame rate and resolution. The user can see the sign language interpreter in real time through the display.

[0959] Specific examples

[0960] Use in conference rooms

[0961] When User A gives a presentation in a conference room, the terminal (the conference room's microphone and computer system) collects User A's speech and sends it to the server. The server converts the speech data into text data, and then converts the text data into sign language steps. The server then generates a sign language video and displays it on a monitor in the conference room. User B, who is hearing impaired, can understand what User A is saying in real time by watching the sign language video displayed on the monitor.

[0962] Personal use

[0963] When User X starts a conversation with a friend at a cafe, the device (smartphone) collects User X's voice and sends it to the server. The server converts the voice data into text data and then converts it into sign language steps. The server then generates a sign language video that is displayed on the smartphone or projected onto smart glasses. User Y, a friend with a hearing impairment, can understand the content of the conversation in real time by viewing the sign language video through the smart glasses.

[0964] In this way, the system of the present invention provides real-time sign language interpretation in a variety of situations, enabling the hearing impaired to obtain information quickly and accurately.

[0965] The processing flow will be explained below.

[0966] Specific processing steps of the program

[0967] Step 1: Collecting voice input

[0968] The user speaks into the microphone. The device collects the user's voice through a built-in or external microphone. Appropriate noise cancellation technology is used to collect the voice, ensuring clear voice data. The collected voice data is stored in digital format.

[0969] Step 2: Sending audio data

[0970] The collected voice data is sent to a server via the Internet in streaming format to maintain real-time performance.

[0971] Step 3: Receiving audio data

[0972] The server receives the transmitted audio data in real time. The received audio data is stored in a buffer. Buffering allows for temporary storage and sequential processing of audio data.

[0973] Step 4: Audio analysis

[0974] The server calls a speech recognition engine (e.g., a cloud-based speech recognition service) and converts the voice data stored in the buffer into text data, which is then temporarily stored in memory.

[0975] Step 5: Translating text data into sign language

[0976] The server sends the stored text data to a sign language translation algorithm, which analyzes the grammar and context of the text data and converts it into appropriate sign language steps. A context analysis algorithm is also used to deepen context understanding.

[0977] Step 6: Generate sign language video

[0978] The server generates sign language video based on the sign language steps. It uses image generation AI and a 3D modeling engine to generate 3D models and animations to visually represent each sign gesture. The generated sign language video is then seamlessly displayed as a series of movements.

[0979] Step 7: Compress the video data

[0980] The server compresses the generated sign language video data to ensure efficient use of bandwidth, using an appropriate codec to maintain data quality.

[0981] Step 8: Sending sign language video

[0982] The server sends the compressed sign language video data to the device via the Internet. The data is sent using an appropriate protocol (e.g., WebRTC) to maintain real-time transmission.

[0983] Step 9: Receiving sign language video

[0984] The terminal receives the transmitted sign language video data in real time, decodes the received video data, and prepares it for playback.

[0985] Step 10: Displaying the sign language video

[0986] The device projects the sign language image onto a display device such as a monitor or smart glasses. The frame rate and resolution of the image are adjusted to ensure smooth playback. The user can view the sign language image on the display device and understand the information.

[0987] The above steps will enable hearing-impaired people to obtain information in real time in a variety of situations, such as lectures and small group work.

[0988] Example 1

[0989] 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."

[0990] The present invention relates to a system that collects voice data in real time, converts the voice data into text data, then converts the text data into sign language steps to generate a sign language video, and finally transmits and displays the sign language video on a display device. Conventional voice recognition and sign language translation technologies have issues with real-time performance and accuracy, making it difficult for hearing-impaired users to obtain information in real time.

[0991] 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.

[0992] In this invention, the server includes means for applying noise canceling technology to process voice data in real time, means for analyzing text data with a sign language translation algorithm, and means for generating realistic sign language images using 3D modeling technology, thereby enabling highly accurate collection and processing of voice data and real-time generation and display of sign language images.

[0993] "Voice data" is a digital representation of voice, and is data collected from a user's speech via a microphone or the like.

[0994] "Text data" is character string data converted from voice data using voice recognition technology, and is data in a format that can be read by humans.

[0995] "Sign language steps" are a series of instructions and commands that express each step of sign language movement based on text data, and are intermediate data for generating sign language images.

[0996] "Sign language video" is a visual sign language expression generated based on sign language steps, and is video data expressed as animation using 3D modeling technology, etc.

[0997] A "display device" is a device that visually presents sign language images to users, and examples include monitors and smart glasses.

[0998] "Real-time processing" refers to a process in which the entire process, from data collection to display, is carried out in an extremely short time with almost no delay.

[0999] "Noise canceling technology" is a technology that suppresses background noise when collecting voice data, with the aim of clearly capturing only the user's speech.

