system

Abstract

Provide a system. A method for receiving voice data and converting the voice data into text data; means for receiving image data and extracting necessary information from the image data; means for integrating the text data and the necessary information to generate a final message; means for converting said final message into sign language indicia; means for generating motion data based on the sign language indicators; means for applying the motion data to a virtual character to display sign language; A system including:

Description

[Technical Field]

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

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

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

[0004] Conventionally, there have been limited means for users with hearing impairments to convert speech and images into sign language, and there has been a lack of technology that provides real-time, accurate sign language translation. In particular, there is no system that can simultaneously process speech and images, integrate them, and translate them into sign language, making communication difficult for many users. The present invention aims to solve these problems and provide a system that can accurately translate speech and images into sign language in real time. [Means for solving the problem]

[0005] The present invention is a system including: means for receiving voice data and converting the voice data into text data; means for receiving image data and extracting necessary information from the image data; means for integrating the text data and the necessary information to generate a final message; means for converting the final message into sign language indicators; means for generating motion data based on the sign language indicators; and means for applying the motion data to a virtual character to display sign language. The system also uses a voice recognition engine to convert the voice data into text data and an image recognition engine to extract necessary information from the image data, thereby integrating voice and image information to provide accurate sign language translation in real time.

[0006] "Voice data" refers to data that electronically records words spoken by a user.

[0007] "Image data" is electronic data that includes information about still images or videos uploaded by users.

[0008] "Text data" is character information extracted from audio data or image data.

[0009] The "required information" is detailed indicators and features of the sign language recognized from the image data.

[0010] The "final message" is a text containing integrated information generated from the audio data and the image data.

[0011] "Sign language indicators" are specific instructions such as hand shapes, movements, and position information used to express sign language.

[0012] "Motion data" is action information for giving movement to a virtual character based on sign language indicators.

[0013] A "virtual character" is a fictitious person or character that appears on the screen of a tablet device. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The system of the present invention translates voice data and image data into sign language in real time and displays the translated data using a virtual character. Each component of this system will now be described.

[0036] System Overview

[0037] 1. User Action:

[0038] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[0039] 2. Data Collection and Transmission:

[0040] The device captures the voice input and transmits the voice data to the server.

[0041] The terminal also transmits the uploaded image data to the server.

[0042] 3. Data analysis and synthesis:

[0043] The server converts the received voice data into text data using a voice recognition engine.

[0044] The server analyzes the received image data using an image recognition engine and extracts the information necessary for sign language.

[0045] The server integrates the information extracted from the audio and image data to generate the final message.

[0046] 4. Sign Language Translation:

[0047] The server converts the final message into sign language indicators and generates motion data.

[0048] 5. Virtual character display:

[0049] The server transmits the generated motion data to the terminal.

[0050] Based on the received motion data, the device moves the virtual character and displays sign language.

[0051] Program processing and specific examples

[0052] 1. Audio data processing:

[0053] The user speaks "Hello!"

[0054] The device captures the audio data and sends it to the server.

[0055] The server uses a voice recognition engine to convert the voice data into text data such as "hello."

[0056] 2. Image data processing:

[0057] The user uploads a sign language reference image to the tablet.

[0058] The terminal transmits the image data to the server.

[0059] The server uses an image recognition engine to extract the information necessary for sign language from the image data.

[0060] 3. Data Integration and Sign Language Translation:

[0061] The server combines the text "hello" obtained from speech recognition with the sign language information obtained from image recognition.

[0062] The server converts the synthesized messages into sign language indicators.

[0063] The server generates motion data based on the indicators.

[0064] 4. Sign Language Display:

[0065] The server transmits the generated motion data to the terminal.

[0066] Based on the received motion data, the device makes the virtual character on the tablet screen sign "hello."

[0067] These steps enable hearing-impaired users to understand sign language accurately and in real time. This invention is a technology that extracts information from both audio and images, converts it into sign language, and displays it, thereby solving problems that conventional systems could not solve.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The user speaks "Hello!" into the tablet device or uploads a reference image of the sign language.

[0071] Step 2:

[0072] The user taps the "Start Recording" button to start voice input, or selects an image on the image file selection screen and taps the send button.

[0073] Step 3:

[0074] The device captures the audio input and stores it in a temporary buffer as digital audio data, or in local storage as an image file.

[0075] Step 4:

[0076] The terminal sends the saved audio data and image data to the server as an HTTP request.

[0077] Step 5:

[0078] The server passes the received voice data to a voice recognition engine, which generates the text data "Hello."

[0079] Step 6:

[0080] The server inputs the received image data into an image recognition engine and extracts the information necessary for sign language.

[0081] Step 7:

[0082] The server combines the text obtained from the voice data with the sign language information obtained from the image data to generate the final message.

[0083] Step 8:

[0084] The server converts the final message into sign language indicators and generates sign language motion data.

[0085] Step 9:

[0086] The server encodes the generated sign language motion data and sends it to the terminal as an HTTP response.

[0087] Step 10:

[0088] The device decodes the received sign language motion data and uses a 3D graphics engine to make the virtual character perform the sign language movements.

[0089] Step 11:

[0090] The device displays the sign language "hello" from a virtual character on the screen.

[0091] By performing these steps in succession, accurate sign language translation from audio and images can be achieved in real time, allowing a virtual character to provide visual sign language interpretation to the user.

[0092] Example 1

[0093] 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."

[0094] It is difficult for the hearing impaired to recognize audio and image information as sign language in real time. Furthermore, conventional systems process audio data and image data separately, which can result in a time lag when integrating them. The present invention aims to solve these problems by providing a system that converts audio and images into sign language and displays them in real time.

[0095] 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.

[0096] In this invention, the server includes means for receiving voice data from a user and converting the voice data into text data, means for receiving image data from a user and extracting necessary information from the image data, means for integrating the text data and the necessary information to generate a final message, means for converting the final message into a sign language indicator, means for generating motion data based on the sign language indicator, means for applying the motion data to a virtual character and displaying the sign language, means for receiving the voice data and image data by a terminal and transmitting them to the server, and means for displaying the virtual character by the terminal. This makes it possible to convert voice and image data into sign language in real time and display it using a virtual character.

[0097] "User" means an individual who operates the system or a customer who uses its services.

[0098] "Voice data" refers to data that is a digital recording of a user's speech.

[0099] "Text data" is character string information obtained by analyzing voice data.

[0100] "Image data" refers to digital information of still images or videos uploaded by users.

[0101] The "necessary information" is partial information necessary for the sign language actions extracted from the image data.

[0102] "Synthesis" is the process of combining information from multiple data sources into a single, consistent output.

[0103] The "final message" is the overall information obtained by integrating the information obtained from the audio data and the image data.

[0104] "Sign language indicators" are marker information that serves as the basis for expressing sign language actions.

[0105] "Motion data" refers to character movement data generated from sign language indicators.

[0106] A "virtual character" is an animated character that performs sign language in a digital space.

[0107] A "terminal" is an electronic device used by a user to perform input, and is a device that has the function of capturing audio or image data and transmitting it to a server.

[0108] A "voice recognition engine" is a software or hardware configuration that analyzes voice data and converts it into text data.

[0109] An "image recognition engine" is a software or hardware configuration that analyzes image data and extracts the information necessary for sign language.

[0110] The system of the present invention translates voice data and image data into sign language in real time and displays the translated data using a virtual character. Each component for implementing this system will now be described.

[0111] First, the user can use the tablet device to give voice instructions or upload a reference image of the sign language. This applies to situations where the user says "hello" or selects and uploads a reference image of the sign language.

[0112] Next, the device receives user input, captures voice data, and sends it to the server. To capture voice data, the tablet device's microphone and a voice capture library (e.g., WebRTC) are used. To upload image data, the HTML5 File API is used to send the image data from the tablet's storage to the server.

[0113] The server converts the received voice data into text data using a speech recognition engine such as the Google® Cloud Speech-to-Text API. For example, voice data such as "Hello" is converted into text data such as "Hello." Similarly, the received image data is analyzed using the Google Cloud Vision API or Amazon Rekognition, and the information necessary for sign language is extracted. For example, hand positions and movements are extracted from uploaded sign language images.

[0114] The server then combines the text data obtained from speech recognition with the sign language information obtained from image recognition. The combined message is converted into sign language indicators and motion data is generated using a custom sign language indicator conversion algorithm. At this stage, specific motion data is obtained to create the sign language action for "hello."

[0115] Finally, the server sends the generated motion data to the device, which then uses Unity to move the virtual character based on the received motion data, resulting in the sign language "hello" being displayed on the tablet screen.

[0116] This allows hearing-impaired users to understand sign language accurately and in real time. The system provides a unique technology that integrates audio and image data, translates them into sign language, and displays them.

[0117] Specifically, by inputting the following prompt sentence into the generative AI model, text explaining the system's behavior can be generated.

[0118] "When a user speaks "hello" into the tablet, the voice data is captured on the device and sent to the server. The server converts the voice into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text API), and the text data is converted into the motion data required for sign language. The motion data is then sent to the device, and a virtual character displays the sign for "hello."

[0119] In this way, the system of the present invention can process the user's voice and image data in real time and display them as sign language using a virtual character.

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1:

[0122] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[0123] Specifically, users can say "hello" into the microphone on the tablet device, or select a reference image of sign language and press the upload button.

[0124] Input: User's voice or image data.

[0125] Output: Audio or image data is captured on the device.

[0126] Step 2:

[0127] The device captures the user's voice data and sends it to the server, as well as the uploaded image data.

[0128] Specifically, it uses a voice capture library such as WebRTC to capture audio data and send it to the server, while image data is sent to the server using the HTML5 File API.

[0129] Input: Captured audio data or uploaded image data.

[0130] Output: Audio and image data sent to the server.

[0131] Step 3:

[0132] The server converts the received voice data into text data using a voice recognition engine, and also analyzes the received image data using an image recognition engine to extract the information necessary for sign language.

[0133] Specifically, the Google Cloud Speech-to-Text API is used to convert the audio data of "Hello" into the text "Hello." The Google Cloud Vision API is used to analyze the uploaded sign language reference image.

[0134] Input: Audio and image data received by the server.

[0135] Output: Text data and information required for sign language.

[0136] Step 4:

[0137] The server integrates the text data obtained from speech recognition with the sign language information obtained from image recognition, converts the integrated data into sign language indicators, and generates motion data using the indicators.

[0138] Specifically, a custom sign language index conversion algorithm is used to integrate the text "Hello" with sign language information and generate sign language motion data.

[0139] Input: Text data generated by speech recognition and sign language information generated by image recognition.

[0140] Output: Sign language motion data.

[0141] Step 5:

[0142] The server transmits the generated motion data to the terminal.

[0143] Specifically, the motion data is transmitted to the terminal via the network.

[0144] Input: Sign language motion data.

[0145] Output: The motion data sent to the device.

[0146] Step 6:

[0147] The terminal moves the virtual character and displays the sign language based on the received motion data.

[0148] Specifically, it uses Unity to animate a virtual character and display the sign for "hello" on the tablet screen.

[0149] Input: Motion data sent from the server.

[0150] Output: Sign language displayed by a virtual character.

[0151] This allows for a process of converting voice and image data into sign language in real time and displaying it using a virtual character.

[0152] (Application example 1)

[0153] 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."

[0154] Communication between hearing-impaired customers and store staff has traditionally been difficult because it requires knowledge of sign language. Furthermore, there is no way to display sign language in real time in typical customer service situations, making smooth communication difficult. As a result, hearing-impaired customers often lack the ability to receive adequate service and are often forced to endure inconvenience.

[0155] 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.

[0156] In this invention, the server includes means for receiving voice data and converting the voice data into text data, means for receiving image data and extracting necessary information from the image data, and means for integrating the text data and the necessary information to generate a final message. This makes it possible to use voice data or image data to assist communication between customers and store clerks when serving customers in a physical store.

[0157] "Voice data" is digital information that is a recording or real-time capture of a user's voice.

[0158] "Text data" is character string information converted from voice data.

[0159] "Image data" is digital data that includes visual information captured by a camera, scanner, etc.

[0160] "Sign language" is a form of linguistic expression using hands and facial expressions that is used by people with hearing impairments to communicate.

[0161] "Virtual character" refers to an imaginary person or character that operates in a digital environment.

[0162] "Integrating" refers to combining multiple pieces of data or information into a single entity.

[0163] "Motion data" is digital information that includes a series of instructions for causing a virtual character to perform a particular movement.

[0164] A "physical store" refers to a commercial establishment that exists in a physical location and where customers can visit and receive goods or services in person.

[0165] "Customer" refers to a person who visits a physical store and receives goods or services.

[0166] A "store clerk" refers to someone who provides products and services to customers in a physical store.

[0167] This invention describes a specific embodiment of a system that translates voice data and image data into sign language in real time and displays them as a virtual character. An example of this system that facilitates communication between customers and store clerks in a brick-and-mortar store is shown below.

[0168] System infrastructure

[0169] 1. Audio and text data processing

[0170] The user (store clerk or customer) inputs voice data using the microphone on their smartphone. This voice data is captured by the device and sent to the server. The server then uses voice recognition software (e.g., Google Speech Recognition API) to convert the voice data into text data.

[0171] 2. Image data processing

[0172] The user uploads a reference image of the sign language to their smartphone. The device then sends this image data to the server. The server then uses an image recognition engine (e.g., OpenCV) to extract the information necessary for the sign language from the image data.

[0173] 3. Data integration and sign language translation

[0174] The server integrates the text data obtained from speech recognition with the sign language information obtained from image recognition. The server then converts the integrated message into sign language indicators and generates motion data. This is done using a dedicated sign language translation model (generative AI model).

[0175] 4. Display of virtual characters

[0176] The server then sends the generated motion data to the smartphone, which then uses the received motion data to move the virtual character on the smartphone screen and display sign language, allowing hearing-impaired customers to easily communicate with store staff.

[0177] Specific examples

[0178] For example, if a customer voice-inputs "Hello!", this is converted into text data "Hello." Reference images of the sign language are then uploaded to extract features, and the voice and image data are combined to create the final message. A virtual character then displays "Hello" in sign language.

[0179] Specific examples of hardware and software used

[0180] Hardware: Smartphones, tablets

[0181] Software: Google Speech Recognition API (voice recognition), OpenCV (image recognition)

[0182] Generative AI models: Custom-developed or pre-trained sign language translation models

[0183] Prompt Sentence Examples

[0184] "Generate a program that converts audio data into text, extracts the necessary sign language information from reference images, and makes a virtual character perform sign language movements."

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] The user inputs voice data into the smartphone's microphone. The voice data is sent to the device as an analog signal. The user also uploads reference images of sign language to the smartphone. The image data is sent to the device as a digital signal.

[0188] Step 2:

[0189] The terminal captures the audio data and converts it into a digital audio file, for example, in an audio file format (such as WAV), which is then sent to the server. At the same time, the image data is sent to the server in digital format. The input is an analog audio signal and digital image data, and the output is a digital audio file and image data.

[0190] Step 3:

[0191] The server uses a speech recognition engine (e.g., Google Speech Recognition API) to convert the audio file into text data. The server converts the audio file into text format and stores it in its internal memory. The input is a digital audio file, and the output is text data.

[0192] Step 4:

[0193] The server uses an image recognition engine (e.g., OpenCV) to extract the features required for sign language from the uploaded image data. It analyzes the image data and extracts features such as joint positions and movement vectors required for sign language movements. The input is image data, and the output is feature data required for sign language.

[0194] Step 5:

[0195] The server combines the text data obtained from the speech recognition engine with the sign language information obtained from the image recognition engine, thereby generating the final message. The input is text data and sign language feature data, which are combined to create a single integrated message. The output is the integrated message.

[0196] Step 6:

[0197] A generative AI model (sign language translation model) is used to convert the integrated message into sign language movements. Specifically, the integrated message is used as input, converted into sign language indicators, and motion data is generated. The input is the integrated message, and the output is motion data.

[0198] Step 7:

[0199] The server generates motion data and sends it to the device. The device receives this motion data and uses it to move the virtual character. The input is the motion data, and the output is the motion data sent to the device.

[0200] Step 8:

[0201] Based on the motion data received by the device, a virtual character is displayed on the smartphone screen. The virtual character then performs greetings such as "hello" in sign language. The input is motion data, and the output is the sign language movements of the virtual character.

[0202] 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.

[0203] The system of the present invention translates voice data and image data into sign language in real time and displays it using a virtual character. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more natural and rich communication is realized. The components of this system and the program processing are explained below.

[0204] System Overview

[0205] 1. User Action:

[0206] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[0207] The system also recognizes emotions from the user's voice and facial expressions and generates sign language that corresponds to the emotion.

[0208] 2. Data Collection and Transmission:

[0209] The device captures the voice input and transmits the voice data to the server.

[0210] The terminal also transmits the uploaded image data to the server.

[0211] 3. Data analysis and synthesis:

[0212] The server converts the received voice data into text data using a voice recognition engine.

[0213] The server analyzes the received image data using an image recognition engine and extracts the information necessary for sign language.

[0214] The server uses an emotion engine to recognize the user's emotion from the voice data and image data.

[0215] The server integrates the information extracted from the audio and image data with the recognized emotions to generate the final message.

[0216] 4. Sign Language Translation:

[0217] The server converts the final message into sign language indicators and generates motion data.

[0218] The virtual character's facial expressions and movements are also modified based on the generated sign language indicators and emotions.

[0219] 5. Virtual character display:

[0220] The server transmits the generated motion data to the terminal.

[0221] The device moves the virtual character based on the received motion data and displays emotional expressions along with sign language.

[0222] Program processing and specific examples

[0223] 1. Audio data processing:

[0224] The user speaks "Hello!"

[0225] The device captures the audio data and sends it to the server.

[0226] The server uses a voice recognition engine to convert the voice data into text data such as "hello."

[0227] 2. Image data processing:

[0228] The user uploads a sign language reference image to the tablet.

[0229] The terminal transmits the image data to the server.

[0230] The server uses an image recognition engine to extract the information necessary for sign language from the image data.

[0231] 3. Emotion Recognition:

[0232] The server passes the voice data and image data to an emotion engine to recognize the user's emotion.

[0233] For example, the emotion "happy" can be recognized by analyzing the tone of voice and facial expressions in images.