[1000] A "sign language translation algorithm" is a formula or procedure for converting text data into sign language steps, and applies natural language processing technology.

[1001] "3D modeling technology" is a technique for creating visual objects using three-dimensional computer graphics, and is used to realistically represent each movement in sign language videos.

[1002] The present invention is a system that collects voice data in real time, converts the voice data into text data, further converts the text data into sign language steps to generate sign language video, and finally transmits the sign language video to a display device for display.

[1003] Overall system overview

[1004] This system consists of a user, a terminal, and a server. The user speaks into a microphone, and the terminal collects the voice data. The terminal sends the collected voice data to the server in real time. The server analyzes the voice data and converts it into text data, which is then converted into sign language steps. The server then generates a sign language video based on the sign language steps and sends the generated sign language video to the terminal in real time. The terminal decodes the sign language video and displays it on a display device, allowing the user to view the sign language video.

[1005] Specific examples of hardware and software used

[1006] Microphone: Collects user utterances.

[1007] Terminal: A communication device such as a personal computer or smartphone.

[1008] Server: Responsible for data processing and storage. Uses a speech recognition engine (e.g., a cloud-based speech recognition service).

[1009] Display devices: monitors, smart glasses, etc.

[1010] Specific examples of software include:

[1011] Speech recognition engine: Google Cloud Speech-to-Text API, etc.

[1012] Noise cancelling technologies: NVIDIA RTX Voice, Dolby Voice, etc.

[1013] Sign language translation algorithms: BERT and GPT models using natural language processing (NLP) techniques.

[1014] 3D modeling software: Blender and Unity are used to generate sign language videos.

[1015] Example of system operation

[1016] Example of use in a conference room

[1017] When User A gives a presentation in a conference room, a terminal (a microphone and computer system located in the conference room) collects User A's speech and converts it into digital audio data. This data is sent to a server in real time. The server receives the speech data and converts it into text data using a speech recognition engine. The converted text data is then converted into sign language steps using a sign language translation algorithm, and a sign language video is generated using 3D modeling software. The generated sign language video is sent to the terminal and displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can view the sign language video on the monitor and understand what User A is saying in real time.

[1018] Examples of personal use

[1019] When User X is talking with a friend at a cafe, the device (smartphone) collects User X's speech and sends it to a server in real time. The server receives the voice data and converts it into text data using a cloud-based speech recognition engine. The converted text data is converted into sign language steps using a sign language translation algorithm, and a sign language video is generated using 3D modeling software based on the sign language steps. The generated sign language video is sent to the smartphone and projected onto the smartphone screen or smart glasses. User Y, the friend who is hearing impaired, can view the sign language video through the smart glasses and understand the content of the conversation in real time.

[1020] Example prompts for generative AI models

[1021] "Collect audio data in real time as the user speaks into a microphone, and convert that audio data into text data. Next, convert the text data into sign language steps, and generate a sign language video using 3D modeling software. Then, send the generated sign language video to a display device and display it."

[1022] This system provides real-time sign language interpretation in a variety of situations, enabling the hearing impaired to obtain information quickly and accurately.

[1023] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1024] Step 1:

[1025] The user speaks into the microphone. Voice data (analog signal) is captured by the microphone. This data is input into the device's voice collection module. The voice collection module converts the voice signal into digital form and applies noise cancellation technology. Specifically, the user's speech is collected by the microphone, and the device's voice input device (e.g., built-in microphone or external microphone) converts the analog signal into digital data, which is then subjected to noise cancellation processing. The output is digitized, noise-removed voice data.

[1026] Step 2:

[1027] The terminal transmits digital audio data to the server in real time. The audio data is buffered by the terminal's communication module and divided into packets of a certain size before being transmitted. Specifically, the terminal's communication protocol (e.g., TCP / IP) divides the audio data into packets and transmits them to the server via the network. The output is the packets of digital audio data transmitted to the server.

[1028] Step 3:

[1029] The server passes the received voice data to a voice recognition engine, which converts it into text data. In this process, the voice recognition engine (for example, Google Cloud Speech-to-Text) analyzes the voice signal, breaks it down into phonemes and phrases, and generates text data as a string of characters. Specifically, the voice recognition engine analyzes the voice waveform data and converts it into corresponding text data. The input is digital voice data, and the output is converted text data.

[1030] Step 4:

[1031] The server sends the text data to a sign language translation algorithm, which converts it into sign language steps. The sign language translation algorithm uses natural language processing (NLP) to analyze the grammar and context of the text and convert it into appropriate sign language expressions. Specifically, an NLP model (e.g., BERT) analyzes the context and determines the sign language steps for each word or phrase by referring to a sign language dictionary. The input is text data, and the output is sign language step data.

[1032] Step 5:

[1033] The server generates sign language video based on the sign language steps. Each sign gesture is animated using 3D modeling software (e.g., Blender or Unity). Specifically, the sign language steps are converted into 3D character movements, and keyframe technology and smooth transitions are used to generate realistic movements. The input is sign language step data, and the output is a 3D animated video of the sign language.

[1034] Step 6:

[1035] The server transmits the generated sign language video to the terminal in real time. The video data is encoded in real time and transmitted to the terminal. Specifically, the server's video encoding module compresses the sign language video in real time and transmits it to the terminal via the Internet. The output is sign language video data transmitted in real time.

[1036] Step 7:

[1037] The device decodes the received sign language video and displays it on a display device at an appropriate frame rate and resolution. The display device can be a monitor or smart glasses, through which the user can view the sign language interpreter in real time. Specifically, the device's decoding library (e.g., FFmpeg) decodes the sign language video and outputs it to the display device. The input is the received sign language video data, and the output is the sign language video displayed on the display device.

[1038] (Application example 1)

[1039] 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."

[1040] Conventional conversation and translation systems have had the problem of preventing hearing-impaired people from communicating smoothly with other people. In particular, in public places such as brick-and-mortar stores, there is a lack of appropriate sign language interpretation services when hearing-impaired people communicate with store staff and other customers. This has made it difficult for hearing-impaired people to understand conversations in real time, resulting in reduced communication efficiency.

[1041] 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.

[1042] In this invention, the server includes means for collecting voice data, means for converting the voice data into text data, means for converting the text data into sign language steps, means for generating a sign language video based on the sign language steps, means for transmitting the sign language video to a display device and displaying it, means for applying noise canceling technology to the means for collecting voice data, and means for displaying the sign language video on a display of smart glasses, thereby enabling a hearing-impaired person to understand the content of a conversation as a sign language video in real time.

[1043] "Audio data" refers to sound signals collected by an audio input device.

[1044] "Text data" refers to data that is generated by analyzing voice data and expressing it as text information.

[1045] "Sign language steps" refer to the steps for determining sign language actions based on text data.

[1046] "Sign language video" refers to a moving image display of information generated based on sign language steps.

[1047] "Display device" means a device for visually displaying sign language images. Examples include monitors and smart glasses.

[1048] "Noise canceling technology" refers to technology that reduces ambient noise when collecting audio.

[1049] "Smart glasses" refers to a device in the form of glasses that includes a display and has the ability to visually display information.

[1050] A "server" is a computer system for processing data, transforming data, and providing necessary services.

[1051] "Real-time" is a term that refers to near-simultaneous processing and display.

[1052] System Overview

[1053] This invention is composed of a comprehensive system including various voice input devices, a server, and a display device. Voice data is collected in real time, converted into text data, and then converted into sign language steps to generate sign language images, which are then sent to a display device for display.

[1054] Specific configuration

[1055] 1. Collecting voice input

[1056] The user speaks, and the audio is collected by a microphone in the device. The device then uses appropriate noise-canceling technology to reduce ambient noise and obtain clear audio data. For example, the audio of a staff member in a physical store where a conversation takes place is collected by a microphone built into smart glasses.

[1057] 2. Audio analysis

[1058] The collected voice data is sent to a server in real time and converted into text data using a voice recognition engine (e.g., Google Cloud Speech-to-Text API).

[1059] 3. Sign Language Translation of Text Data

[1060] The server converts this text data into sign language steps using a sign language translation algorithm (e.g., a sign language translation system using a deep learning model). The text data is analyzed for grammar and context, and converted into appropriate sign language gestures.

[1061] 4. Sign Language Video Generation

[1062] The server generates sign language videos based on the sign language steps, using 3D character modeling software (e.g., Blender or Unity) to visually represent realistic sign language gestures.

[1063] 5. Video transmission and display

[1064] The generated sign language image is sent in real time to a display device, specifically a smart glasses display. For example, a hearing-impaired person wearing the smart glasses can understand what a store clerk is saying through the sign language image.

[1065] Specific examples

[1066] Use in physical stores

[1067] When a hearing-impaired person visits a store, the smart glasses collect what the staff say and send it to the server. The server converts the voice data into text data and then converts it into sign language steps. A sign language video is then generated based on the sign language steps and sent to the smart glasses to be displayed in real time.

[1068] Prompt Sentence Examples

[1069] Developed a system that enables hearing-impaired people to understand what store clerks are saying through smart glasses. Explain the application that combines speech recognition and sign language translation.

[1070] ---

[1071] In this way, by specifically explaining the mode for carrying out the invention, a system is provided that enables hearing-impaired people to communicate smoothly in social situations.