[0234] 4. Message Integration and Sign Language Translation:

[0235] The server integrates the text "hello" obtained from speech recognition, the sign language information obtained from image recognition, and the recognized emotions.

[0236] The server converts the synthesized messages into sign language indicators and generates sign language motion data.

[0237] Add facial expressions and movements that correspond to emotions to virtual characters.

[0238] 5. Sign Language and Emotional Expressions:

[0239] The server transmits the generated sign language motion data and emotional expression data to the terminal.

[0240] Based on the data received, the device makes the virtual character on the tablet screen sign "hello" and make a "happy" expression.

[0241] These steps enable accurate and emotional sign language translation from audio and images in real time, allowing virtual characters to provide users with visual and intuitive sign language interpretation.The present invention is a technology that integrates audio, image, and emotional information, converts it into sign language, and displays it, promoting rich communication.

[0242] The processing flow will be explained below.

[0243] Step 1:

[0244] The user speaks "Hello!" into the tablet device or uploads a reference image of the sign language.

[0245] Step 2:

[0246] The user taps the "Start Recording" button to start voice input, or selects an image on the image file selection screen and taps the send button.

[0247] Step 3:

[0248] The device captures the audio input and stores it in a temporary buffer as digital audio data, or in local storage as an image file.

[0249] Step 4:

[0250] The terminal sends the saved audio data and image data to the server as an HTTP request.

[0251] Step 5:

[0252] The server passes the received voice data to a voice recognition engine, which generates the text data "Hello."

[0253] Step 6:

[0254] The server inputs the received image data into an image recognition engine and extracts the information necessary for sign language.

[0255] Step 7:

[0256] The server passes the voice data and image data to an emotion engine to recognize the user's emotion.

[0257] Step 8:

[0258] The server combines the text "Hello" obtained from speech recognition, the sign language information obtained from image recognition, and the user's emotions recognized by the emotion engine to generate the final message.

[0259] Step 9:

[0260] The server converts the final message into sign language indicators and generates sign language motion data, which changes the virtual character's facial expressions and movements based on the user's emotions.

[0261] Step 10:

[0262] The server transmits the generated sign language motion data and emotional expression data to the terminal.

[0263] Step 11:

[0264] The terminal decodes the received sign language motion data and emotional expression data, and uses a 3D graphics engine to make the virtual character perform the sign language movements and facial expressions.

[0265] Step 12:

[0266] The device displays the virtual character's sign language "hello" and a "happy" facial expression based on the recognized emotion on the screen.

[0267] By performing these steps sequentially, the system can extract information, including the user's emotions, from audio and images, and provide accurate and emotive sign language translation in real time. This system provides a more natural and intuitive means of communication for hearing-impaired users.

[0268] Example 2

[0269] 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."

[0270] In today's world, there is a demand for simple and effective sign language interpretation systems as a means of facilitating smooth communication between the hearing impaired and hearing people. However, conventional sign language interpretation systems lack the ability to express emotions, making it difficult to produce natural sign language that accurately reflects the user's emotions. In addition, their inadequate real-time processing of audio and image data can lead to communication delays and misunderstandings.

[0271] 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.

[0272] In this invention, the server includes means for receiving voice data and converting the voice data into text data, means for receiving image data and extracting necessary information from the image data, and means for recognizing a user's emotion from the voice data and the image data and generating sign language based on the emotion, thereby enabling natural and rich sign language expressions that reflect the user's emotion in real time.

[0273] "Audio Data" means data in digital form derived from audio input.

[0274] "Text data" refers to digital data that has been converted from audio data into text information.

[0275] "Image data" refers to image information in digital form obtained from a camera or other image capture device.

[0276] "Necessary information" refers to indicators and features extracted from image data that are necessary for sign language translation.

[0277] The "final message" refers to a message generated by integrating the text data converted from the voice data and the necessary information extracted from the image data.

[0278] A "sign language indicator" is a standard or reference point that translates a sign expression into a form that can be applied as a movement.

[0279] "Motion data" refers to movement information applied to a virtual character.

[0280] A "virtual character" is an imaginary person or character that performs sign language actions within a digital environment.

[0281] "Emotion recognition" refers to the process of detecting and identifying a user's emotions and moods from their voice or image data.

[0282] The system of the present invention translates voice data and image data into sign language in real time and displays it using a virtual character. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more natural and rich communication is realized. Each component of this system and its processing will be specifically explained.

[0283] Components and hardware / software used

[0284] 1. User Action:

[0285] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[0286] In this case, the tablet device needs to be equipped with a high-performance microphone and camera.

[0287] The system uses data from cameras and microphones to recognize emotions from the user's voice and facial expressions.

[0288] 2. Data Collection and Transmission:

[0289] The device sends the captured audio data to a server via the Internet, using the HTTPS communication protocol.

[0290] Image data uploaded by the user is also sent to the server in the same way.

[0291] 3. Data analysis and synthesis:

[0292] Speech recognition engine: The server uses the Google Cloud Speech-to-Text API to convert voice data into text data.

[0293] Image recognition engine: The server uses Amazon Rekognition to extract the information necessary for sign language from the image data.

[0294] Emotion engine: The server uses Microsoft® Azure® Emotion API to recognize the user's emotions from voice data and image data.

[0295] The server integrates the information extracted from the audio and image data with the recognized emotions to generate the final message.

[0296] 4. Sign Language Translation:

[0297] The server converts the synthesized messages into sign language indicators and generates motion data, where the sign language indicators are converted into a form that can be applied as actions.

[0298] The virtual character's facial expressions and movements are also modified based on the generated sign language indicators and emotions.

[0299] 5. Virtual character display:

[0300] The server transmits the generated motion data to the terminal.

[0301] The device moves the virtual character based on the received motion data and displays emotional expressions along with sign language.

[0302] Specific examples

[0303] 1. Audio data processing:

[0304] The user speaks "Hello!"

[0305] The device captures the audio data and sends it to the server.

[0306] The server uses the Google Cloud Speech-to-Text API to convert the audio data into the text "Hello."

[0307] 2. Image data processing:

[0308] The user uploads a sign language reference image to the tablet.

[0309] The terminal transmits the image data to the server.

[0310] The server uses Amazon Rekognition to extract the information needed for sign language from the image data.

[0311] 3. Emotion Recognition:

[0312] The server passes the audio and image data to the Microsoft Azure Emotion API to recognize the user's emotions.

[0313] For example, the emotion "happy" can be recognized by analyzing the tone of voice and facial expressions in images.

[0314] 4. Message Integration and Sign Language Translation:

[0315] The server integrates the text "hello" obtained from speech recognition, the sign language information obtained from image recognition, and the recognized emotions.

[0316] The server converts the synthesized messages into sign language indicators and generates sign language motion data.

[0317] Add facial expressions and movements that correspond to emotions to virtual characters.

[0318] 5. Sign Language and Emotional Expressions:

[0319] The server transmits the generated sign language motion data and emotional expression data to the terminal.

[0320] Based on the data received, the device makes the virtual character on the tablet screen sign "hello" and make a "happy" expression.

[0321] Example prompt sentence:

[0322] When a user says "Hello!" on a tablet, how is that voice processed and how does the virtual character ultimately react? Please explain in detail the steps involved.

[0323] In this way, the present invention provides a technology that integrates audio, image, and emotional information, converts it into sign language, and displays it, thereby realizing rich communication.

[0324] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0325] Step 1: User Speech Input

[0326] The user issues a voice command to the tablet device, for example, "Hello!"

[0327] The device uses its built-in microphone to capture this audio.

[0328] Input: User's voice

[0329] Output: Captured audio data

[0330] Specific behavior:

[0331] The terminal records the voice data uttered by the user and converts it into digital voice data.

[0332] Step 2: Sending audio data

[0333] The device transmits the captured audio data to a server via the Internet.

[0334] HTTPS is used as the communication protocol for transmission.

[0335] Input: Captured audio data

[0336] Output: Audio data sent to the server

[0337] Specific behavior:

[0338] The device divides the captured audio data into packets and securely transmits them to the server.

[0339] Step 3: Voice Recognition

[0340] The server uses the Google Cloud Speech-to-Text API to convert the received voice data into text data.

[0341] Input: Transmitted audio data

[0342] Output: Text data "Hello"

[0343] Specific behavior:

[0344] The server sends the audio data to the Google Cloud Speech-to-Text API, which analyzes the audio and converts it into text data.

[0345] Step 4: Upload user images

[0346] Users upload sign language reference images to a tablet.

[0347] The tablet device stores this image data.

[0348] Input: User uploaded image

[0349] Output: Image data saved on the device

[0350] Specific behavior:

[0351] The user uploads an image of sign language taken with the tablet device's camera, and the device stores the image data.

[0352] Step 5: Sending image data

[0353] The terminal transmits the uploaded image data to a server via the Internet.

[0354] HTTPS is used as the communication protocol for transmission.

[0355] Input: Saved image data

[0356] Output: Image data sent to the server

[0357] Specific behavior:

[0358] The device divides the stored image data into packets and securely transmits them to the server.

[0359] Step 6: Image Recognition

[0360] The server uses Amazon Rekognition to extract the information necessary for sign language from the received image data.

[0361] Input: Image data sent

[0362] Output: Extracted sign language information

[0363] Specific behavior:

[0364] The server sends the image data to Amazon Rekognition, which analyzes the image and extracts the information needed for sign language.

[0365] Step 7: Emotion Recognition

[0366] The server uses the Microsoft Azure Emotion API to recognize the user's emotions from voice and image data.

[0367] Input: Text data and sign language information

[0368] Output: Emotion data "It looks fun"

[0369] Specific behavior:

[0370] The server sends the text and image data to the Microsoft Azure Emotion API, which analyzes and recognizes the user's emotions.

[0371] Step 8: Message Integration

[0372] The server integrates the text data obtained from speech recognition, the sign language information obtained from image recognition, and the emotion data obtained from emotion recognition to generate the final message.

[0373] Input: Text data, sign language information, emotion data

[0374] Output: Final consolidated message

[0375] Specific behavior:

[0376] The server consolidates the information obtained from each data source and generates a final message as a response to the user.

[0377] Step 9: Sign Language Translation

[0378] The server converts the synthesized messages into sign language indicators and generates sign language motion data.

[0379] Input: Final consolidated message

[0380] Output: Sign language motion data

[0381] Specific behavior:

[0382] The server converts the final message into sign language movements and gestures and generates the motion data.

[0383] Step 10: Sending motion data

[0384] The server transmits the generated sign language motion data to the terminal.

[0385] Input: Sign language motion data

[0386] Output: Motion data sent to the device

[0387] Specific behavior:

[0388] The sign language motion data generated by the server is divided into packets and securely transmitted to the terminal.

[0389] Step 11: Virtual Character Movement

[0390] The device controls the virtual character based on the received motion data.

[0391] The character signs "hello" and expresses a "happy" expression.

[0392] Input: Received motion data

[0393] Output: A moving virtual character

[0394] Specific behavior:

[0395] Based on the motion data received by the terminal, an animation of the virtual character is generated and displayed to the user.

[0396] (Application example 2)

[0397] 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."

[0398] Previously, there was no smooth means of communication with hearing-impaired employees in factories, making it difficult to give instructions or feedback on work, resulting in a lack of safety and efficiency.In addition, there was a lack of means to understand employees' emotions and provide appropriate feedback, so improvements to the working environment were required.

[0399] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving voice data and converting the voice data into text data, means for receiving image data and extracting necessary information from the image data, means for receiving emotion data and evaluating the emotion data, means for integrating the text data and the necessary information and generating a final message, means for converting the final message into a sign language indicator, means for generating motion data based on the sign language indicator, means for integrating emotional expressions into the motion data, means for applying the motion data to a virtual character and displaying sign language, and means for controlling the operation of a factory robot. This enables smooth and safe communication with hearing-impaired employees.

[0400] "Audio data" refers to sound information acquired through a voice input device.

[0401] "Text data" is information in which voice data is expressed as a string of characters.

[0402] "Image data" is visual information acquired by an image input device.

[0403] "Necessary information" is specific important data that is analyzed and extracted from the image data.

[0404] The "final message" is detailed instructions or information generated by integrating information obtained from audio and visual data.

[0405] "Sign language indicators" are information representing sign language actions created based on the final message.

[0406] "Motion data" refers to the movement data of a virtual character created based on sign language indicators.

[0407] A "virtual character" is a fictitious person or character that appears digitally.

[0408] "Emotion data" is data that expresses the user's emotional state.

[0409] A "factory robot" is an autonomous or remotely controlled mechanical device that performs various tasks in a factory.

[0410] A "speech recognition engine" is software or an algorithm for analyzing voice data and converting it into text data.

[0411] An "image recognition engine" is software or algorithms that analyze image data and extract necessary information.

[0412] An "emotion engine" is software or an algorithm that uses voice data and image data to evaluate and analyze a user's emotions.

[0413] This invention relates to a system for realizing smooth communication with hearing-impaired employees in a factory environment. This system translates voice data and image data into sign language in real time, and combines this with emotion recognition to enable factory robots to understand instructions and perform tasks. Detailed embodiments of this invention are described below.

[0414] Hardware and software used

[0415] 1. Hardware

[0416] Audio input device: Use a microphone or other audio capture device.

[0417] Image Input Device: Use an image capture device such as a camera.

[0418] Factory robots: Use autonomous or remotely controlled machinery to perform various tasks within a factory.

[0419] Smart glasses or head-mounted display (HMD): worn by the operator and used to provide instructions and display sign language.

[0420] 2. Software

[0421] Speech recognition engine: Converts voice data into text data using Google Cloud Speech-to-Text or similar.

[0422] Image recognition engine: Uses Tensorflow (registered trademark), OpenCV, etc. to extract necessary information from image data.

[0423] Emotion engine: Recognizes and evaluates user emotions using Microsoft Azure Emotion Recognition, etc.

[0424] Motion Generation Engine: Use the Custom Motion Engine to generate sign language motion data.

[0425] Virtual character control software: Use software such as Unity to control a virtual character and display sign language and emotional expressions.

[0426] System Operation

[0427] 1. Receiving and processing audio data

[0428] When a user issues a command by voice, the voice input device captures the voice data and sends it to the server, which uses a voice recognition engine to convert the voice data into text data.

[0429] 2. Receiving and processing image data

[0430] When a user signs through the smart glasses or HMD, the image input device captures the image data of the sign and sends it to the server, which uses an image recognition engine to extract the information needed for the sign from the image data.

[0431] 3. Receiving and processing emotional data

[0432] The server uses an emotion engine to recognize and evaluate the user's emotions using voice and image data. For example, it analyzes the tone of the voice and facial expressions when signing and recognizes emotions such as "feeling happy."

[0433] 4. Producing the final message

[0434] The server combines the text data obtained from speech recognition, the sign language information obtained from image recognition, and the emotion data to generate a final message. Based on this message, it generates sign language indicators and motion data.

[0435] 5. Sign Language and Emotional Expressions

[0436] The server applies the generated motion data and emotional expression data to the virtual character using virtual character control software, which allows the sign language and emotional expressions to be displayed in real time.

[0437] 6. Control of factory robots

[0438] Instructions based on the final message are transmitted to the factory robot, causing it to perform the appropriate task.

[0439] Specific examples

[0440] For example, in a factory, an operator signs to say, "Please carry the luggage." The voice input device captures the voice, and the image input device captures the sign. The server processes this data and transmits the command "carry the luggage" to a factory robot. At the same time, the smart glasses display visualizes feedback according to the operator's emotions and conveys it to the operator.

[0441] Prompt Sentence Examples

[0442] Voice: "Please carry my luggage."

[0443] Emotion: "It looks fun"

[0444] Sign Language: "Sign language gestures for carrying luggage"

[0445] This dramatically improves communication with hearing-impaired employees, creating a safer and more efficient working environment.

[0446] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0447] Step 1:

[0448] The user issues a command by voice. The voice input device captures the voice data and sends it to the terminal. The terminal then sends the voice data to the server.

[0449] Input: User's voice

[0450] Output: Audio data

[0451] Step 2:

[0452] The server uses a speech recognition engine to convert the voice data into text data. Specifically, the speech recognition algorithm analyzes the waveform of the voice and generates a corresponding string of characters.

[0453] Input: Audio data

[0454] Output: Text data

[0455] Step 3:

[0456] The user signs through smart glasses or an HMD. The image input device captures the sign image data and sends it to the device. The device then sends the image data to the server.

[0457] Input: Sign language gesture

[0458] Output: Image data

[0459] Step 4:

[0460] The server uses an image recognition engine to extract the necessary sign language information from the image data. Specifically, the image recognition algorithm analyzes the image data and generates information corresponding to sign language gestures.

[0461] Input: Image data

[0462] Output: Sign language information

[0463] Step 5:

[0464] The server passes the voice and image data to the emotion engine to recognize the user's emotions. Specifically, it analyzes the tone of the voice and facial expressions when signing, and generates an emotional status.

[0465] Input: Audio data, image data

[0466] Output: Emotion data

[0467] Step 6:

[0468] The server combines the text data obtained from speech recognition, the sign language information obtained from image recognition, and the emotion data to generate the final message, which then generates the sign language indicators and motion data.

[0469] Input: Text data, sign language information, emotion data

[0470] Output: Final message

[0471] Step 7:

[0472] The server converts the final message into sign language indicators and generates sign language motion data. Specifically, it references a sign language database and obtains the corresponding motion data from the sign language indicators.

[0473] Input: Final message

[0474] Output: Sign Language Index

[0475] Step 8:

[0476] The server applies the generated motion data and emotional expression data to the virtual character using virtual character control software, and the virtual character displays sign language and emotional expressions in real time.

[0477] Input: Sign language indicators, emotion data

[0478] Output: Virtual character movement data

[0479] Step 9:

[0480] The server transmits instructions based on the final message to the factory robot to cause it to perform the appropriate task, specifically, inputs the final message into the control system of the factory robot and generates a corresponding operation command.

[0481] Input: Final message

[0482] Output: Robot movement instructions

[0483] In this way, by appropriately processing and integrating voice data, image data, and emotional data at each step, it is demonstrated that this system enables communication with hearing-impaired employees in a factory environment.

[0484] 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.

[0485] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0486] 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.

[0487] [Second embodiment]

[0488] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0489] 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.

[0490] 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).

[0491] 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.

[0492] 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.

[0493] 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).

[0494] 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.

[0495] 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.

[0496] 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.

[0497] 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.

[0498] 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.

[0499] 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."

[0500] The system of the present invention translates voice data and image data into sign language in real time and displays the translated data using a virtual character. Each component of this system will now be described.

[0501] System Overview

[0502] 1. User Action:

[0503] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[0504] 2. Data Collection and Transmission:

[0505] The device captures the voice input and transmits the voice data to the server.

[0506] The terminal also transmits the uploaded image data to the server.

[0507] 3. Data analysis and synthesis:

[0508] The server converts the received voice data into text data using a voice recognition engine.

[0509] The server analyzes the received image data using an image recognition engine and extracts the information necessary for sign language.

[0510] The server integrates the information extracted from the audio and image data to generate the final message.

[0511] 4. Sign Language Translation:

[0512] The server converts the final message into sign language indicators and generates motion data.

[0513] 5. Virtual character display:

[0514] The server transmits the generated motion data to the terminal.

[0515] Based on the received motion data, the device moves the virtual character and displays sign language.

[0516] Program processing and specific examples

[0517] 1. Audio data processing:

[0518] The user speaks "Hello!"

[0519] The device captures the audio data and sends it to the server.

[0520] The server uses a voice recognition engine to convert the voice data into text data such as "hello."

[0521] 2. Image data processing:

[0522] The user uploads a sign language reference image to the tablet.

[0523] The terminal transmits the image data to the server.

[0524] The server uses an image recognition engine to extract the information necessary for sign language from the image data.

[0525] 3. Data Integration and Sign Language Translation:

[0526] The server combines the text "hello" obtained from speech recognition with the sign language information obtained from image recognition.

[0527] The server converts the synthesized messages into sign language indicators.

[0528] The server generates motion data based on the indicators.

[0529] 4. Sign Language Display:

[0530] The server transmits the generated motion data to the terminal.

[0531] Based on the received motion data, the device makes the virtual character on the tablet screen sign "hello."

[0532] These steps enable hearing-impaired users to understand sign language accurately and in real time. This invention is a technology that extracts information from both audio and images, converts it into sign language, and displays it, thereby solving problems that conventional systems could not solve.

[0533] The processing flow will be explained below.

[0534] Step 1:

[0535] The user speaks "Hello!" into the tablet device or uploads a reference image of the sign language.

[0536] Step 2:

[0537] The user taps the "Start Recording" button to start voice input, or selects an image on the image file selection screen and taps the send button.

[0538] Step 3:

[0539] The device captures the audio input and stores it in a temporary buffer as digital audio data, or in local storage as an image file.

[0540] Step 4:

[0541] The terminal sends the saved audio data and image data to the server as an HTTP request.

[0542] Step 5:

[0543] The server passes the received voice data to a voice recognition engine, which generates the text data "Hello."

[0544] Step 6:

[0545] The server inputs the received image data into an image recognition engine and extracts the information necessary for sign language.

[0546] Step 7:

[0547] The server combines the text obtained from the voice data with the sign language information obtained from the image data to generate the final message.

[0548] Step 8:

[0549] The server converts the final message into sign language indicators and generates sign language motion data.

[0550] Step 9:

[0551] The server encodes the generated sign language motion data and sends it to the terminal as an HTTP response.

[0552] Step 10:

[0553] The device decodes the received sign language motion data and uses a 3D graphics engine to make the virtual character perform the sign language movements.

[0554] Step 11:

[0555] The device displays the sign language "hello" from a virtual character on the screen.

[0556] By performing these steps in succession, accurate sign language translation from audio and images can be achieved in real time, allowing a virtual character to provide visual sign language interpretation to the user.

[0557] Example 1

[0558] 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."

[0559] It is difficult for the hearing impaired to recognize audio and image information as sign language in real time. Furthermore, conventional systems process audio data and image data separately, which can result in a time lag when integrating them. The present invention aims to solve these problems by providing a system that converts audio and images into sign language and displays them in real time.

[0560] 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.

[0561] In this invention, the server includes means for receiving voice data from a user and converting the voice data into text data, means for receiving image data from a user and extracting necessary information from the image data, means for integrating the text data and the necessary information to generate a final message, means for converting the final message into a sign language indicator, means for generating motion data based on the sign language indicator, means for applying the motion data to a virtual character and displaying the sign language, means for receiving the voice data and image data by a terminal and transmitting them to the server, and means for displaying the virtual character by the terminal. This makes it possible to convert voice and image data into sign language in real time and display it using a virtual character.

[0562] "User" means an individual who operates the system or a customer who uses its services.

[0563] "Voice data" refers to data that is a digital recording of a user's speech.

[0564] "Text data" is character string information obtained by analyzing voice data.

[0565] "Image data" refers to digital information of still images or videos uploaded by users.

[0566] The "necessary information" is partial information necessary for the sign language actions extracted from the image data.

[0567] "Synthesis" is the process of combining information from multiple data sources into a single, consistent output.

[0568] The "final message" is the overall information obtained by integrating the information obtained from the audio data and the image data.

[0569] "Sign language indicators" are marker information that serves as the basis for expressing sign language actions.

[0570] "Motion data" refers to character movement data generated from sign language indicators.

[0571] A "virtual character" is an animated character that performs sign language in a digital space.

[0572] A "terminal" is an electronic device used by a user to perform input, and is a device that has the function of capturing audio or image data and transmitting it to a server.

[0573] A "voice recognition engine" is a software or hardware configuration that analyzes voice data and converts it into text data.

[0574] An "image recognition engine" is a software or hardware configuration that analyzes image data and extracts the information necessary for sign language.

[0575] The system of the present invention translates voice data and image data into sign language in real time and displays the translated data using a virtual character. Each component for implementing this system will now be described.

[0576] First, the user can use the tablet device to give voice instructions or upload a reference image of the sign language. This applies to situations where the user says "hello" or selects and uploads a reference image of the sign language.

[0577] Next, the device receives user input, captures voice data, and sends it to the server. To capture voice data, the tablet device's microphone and a voice capture library (e.g., WebRTC) are used. To upload image data, the HTML5 File API is used to send the image data from the tablet's storage to the server.

[0578] The server converts the received voice data into text data using a speech recognition engine such as the Google Cloud Speech-to-Text API. For example, voice data such as "Hello" is converted into text data such as "Hello." Similarly, the received image data is analyzed using the Google Cloud Vision API or Amazon Rekognition to extract the information necessary for sign language. For example, hand positions and movements are extracted from uploaded sign language images.

[0579] The server then combines the text data obtained from speech recognition with the sign language information obtained from image recognition. The combined message is converted into sign language indicators and motion data is generated using a custom sign language indicator conversion algorithm. At this stage, specific motion data is obtained to create the sign language action for "hello."

[0580] Finally, the server sends the generated motion data to the device, which then uses Unity to move the virtual character based on the received motion data, resulting in the sign language "hello" being displayed on the tablet screen.

[0581] This allows hearing-impaired users to understand sign language accurately and in real time. The system provides a unique technology that integrates audio and image data, translates them into sign language, and displays them.

[0582] Specifically, by inputting the following prompt sentence into the generative AI model, text explaining the system's behavior can be generated.

[0583] "When a user speaks "hello" into the tablet, the voice data is captured on the device and sent to the server. The server converts the voice into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text API), and the text data is converted into the motion data required for sign language. The motion data is then sent to the device, and a virtual character displays the sign for "hello."

[0584] In this way, the system of the present invention can process the user's voice and image data in real time and display them as sign language using a virtual character.

[0585] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0586] Step 1:

[0587] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[0588] Specifically, users can say "hello" into the microphone on the tablet device, or select a reference image of sign language and press the upload button.

[0589] Input: User's voice or image data.

[0590] Output: Audio or image data is captured on the device.

[0591] Step 2:

[0592] The device captures the user's voice data and sends it to the server, as well as the uploaded image data.

[0593] Specifically, it uses a voice capture library such as WebRTC to capture audio data and send it to the server, while image data is sent to the server using the HTML5 File API.

[0594] Input: Captured audio data or uploaded image data.

[0595] Output: Audio and image data sent to the server.

[0596] Step 3:

[0597] The server converts the received voice data into text data using a voice recognition engine, and also analyzes the received image data using an image recognition engine to extract the information necessary for sign language.

[0598] Specifically, the Google Cloud Speech-to-Text API is used to convert the audio data of "Hello" into the text "Hello." The Google Cloud Vision API is used to analyze the uploaded sign language reference image.

[0599] Input: Audio and image data received by the server.

[0600] Output: Text data and information required for sign language.

[0601] Step 4:

[0602] The server integrates the text data obtained from speech recognition with the sign language information obtained from image recognition, converts the integrated data into sign language indicators, and generates motion data using the indicators.

[0603] Specifically, a custom sign language index conversion algorithm is used to integrate the text "Hello" with sign language information and generate sign language motion data.

[0604] Input: Text data generated by speech recognition and sign language information generated by image recognition.

[0605] Output: Sign language motion data.

[0606] Step 5:

[0607] The server transmits the generated motion data to the terminal.

[0608] Specifically, the motion data is transmitted to the terminal via the network.

[0609] Input: Sign language motion data.

[0610] Output: The motion data sent to the device.

[0611] Step 6:

[0612] The terminal moves the virtual character and displays the sign language based on the received motion data.

[0613] Specifically, it uses Unity to animate a virtual character and display the sign for "hello" on the tablet screen.

[0614] Input: Motion data sent from the server.

[0615] Output: Sign language displayed by a virtual character.

[0616] This allows for a process of converting voice and image data into sign language in real time and displaying it using a virtual character.

[0617] (Application example 1)

[0618] 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."

[0619] Communication between hearing-impaired customers and store staff has traditionally been difficult because it requires knowledge of sign language. Furthermore, there is no way to display sign language in real time in typical customer service situations, making smooth communication difficult. As a result, hearing-impaired customers often lack the ability to receive adequate service and are often forced to endure inconvenience.

[0620] 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.

[0621] In this invention, the server includes means for receiving voice data and converting the voice data into text data, means for receiving image data and extracting necessary information from the image data, and means for integrating the text data and the necessary information to generate a final message. This makes it possible to use voice data or image data to assist communication between customers and store clerks when serving customers in a physical store.

[0622] "Voice data" is digital information that is a recording or real-time capture of a user's voice.

[0623] "Text data" is character string information converted from voice data.

[0624] "Image data" is digital data that includes visual information captured by a camera, scanner, etc.

[0625] "Sign language" is a form of linguistic expression using hands and facial expressions that is used by people with hearing impairments to communicate.

[0626] "Virtual character" refers to an imaginary person or character that operates in a digital environment.

[0627] "Integrating" refers to combining multiple pieces of data or information into a single entity.

[0628] "Motion data" is digital information that includes a series of instructions for causing a virtual character to perform a particular movement.

[0629] A "physical store" refers to a commercial establishment that exists in a physical location and where customers can visit and receive goods or services in person.

[0630] "Customer" refers to a person who visits a physical store and receives goods or services.

[0631] A "store clerk" refers to someone who provides products and services to customers in a physical store.

[0632] This invention describes a specific embodiment of a system that translates voice data and image data into sign language in real time and displays them as a virtual character. An example of this system that facilitates communication between customers and store clerks in a brick-and-mortar store is shown below.

[0633] System infrastructure

[0634] 1. Audio and text data processing

[0635] The user (store clerk or customer) inputs voice data using the microphone on their smartphone. This voice data is captured by the device and sent to the server. The server then uses voice recognition software (e.g., Google Speech Recognition API) to convert the voice data into text data.

[0636] 2. Image data processing

[0637] The user uploads a reference image of the sign language to their smartphone. The device then sends this image data to the server. The server then uses an image recognition engine (e.g., OpenCV) to extract the information necessary for the sign language from the image data.

[0638] 3. Data integration and sign language translation

[0639] The server integrates the text data obtained from speech recognition with the sign language information obtained from image recognition. The server then converts the integrated message into sign language indicators and generates motion data. This is done using a dedicated sign language translation model (generative AI model).

[0640] 4. Display of virtual characters

[0641] The server then sends the generated motion data to the smartphone, which then uses the received motion data to move the virtual character on the smartphone screen and display sign language, allowing hearing-impaired customers to easily communicate with store staff.

[0642] Specific examples

[0643] For example, if a customer voice-inputs "Hello!", this is converted into text data "Hello." Reference images of the sign language are then uploaded to extract features, and the voice and image data are combined to create the final message. A virtual character then displays "Hello" in sign language.

[0644] Specific examples of hardware and software used

[0645] Hardware: Smartphones, tablets

[0646] Software: Google Speech Recognition API (voice recognition), OpenCV (image recognition)

[0647] Generative AI models: Custom-developed or pre-trained sign language translation models

[0648] Prompt Sentence Examples

[0649] "Generate a program that converts audio data into text, extracts the necessary sign language information from reference images, and makes a virtual character perform sign language movements."

[0650] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0651] Step 1:

[0652] The user inputs voice data into the smartphone's microphone. The voice data is sent to the device as an analog signal. The user also uploads reference images of sign language to the smartphone. The image data is sent to the device as a digital signal.

[0653] Step 2:

[0654] The terminal captures the audio data and converts it into a digital audio file, for example, in an audio file format (such as WAV), which is then sent to the server. At the same time, the image data is sent to the server in digital format. The input is an analog audio signal and digital image data, and the output is a digital audio file and image data.

[0655] Step 3:

[0656] The server uses a speech recognition engine (e.g., Google Speech Recognition API) to convert the audio file into text data. The server converts the audio file into text format and stores it in its internal memory. The input is a digital audio file, and the output is text data.

[0657] Step 4:

[0658] The server uses an image recognition engine (e.g., OpenCV) to extract the features required for sign language from the uploaded image data. It analyzes the image data and extracts features such as joint positions and movement vectors required for sign language movements. The input is image data, and the output is feature data required for sign language.

[0659] Step 5:

[0660] The server combines the text data obtained from the speech recognition engine with the sign language information obtained from the image recognition engine, thereby generating the final message. The input is text data and sign language feature data, which are combined to create a single integrated message. The output is the integrated message.

[0661] Step 6:

[0662] A generative AI model (sign language translation model) is used to convert the integrated message into sign language movements. Specifically, the integrated message is used as input, converted into sign language indicators, and motion data is generated. The input is the integrated message, and the output is motion data.

[0663] Step 7:

[0664] The server generates motion data and sends it to the device. The device receives this motion data and uses it to move the virtual character. The input is the motion data, and the output is the motion data sent to the device.

[0665] Step 8:

[0666] Based on the motion data received by the device, a virtual character is displayed on the smartphone screen. The virtual character then performs greetings such as "hello" in sign language. The input is motion data, and the output is the sign language movements of the virtual character.

[0667] 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.

[0668] The system of the present invention translates voice data and image data into sign language in real time and displays it using a virtual character. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more natural and rich communication is realized. The components of this system and the program processing are explained below.

[0669] System Overview

[0670] 1. User Action:

[0671] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[0672] The system also recognizes emotions from the user's voice and facial expressions and generates sign language that corresponds to the emotion.

[0673] 2. Data Collection and Transmission:

[0674] The device captures the voice input and transmits the voice data to the server.

[0675] The terminal also transmits the uploaded image data to the server.

[0676] 3. Data analysis and synthesis:

[0677] The server converts the received voice data into text data using a voice recognition engine.

[0678] The server analyzes the received image data using an image recognition engine and extracts the information necessary for sign language.

[0679] The server uses an emotion engine to recognize the user's emotion from the voice data and image data.

[0680] The server integrates the information extracted from the audio and image data with the recognized emotions to generate the final message.

[0681] 4. Sign Language Translation:

[0682] The server converts the final message into sign language indicators and generates motion data.

[0683] The virtual character's facial expressions and movements are also modified based on the generated sign language indicators and emotions.

[0684] 5. Virtual character display:

[0685] The server transmits the generated motion data to the terminal.

[0686] The device moves the virtual character based on the received motion data and displays emotional expressions along with sign language.

[0687] Program processing and specific examples

[0688] 1. Audio data processing:

[0689] The user speaks "Hello!"

[0690] The device captures the audio data and sends it to the server.

[0691] The server uses a voice recognition engine to convert the voice data into text data such as "hello."

[0692] 2. Image data processing:

[0693] The user uploads a sign language reference image to the tablet.

[0694] The terminal transmits the image data to the server.

[0695] The server uses an image recognition engine to extract the information necessary for sign language from the image data.

[0696] 3. Emotion Recognition:

[0697] The server passes the voice data and image data to an emotion engine to recognize the user's emotion.

[0698] For example, the emotion "happy" can be recognized by analyzing the tone of voice and facial expressions in images.

[0699] 4. Message Integration and Sign Language Translation:

[0700] The server integrates the text "hello" obtained from speech recognition, the sign language information obtained from image recognition, and the recognized emotions.

[0701] The server converts the synthesized messages into sign language indicators and generates sign language motion data.

[0702] Add facial expressions and movements that correspond to emotions to virtual characters.

[0703] 5. Sign Language and Emotional Expressions:

[0704] The server transmits the generated sign language motion data and emotional expression data to the terminal.

[0705] Based on the data received, the device makes the virtual character on the tablet screen sign "hello" and make a "happy" expression.

[0706] These steps enable accurate and emotional sign language translation from audio and images in real time, allowing virtual characters to provide users with visual and intuitive sign language interpretation.The present invention is a technology that integrates audio, image, and emotional information, converts it into sign language, and displays it, promoting rich communication.

[0707] The processing flow will be explained below.

[0708] Step 1:

[0709] The user speaks "Hello!" into the tablet device or uploads a reference image of the sign language.

[0710] Step 2:

[0711] The user taps the "Start Recording" button to start voice input, or selects an image on the image file selection screen and taps the send button.

[0712] Step 3:

[0713] The device captures the audio input and stores it in a temporary buffer as digital audio data, or in local storage as an image file.

[0714] Step 4:

[0715] The terminal sends the saved audio data and image data to the server as an HTTP request.

[0716] Step 5:

[0717] The server passes the received voice data to a voice recognition engine, which generates the text data "Hello."

[0718] Step 6:

[0719] The server inputs the received image data into an image recognition engine and extracts the information necessary for sign language.

[0720] Step 7:

[0721] The server passes the voice data and image data to an emotion engine to recognize the user's emotion.