[1072] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1073] Step 1: Collecting voice input

[1074] When a user speaks, the audio is picked up by the device's microphone. The device then uses noise-canceling technology to reduce ambient noise and capture clear audio data. To achieve this, the microphone captures the audio data in digital form and temporarily stores it in the device's memory.

[1075] Step 2: Sending audio data

[1076] The device transmits the collected voice data to a server in real time using data streaming technology over the Internet, where the server receives the voice data and temporarily stores it in memory.

[1077] Step 3: Audio analysis

[1078] The server analyzes the received voice data using a speech recognition engine (for example, Google Cloud Speech-to-Text API) and converts it into text data. To analyze the voice data, the server inputs the voice data into the speech recognition engine and obtains text data as output. The converted text data is temporarily stored in the server's memory.

[1079] Step 4: Sign Language Translation

[1080] The server sends the text data to a sign language translation algorithm (e.g., a sign language translation system using a deep learning model) and converts it into sign language steps. For this process, the server inputs the text data and obtains sign language step data. The server temporarily stores these sign language steps in memory.

[1081] Step 5: Generate sign language video

[1082] The server generates sign language video using 3D modeling technology (e.g., Blender or Unity) based on the sign language steps. It uses the sign language steps as input and creates sign language video as output. The generated sign language video is stored in the server's memory.

[1083] Step 6: Sending sign language video

[1084] The server transmits the generated sign language video in real time to the device via the Internet, and the device receives the sign language video and temporarily stores it in its memory.

[1085] Step 7: Displaying the sign language video

[1086] The device receives the sign language video and displays it on the smart glasses' display. It decodes the sign language video and adjusts the frame rate and resolution for display. Specifically, the display software processes the video data so that the user can see the video through the glasses.

[1087] 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.

[1088] This invention combines an emotion recognition engine with a system that collects voice data in real time, converts the voice data into text data, converts the text data into sign language steps to generate sign language video, and finally transmits and displays the sign language video on a display device. This allows the user's emotions to be reflected in the sign language video, enabling more expressive sign language interpretation.

[1089] Overview of program processing

[1090] Collecting voice input

[1091] When a user speaks into the microphone, the device collects the voice, using appropriate noise-canceling technology to obtain clear voice data and store it in digital form. The collected voice data is then sent to a server for real-time processing.

[1092] Analysis of audio data

[1093] The server receives the transmitted voice data in real time and converts it into text data using a voice recognition engine. The converted text data is temporarily stored in memory.

[1094] emotion recognition

[1095] The server uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the voice data. The emotion recognition engine determines the user's emotional state from the user's facial expressions, tone of voice, speech patterns, etc., and reflects this emotional data in the sign language translation process.

[1096] Sign language translation of text data

[1097] The server sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The algorithm uses emotional data as input to adjust the emphasis, rhythm, and expression of sign language gestures and movements.

[1098] Sign language video generation

[1099] The server generates a sign language video based on the sign language steps. During this process, image generation AI and a 3D modeling engine are used to visually represent the sign language gestures. The sign language video is dynamically adjusted based on the emotion data, creating a video that reflects the user's emotions.

[1100] Video data compression and transmission

[1101] The server compresses the generated sign language video data and transmits it to the terminal. The compression uses an appropriate codec to enable efficient transmission.

[1102] Receiving and displaying sign language video

[1103] The device receives and decodes the transmitted sign language video data in real time. The decoded sign language video is then projected onto a display device such as a monitor or smart glasses, allowing the user to view the sign language video in real time, reflecting their emotions.

[1104] Specific examples

[1105] Use in conference rooms

[1106] When User A gives a presentation in a conference room, the device collects User A's audio and video data and sends it to the server. The server converts the audio data into text data and analyzes User A's emotions using an emotion recognition engine. It then converts the text into sign language steps and generates a sign language video. This sign language video is displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can watch the sign language video and understand User A's presentation content and emotions in real time.

[1107] Personal use

[1108] When User X is talking with a friend at a cafe, the device collects User X's voice and video data and sends it to a server. The server converts the voice data into text and uses an emotion recognition engine to analyze the video data and voice intonation. It then converts the text into sign language steps and generates a sign language video. This sign language video is projected onto the smart glasses, allowing User Y to understand the content of the conversation, including the emotions of his friend User X, in real time through the sign language video.

[1109] In this way, the system of the present invention provides real-time sign language interpretation that reflects the user's emotions, enabling hearing-impaired people to obtain information with a richer range of expression.

[1110] The processing flow will be explained below.

[1111] Specific processing steps of the program

[1112] Step 1: Collecting voice input

[1113] The user speaks into the microphone. The device collects the user's voice through a built-in or external microphone. Appropriate noise cancellation technology is used to collect the voice, ensuring clear voice data. The collected voice data is stored in digital format.

[1114] Step 2: Sending audio data

[1115] The collected voice data is sent to a server via the Internet in streaming format to maintain real-time performance.