[0722] Step 8:

[0723] The server combines the text "Hello" obtained from speech recognition, the sign language information obtained from image recognition, and the user's emotions recognized by the emotion engine to generate the final message.

[0724] Step 9:

[0725] The server converts the final message into sign language indicators and generates sign language motion data, which changes the virtual character's facial expressions and movements based on the user's emotions.

[0726] Step 10:

[0727] The server transmits the generated sign language motion data and emotional expression data to the terminal.

[0728] Step 11:

[0729] The terminal decodes the received sign language motion data and emotional expression data, and uses a 3D graphics engine to make the virtual character perform the sign language movements and facial expressions.

[0730] Step 12:

[0731] The device displays the virtual character's sign language "hello" and a "happy" facial expression based on the recognized emotion on the screen.

[0732] By performing these steps sequentially, the system can extract information, including the user's emotions, from audio and images, and provide accurate and emotive sign language translation in real time. This system provides a more natural and intuitive means of communication for hearing-impaired users.

[0733] Example 2

[0734] 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."

[0735] In today's world, there is a demand for simple and effective sign language interpretation systems as a means of facilitating smooth communication between the hearing impaired and hearing people. However, conventional sign language interpretation systems lack the ability to express emotions, making it difficult to produce natural sign language that accurately reflects the user's emotions. In addition, their inadequate real-time processing of audio and image data can lead to communication delays and misunderstandings.

[0736] 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.

[0737] In this invention, the server includes means for receiving voice data and converting the voice data into text data, means for receiving image data and extracting necessary information from the image data, and means for recognizing a user's emotion from the voice data and the image data and generating sign language based on the emotion, thereby enabling natural and rich sign language expressions that reflect the user's emotion in real time.

[0738] "Audio Data" means data in digital form derived from audio input.

[0739] "Text data" refers to digital data that has been converted from audio data into text information.

[0740] "Image data" refers to image information in digital form obtained from a camera or other image capture device.

[0741] "Necessary information" refers to indicators and features extracted from image data that are necessary for sign language translation.

[0742] The "final message" refers to a message generated by integrating the text data converted from the voice data and the necessary information extracted from the image data.

[0743] A "sign language indicator" is a standard or reference point that translates a sign expression into a form that can be applied as a movement.

[0744] "Motion data" refers to movement information applied to a virtual character.

[0745] A "virtual character" is an imaginary person or character that performs sign language actions within a digital environment.

[0746] "Emotion recognition" refers to the process of detecting and identifying a user's emotions and moods from their voice or image data.

[0747] The system of the present invention translates voice data and image data into sign language in real time and displays it using a virtual character. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more natural and rich communication is realized. Each component of this system and its processing will be specifically explained.

[0748] Components and hardware / software used

[0749] 1. User Action:

[0750] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[0751] In this case, the tablet device needs to be equipped with a high-performance microphone and camera.

[0752] The system uses data from cameras and microphones to recognize emotions from the user's voice and facial expressions.

[0753] 2. Data Collection and Transmission:

[0754] The device sends the captured audio data to a server via the Internet, using the HTTPS communication protocol.

[0755] Image data uploaded by the user is also sent to the server in the same way.

[0756] 3. Data analysis and synthesis:

[0757] Speech recognition engine: The server uses the Google Cloud Speech-to-Text API to convert voice data into text data.

[0758] Image recognition engine: The server uses Amazon Rekognition to extract the information necessary for sign language from the image data.

[0759] Emotion engine: The server uses the Microsoft Azure Emotion API to recognize the user's emotions from voice and image data.

[0760] The server integrates the information extracted from the audio and image data with the recognized emotions to generate the final message.

[0761] 4. Sign Language Translation:

[0762] The server converts the synthesized messages into sign language indicators and generates motion data, where the sign language indicators are converted into a form that can be applied as actions.

[0763] The virtual character's facial expressions and movements are also modified based on the generated sign language indicators and emotions.

[0764] 5. Virtual character display:

[0765] The server transmits the generated motion data to the terminal.

[0766] The device moves the virtual character based on the received motion data and displays emotional expressions along with sign language.

[0767] Specific examples

[0768] 1. Audio data processing:

[0769] The user speaks "Hello!"

[0770] The device captures the audio data and sends it to the server.

[0771] The server uses the Google Cloud Speech-to-Text API to convert the audio data into the text "Hello."

[0772] 2. Image data processing:

[0773] The user uploads a sign language reference image to the tablet.

[0774] The terminal transmits the image data to the server.

[0775] The server uses Amazon Rekognition to extract the information needed for sign language from the image data.

[0776] 3. Emotion Recognition:

[0777] The server passes the audio and image data to the Microsoft Azure Emotion API to recognize the user's emotions.

[0778] For example, the emotion "happy" can be recognized by analyzing the tone of voice and facial expressions in images.

[0779] 4. Message Integration and Sign Language Translation:

[0780] The server integrates the text "hello" obtained from speech recognition, the sign language information obtained from image recognition, and the recognized emotions.

[0781] The server converts the synthesized messages into sign language indicators and generates sign language motion data.

[0782] Add facial expressions and movements that correspond to emotions to virtual characters.

[0783] 5. Sign Language and Emotional Expressions:

[0784] The server transmits the generated sign language motion data and emotional expression data to the terminal.

[0785] Based on the data received, the device makes the virtual character on the tablet screen sign "hello" and make a "happy" expression.

[0786] Example prompt sentence:

[0787] When a user says "Hello!" on a tablet, how is that voice processed and how does the virtual character ultimately react? Please explain in detail the steps involved.

[0788] In this way, the present invention provides a technology that integrates audio, image, and emotional information, converts it into sign language, and displays it, thereby realizing rich communication.

[0789] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0790] Step 1: User Speech Input

[0791] The user issues a voice command to the tablet device, for example, "Hello!"

[0792] The device uses its built-in microphone to capture this audio.

[0793] Input: User's voice

[0794] Output: Captured audio data

[0795] Specific behavior:

[0796] The terminal records the voice data uttered by the user and converts it into digital voice data.

[0797] Step 2: Sending audio data

[0798] The device transmits the captured audio data to a server via the Internet.

[0799] HTTPS is used as the communication protocol for transmission.

[0800] Input: Captured audio data

[0801] Output: Audio data sent to the server

[0802] Specific behavior:

[0803] The device divides the captured audio data into packets and securely transmits them to the server.

[0804] Step 3: Voice Recognition

[0805] The server uses the Google Cloud Speech-to-Text API to convert the received voice data into text data.

[0806] Input: Transmitted audio data

[0807] Output: Text data "Hello"

[0808] Specific behavior:

[0809] The server sends the audio data to the Google Cloud Speech-to-Text API, which analyzes the audio and converts it into text data.

[0810] Step 4: Upload user images

[0811] Users upload sign language reference images to a tablet.

[0812] The tablet device stores this image data.

[0813] Input: User uploaded image

[0814] Output: Image data saved on the device

[0815] Specific behavior:

[0816] The user uploads an image of sign language taken with the tablet device's camera, and the device stores the image data.

[0817] Step 5: Sending image data

[0818] The terminal transmits the uploaded image data to a server via the Internet.

[0819] HTTPS is used as the communication protocol for transmission.

[0820] Input: Saved image data

[0821] Output: Image data sent to the server

[0822] Specific behavior:

[0823] The device divides the stored image data into packets and securely transmits them to the server.

[0824] Step 6: Image Recognition

[0825] The server uses Amazon Rekognition to extract the information necessary for sign language from the received image data.

[0826] Input: Image data sent

[0827] Output: Extracted sign language information

[0828] Specific behavior:

[0829] The server sends the image data to Amazon Rekognition, which analyzes the image and extracts the information needed for sign language.

[0830] Step 7: Emotion Recognition

[0831] The server uses the Microsoft Azure Emotion API to recognize the user's emotions from voice and image data.

[0832] Input: Text data and sign language information

[0833] Output: Emotion data "It looks fun"

[0834] Specific behavior:

[0835] The server sends the text and image data to the Microsoft Azure Emotion API, which analyzes and recognizes the user's emotions.

[0836] Step 8: Message Integration

[0837] The server integrates the text data obtained from speech recognition, the sign language information obtained from image recognition, and the emotion data obtained from emotion recognition to generate the final message.

[0838] Input: Text data, sign language information, emotion data

[0839] Output: Final consolidated message

[0840] Specific behavior:

[0841] The server consolidates the information obtained from each data source and generates a final message as a response to the user.

[0842] Step 9: Sign Language Translation

[0843] The server converts the synthesized messages into sign language indicators and generates sign language motion data.

[0844] Input: Final consolidated message

[0845] Output: Sign language motion data

[0846] Specific behavior:

[0847] The server converts the final message into sign language movements and gestures and generates the motion data.

[0848] Step 10: Sending motion data

[0849] The server transmits the generated sign language motion data to the terminal.

[0850] Input: Sign language motion data

[0851] Output: Motion data sent to the device

[0852] Specific behavior:

[0853] The sign language motion data generated by the server is divided into packets and securely transmitted to the terminal.

[0854] Step 11: Virtual Character Movement

[0855] The device controls the virtual character based on the received motion data.

[0856] The character signs "hello" and expresses a "happy" expression.

[0857] Input: Received motion data

[0858] Output: A moving virtual character

[0859] Specific behavior:

[0860] Based on the motion data received by the terminal, an animation of the virtual character is generated and displayed to the user.

[0861] (Application example 2)

[0862] 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."

[0863] Previously, there was no smooth means of communication with hearing-impaired employees in factories, making it difficult to give instructions or feedback on work, resulting in a lack of safety and efficiency.In addition, there was a lack of means to understand employees' emotions and provide appropriate feedback, so improvements to the working environment were required.

[0864] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving voice data and converting the voice data into text data, means for receiving image data and extracting necessary information from the image data, means for receiving emotion data and evaluating the emotion data, means for integrating the text data and the necessary information and generating a final message, means for converting the final message into a sign language indicator, means for generating motion data based on the sign language indicator, means for integrating emotional expressions into the motion data, means for applying the motion data to a virtual character and displaying sign language, and means for controlling the operation of a factory robot. This enables smooth and safe communication with hearing-impaired employees.

[0865] "Audio data" refers to sound information acquired through a voice input device.

[0866] "Text data" is information in which voice data is expressed as a string of characters.

[0867] "Image data" is visual information acquired by an image input device.

[0868] "Necessary information" is specific important data that is analyzed and extracted from the image data.

[0869] The "final message" is detailed instructions or information generated by integrating information obtained from audio and visual data.

[0870] "Sign language indicators" are information representing sign language actions created based on the final message.

[0871] "Motion data" refers to the movement data of a virtual character created based on sign language indicators.

[0872] A "virtual character" is a fictitious person or character that appears digitally.

[0873] "Emotion data" is data that expresses the user's emotional state.

[0874] A "factory robot" is an autonomous or remotely controlled mechanical device that performs various tasks in a factory.

[0875] A "speech recognition engine" is software or an algorithm for analyzing voice data and converting it into text data.

[0876] An "image recognition engine" is software or algorithms that analyze image data and extract necessary information.

[0877] An "emotion engine" is software or an algorithm that uses voice data and image data to evaluate and analyze a user's emotions.

[0878] This invention relates to a system for realizing smooth communication with hearing-impaired employees in a factory environment. This system translates voice data and image data into sign language in real time, and combines this with emotion recognition to enable factory robots to understand instructions and perform tasks. Detailed embodiments of this invention are described below.

[0879] Hardware and software used

[0880] 1. Hardware

[0881] Audio input device: Use a microphone or other audio capture device.

[0882] Image Input Device: Use an image capture device such as a camera.

[0883] Factory robots: Use autonomous or remotely controlled machinery to perform various tasks within a factory.

[0884] Smart glasses or head-mounted display (HMD): worn by the operator and used to provide instructions and display sign language.

[0885] 2. Software

[0886] Speech recognition engine: Converts voice data into text data using Google Cloud Speech-to-Text or similar.

[0887] Image recognition engine: Uses TensorFlow, OpenCV, etc. to extract necessary information from image data.

[0888] Emotion engine: Recognizes and evaluates user emotions using Microsoft Azure Emotion Recognition, etc.

[0889] Motion Generation Engine: Use the Custom Motion Engine to generate sign language motion data.

[0890] Virtual character control software: Use software such as Unity to control a virtual character and display sign language and emotional expressions.

[0891] System Operation

[0892] 1. Receiving and processing audio data

[0893] When a user issues a command by voice, the voice input device captures the voice data and sends it to the server, which uses a voice recognition engine to convert the voice data into text data.

[0894] 2. Receiving and processing image data

[0895] When a user signs through the smart glasses or HMD, the image input device captures the image data of the sign and sends it to the server, which uses an image recognition engine to extract the information needed for the sign from the image data.

[0896] 3. Receiving and processing emotional data

[0897] The server uses an emotion engine to recognize and evaluate the user's emotions using voice and image data. For example, it analyzes the tone of the voice and facial expressions when signing and recognizes emotions such as "feeling happy."

[0898] 4. Producing the final message

[0899] The server combines the text data obtained from speech recognition, the sign language information obtained from image recognition, and the emotion data to generate a final message. Based on this message, it generates sign language indicators and motion data.

[0900] 5. Sign Language and Emotional Expressions

[0901] The server applies the generated motion data and emotional expression data to the virtual character using virtual character control software, which allows the sign language and emotional expressions to be displayed in real time.

[0902] 6. Control of factory robots

[0903] Instructions based on the final message are transmitted to the factory robot, causing it to perform the appropriate task.

[0904] Specific examples

[0905] For example, in a factory, an operator signs to say, "Please carry the luggage." The voice input device captures the voice, and the image input device captures the sign. The server processes this data and transmits the command "carry the luggage" to a factory robot. At the same time, the smart glasses display visualizes feedback according to the operator's emotions and conveys it to the operator.

[0906] Prompt Sentence Examples

[0907] Voice: "Please carry my luggage."

[0908] Emotion: "It looks fun"

[0909] Sign Language: "Sign language gestures for carrying luggage"

[0910] This dramatically improves communication with hearing-impaired employees, creating a safer and more efficient working environment.

[0911] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0912] Step 1:

[0913] The user issues a command by voice. The voice input device captures the voice data and sends it to the terminal. The terminal then sends the voice data to the server.

[0914] Input: User's voice

[0915] Output: Audio data

[0916] Step 2:

[0917] The server uses a speech recognition engine to convert the voice data into text data. Specifically, the speech recognition algorithm analyzes the waveform of the voice and generates a corresponding string of characters.

[0918] Input: Audio data

[0919] Output: Text data

[0920] Step 3:

[0921] The user signs through smart glasses or an HMD. The image input device captures the sign image data and sends it to the device. The device then sends the image data to the server.

[0922] Input: Sign language gesture

[0923] Output: Image data

[0924] Step 4:

[0925] The server uses an image recognition engine to extract the necessary sign language information from the image data. Specifically, the image recognition algorithm analyzes the image data and generates information corresponding to sign language gestures.

[0926] Input: Image data

[0927] Output: Sign language information

[0928] Step 5:

[0929] The server passes the voice and image data to the emotion engine to recognize the user's emotions. Specifically, it analyzes the tone of the voice and facial expressions when signing, and generates an emotional status.

[0930] Input: Audio data, image data

[0931] Output: Emotion data

[0932] Step 6:

[0933] The server combines the text data obtained from speech recognition, the sign language information obtained from image recognition, and the emotion data to generate the final message, which then generates the sign language indicators and motion data.

[0934] Input: Text data, sign language information, emotion data

[0935] Output: Final message

[0936] Step 7:

[0937] The server converts the final message into sign language indicators and generates sign language motion data. Specifically, it references a sign language database and obtains the corresponding motion data from the sign language indicators.

[0938] Input: Final message

[0939] Output: Sign Language Index

[0940] Step 8:

[0941] The server applies the generated motion data and emotional expression data to the virtual character using virtual character control software, and the virtual character displays sign language and emotional expressions in real time.

[0942] Input: Sign language indicators, emotion data

[0943] Output: Virtual character movement data

[0944] Step 9:

[0945] The server transmits instructions based on the final message to the factory robot to cause it to perform the appropriate task, specifically, inputs the final message into the control system of the factory robot and generates a corresponding operation command.

[0946] Input: Final message

[0947] Output: Robot movement instructions

[0948] In this way, by appropriately processing and integrating voice data, image data, and emotional data at each step, it is demonstrated that this system enables communication with hearing-impaired employees in a factory environment.

[0949] 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.

[0950] 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.

[0951] 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.

[0952] [Third embodiment]

[0953] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0954] 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.

[0955] 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).

[0956] 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.

[0957] 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.

[0958] 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).

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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.

[0964] 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."

[0965] The system of the present invention translates voice data and image data into sign language in real time and displays the translated data using a virtual character. Each component of this system will now be described.

[0966] System Overview

[0967] 1. User Action:

[0968] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[0969] 2. Data Collection and Transmission:

[0970] The device captures the voice input and transmits the voice data to the server.

[0971] The terminal also transmits the uploaded image data to the server.

[0972] 3. Data analysis and synthesis:

[0973] The server converts the received voice data into text data using a voice recognition engine.

[0974] The server analyzes the received image data using an image recognition engine and extracts the information necessary for sign language.

[0975] The server integrates the information extracted from the audio and image data to generate the final message.

[0976] 4. Sign Language Translation:

[0977] The server converts the final message into sign language indicators and generates motion data.

[0978] 5. Virtual character display:

[0979] The server transmits the generated motion data to the terminal.

[0980] Based on the received motion data, the device moves the virtual character and displays sign language.

[0981] Program processing and specific examples

[0982] 1. Audio data processing:

[0983] The user speaks "Hello!"

[0984] The device captures the audio data and sends it to the server.

[0985] The server uses a voice recognition engine to convert the voice data into text data such as "hello."

[0986] 2. Image data processing:

[0987] The user uploads a sign language reference image to the tablet.

[0988] The terminal transmits the image data to the server.

[0989] The server uses an image recognition engine to extract the information necessary for sign language from the image data.

[0990] 3. Data Integration and Sign Language Translation:

[0991] The server combines the text "hello" obtained from speech recognition with the sign language information obtained from image recognition.

[0992] The server converts the synthesized messages into sign language indicators.