[1116] Step 3: Receiving audio data

[1117] The server receives the transmitted audio data in real time. The received audio data is stored in a buffer. Buffering allows for temporary storage and sequential processing of audio data.

[1118] Step 4: Audio analysis

[1119] The server invokes the speech recognition engine and converts the voice data stored in the buffer into text data, which is then temporarily stored in memory.

[1120] Step 5: Emotion Recognition

[1121] The server uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the voice data. The emotion recognition engine determines the user's emotional state from the user's facial expressions, tone of voice, speech patterns, etc., and reflects this emotional data in the sign language translation process.

[1122] Step 6: Translating text data into sign language

[1123] The server sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The algorithm uses emotional data as input to adjust the emphasis, rhythm, and expression of sign language gestures and movements.

[1124] Step 7: Generate sign language video

[1125] The server generates sign language video based on the sign language steps. It uses image generation AI and a 3D modeling engine to generate 3D models and animations to visually represent each sign gesture. The sign language video is dynamically adjusted based on emotion data, creating a video that reflects the user's emotions.

[1126] Step 8: Compress the video data

[1127] The server compresses the generated sign language video data to ensure efficient use of bandwidth, using an appropriate codec to maintain data quality.

[1128] Step 9: Sending sign language video

[1129] The server sends the compressed sign language video data to the terminal via the Internet. The data is sent in real time using an appropriate protocol.

[1130] Step 10: Receiving sign language video

[1131] The terminal receives the transmitted sign language video data in real time, decodes the received video data, and prepares it for playback.

[1132] Step 11: Displaying the sign language video

[1133] The device projects the sign language image onto a display device such as a monitor or smart glasses. The frame rate and resolution of the image are adjusted to ensure smooth playback. The user can view the sign language image on the display device and understand the information.

[1134] Specific examples

[1135] Example 1: Use in a conference room

[1136] When User A gives a presentation in a conference room, the device collects User A's audio and video data and sends it to the server. The server converts the audio data into text data and analyzes User A's emotions using an emotion recognition engine. It then converts the text into sign language steps and generates a sign language video. This sign language video is displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can watch the sign language video and understand User A's presentation content and emotions in real time.

[1137] Example 2: Personal use

[1138] When User X is having a conversation with a friend at a cafe, the device collects User X's voice and video data and sends it to the server. The server converts the voice data into text and uses an emotion recognition engine to analyze the video data and voice intonation. It then converts the text into sign language steps and generates a sign language video. This sign language video is projected onto the smart glasses, allowing User Y to understand the content of the conversation, including the emotions of his friend User X, through the sign language video in real time.

[1139] As described above, the system of the present invention provides real-time sign language interpretation that reflects the user's emotions, enabling hearing-impaired people to obtain information with a richer range of expressions.

[1140] Example 2

[1141] 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."

[1142] In today's communication environment, it is extremely difficult for the hearing impaired to understand audio information in real time. In particular, information containing emotional nuances in audio cannot be fully conveyed by conventional sign language interpretation systems. For this reason, there is a demand for a system that can convert audio information, including emotions, into sign language images that can be understood by the hearing impaired in real time.

[1143] 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.

[1144] In this invention, the server includes means for collecting voice data, means for converting the voice data into text data, means for analyzing the text data to generate emotion data, means for converting the text data and emotion data into sign language steps, means for generating a sign language video based on the sign language steps and emotion data, and means for transmitting the sign language video to a display device and displaying it. This converts the voice information, including emotions, into a sign language video, enabling hearing-impaired people to understand the voice information and its emotional nuances in real time.

[1145] "Audio data" refers to data that is a digital recording of a user's speech or other sounds.

[1146] A "means" is a device, method, or technique used to achieve a particular purpose.

[1147] "Text data" is digital data that has been converted from voice data into character information.

[1148] "Emotion data" is digital data that indicates the emotional state of the user analyzed from the user's voice and video.

[1149] A "sign language step" is a group of instructions or commands of sign language actions generated based on text data and emotion data.

[1150] "Sign language video" is video data that is visually expressed based on sign language steps.

[1151] "Display device" refers to a device for displaying sign language images, including monitors and smart glasses.

[1152] "Real time" refers to a state in which the processing from collecting audio data to generating and displaying sign language images is carried out without delay.

[1153] This invention is a system that collects voice data in real time, converts the voice data into text data, converts the text data into sign language steps to generate sign language video, and finally transmits the sign language video to a display device for display. This system incorporates an emotion recognition engine, which allows the user's emotions to be reflected in the sign language video, thereby achieving more expressive sign language interpretation.

[1154] System Configuration

[1155] This system mainly consists of the following components:

[1156] 1. Terminal: Collects audio and video data and sends it to the server.