[0993] The server generates motion data based on the indicators.

[0994] 4. Sign Language Display:

[0995] The server transmits the generated motion data to the terminal.

[0996] Based on the received motion data, the device makes the virtual character on the tablet screen sign "hello."

[0997] These steps enable hearing-impaired users to understand sign language accurately and in real time. This invention is a technology that extracts information from both audio and images, converts it into sign language, and displays it, thereby solving problems that conventional systems could not solve.

[0998] The processing flow will be explained below.

[0999] Step 1:

[1000] The user speaks "Hello!" into the tablet device or uploads a reference image of the sign language.

[1001] Step 2:

[1002] The user taps the "Start Recording" button to start voice input, or selects an image on the image file selection screen and taps the send button.

[1003] Step 3:

[1004] The device captures the audio input and stores it in a temporary buffer as digital audio data, or in local storage as an image file.

[1005] Step 4:

[1006] The terminal sends the saved audio data and image data to the server as an HTTP request.

[1007] Step 5:

[1008] The server passes the received voice data to a voice recognition engine, which generates the text data "Hello."

[1009] Step 6:

[1010] The server inputs the received image data into an image recognition engine and extracts the information necessary for sign language.

[1011] Step 7:

[1012] The server combines the text obtained from the voice data with the sign language information obtained from the image data to generate the final message.

[1013] Step 8:

[1014] The server converts the final message into sign language indicators and generates sign language motion data.

[1015] Step 9:

[1016] The server encodes the generated sign language motion data and sends it to the terminal as an HTTP response.

[1017] Step 10:

[1018] The device decodes the received sign language motion data and uses a 3D graphics engine to make the virtual character perform the sign language movements.

[1019] Step 11:

[1020] The device displays the sign language "hello" from a virtual character on the screen.

[1021] By performing these steps in succession, accurate sign language translation from audio and images can be achieved in real time, allowing a virtual character to provide visual sign language interpretation to the user.

[1022] Example 1

[1023] 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."

[1024] It is difficult for the hearing impaired to recognize audio and image information as sign language in real time. Furthermore, conventional systems process audio data and image data separately, which can result in a time lag when integrating them. The present invention aims to solve these problems by providing a system that converts audio and images into sign language and displays them in real time.

[1025] 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.

[1026] In this invention, the server includes means for receiving voice data from a user and converting the voice data into text data, means for receiving image data from a user and extracting necessary information from the image data, means for integrating the text data and the necessary information to generate a final message, means for converting the final message into a sign language indicator, means for generating motion data based on the sign language indicator, means for applying the motion data to a virtual character and displaying the sign language, means for receiving the voice data and image data by a terminal and transmitting them to the server, and means for displaying the virtual character by the terminal. This makes it possible to convert voice and image data into sign language in real time and display it using a virtual character.

[1027] "User" means an individual who operates the system or a customer who uses its services.

[1028] "Voice data" refers to data that is a digital recording of a user's speech.

[1029] "Text data" is character string information obtained by analyzing voice data.

[1030] "Image data" refers to digital information of still images or videos uploaded by users.

[1031] The "necessary information" is partial information necessary for the sign language actions extracted from the image data.

[1032] "Synthesis" is the process of combining information from multiple data sources into a single, consistent output.

[1033] The "final message" is the overall information obtained by integrating the information obtained from the audio data and the image data.

[1034] "Sign language indicators" are marker information that serves as the basis for expressing sign language actions.

[1035] "Motion data" refers to character movement data generated from sign language indicators.

[1036] A "virtual character" is an animated character that performs sign language in a digital space.

[1037] A "terminal" is an electronic device used by a user to perform input, and is a device that has the function of capturing audio or image data and transmitting it to a server.

[1038] A "voice recognition engine" is a software or hardware configuration that analyzes voice data and converts it into text data.

[1039] An "image recognition engine" is a software or hardware configuration that analyzes image data and extracts the information necessary for sign language.

[1040] The system of the present invention translates voice data and image data into sign language in real time and displays the translated data using a virtual character. Each component for implementing this system will now be described.

[1041] First, the user can use the tablet device to give voice instructions or upload a reference image of the sign language. This applies to situations where the user says "hello" or selects and uploads a reference image of the sign language.

[1042] Next, the device receives user input, captures voice data, and sends it to the server. To capture voice data, the tablet device's microphone and a voice capture library (e.g., WebRTC) are used. To upload image data, the HTML5 File API is used to send the image data from the tablet's storage to the server.

[1043] The server converts the received voice data into text data using a speech recognition engine such as the Google Cloud Speech-to-Text API. For example, voice data such as "Hello" is converted into text data such as "Hello." Similarly, the received image data is analyzed using the Google Cloud Vision API or Amazon Rekognition to extract the information necessary for sign language. For example, hand positions and movements are extracted from uploaded sign language images.

[1044] The server then combines the text data obtained from speech recognition with the sign language information obtained from image recognition. The combined message is converted into sign language indicators and motion data is generated using a custom sign language indicator conversion algorithm. At this stage, specific motion data is obtained to create the sign language action for "hello."

[1045] Finally, the server sends the generated motion data to the device, which then uses Unity to move the virtual character based on the received motion data, resulting in the sign language "hello" being displayed on the tablet screen.

[1046] This allows hearing-impaired users to understand sign language accurately and in real time. The system provides a unique technology that integrates audio and image data, translates them into sign language, and displays them.

[1047] Specifically, by inputting the following prompt sentence into the generative AI model, text explaining the system's behavior can be generated.

[1048] "When a user speaks "hello" into the tablet, the voice data is captured on the device and sent to the server. The server converts the voice into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text API), and the text data is converted into the motion data required for sign language. The motion data is then sent to the device, and a virtual character displays the sign for "hello."

[1049] In this way, the system of the present invention can process the user's voice and image data in real time and display them as sign language using a virtual character.

[1050] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1051] Step 1:

[1052] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[1053] Specifically, users can say "hello" into the microphone on the tablet device, or select a reference image of sign language and press the upload button.

[1054] Input: User's voice or image data.

[1055] Output: Audio or image data is captured on the device.

[1056] Step 2:

[1057] The device captures the user's voice data and sends it to the server, as well as the uploaded image data.

[1058] Specifically, it uses a voice capture library such as WebRTC to capture audio data and send it to the server, while image data is sent to the server using the HTML5 File API.

[1059] Input: Captured audio data or uploaded image data.

[1060] Output: Audio and image data sent to the server.

[1061] Step 3:

[1062] The server converts the received voice data into text data using a voice recognition engine, and also analyzes the received image data using an image recognition engine to extract the information necessary for sign language.

[1063] Specifically, the Google Cloud Speech-to-Text API is used to convert the audio data of "Hello" into the text "Hello." The Google Cloud Vision API is used to analyze the uploaded sign language reference image.

[1064] Input: Audio and image data received by the server.

[1065] Output: Text data and information required for sign language.

[1066] Step 4:

[1067] The server integrates the text data obtained from speech recognition with the sign language information obtained from image recognition, converts the integrated data into sign language indicators, and generates motion data using the indicators.

[1068] Specifically, a custom sign language index conversion algorithm is used to integrate the text "Hello" with sign language information and generate sign language motion data.

[1069] Input: Text data generated by speech recognition and sign language information generated by image recognition.

[1070] Output: Sign language motion data.

[1071] Step 5:

[1072] The server transmits the generated motion data to the terminal.

[1073] Specifically, the motion data is transmitted to the terminal via the network.

[1074] Input: Sign language motion data.

[1075] Output: The motion data sent to the device.

[1076] Step 6:

[1077] The terminal moves the virtual character and displays the sign language based on the received motion data.

[1078] Specifically, it uses Unity to animate a virtual character and display the sign for "hello" on the tablet screen.

[1079] Input: Motion data sent from the server.

[1080] Output: Sign language displayed by a virtual character.

[1081] This allows for a process of converting voice and image data into sign language in real time and displaying it using a virtual character.

[1082] (Application example 1)

[1083] 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."

[1084] Communication between hearing-impaired customers and store staff has traditionally been difficult because it requires knowledge of sign language. Furthermore, there is no way to display sign language in real time in typical customer service situations, making smooth communication difficult. As a result, hearing-impaired customers often lack the ability to receive adequate service and are often forced to endure inconvenience.

[1085] 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.

[1086] In this invention, the server includes means for receiving voice data and converting the voice data into text data, means for receiving image data and extracting necessary information from the image data, and means for integrating the text data and the necessary information to generate a final message. This makes it possible to use voice data or image data to assist communication between customers and store clerks when serving customers in a physical store.

[1087] "Voice data" is digital information that is a recording or real-time capture of a user's voice.

[1088] "Text data" is character string information converted from voice data.

[1089] "Image data" is digital data that includes visual information captured by a camera, scanner, etc.

[1090] "Sign language" is a form of linguistic expression using hands and facial expressions that is used by people with hearing impairments to communicate.

[1091] "Virtual character" refers to an imaginary person or character that operates in a digital environment.

[1092] "Integrating" refers to combining multiple pieces of data or information into a single entity.

[1093] "Motion data" is digital information that includes a series of instructions for causing a virtual character to perform a particular movement.

[1094] A "physical store" refers to a commercial establishment that exists in a physical location and where customers can visit and receive goods or services in person.

[1095] "Customer" refers to a person who visits a physical store and receives goods or services.

[1096] A "store clerk" refers to someone who provides products and services to customers in a physical store.

[1097] This invention describes a specific embodiment of a system that translates voice data and image data into sign language in real time and displays them as a virtual character. An example of this system that facilitates communication between customers and store clerks in a brick-and-mortar store is shown below.

[1098] System infrastructure

[1099] 1. Audio and text data processing

[1100] The user (store clerk or customer) inputs voice data using the microphone on their smartphone. This voice data is captured by the device and sent to the server. The server then uses voice recognition software (e.g., Google Speech Recognition API) to convert the voice data into text data.

[1101] 2. Image data processing

[1102] The user uploads a reference image of the sign language to their smartphone. The device then sends this image data to the server. The server then uses an image recognition engine (e.g., OpenCV) to extract the information necessary for the sign language from the image data.

[1103] 3. Data integration and sign language translation

[1104] The server integrates the text data obtained from speech recognition with the sign language information obtained from image recognition. The server then converts the integrated message into sign language indicators and generates motion data. This is done using a dedicated sign language translation model (generative AI model).

[1105] 4. Display of virtual characters

[1106] The server then sends the generated motion data to the smartphone, which then uses the received motion data to move the virtual character on the smartphone screen and display sign language, allowing hearing-impaired customers to easily communicate with store staff.

[1107] Specific examples

[1108] For example, if a customer voice-inputs "Hello!", this is converted into text data "Hello." Reference images of the sign language are then uploaded to extract features, and the voice and image data are combined to create the final message. A virtual character then displays "Hello" in sign language.

[1109] Specific examples of hardware and software used

[1110] Hardware: Smartphones, tablets

[1111] Software: Google Speech Recognition API (voice recognition), OpenCV (image recognition)

[1112] Generative AI models: Custom-developed or pre-trained sign language translation models

[1113] Prompt Sentence Examples

[1114] "Generate a program that converts audio data into text, extracts the necessary sign language information from reference images, and makes a virtual character perform sign language movements."

[1115] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1116] Step 1:

[1117] The user inputs voice data into the smartphone's microphone. The voice data is sent to the device as an analog signal. The user also uploads reference images of sign language to the smartphone. The image data is sent to the device as a digital signal.

[1118] Step 2:

[1119] The terminal captures the audio data and converts it into a digital audio file, for example, in an audio file format (such as WAV), which is then sent to the server. At the same time, the image data is sent to the server in digital format. The input is an analog audio signal and digital image data, and the output is a digital audio file and image data.

[1120] Step 3:

[1121] The server uses a speech recognition engine (e.g., Google Speech Recognition API) to convert the audio file into text data. The server converts the audio file into text format and stores it in its internal memory. The input is a digital audio file, and the output is text data.

[1122] Step 4:

[1123] The server uses an image recognition engine (e.g., OpenCV) to extract the features required for sign language from the uploaded image data. It analyzes the image data and extracts features such as joint positions and movement vectors required for sign language movements. The input is image data, and the output is feature data required for sign language.

[1124] Step 5:

[1125] The server combines the text data obtained from the speech recognition engine with the sign language information obtained from the image recognition engine, thereby generating the final message. The input is text data and sign language feature data, which are combined to create a single integrated message. The output is the integrated message.

[1126] Step 6:

[1127] A generative AI model (sign language translation model) is used to convert the integrated message into sign language movements. Specifically, the integrated message is used as input, converted into sign language indicators, and motion data is generated. The input is the integrated message, and the output is motion data.

[1128] Step 7:

[1129] The server generates motion data and sends it to the device. The device receives this motion data and uses it to move the virtual character. The input is the motion data, and the output is the motion data sent to the device.

[1130] Step 8:

[1131] Based on the motion data received by the device, a virtual character is displayed on the smartphone screen. The virtual character then performs greetings such as "hello" in sign language. The input is motion data, and the output is the sign language movements of the virtual character.

[1132] 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.

[1133] The system of the present invention translates voice data and image data into sign language in real time and displays it using a virtual character. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more natural and rich communication is realized. The components of this system and the program processing are explained below.

[1134] System Overview

[1135] 1. User Action:

[1136] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[1137] The system also recognizes emotions from the user's voice and facial expressions and generates sign language that corresponds to the emotion.

[1138] 2. Data Collection and Transmission:

[1139] The device captures the voice input and transmits the voice data to the server.

[1140] The terminal also transmits the uploaded image data to the server.

[1141] 3. Data analysis and synthesis:

[1142] The server converts the received voice data into text data using a voice recognition engine.

[1143] The server analyzes the received image data using an image recognition engine and extracts the information necessary for sign language.

[1144] The server uses an emotion engine to recognize the user's emotion from the voice data and image data.

[1145] The server integrates the information extracted from the audio and image data with the recognized emotions to generate the final message.

[1146] 4. Sign Language Translation:

[1147] The server converts the final message into sign language indicators and generates motion data.

[1148] The virtual character's facial expressions and movements are also modified based on the generated sign language indicators and emotions.

[1149] 5. Virtual character display:

[1150] The server transmits the generated motion data to the terminal.

[1151] The device moves the virtual character based on the received motion data and displays emotional expressions along with sign language.

[1152] Program processing and specific examples

[1153] 1. Audio data processing:

[1154] The user speaks "Hello!"

[1155] The device captures the audio data and sends it to the server.

[1156] The server uses a voice recognition engine to convert the voice data into text data such as "hello."

[1157] 2. Image data processing:

[1158] The user uploads a sign language reference image to the tablet.

[1159] The terminal transmits the image data to the server.

[1160] The server uses an image recognition engine to extract the information necessary for sign language from the image data.

[1161] 3. Emotion Recognition:

[1162] The server passes the voice data and image data to an emotion engine to recognize the user's emotion.

[1163] For example, the emotion "happy" can be recognized by analyzing the tone of voice and facial expressions in images.

[1164] 4. Message Integration and Sign Language Translation:

[1165] The server integrates the text "hello" obtained from speech recognition, the sign language information obtained from image recognition, and the recognized emotions.

[1166] The server converts the synthesized messages into sign language indicators and generates sign language motion data.

[1167] Add facial expressions and movements that correspond to emotions to virtual characters.

[1168] 5. Sign Language and Emotional Expressions:

[1169] The server transmits the generated sign language motion data and emotional expression data to the terminal.

[1170] Based on the data received, the device makes the virtual character on the tablet screen sign "hello" and make a "happy" expression.

[1171] These steps enable accurate and emotional sign language translation from audio and images in real time, allowing virtual characters to provide users with visual and intuitive sign language interpretation.The present invention is a technology that integrates audio, image, and emotional information, converts it into sign language, and displays it, promoting rich communication.

[1172] The processing flow will be explained below.

[1173] Step 1:

[1174] The user speaks "Hello!" into the tablet device or uploads a reference image of the sign language.

[1175] Step 2:

[1176] The user taps the "Start Recording" button to start voice input, or selects an image on the image file selection screen and taps the send button.

[1177] Step 3:

[1178] The device captures the audio input and stores it in a temporary buffer as digital audio data, or in local storage as an image file.

[1179] Step 4:

[1180] The terminal sends the saved audio data and image data to the server as an HTTP request.

[1181] Step 5:

[1182] The server passes the received voice data to a voice recognition engine, which generates the text data "Hello."

[1183] Step 6:

[1184] The server inputs the received image data into an image recognition engine and extracts the information necessary for sign language.

[1185] Step 7:

[1186] The server passes the voice data and image data to an emotion engine to recognize the user's emotion.

[1187] Step 8:

[1188] The server combines the text "Hello" obtained from speech recognition, the sign language information obtained from image recognition, and the user's emotions recognized by the emotion engine to generate the final message.

[1189] Step 9:

[1190] The server converts the final message into sign language indicators and generates sign language motion data, which changes the virtual character's facial expressions and movements based on the user's emotions.

[1191] Step 10:

[1192] The server transmits the generated sign language motion data and emotional expression data to the terminal.

[1193] Step 11:

[1194] The terminal decodes the received sign language motion data and emotional expression data, and uses a 3D graphics engine to make the virtual character perform the sign language movements and facial expressions.

[1195] Step 12:

[1196] The device displays the virtual character's sign language "hello" and a "happy" facial expression based on the recognized emotion on the screen.

[1197] By performing these steps sequentially, the system can extract information, including the user's emotions, from audio and images, and provide accurate and emotive sign language translation in real time. This system provides a more natural and intuitive means of communication for hearing-impaired users.

[1198] Example 2

[1199] 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."

[1200] In today's world, there is a demand for simple and effective sign language interpretation systems as a means of facilitating smooth communication between the hearing impaired and hearing people. However, conventional sign language interpretation systems lack the ability to express emotions, making it difficult to produce natural sign language that accurately reflects the user's emotions. In addition, their inadequate real-time processing of audio and image data can lead to communication delays and misunderstandings.

[1201] 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.

[1202] In this invention, the server includes means for receiving voice data and converting the voice data into text data, means for receiving image data and extracting necessary information from the image data, and means for recognizing a user's emotion from the voice data and the image data and generating sign language based on the emotion, thereby enabling natural and rich sign language expressions that reflect the user's emotion in real time.