[1157] 2. Server: Converts voice data into text data, analyzes emotion data, and generates sign language steps and sign language images.

[1158] 3. Display device: Displays the generated sign language image.

[1159] Hardware and Software

[1160] Devices: High-performance microphones (e.g., Bose QuietComfort or Apple AirPods Pro) are used to collect audio data, and high-resolution cameras (e.g., Logitech HD Pro Webcam) are used to collect video data. Noise-cancelling technology reduces background noise.

[1161] Server: We use high-performance cloud servers (e.g., Amazon Web Services and Microsoft Azure) for data processing, Google Cloud Speech-to-Text for speech recognition, and Microsoft Azure Emotion API for emotion recognition.

[1162] Display devices: Use monitors or smart glasses (e.g., Microsoft HoloLens or Google Glass) to display sign language images.

[1163] Specific examples

[1164] Use in conference rooms

[1165] When User A gives a presentation in a conference room, the device collects User A's audio and video data and sends it to the server in real time. The server converts the audio data into text data and analyzes User A's emotions using an emotion recognition engine. The server then converts the text data into sign language steps and generates a sign language video. This sign language video is displayed in real time on a monitor in the conference room. User B, who is hearing impaired, can watch the sign language video and understand User A's presentation content and emotions in real time.

[1166] Personal use

[1167] When User X is talking with a friend at a cafe, the device collects User X's voice and video data and sends it to the server in real time. The server converts the voice data into text data and uses an emotion recognition engine to analyze the video data and voice intonation. The server then converts the text data into sign language steps and generates a sign language video. This sign language video is projected onto the smart glasses, allowing User Y to understand the content of the conversation, including the emotions of his friend User X, through the sign language video in real time.

[1168] Prompt Sentence Examples

[1169] "Please explain the process steps of your system to convert speech during a presentation into text and then convert it into emotionally relevant sign language video."

[1170] The above configuration enables real-time sign language interpretation that reflects the user's emotions, allowing hearing-impaired people to obtain information with a richer range of expressions.

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

[1172] Step 1:

[1173] When a user speaks into the microphone, the device collects voice data. The input is the user's voice, and the device uses the microphone to record this voice in digital format (e.g., WAV format). The device uses noise-canceling technology to reduce background noise and save clear voice data. The output is the collected voice data, which is sent to the server in real time.

[1174] Step 2:

[1175] The server runs the received voice data through a speech recognition engine. The input is voice data, and the server converts the voice data into text data using speech recognition software such as Google Cloud Speech-to-Text or IBM Watson Speech to Text. The speech recognition engine converts the voice into text using an acoustic model and a language model. The output is text data, which is temporarily stored in the server's memory.

[1176] Step 3:

[1177] The server analyzes the text data, as well as the user's video data and voice intonation. The inputs are the text data, the user's video data, and the voice intonation. The server uses an emotion recognition engine (for example, Microsoft Azure Emotion API) to determine the user's emotion. The analysis includes factors such as facial expression recognition, voice tone, and speech pattern. The output is emotion data that indicates the user's emotional state.

[1178] Step 4:

[1179] The server converts text data, including emotion data, into sign language steps. The input is text data and emotion data. The server uses a sign language translation algorithm such as SignAll to generate sign language steps from the text data and emotion data. The strength and speed of the gestures are adjusted based on the emotion data. The output is sign language steps. These sign language steps are expressed as action instructions or commands.

[1180] Step 5:

[1181] The server generates a sign language video based on the sign language steps. The input is the sign language steps and emotion data. The server uses image generation AI or a 3D modeling engine such as Unity 3D or Blender to create a sign language video that visually expresses the sign language steps. The character's facial expressions and movements are dynamically adjusted based on the emotion data. The output is the generated sign language video data.

[1182] Step 6:

[1183] The server compresses the generated sign language video data. The input is sign language video data, and the server efficiently compresses the video using the H.264 codec. The frame rate and bit rate are adjusted to reduce the amount of data while maintaining the video quality. The output is compressed sign language video data. After compression, it is sent to the terminal.

[1184] Step 7:

[1185] The device receives the transmitted sign language video data and decompresses it in real time. The input is compressed sign language video data, and the device decompresses the data using a decoding engine such as VLC Media Player or FFmpeg. The decoded sign language video is projected onto a display device such as a monitor or smart glasses. The output is a visually expressed sign language video, allowing the user to check the sign language video reflecting emotions in real time.

[1186] (Application example 2)

[1187] 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."

[1188] Conventional sign language translation systems are limited to converting audio data into text data and generating sign language images, which makes it difficult to reflect the user's emotions and nuances in the sign language. Furthermore, in noisy environments such as factories, audio communication is difficult, making it difficult to convey appropriate instructions to hearing-impaired employees in real time.