[1203] "Audio Data" means data in digital form derived from audio input.

[1204] "Text data" refers to digital data that has been converted from audio data into text information.

[1205] "Image data" refers to image information in digital form obtained from a camera or other image capture device.

[1206] "Necessary information" refers to indicators and features extracted from image data that are necessary for sign language translation.

[1207] The "final message" refers to a message generated by integrating the text data converted from the voice data and the necessary information extracted from the image data.

[1208] A "sign language indicator" is a standard or reference point that translates a sign expression into a form that can be applied as a movement.

[1209] "Motion data" refers to movement information applied to a virtual character.

[1210] A "virtual character" is an imaginary person or character that performs sign language actions within a digital environment.

[1211] "Emotion recognition" refers to the process of detecting and identifying a user's emotions and moods from their voice or image data.

[1212] The system of the present invention translates voice data and image data into sign language in real time and displays it using a virtual character. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more natural and rich communication is realized. Each component of this system and its processing will be specifically explained.

[1213] Components and hardware / software used

[1214] 1. User Action:

[1215] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[1216] In this case, the tablet device needs to be equipped with a high-performance microphone and camera.

[1217] The system uses data from cameras and microphones to recognize emotions from the user's voice and facial expressions.

[1218] 2. Data Collection and Transmission:

[1219] The device sends the captured audio data to a server via the Internet, using the HTTPS communication protocol.

[1220] Image data uploaded by the user is also sent to the server in the same way.

[1221] 3. Data analysis and synthesis:

[1222] Speech recognition engine: The server uses the Google Cloud Speech-to-Text API to convert voice data into text data.

[1223] Image recognition engine: The server uses Amazon Rekognition to extract the information necessary for sign language from the image data.

[1224] Emotion engine: The server uses the Microsoft Azure Emotion API to recognize the user's emotions from voice and image data.

[1225] The server integrates the information extracted from the audio and image data with the recognized emotions to generate the final message.

[1226] 4. Sign Language Translation:

[1227] The server converts the synthesized messages into sign language indicators and generates motion data, where the sign language indicators are converted into a form that can be applied as actions.

[1228] The virtual character's facial expressions and movements are also modified based on the generated sign language indicators and emotions.

[1229] 5. Virtual character display:

[1230] The server transmits the generated motion data to the terminal.

[1231] The device moves the virtual character based on the received motion data and displays emotional expressions along with sign language.

[1232] Specific examples

[1233] 1. Audio data processing:

[1234] The user speaks "Hello!"

[1235] The device captures the audio data and sends it to the server.

[1236] The server uses the Google Cloud Speech-to-Text API to convert the audio data into the text "Hello."

[1237] 2. Image data processing:

[1238] The user uploads a sign language reference image to the tablet.

[1239] The terminal transmits the image data to the server.

[1240] The server uses Amazon Rekognition to extract the information needed for sign language from the image data.

[1241] 3. Emotion Recognition:

[1242] The server passes the audio and image data to the Microsoft Azure Emotion API to recognize the user's emotions.

[1243] For example, the emotion "happy" can be recognized by analyzing the tone of voice and facial expressions in images.

[1244] 4. Message Integration and Sign Language Translation:

[1245] The server integrates the text "hello" obtained from speech recognition, the sign language information obtained from image recognition, and the recognized emotions.

[1246] The server converts the synthesized messages into sign language indicators and generates sign language motion data.

[1247] Add facial expressions and movements that correspond to emotions to virtual characters.

[1248] 5. Sign Language and Emotional Expressions:

[1249] The server transmits the generated sign language motion data and emotional expression data to the terminal.

[1250] Based on the data received, the device makes the virtual character on the tablet screen sign "hello" and make a "happy" expression.

[1251] Example prompt sentence:

[1252] When a user says "Hello!" on a tablet, how is that voice processed and how does the virtual character ultimately react? Please explain in detail the steps involved.

[1253] In this way, the present invention provides a technology that integrates audio, image, and emotional information, converts it into sign language, and displays it, thereby realizing rich communication.

[1254] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1255] Step 1: User Speech Input

[1256] The user issues a voice command to the tablet device, for example, "Hello!"

[1257] The device uses its built-in microphone to capture this audio.

[1258] Input: User's voice

[1259] Output: Captured audio data

[1260] Specific behavior:

[1261] The terminal records the voice data uttered by the user and converts it into digital voice data.

[1262] Step 2: Sending audio data

[1263] The device transmits the captured audio data to a server via the Internet.

[1264] HTTPS is used as the communication protocol for transmission.

[1265] Input: Captured audio data

[1266] Output: Audio data sent to the server

[1267] Specific behavior:

[1268] The device divides the captured audio data into packets and securely transmits them to the server.

[1269] Step 3: Voice Recognition

[1270] The server uses the Google Cloud Speech-to-Text API to convert the received voice data into text data.

[1271] Input: Transmitted audio data

[1272] Output: Text data "Hello"

[1273] Specific behavior:

[1274] The server sends the audio data to the Google Cloud Speech-to-Text API, which analyzes the audio and converts it into text data.

[1275] Step 4: Upload user images

[1276] Users upload sign language reference images to a tablet.

[1277] The tablet device stores this image data.

[1278] Input: User uploaded image

[1279] Output: Image data saved on the device

[1280] Specific behavior:

[1281] The user uploads an image of sign language taken with the tablet device's camera, and the device stores the image data.

[1282] Step 5: Sending image data

[1283] The terminal transmits the uploaded image data to a server via the Internet.

[1284] HTTPS is used as the communication protocol for transmission.

[1285] Input: Saved image data

[1286] Output: Image data sent to the server

[1287] Specific behavior:

[1288] The device divides the stored image data into packets and securely transmits them to the server.

[1289] Step 6: Image Recognition

[1290] The server uses Amazon Rekognition to extract the information necessary for sign language from the received image data.

[1291] Input: Image data sent

[1292] Output: Extracted sign language information

[1293] Specific behavior:

[1294] The server sends the image data to Amazon Rekognition, which analyzes the image and extracts the information needed for sign language.

[1295] Step 7: Emotion Recognition

[1296] The server uses the Microsoft Azure Emotion API to recognize the user's emotions from voice and image data.

[1297] Input: Text data and sign language information

[1298] Output: Emotion data "It looks fun"

[1299] Specific behavior:

[1300] The server sends the text and image data to the Microsoft Azure Emotion API, which analyzes and recognizes the user's emotions.

[1301] Step 8: Message Integration

[1302] The server integrates the text data obtained from speech recognition, the sign language information obtained from image recognition, and the emotion data obtained from emotion recognition to generate the final message.

[1303] Input: Text data, sign language information, emotion data

[1304] Output: Final consolidated message

[1305] Specific behavior:

[1306] The server consolidates the information obtained from each data source and generates a final message as a response to the user.

[1307] Step 9: Sign Language Translation

[1308] The server converts the synthesized messages into sign language indicators and generates sign language motion data.

[1309] Input: Final consolidated message

[1310] Output: Sign language motion data

[1311] Specific behavior:

[1312] The server converts the final message into sign language movements and gestures and generates the motion data.

[1313] Step 10: Sending motion data

[1314] The server transmits the generated sign language motion data to the terminal.

[1315] Input: Sign language motion data

[1316] Output: Motion data sent to the device

[1317] Specific behavior:

[1318] The sign language motion data generated by the server is divided into packets and securely transmitted to the terminal.

[1319] Step 11: Virtual Character Movement

[1320] The device controls the virtual character based on the received motion data.

[1321] The character signs "hello" and expresses a "happy" expression.

[1322] Input: Received motion data

[1323] Output: A moving virtual character

[1324] Specific behavior:

[1325] Based on the motion data received by the terminal, an animation of the virtual character is generated and displayed to the user.

[1326] (Application example 2)

[1327] 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."

[1328] Previously, there was no smooth means of communication with hearing-impaired employees in factories, making it difficult to give instructions or feedback on work, resulting in a lack of safety and efficiency.In addition, there was a lack of means to understand employees' emotions and provide appropriate feedback, so improvements to the working environment were required.

[1329] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving voice data and converting the voice data into text data, means for receiving image data and extracting necessary information from the image data, means for receiving emotion data and evaluating the emotion data, means for integrating the text data and the necessary information and generating a final message, means for converting the final message into a sign language indicator, means for generating motion data based on the sign language indicator, means for integrating emotional expressions into the motion data, means for applying the motion data to a virtual character and displaying sign language, and means for controlling the operation of a factory robot. This enables smooth and safe communication with hearing-impaired employees.

[1330] "Audio data" refers to sound information acquired through a voice input device.

[1331] "Text data" is information in which voice data is expressed as a string of characters.

[1332] "Image data" is visual information acquired by an image input device.

[1333] "Necessary information" is specific important data that is analyzed and extracted from the image data.

[1334] The "final message" is detailed instructions or information generated by integrating information obtained from audio and visual data.

[1335] "Sign language indicators" are information representing sign language actions created based on the final message.

[1336] "Motion data" refers to the movement data of a virtual character created based on sign language indicators.

[1337] A "virtual character" is a fictitious person or character that appears digitally.

[1338] "Emotion data" is data that expresses the user's emotional state.

[1339] A "factory robot" is an autonomous or remotely controlled mechanical device that performs various tasks in a factory.

[1340] A "speech recognition engine" is software or an algorithm for analyzing voice data and converting it into text data.

[1341] An "image recognition engine" is software or algorithms that analyze image data and extract necessary information.

[1342] An "emotion engine" is software or an algorithm that uses voice data and image data to evaluate and analyze a user's emotions.

[1343] This invention relates to a system for realizing smooth communication with hearing-impaired employees in a factory environment. This system translates voice data and image data into sign language in real time, and combines this with emotion recognition to enable factory robots to understand instructions and perform tasks. Detailed embodiments of this invention are described below.

[1344] Hardware and software used

[1345] 1. Hardware

[1346] Audio input device: Use a microphone or other audio capture device.

[1347] Image Input Device: Use an image capture device such as a camera.

[1348] Factory robots: Use autonomous or remotely controlled machinery to perform various tasks within a factory.

[1349] Smart glasses or head-mounted display (HMD): worn by the operator and used to provide instructions and display sign language.

[1350] 2. Software

[1351] Speech recognition engine: Converts voice data into text data using Google Cloud Speech-to-Text or similar.

[1352] Image recognition engine: Uses TensorFlow, OpenCV, etc. to extract necessary information from image data.

[1353] Emotion engine: Recognizes and evaluates user emotions using Microsoft Azure Emotion Recognition, etc.

[1354] Motion Generation Engine: Use the Custom Motion Engine to generate sign language motion data.

[1355] Virtual character control software: Use software such as Unity to control a virtual character and display sign language and emotional expressions.

[1356] System Operation

[1357] 1. Receiving and processing audio data

[1358] When a user issues a command by voice, the voice input device captures the voice data and sends it to the server, which uses a voice recognition engine to convert the voice data into text data.

[1359] 2. Receiving and processing image data

[1360] When a user signs through the smart glasses or HMD, the image input device captures the image data of the sign and sends it to the server, which uses an image recognition engine to extract the information needed for the sign from the image data.

[1361] 3. Receiving and processing emotional data

[1362] The server uses an emotion engine to recognize and evaluate the user's emotions using voice and image data. For example, it analyzes the tone of the voice and facial expressions when signing and recognizes emotions such as "feeling happy."

[1363] 4. Producing the final message

[1364] The server combines the text data obtained from speech recognition, the sign language information obtained from image recognition, and the emotion data to generate a final message. Based on this message, it generates sign language indicators and motion data.

[1365] 5. Sign Language and Emotional Expressions

[1366] The server applies the generated motion data and emotional expression data to the virtual character using virtual character control software, which allows the sign language and emotional expressions to be displayed in real time.

[1367] 6. Control of factory robots

[1368] Instructions based on the final message are transmitted to the factory robot, causing it to perform the appropriate task.

[1369] Specific examples

[1370] For example, in a factory, an operator signs to say, "Please carry the luggage." The voice input device captures the voice, and the image input device captures the sign. The server processes this data and transmits the command "carry the luggage" to a factory robot. At the same time, the smart glasses display visualizes feedback according to the operator's emotions and conveys it to the operator.

[1371] Prompt Sentence Examples

[1372] Voice: "Please carry my luggage."

[1373] Emotion: "It looks fun"

[1374] Sign Language: "Sign language gestures for carrying luggage"

[1375] This dramatically improves communication with hearing-impaired employees, creating a safer and more efficient working environment.

[1376] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1377] Step 1:

[1378] The user issues a command by voice. The voice input device captures the voice data and sends it to the terminal. The terminal then sends the voice data to the server.

[1379] Input: User's voice

[1380] Output: Audio data

[1381] Step 2:

[1382] The server uses a speech recognition engine to convert the voice data into text data. Specifically, the speech recognition algorithm analyzes the waveform of the voice and generates a corresponding string of characters.

[1383] Input: Audio data

[1384] Output: Text data

[1385] Step 3:

[1386] The user signs through smart glasses or an HMD. The image input device captures the sign image data and sends it to the device. The device then sends the image data to the server.

[1387] Input: Sign language gesture

[1388] Output: Image data

[1389] Step 4:

[1390] The server uses an image recognition engine to extract the necessary sign language information from the image data. Specifically, the image recognition algorithm analyzes the image data and generates information corresponding to sign language gestures.

[1391] Input: Image data

[1392] Output: Sign language information

[1393] Step 5:

[1394] The server passes the voice and image data to the emotion engine to recognize the user's emotions. Specifically, it analyzes the tone of the voice and facial expressions when signing, and generates an emotional status.

[1395] Input: Audio data, image data

[1396] Output: Emotion data

[1397] Step 6:

[1398] The server combines the text data obtained from speech recognition, the sign language information obtained from image recognition, and the emotion data to generate the final message, which then generates the sign language indicators and motion data.

[1399] Input: Text data, sign language information, emotion data

[1400] Output: Final message

[1401] Step 7:

[1402] The server converts the final message into sign language indicators and generates sign language motion data. Specifically, it references a sign language database and obtains the corresponding motion data from the sign language indicators.

[1403] Input: Final message

[1404] Output: Sign Language Index

[1405] Step 8:

[1406] The server applies the generated motion data and emotional expression data to the virtual character using virtual character control software, and the virtual character displays sign language and emotional expressions in real time.

[1407] Input: Sign language indicators, emotion data

[1408] Output: Virtual character movement data

[1409] Step 9:

[1410] The server transmits instructions based on the final message to the factory robot to cause it to perform the appropriate task, specifically, inputs the final message into the control system of the factory robot and generates a corresponding operation command.

[1411] Input: Final message

[1412] Output: Robot movement instructions

[1413] In this way, by appropriately processing and integrating voice data, image data, and emotional data at each step, it is demonstrated that this system enables communication with hearing-impaired employees in a factory environment.

[1414] 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.

[1415] 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.

[1416] 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.

[1417] [Fourth embodiment]

[1418] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1419] 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.

[1420] 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).

[1421] 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.

[1422] 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.

[1423] 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).

[1424] 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.

[1425] 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.

[1426] 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.

[1427] 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.

[1428] 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.

[1429] 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.

[1430] 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."

[1431] The system of the present invention translates voice data and image data into sign language in real time and displays the translated data using a virtual character. Each component of this system will now be described.

[1432] System Overview

[1433] 1. User Action:

[1434] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[1435] 2. Data Collection and Transmission:

[1436] The device captures the voice input and transmits the voice data to the server.

[1437] The terminal also transmits the uploaded image data to the server.

[1438] 3. Data analysis and synthesis:

[1439] The server converts the received voice data into text data using a voice recognition engine.

[1440] The server analyzes the received image data using an image recognition engine and extracts the information necessary for sign language.

[1441] The server integrates the information extracted from the audio and image data to generate the final message.

[1442] 4. Sign Language Translation:

[1443] The server converts the final message into sign language indicators and generates motion data.

[1444] 5. Virtual character display:

[1445] The server transmits the generated motion data to the terminal.

[1446] Based on the received motion data, the device moves the virtual character and displays sign language.

[1447] Program processing and specific examples

[1448] 1. Audio data processing:

[1449] The user speaks "Hello!"

[1450] The device captures the audio data and sends it to the server.

[1451] The server uses a voice recognition engine to convert the voice data into text data such as "hello."

[1452] 2. Image data processing:

[1453] The user uploads a sign language reference image to the tablet.

[1454] The terminal transmits the image data to the server.

[1455] The server uses an image recognition engine to extract the information necessary for sign language from the image data.

[1456] 3. Data Integration and Sign Language Translation:

[1457] The server combines the text "hello" obtained from speech recognition with the sign language information obtained from image recognition.

[1458] The server converts the synthesized messages into sign language indicators.

[1459] The server generates motion data based on the indicators.

[1460] 4. Sign Language Display:

[1461] The server transmits the generated motion data to the terminal.

[1462] Based on the received motion data, the device makes the virtual character on the tablet screen sign "hello."

[1463] These steps enable hearing-impaired users to understand sign language accurately and in real time. This invention is a technology that extracts information from both audio and images, converts it into sign language, and displays it, thereby solving problems that conventional systems could not solve.

[1464] The processing flow will be explained below.

[1465] Step 1:

[1466] The user speaks "Hello!" into the tablet device or uploads a reference image of the sign language.

[1467] Step 2:

[1468] The user taps the "Start Recording" button to start voice input, or selects an image on the image file selection screen and taps the send button.

[1469] Step 3:

[1470] The device captures the audio input and stores it in a temporary buffer as digital audio data, or in local storage as an image file.

[1471] Step 4:

[1472] The terminal sends the saved audio data and image data to the server as an HTTP request.

[1473] Step 5:

[1474] The server passes the received voice data to a voice recognition engine, which generates the text data "Hello."

[1475] Step 6:

[1476] The server inputs the received image data into an image recognition engine and extracts the information necessary for sign language.

[1477] Step 7:

[1478] The server combines the text obtained from the voice data with the sign language information obtained from the image data to generate the final message.

[1479] Step 8:

[1480] The server converts the final message into sign language indicators and generates sign language motion data.

[1481] Step 9:

[1482] The server encodes the generated sign language motion data and sends it to the terminal as an HTTP response.

[1483] Step 10:

[1484] The device decodes the received sign language motion data and uses a 3D graphics engine to make the virtual character perform the sign language movements.

[1485] Step 11:

[1486] The device displays the sign language "hello" from a virtual character on the screen.

[1487] By performing these steps in succession, accurate sign language translation from audio and images can be achieved in real time, allowing a virtual character to provide visual sign language interpretation to the user.

[1488] Example 1

[1489] 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."

[1490] It is difficult for the hearing impaired to recognize audio and image information as sign language in real time. Furthermore, conventional systems process audio data and image data separately, which can result in a time lag when integrating them. The present invention aims to solve these problems by providing a system that converts audio and images into sign language and displays them in real time.

[1491] 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.

[1492] In this invention, the server includes means for receiving voice data from a user and converting the voice data into text data, means for receiving image data from a user and extracting necessary information from the image data, means for integrating the text data and the necessary information to generate a final message, means for converting the final message into a sign language indicator, means for generating motion data based on the sign language indicator, means for applying the motion data to a virtual character and displaying the sign language, means for receiving the voice data and image data by a terminal and transmitting them to the server, and means for displaying the virtual character by the terminal. This makes it possible to convert voice and image data into sign language in real time and display it using a virtual character.

[1493] "User" means an individual who operates the system or a customer who uses its services.

[1494] "Voice data" refers to data that is a digital recording of a user's speech.

[1495] "Text data" is character string information obtained by analyzing voice data.

[1496] "Image data" refers to digital information of still images or videos uploaded by users.

[1497] The "necessary information" is partial information necessary for the sign language actions extracted from the image data.

[1498] "Synthesis" is the process of combining information from multiple data sources into a single, consistent output.

[1499] The "final message" is the overall information obtained by integrating the information obtained from the audio data and the image data.

[1500] "Sign language indicators" are marker information that serves as the basis for expressing sign language actions.

[1501] "Motion data" refers to character movement data generated from sign language indicators.

[1502] A "virtual character" is an animated character that performs sign language in a digital space.

[1503] A "terminal" is an electronic device used by a user to perform input, and is a device that has the function of capturing audio or image data and transmitting it to a server.

[1504] A "voice recognition engine" is a software or hardware configuration that analyzes voice data and converts it into text data.

[1505] An "image recognition engine" is a software or hardware configuration that analyzes image data and extracts the information necessary for sign language.

[1506] The system of the present invention translates voice data and image data into sign language in real time and displays the translated data using a virtual character. Each component for implementing this system will now be described.

[1507] First, the user can use the tablet device to give voice instructions or upload a reference image of the sign language. This applies to situations where the user says "hello" or selects and uploads a reference image of the sign language.

[1508] Next, the device receives user input, captures voice data, and sends it to the server. To capture voice data, the tablet device's microphone and a voice capture library (e.g., WebRTC) are used. To upload image data, the HTML5 File API is used to send the image data from the tablet's storage to the server.

[1509] The server converts the received voice data into text data using a speech recognition engine such as the Google Cloud Speech-to-Text API. For example, voice data such as "Hello" is converted into text data such as "Hello." Similarly, the received image data is analyzed using the Google Cloud Vision API or Amazon Rekognition to extract the information necessary for sign language. For example, hand positions and movements are extracted from uploaded sign language images.

[1510] The server then combines the text data obtained from speech recognition with the sign language information obtained from image recognition. The combined message is converted into sign language indicators and motion data is generated using a custom sign language indicator conversion algorithm. At this stage, specific motion data is obtained to create the sign language action for "hello."

[1511] Finally, the server sends the generated motion data to the device, which then uses Unity to move the virtual character based on the received motion data, resulting in the sign language "hello" being displayed on the tablet screen.

[1512] This allows hearing-impaired users to understand sign language accurately and in real time. The system provides a unique technology that integrates audio and image data, translates them into sign language, and displays them.

[1513] Specifically, by inputting the following prompt sentence into the generative AI model, text explaining the system's behavior can be generated.

[1514] "When a user speaks "hello" into the tablet, the voice data is captured on the device and sent to the server. The server converts the voice into text using a speech recognition engine (e.g., Google Cloud Speech-to-Text API), and the text data is converted into the motion data required for sign language. The motion data is then sent to the device, and a virtual character displays the sign for "hello."

[1515] In this way, the system of the present invention can process the user's voice and image data in real time and display them as sign language using a virtual character.

[1516] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1517] Step 1:

[1518] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[1519] Specifically, users can say "hello" into the microphone on the tablet device, or select a reference image of sign language and press the upload button.

[1520] Input: User's voice or image data.

[1521] Output: Audio or image data is captured on the device.

[1522] Step 2:

[1523] The device captures the user's voice data and sends it to the server, as well as the uploaded image data.

[1524] Specifically, it uses a voice capture library such as WebRTC to capture audio data and send it to the server, while image data is sent to the server using the HTML5 File API.

[1525] Input: Captured audio data or uploaded image data.

[1526] Output: Audio and image data sent to the server.

[1527] Step 3:

[1528] The server converts the received voice data into text data using a voice recognition engine, and also analyzes the received image data using an image recognition engine to extract the information necessary for sign language.

[1529] Specifically, the Google Cloud Speech-to-Text API is used to convert the audio data of "Hello" into the text "Hello." The Google Cloud Vision API is used to analyze the uploaded sign language reference image.

[1530] Input: Audio and image data received by the server.

[1531] Output: Text data and information required for sign language.

[1532] Step 4:

[1533] The server integrates the text data obtained from speech recognition with the sign language information obtained from image recognition, converts the integrated data into sign language indicators, and generates motion data using the indicators.

[1534] Specifically, a custom sign language index conversion algorithm is used to integrate the text "Hello" with sign language information and generate sign language motion data.

[1535] Input: Text data generated by speech recognition and sign language information generated by image recognition.

[1536] Output: Sign language motion data.

[1537] Step 5:

[1538] The server transmits the generated motion data to the terminal.

[1539] Specifically, the motion data is transmitted to the terminal via the network.

[1540] Input: Sign language motion data.

[1541] Output: The motion data sent to the device.

[1542] Step 6:

[1543] The terminal moves the virtual character and displays the sign language based on the received motion data.

[1544] Specifically, it uses Unity to animate a virtual character and display the sign for "hello" on the tablet screen.

[1545] Input: Motion data sent from the server.

[1546] Output: Sign language displayed by a virtual character.

[1547] This allows for a process of converting voice and image data into sign language in real time and displaying it using a virtual character.

[1548] (Application example 1)

[1549] 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."

[1550] Communication between hearing-impaired customers and store staff has traditionally been difficult because it requires knowledge of sign language. Furthermore, there is no way to display sign language in real time in typical customer service situations, making smooth communication difficult. As a result, hearing-impaired customers often lack the ability to receive adequate service and are often forced to endure inconvenience.

[1551] 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.

[1552] In this invention, the server includes means for receiving voice data and converting the voice data into text data, means for receiving image data and extracting necessary information from the image data, and means for integrating the text data and the necessary information to generate a final message. This makes it possible to use voice data or image data to assist communication between customers and store clerks when serving customers in a physical store.

[1553] "Voice data" is digital information that is a recording or real-time capture of a user's voice.

[1554] "Text data" is character string information converted from voice data.

[1555] "Image data" is digital data that includes visual information captured by a camera, scanner, etc.

[1556] "Sign language" is a form of linguistic expression using hands and facial expressions that is used by people with hearing impairments to communicate.

[1557] "Virtual character" refers to an imaginary person or character that operates in a digital environment.

[1558] "Integrating" refers to combining multiple pieces of data or information into a single entity.

[1559] "Motion data" is digital information that includes a series of instructions for causing a virtual character to perform a particular movement.

[1560] A "physical store" refers to a commercial establishment that exists in a physical location and where customers can visit and receive goods or services in person.

[1561] "Customer" refers to a person who visits a physical store and receives goods or services.

[1562] A "store clerk" refers to someone who provides products and services to customers in a physical store.

[1563] This invention describes a specific embodiment of a system that translates voice data and image data into sign language in real time and displays them as a virtual character. An example of this system that facilitates communication between customers and store clerks in a brick-and-mortar store is shown below.

[1564] System infrastructure

[1565] 1. Audio and text data processing

[1566] The user (store clerk or customer) inputs voice data using the microphone on their smartphone. This voice data is captured by the device and sent to the server. The server then uses voice recognition software (e.g., Google Speech Recognition API) to convert the voice data into text data.

[1567] 2. Image data processing

[1568] The user uploads a reference image of the sign language to their smartphone. The device then sends this image data to the server. The server then uses an image recognition engine (e.g., OpenCV) to extract the information necessary for the sign language from the image data.

[1569] 3. Data integration and sign language translation

[1570] The server integrates the text data obtained from speech recognition with the sign language information obtained from image recognition. The server then converts the integrated message into sign language indicators and generates motion data. This is done using a dedicated sign language translation model (generative AI model).

[1571] 4. Display of virtual characters

[1572] The server then sends the generated motion data to the smartphone, which then uses the received motion data to move the virtual character on the smartphone screen and display sign language, allowing hearing-impaired customers to easily communicate with store staff.

[1573] Specific examples

[1574] For example, if a customer voice-inputs "Hello!", this is converted into text data "Hello." Reference images of the sign language are then uploaded to extract features, and the voice and image data are combined to create the final message. A virtual character then displays "Hello" in sign language.

[1575] Specific examples of hardware and software used

[1576] Hardware: Smartphones, tablets

[1577] Software: Google Speech Recognition API (voice recognition), OpenCV (image recognition)

[1578] Generative AI models: Custom-developed or pre-trained sign language translation models

[1579] Prompt Sentence Examples

[1580] "Generate a program that converts audio data into text, extracts the necessary sign language information from reference images, and makes a virtual character perform sign language movements."

[1581] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1582] Step 1:

[1583] The user inputs voice data into the smartphone's microphone. The voice data is sent to the device as an analog signal. The user also uploads reference images of sign language to the smartphone. The image data is sent to the device as a digital signal.

[1584] Step 2:

[1585] The terminal captures the audio data and converts it into a digital audio file, for example, in an audio file format (such as WAV), which is then sent to the server. At the same time, the image data is sent to the server in digital format. The input is an analog audio signal and digital image data, and the output is a digital audio file and image data.

[1586] Step 3:

[1587] The server uses a speech recognition engine (e.g., Google Speech Recognition API) to convert the audio file into text data. The server converts the audio file into text format and stores it in its internal memory. The input is a digital audio file, and the output is text data.

[1588] Step 4:

[1589] The server uses an image recognition engine (e.g., OpenCV) to extract the features required for sign language from the uploaded image data. It analyzes the image data and extracts features such as joint positions and movement vectors required for sign language movements. The input is image data, and the output is feature data required for sign language.

[1590] Step 5:

[1591] The server combines the text data obtained from the speech recognition engine with the sign language information obtained from the image recognition engine, thereby generating the final message. The input is text data and sign language feature data, which are combined to create a single integrated message. The output is the integrated message.

[1592] Step 6:

[1593] A generative AI model (sign language translation model) is used to convert the integrated message into sign language movements. Specifically, the integrated message is used as input, converted into sign language indicators, and motion data is generated. The input is the integrated message, and the output is motion data.

[1594] Step 7:

[1595] The server generates motion data and sends it to the device. The device receives this motion data and uses it to move the virtual character. The input is the motion data, and the output is the motion data sent to the device.

[1596] Step 8:

[1597] Based on the motion data received by the device, a virtual character is displayed on the smartphone screen. The virtual character then performs greetings such as "hello" in sign language. The input is motion data, and the output is the sign language movements of the virtual character.

[1598] 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.

[1599] The system of the present invention translates voice data and image data into sign language in real time and displays it using a virtual character. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more natural and rich communication is realized. The components of this system and the program processing are explained below.

[1600] System Overview

[1601] 1. User Action:

[1602] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[1603] The system also recognizes emotions from the user's voice and facial expressions and generates sign language that corresponds to the emotion.

[1604] 2. Data Collection and Transmission:

[1605] The device captures the voice input and transmits the voice data to the server.

[1606] The terminal also transmits the uploaded image data to the server.

[1607] 3. Data analysis and synthesis:

[1608] The server converts the received voice data into text data using a voice recognition engine.

[1609] The server analyzes the received image data using an image recognition engine and extracts the information necessary for sign language.

[1610] The server uses an emotion engine to recognize the user's emotion from the voice data and image data.

[1611] The server integrates the information extracted from the audio and image data with the recognized emotions to generate the final message.

[1612] 4. Sign Language Translation:

[1613] The server converts the final message into sign language indicators and generates motion data.

[1614] The virtual character's facial expressions and movements are also modified based on the generated sign language indicators and emotions.

[1615] 5. Virtual character display:

[1616] The server transmits the generated motion data to the terminal.

[1617] The device moves the virtual character based on the received motion data and displays emotional expressions along with sign language.

[1618] Program processing and specific examples

[1619] 1. Audio data processing:

[1620] The user speaks "Hello!"

[1621] The device captures the audio data and sends it to the server.

[1622] The server uses a voice recognition engine to convert the voice data into text data such as "hello."

[1623] 2. Image data processing:

[1624] The user uploads a sign language reference image to the tablet.

[1625] The terminal transmits the image data to the server.

[1626] The server uses an image recognition engine to extract the information necessary for sign language from the image data.

[1627] 3. Emotion Recognition:

[1628] The server passes the voice data and image data to an emotion engine to recognize the user's emotion.

[1629] For example, the emotion "happy" can be recognized by analyzing the tone of voice and facial expressions in images.

[1630] 4. Message Integration and Sign Language Translation:

[1631] The server integrates the text "hello" obtained from speech recognition, the sign language information obtained from image recognition, and the recognized emotions.

[1632] The server converts the synthesized messages into sign language indicators and generates sign language motion data.

[1633] Add facial expressions and movements that correspond to emotions to virtual characters.

[1634] 5. Sign Language and Emotional Expressions:

[1635] The server transmits the generated sign language motion data and emotional expression data to the terminal.

[1636] Based on the data received, the device makes the virtual character on the tablet screen sign "hello" and make a "happy" expression.

[1637] These steps enable accurate and emotional sign language translation from audio and images in real time, allowing virtual characters to provide users with visual and intuitive sign language interpretation.The present invention is a technology that integrates audio, image, and emotional information, converts it into sign language, and displays it, promoting rich communication.

[1638] The processing flow will be explained below.

[1639] Step 1:

[1640] The user speaks "Hello!" into the tablet device or uploads a reference image of the sign language.

[1641] Step 2:

[1642] The user taps the "Start Recording" button to start voice input, or selects an image on the image file selection screen and taps the send button.

[1643] Step 3:

[1644] The device captures the audio input and stores it in a temporary buffer as digital audio data, or in local storage as an image file.

[1645] Step 4:

[1646] The terminal sends the saved audio data and image data to the server as an HTTP request.

[1647] Step 5:

[1648] The server passes the received voice data to a voice recognition engine, which generates the text data "Hello."

[1649] Step 6:

[1650] The server inputs the received image data into an image recognition engine and extracts the information necessary for sign language.

[1651] Step 7:

[1652] The server passes the voice data and image data to an emotion engine to recognize the user's emotion.

[1653] Step 8:

[1654] The server combines the text "Hello" obtained from speech recognition, the sign language information obtained from image recognition, and the user's emotions recognized by the emotion engine to generate the final message.

[1655] Step 9:

[1656] The server converts the final message into sign language indicators and generates sign language motion data, which changes the virtual character's facial expressions and movements based on the user's emotions.

[1657] Step 10:

[1658] The server transmits the generated sign language motion data and emotional expression data to the terminal.

[1659] Step 11:

[1660] The terminal decodes the received sign language motion data and emotional expression data, and uses a 3D graphics engine to make the virtual character perform the sign language movements and facial expressions.

[1661] Step 12:

[1662] The device displays the virtual character's sign language "hello" and a "happy" facial expression based on the recognized emotion on the screen.

[1663] By performing these steps sequentially, the system can extract information, including the user's emotions, from audio and images, and provide accurate and emotive sign language translation in real time. This system provides a more natural and intuitive means of communication for hearing-impaired users.

[1664] Example 2

[1665] 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."

[1666] In today's world, there is a demand for simple and effective sign language interpretation systems as a means of facilitating smooth communication between the hearing impaired and hearing people. However, conventional sign language interpretation systems lack the ability to express emotions, making it difficult to produce natural sign language that accurately reflects the user's emotions. In addition, their inadequate real-time processing of audio and image data can lead to communication delays and misunderstandings.

[1667] 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.

[1668] In this invention, the server includes means for receiving voice data and converting the voice data into text data, means for receiving image data and extracting necessary information from the image data, and means for recognizing a user's emotion from the voice data and the image data and generating sign language based on the emotion, thereby enabling natural and rich sign language expressions that reflect the user's emotion in real time.

[1669] "Audio Data" means data in digital form derived from audio input.

[1670] "Text data" refers to digital data that has been converted from audio data into text information.

[1671] "Image data" refers to image information in digital form obtained from a camera or other image capture device.

[1672] "Necessary information" refers to indicators and features extracted from image data that are necessary for sign language translation.

[1673] The "final message" refers to a message generated by integrating the text data converted from the voice data and the necessary information extracted from the image data.

[1674] A "sign language indicator" is a standard or reference point that translates a sign expression into a form that can be applied as a movement.

[1675] "Motion data" refers to movement information applied to a virtual character.

[1676] A "virtual character" is an imaginary person or character that performs sign language actions within a digital environment.

[1677] "Emotion recognition" refers to the process of detecting and identifying a user's emotions and moods from their voice or image data.

[1678] The system of the present invention translates voice data and image data into sign language in real time and displays it using a virtual character. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, more natural and rich communication is realized. Each component of this system and its processing will be specifically explained.

[1679] Components and hardware / software used

[1680] 1. User Action:

[1681] Users can either give voice instructions to the tablet device or upload reference images of the sign language.

[1682] In this case, the tablet device needs to be equipped with a high-performance microphone and camera.

[1683] The system uses data from cameras and microphones to recognize emotions from the user's voice and facial expressions.

[1684] 2. Data Collection and Transmission:

[1685] The device sends the captured audio data to a server via the Internet, using the HTTPS communication protocol.

[1686] Image data uploaded by the user is also sent to the server in the same way.