[1189] The specification process by the specification 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 collecting voice data, means for converting the voice data into text data, means for converting the text data into sign language steps, means for generating a sign language gesture video, means for determining the user's emotions using an emotion recognition engine, and means for reflecting the emotion data in the sign language translation process. This makes it possible to generate expressive sign language videos that incorporate the user's emotions and nuances in real time, and to convey appropriate and clear instructions to hearing-impaired employees, especially in noisy environments such as factories.

[1190] "Voice data" is data that represents human voice in digital form.

[1191] "Text data" is character information obtained by analyzing voice data.

[1192] A "sign language step" is a series of instructions for creating sign language gestures based on text data.

[1193] "Sign language video" is video data showing visual sign language expressions generated based on sign language steps.

[1194] "Display device" refers to hardware for visually displaying sign language images, including monitors and smart glasses.

[1195] A "server" is a computer system that analyzes audio data, generates sign language steps, and generates sign language images.

[1196] An "emotion recognition engine" is software that analyzes and determines a user's emotions from audio and video data.

[1197] "Emotion data" is user emotion information obtained as a result of analysis by the emotion recognition engine.

[1198] The "sign language translation process" is a series of processes that generate sign language steps and sign language images based on text data and emotion data.

[1199] A "visual projection device" is a device for displaying sign language images in real time, and includes smart glasses and in-factory displays.

[1200] This invention combines an emotion recognition engine with a system that collects voice data, converts the voice data into text data, converts the text data into sign language steps to generate sign language images, and finally transmits and displays the sign language images on a display device. This allows the user's emotions to be reflected in the sign language images, enabling real-time communication, particularly in environments such as factories.

[1201] The system operates in the following manner:

[1202] First, the device collects the user's voice data. It uses noise-canceling technology to capture clear voice data, stores it digitally, and transmits it to a server in real time.

[1203] The server then receives the voice data and converts it into text data using a speech recognition engine, which is then temporarily stored in memory.

[1204] The server then uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the audio data. The emotion recognition engine determines the user's emotional state from the user's facial expressions, tone of voice, speech patterns, etc., and reflects this emotional data in the sign language translation process.

[1205] The server then sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The algorithm uses the emotional data as input to adjust the emphasis, rhythm, and expression of the sign language gestures and movements.

[1206] The server then generates a sign language video based on the sign language steps. This process uses image generation AI and a 3D modeling engine to visually represent the sign language gestures. The sign language video is dynamically adjusted based on the emotion data, creating a video that reflects the user's emotions.

[1207] The generated sign language video data is compressed by the server and sent to the terminal using an appropriate codec (e.g., H.264) to enable efficient transmission.

[1208] Finally, the device receives and decodes the transmitted sign language video data in real time. The decoded sign language video is then projected onto a display device such as a monitor or smart glasses, allowing the user to view the sign language video in real time, reflecting their emotions.

[1209] As a concrete example, the system can be used in factories where verbal communication is difficult, allowing hearing-impaired employees to receive visual instructions in real time, significantly improving work efficiency and reducing errors.

[1210] Example prompt sentence:

[1211] "You will be asked to create a program that will collect the audio and video of the user speaking into a microphone and convert it into sign language video in real time. The sign language video will need to reflect the user's emotions using an emotion recognition engine. This sign language video will then be sent to a display device in the factory in real time and displayed."

[1212] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1213] Step 1:

[1214] Audio data collection

[1215] The device collects the user's voice data. Specifically, when the user speaks into the microphone, the device uses noise-canceling technology to capture clear voice data. The input is the user's voice, and the output is digital voice data. This voice data is sent to the server in real time.

[1216] Step 2:

[1217] Converting audio data to text

[1218] The server receives the voice data and converts it into text data using a speech recognition engine. The input is digital voice data, and the output is text data as character information. The server temporarily stores this text data in memory.

[1219] Step 3:

[1220] emotion recognition

[1221] The server uses an emotion recognition engine to analyze the user's video data and voice intonation sent along with the voice data. The input is the user's facial expression and tone of voice, and the output is emotion data that indicates the user's emotional state. The server acquires this emotion data and reflects it in the sign language translation process.

[1222] Step 4:

[1223] Step-by-step conversion of text data into sign language

[1224] The server sends the text data in memory to a sign language translation algorithm, which converts it into sign language steps. The input is text data and emotional data, and the output is sign language steps, which are a series of sign language gesture instructions. The sign language translation algorithm adjusts the emphasis, rhythm, and expression of the sign language gestures based on the emotional data.

[1225] Step 5:

[1226] Sign language video generation

[1227] The server generates a sign language video based on the sign language steps. This process uses image generation AI and a 3D modeling engine. The input is the sign language steps, and the output is a visual sign language video. The generated sign language video reflects the user's emotions.