[1687] 3. Data analysis and synthesis:

[1688] Speech recognition engine: The server uses the Google Cloud Speech-to-Text API to convert voice data into text data.

[1689] Image recognition engine: The server uses Amazon Rekognition to extract the information necessary for sign language from the image data.

[1690] Emotion engine: The server uses the Microsoft Azure Emotion API to recognize the user's emotions from voice and image data.

[1691] The server integrates the information extracted from the audio and image data with the recognized emotions to generate the final message.

[1692] 4. Sign Language Translation:

[1693] The server converts the synthesized messages into sign language indicators and generates motion data, where the sign language indicators are converted into a form that can be applied as actions.

[1694] The virtual character's facial expressions and movements are also modified based on the generated sign language indicators and emotions.

[1695] 5. Virtual character display:

[1696] The server transmits the generated motion data to the terminal.

[1697] The device moves the virtual character based on the received motion data and displays emotional expressions along with sign language.

[1698] Specific examples

[1699] 1. Audio data processing:

[1700] The user speaks "Hello!"

[1701] The device captures the audio data and sends it to the server.

[1702] The server uses the Google Cloud Speech-to-Text API to convert the audio data into the text "Hello."

[1703] 2. Image data processing:

[1704] The user uploads a sign language reference image to the tablet.

[1705] The terminal transmits the image data to the server.

[1706] The server uses Amazon Rekognition to extract the information needed for sign language from the image data.

[1707] 3. Emotion Recognition:

[1708] The server passes the audio and image data to the Microsoft Azure Emotion API to recognize the user's emotions.

[1709] For example, the emotion "happy" can be recognized by analyzing the tone of voice and facial expressions in images.

[1710] 4. Message Integration and Sign Language Translation:

[1711] The server integrates the text "hello" obtained from speech recognition, the sign language information obtained from image recognition, and the recognized emotions.

[1712] The server converts the synthesized messages into sign language indicators and generates sign language motion data.

[1713] Add facial expressions and movements that correspond to emotions to virtual characters.

[1714] 5. Sign Language and Emotional Expressions:

[1715] The server transmits the generated sign language motion data and emotional expression data to the terminal.

[1716] Based on the data received, the device makes the virtual character on the tablet screen sign "hello" and make a "happy" expression.

[1717] Example prompt sentence:

[1718] When a user says "Hello!" on a tablet, how is that voice processed and how does the virtual character ultimately react? Please explain in detail the steps involved.

[1719] In this way, the present invention provides a technology that integrates audio, image, and emotional information, converts it into sign language, and displays it, thereby realizing rich communication.

[1720] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1721] Step 1: User Speech Input

[1722] The user issues a voice command to the tablet device, for example, "Hello!"

[1723] The device uses its built-in microphone to capture this audio.

[1724] Input: User's voice

[1725] Output: Captured audio data

[1726] Specific behavior:

[1727] The terminal records the voice data uttered by the user and converts it into digital voice data.

[1728] Step 2: Sending audio data

[1729] The device transmits the captured audio data to a server via the Internet.

[1730] HTTPS is used as the communication protocol for transmission.

[1731] Input: Captured audio data

[1732] Output: Audio data sent to the server

[1733] Specific behavior:

[1734] The device divides the captured audio data into packets and securely transmits them to the server.

[1735] Step 3: Voice Recognition

[1736] The server uses the Google Cloud Speech-to-Text API to convert the received voice data into text data.

[1737] Input: Transmitted audio data

[1738] Output: Text data "Hello"

[1739] Specific behavior:

[1740] The server sends the audio data to the Google Cloud Speech-to-Text API, which analyzes the audio and converts it into text data.

[1741] Step 4: Upload user images

[1742] Users upload sign language reference images to a tablet.

[1743] The tablet device stores this image data.

[1744] Input: User uploaded image

[1745] Output: Image data saved on the device

[1746] Specific behavior:

[1747] The user uploads an image of sign language taken with the tablet device's camera, and the device stores the image data.

[1748] Step 5: Sending image data

[1749] The terminal transmits the uploaded image data to a server via the Internet.

[1750] HTTPS is used as the communication protocol for transmission.

[1751] Input: Saved image data

[1752] Output: Image data sent to the server

[1753] Specific behavior:

[1754] The device divides the stored image data into packets and securely transmits them to the server.

[1755] Step 6: Image Recognition

[1756] The server uses Amazon Rekognition to extract the information necessary for sign language from the received image data.

[1757] Input: Image data sent

[1758] Output: Extracted sign language information

[1759] Specific behavior:

[1760] The server sends the image data to Amazon Rekognition, which analyzes the image and extracts the information needed for sign language.

[1761] Step 7: Emotion Recognition

[1762] The server uses the Microsoft Azure Emotion API to recognize the user's emotions from voice and image data.

[1763] Input: Text data and sign language information

[1764] Output: Emotion data "It looks fun"

[1765] Specific behavior:

[1766] The server sends the text and image data to the Microsoft Azure Emotion API, which analyzes and recognizes the user's emotions.

[1767] Step 8: Message Integration

[1768] The server integrates the text data obtained from speech recognition, the sign language information obtained from image recognition, and the emotion data obtained from emotion recognition to generate the final message.

[1769] Input: Text data, sign language information, emotion data

[1770] Output: Final consolidated message

[1771] Specific behavior:

[1772] The server consolidates the information obtained from each data source and generates a final message as a response to the user.

[1773] Step 9: Sign Language Translation

[1774] The server converts the synthesized messages into sign language indicators and generates sign language motion data.

[1775] Input: Final consolidated message

[1776] Output: Sign language motion data

[1777] Specific behavior:

[1778] The server converts the final message into sign language movements and gestures and generates the motion data.

[1779] Step 10: Sending motion data

[1780] The server transmits the generated sign language motion data to the terminal.

[1781] Input: Sign language motion data

[1782] Output: Motion data sent to the device

[1783] Specific behavior:

[1784] The sign language motion data generated by the server is divided into packets and securely transmitted to the terminal.

[1785] Step 11: Virtual Character Movement

[1786] The device controls the virtual character based on the received motion data.

[1787] The character signs "hello" and expresses a "happy" expression.

[1788] Input: Received motion data

[1789] Output: A moving virtual character

[1790] Specific behavior:

[1791] Based on the motion data received by the terminal, an animation of the virtual character is generated and displayed to the user.

[1792] (Application example 2)

[1793] 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."

[1794] Previously, there was no smooth means of communication with hearing-impaired employees in factories, making it difficult to give instructions or feedback on work, resulting in a lack of safety and efficiency.In addition, there was a lack of means to understand employees' emotions and provide appropriate feedback, so improvements to the working environment were required.

[1795] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving voice data and converting the voice data into text data, means for receiving image data and extracting necessary information from the image data, means for receiving emotion data and evaluating the emotion data, means for integrating the text data and the necessary information and generating a final message, means for converting the final message into a sign language indicator, means for generating motion data based on the sign language indicator, means for integrating emotional expressions into the motion data, means for applying the motion data to a virtual character and displaying sign language, and means for controlling the operation of a factory robot. This enables smooth and safe communication with hearing-impaired employees.

[1796] "Audio data" refers to sound information acquired through a voice input device.

[1797] "Text data" is information in which voice data is expressed as a string of characters.

[1798] "Image data" is visual information acquired by an image input device.

[1799] "Necessary information" is specific important data that is analyzed and extracted from the image data.

[1800] The "final message" is detailed instructions or information generated by integrating information obtained from audio and visual data.

[1801] "Sign language indicators" are information representing sign language actions created based on the final message.

[1802] "Motion data" refers to the movement data of a virtual character created based on sign language indicators.

[1803] A "virtual character" is a fictitious person or character that appears digitally.

[1804] "Emotion data" is data that expresses the user's emotional state.

[1805] A "factory robot" is an autonomous or remotely controlled mechanical device that performs various tasks in a factory.

[1806] A "speech recognition engine" is software or an algorithm for analyzing voice data and converting it into text data.

[1807] An "image recognition engine" is software or algorithms that analyze image data and extract necessary information.

[1808] An "emotion engine" is software or an algorithm that uses voice data and image data to evaluate and analyze a user's emotions.

[1809] This invention relates to a system for realizing smooth communication with hearing-impaired employees in a factory environment. This system translates voice data and image data into sign language in real time, and combines this with emotion recognition to enable factory robots to understand instructions and perform tasks. Detailed embodiments of this invention are described below.

[1810] Hardware and software used

[1811] 1. Hardware

[1812] Audio input device: Use a microphone or other audio capture device.

[1813] Image Input Device: Use an image capture device such as a camera.

[1814] Factory robots: Use autonomous or remotely controlled machinery to perform various tasks within a factory.

[1815] Smart glasses or head-mounted display (HMD): worn by the operator and used to provide instructions and display sign language.

[1816] 2. Software

[1817] Speech recognition engine: Converts voice data into text data using Google Cloud Speech-to-Text or similar.

[1818] Image recognition engine: Uses TensorFlow, OpenCV, etc. to extract necessary information from image data.

[1819] Emotion engine: Recognizes and evaluates user emotions using Microsoft Azure Emotion Recognition, etc.

[1820] Motion Generation Engine: Use the Custom Motion Engine to generate sign language motion data.

[1821] Virtual character control software: Use software such as Unity to control a virtual character and display sign language and emotional expressions.

[1822] System Operation

[1823] 1. Receiving and processing audio data

[1824] When a user issues a command by voice, the voice input device captures the voice data and sends it to the server, which uses a voice recognition engine to convert the voice data into text data.

[1825] 2. Receiving and processing image data

[1826] When a user signs through the smart glasses or HMD, the image input device captures the image data of the sign and sends it to the server, which uses an image recognition engine to extract the information needed for the sign from the image data.

[1827] 3. Receiving and processing emotional data

[1828] The server uses an emotion engine to recognize and evaluate the user's emotions using voice and image data. For example, it analyzes the tone of the voice and facial expressions when signing and recognizes emotions such as "feeling happy."

[1829] 4. Producing the final message

[1830] The server combines the text data obtained from speech recognition, the sign language information obtained from image recognition, and the emotion data to generate a final message. Based on this message, it generates sign language indicators and motion data.

[1831] 5. Sign Language and Emotional Expressions

[1832] The server applies the generated motion data and emotional expression data to the virtual character using virtual character control software, which allows the sign language and emotional expressions to be displayed in real time.

[1833] 6. Control of factory robots

[1834] Instructions based on the final message are transmitted to the factory robot, causing it to perform the appropriate task.

[1835] Specific examples

[1836] For example, in a factory, an operator signs to say, "Please carry the luggage." The voice input device captures the voice, and the image input device captures the sign. The server processes this data and transmits the command "carry the luggage" to a factory robot. At the same time, the smart glasses display visualizes feedback according to the operator's emotions and conveys it to the operator.

[1837] Prompt Sentence Examples

[1838] Voice: "Please carry my luggage."

[1839] Emotion: "It looks fun"

[1840] Sign Language: "Sign language gestures for carrying luggage"

[1841] This dramatically improves communication with hearing-impaired employees, creating a safer and more efficient working environment.

[1842] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1843] Step 1:

[1844] The user issues a command by voice. The voice input device captures the voice data and sends it to the terminal. The terminal then sends the voice data to the server.

[1845] Input: User's voice

[1846] Output: Audio data

[1847] Step 2:

[1848] The server uses a speech recognition engine to convert the voice data into text data. Specifically, the speech recognition algorithm analyzes the waveform of the voice and generates a corresponding string of characters.

[1849] Input: Audio data

[1850] Output: Text data

[1851] Step 3:

[1852] The user signs through smart glasses or an HMD. The image input device captures the sign image data and sends it to the device. The device then sends the image data to the server.

[1853] Input: Sign language gesture

[1854] Output: Image data

[1855] Step 4:

[1856] The server uses an image recognition engine to extract the necessary sign language information from the image data. Specifically, the image recognition algorithm analyzes the image data and generates information corresponding to sign language gestures.

[1857] Input: Image data

[1858] Output: Sign language information

[1859] Step 5:

[1860] The server passes the voice and image data to the emotion engine to recognize the user's emotions. Specifically, it analyzes the tone of the voice and facial expressions when signing, and generates an emotional status.

[1861] Input: Audio data, image data

[1862] Output: Emotion data

[1863] Step 6:

[1864] The server combines the text data obtained from speech recognition, the sign language information obtained from image recognition, and the emotion data to generate the final message, which then generates the sign language indicators and motion data.

[1865] Input: Text data, sign language information, emotion data

[1866] Output: Final message

[1867] Step 7:

[1868] The server converts the final message into sign language indicators and generates sign language motion data. Specifically, it references a sign language database and obtains the corresponding motion data from the sign language indicators.

[1869] Input: Final message

[1870] Output: Sign Language Index

[1871] Step 8:

[1872] The server applies the generated motion data and emotional expression data to the virtual character using virtual character control software, and the virtual character displays sign language and emotional expressions in real time.

[1873] Input: Sign language indicators, emotion data

[1874] Output: Virtual character movement data

[1875] Step 9:

[1876] The server transmits instructions based on the final message to the factory robot to cause it to perform the appropriate task, specifically, inputs the final message into the control system of the factory robot and generates a corresponding operation command.

[1877] Input: Final message

[1878] Output: Robot movement instructions

[1879] In this way, by appropriately processing and integrating voice data, image data, and emotional data at each step, it is demonstrated that this system enables communication with hearing-impaired employees in a factory environment.

[1880] 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.

[1881] 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.

[1882] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1883] 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.

[1884] 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.

[1885] 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.

[1886] 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).

[1887] 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.

[1888] 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."

[1889] 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.

[1890] 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).

[1891] 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.

[1892] 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.

[1893] 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.

[1894] 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.

[1895] 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.

[1896] 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.

[1897] 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.

[1898] 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.

[1899] 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.

[1900] 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.

[1901] The following is further disclosed regarding the above embodiment.

[1902] (Claim 1)

[1903] means for receiving voice data and converting the voice data into text data;

[1904] means for receiving image data and extracting necessary information from the image data;

[1905] means for integrating the text data and the necessary information to generate a final message;

[1906] means for converting said final message into sign language indicia;

[1907] means for generating motion data based on the sign language indicators;

[1908] means for applying the motion data to a virtual character to display sign language;

[1909] A system including:

[1910] (Claim 2)

[1911] 10. The system of claim 1, further comprising means for converting voice data into text data using a voice recognition engine.

[1912] (Claim 3)

[1913] 10. The system of claim 1, further comprising means for extracting required information from the image data using an image recognition engine.

[1914] "Example 1"

[1915] (Claim 1)

[1916] means for receiving voice data from a user and converting the voice data into text data;

[1917] means for receiving image data from a user and extracting necessary information from the image data;

[1918] means for integrating the text data and the necessary information to generate a final message;

[1919] means for converting said final message into sign language indicia;

[1920] means for generating motion data based on the sign language indicators;

[1921] means for applying the motion data to a virtual character to display sign language;

[1922] means for receiving voice data and image data by a terminal and transmitting the data to a server;

[1923] A means for displaying a virtual character by a terminal;

[1924] A system including:

[1925] (Claim 2)

[1926] 10. The system of claim 1, further comprising means for converting voice data into text data using a voice recognition engine.

[1927] (Claim 3)

[1928] 10. The system of claim 1, further comprising means for extracting required information from the image data using an image recognition engine.

[1929] "Application Example 1"

[1930] (Claim 1)

[1931] means for receiving voice data and converting the voice data into text data;

[1932] means for receiving image data and extracting necessary information from the image data;

[1933] means for integrating the text data and the necessary information to generate a final message;

[1934] means for converting said final message into sign language indicia;

[1935] means for generating motion data based on the sign language indicators;

[1936] means for applying the motion data to a virtual character to display sign language;

[1937] A means for assisting communication between a customer and a store clerk using voice data or image data when serving customers in a physical store;

[1938] A system including:

[1939] (Claim 2)

[1940] 10. The system of claim 1, further comprising means for converting voice data into text data using a voice recognition engine.

[1941] (Claim 3)

[1942] 10. The system of claim 1, further comprising means for extracting required information from the image data using an image recognition engine.

[1943] "Example 2: Combining Emotion Engines"

[1944] (Claim 1)

[1945] means for receiving voice data and converting the voice data into text data;

[1946] means for receiving image data and extracting necessary information from the image data;

[1947] means for integrating the text data and the necessary information to generate a final message;

[1948] means for converting said final message into sign language indicia;

[1949] means for generating motion data based on the sign language indicators;

[1950] means for applying the motion data to a virtual character to display sign language;

[1951] means for recognizing a user's emotion from the voice data and image data and generating sign language based on the emotion;

[1952] A system including:

[1953] (Claim 2)

[1954] 10. The system of claim 1, further comprising means for converting voice data into text data using a voice recognition engine.

[1955] (Claim 3)

[1956] 10. The system of claim 1, further comprising means for extracting required information from the image data using an image recognition engine.

[1957] "Application example 2 when combining emotion engines"

[1958] (Claim 1)

[1959] means for receiving voice data and converting the voice data into text data;

[1960] means for receiving image data and extracting necessary information from the image data;

[1961] means for integrating the text data and the necessary information to generate a final message;

[1962] means for converting said final message into sign language indicia;

[1963] means for generating motion data based on the sign language indicators;

[1964] means for applying the motion data to a virtual character to display sign language;

[1965] means for receiving emotion data and evaluating the emotion data;

[1966] means for integrating emotional expressions into the motion data;

[1967] A means for controlling the operation of a factory work robot;

[1968] A system including:

[1969] (Claim 2)

[1970] 10. The system of claim 1, further comprising means for converting voice data into text data using a voice recognition engine.

[1971] (Claim 3)

[1972] 10. The system of claim 1, further comprising means for extracting required information from the image data using an image recognition engine. [Explanation of symbols]

[1973] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving voice data and converting the voice data into text data; means for receiving image data and extracting necessary information from the image data; means for integrating the text data and the necessary information to generate a final message; means for converting said final message into sign language indicia; means for generating motion data based on the sign language indicators; means for applying the motion data to a virtual character to display sign language; A system including:

2. 10. The system of claim 1, further comprising means for converting voice data into text data using a voice recognition engine.

3. The system of claim 1 further comprising means for extracting required information from the image data using an image recognition engine.

Images