[1228] Step 6:

[1229] Video data compression

[1230] The server compresses the generated sign language video data. The input is the sign language video and the output is the compressed video data. The compression uses an appropriate codec (e.g., H.264) to enable efficient data transmission.

[1231] Step 7:

[1232] Sign language video transmission and display

[1233] The device receives and decodes the transmitted sign language video data in real time. The input is compressed video data, and the output is decoded sign language video. The device then projects this sign language video onto a display device such as a monitor or smart glasses. The user can view the sign language video that reflects emotions in real time.

[1234] 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.

[1235] 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.

[1236] 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 robot 414.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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).

[1241] 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.

[1242] 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."

[1243] 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.

[1244] 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).

[1245] 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.

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] 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.

[1253] 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.

[1254] 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.

[1255] The following is further disclosed regarding the above embodiment.

[1256] (Claim 1)

[1257] means for collecting audio data;

[1258] means for converting the voice data into text data;

[1259] means for converting the text data into sign language steps;

[1260] means for generating a sign language image based on the sign language step;

[1261] means for transmitting the sign language video to a display device and displaying it;

[1262] A system including:

[1263] (Claim 2)

[1264] 10. The system of claim 1, wherein the audio data is collected in real time and the sign language video is generated in real time.

[1265] (Claim 3)

[1266] The system of claim 1, further comprising: means for displaying the sign language video on smart glasses.

[1267] "Example 1"

[1268] (Claim 1)

[1269] means for collecting audio data;

[1270] means for converting the voice data into text data;

[1271] means for converting the text data into sign language steps;

[1272] means for generating a sign language image based on the sign language step;

[1273] means for transmitting the sign language video to a display device and displaying it;

[1274] means for applying noise cancelling techniques to process said audio data in real time;

[1275] means for analyzing the text data using a sign language translation algorithm;

[1276] A method for generating realistic sign language images using 3D modeling technology, and

[1277] A system including:

[1278] (Claim 2)

[1279] 10. The system of claim 1, wherein the audio data is collected in real time and the sign language video is generated in real time.

[1280] (Claim 3)

[1281] 10. The system of claim 1, further comprising means for displaying said sign language video on a visual device of a user.

[1282] "Application Example 1"

[1283] (Claim 1)

[1284] means for collecting audio data;

[1285] means for converting the voice data into text data;

[1286] means for converting the text data into sign language steps;

[1287] means for generating a sign language image based on the sign language step;

[1288] means for transmitting the sign language video to a display device and displaying it;

[1289] means for applying noise canceling technology to said means for collecting audio data;

[1290] means for displaying the sign language video on a display of smart glasses;

[1291] A system including:

[1292] (Claim 2)

[1293] 10. The system of claim 1, wherein the audio data is collected in real time and the sign language video is generated in real time.

[1294] (Claim 3)

[1295] 10. The system of claim 1, further comprising: means for displaying the sign language video on smart glasses.

[1296] "Example 2: Combining Emotion Engines"

[1297] (Claim 1)

[1298] means for collecting audio data;

[1299] means for converting the voice data into text data;

[1300] means for analyzing the text data and generating emotion data;

[1301] means for converting the text data including the emotion data into sign language steps;

[1302] means for generating a sign language image based on the sign language step and the emotion data;

[1303] means for transmitting the sign language video to a display device and displaying it;

[1304] A system including:

[1305] (Claim 2)

[1306] The system of claim 1 , wherein the voice data and the emotion data are collected in real time, and the sign language video is generated in real time.

[1307] (Claim 3)

[1308] 10. The system of claim 1, further comprising means for displaying said sign language video on a display device in real time.

[1309] "Application example 2 when combining emotion engines"

[1310] (Claim 1)

[1311] means for collecting audio data;

[1312] means for converting the voice data into text data;

[1313] means for converting the text data into sign language steps;

[1314] means for generating a sign language image based on the sign language step;

[1315] means for transmitting the sign language video to a display device and displaying it;

[1316] means for determining a user's emotion using an emotion recognition engine;

[1317] means for reflecting the emotion data in a sign language translation process;

[1318] A system including:

[1319] (Claim 2)

[1320] 10. The system of claim 1, wherein the audio data is collected in real time and the sign language video is generated in real time.

[1321] (Claim 3)

[1322] 10. The system of claim 1, further comprising means for displaying said sign language image on a visual projection device. [Explanation of symbols]

[1323] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting audio data; means for converting the voice data into text data; means for converting the text data into sign language steps; means for generating a sign language image based on the sign language step; means for transmitting the sign language video to a display device and displaying it; A system including:

2. The system of claim 1 , wherein the audio data is collected in real time and the sign language video is generated in real time.

3. The system of claim 1 , further comprising: means for displaying the sign language video on smart glasses.

Images